diff --git a/.buildlibrary b/.buildlibrary index 490cc6b7..7fb53040 100644 --- a/.buildlibrary +++ b/.buildlibrary @@ -1,4 +1,4 @@ -ValidationKey: '27780630' +ValidationKey: '27996960' AcceptedWarnings: - 'Warning: package ''.*'' was built under R version' - 'Warning: namespace ''.*'' is not available and has been replaced' diff --git a/.github/workflows/check.yaml b/.github/workflows/check.yaml index 46f518a2..f6ea5d40 100644 --- a/.github/workflows/check.yaml +++ b/.github/workflows/check.yaml @@ -11,7 +11,7 @@ jobs: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: r-lib/actions/setup-pandoc@v2 @@ -23,7 +23,6 @@ jobs: - uses: r-lib/actions/setup-r-dependencies@v2 with: extra-packages: | - gamstransfer=?ignore any::lucode2 any::covr any::madrat @@ -36,7 +35,7 @@ jobs: # gms, goxygen, GDPuc) will usually have an outdated binary version # available; by using extra-packages we get the newest version - - uses: actions/setup-python@v4 + - uses: actions/setup-python@v5 with: python-version: 3.9 diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 243f46a1..62f13da6 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -3,7 +3,7 @@ exclude: '^tests/testthat/_snaps/.*$' repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.5.0 + rev: 2c9f875913ee60ca25ce70243dc24d5b6415598c # frozen: v4.6.0 hooks: - id: check-case-conflict - id: check-json @@ -15,7 +15,7 @@ repos: - id: mixed-line-ending - repo: https://github.com/lorenzwalthert/precommit - rev: v0.4.0 + rev: 7910e0323d7213f34275a7a562b9ef0fde8ce1b9 # frozen: v0.4.2 hooks: - id: parsable-R - id: deps-in-desc diff --git a/CITATION.cff b/CITATION.cff index e882a892..cd71ae7b 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -2,8 +2,8 @@ cff-version: 1.2.0 message: If you use this software, please cite it using the metadata from this file. type: software title: 'mrcommons: MadRat commons Input Data Library' -version: 1.40.2 -date-released: '2024-04-02' +version: 1.41.0 +date-released: '2024-05-13' abstract: Provides useful functions and a common structure to all the input data required to run models like MAgPIE and REMIND of model input data. authors: diff --git a/DESCRIPTION b/DESCRIPTION index 1f1472ec..1394e426 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,8 +1,8 @@ Package: mrcommons Type: Package Title: MadRat commons Input Data Library -Version: 1.40.2 -Date: 2024-04-02 +Version: 1.41.0 +Date: 2024-05-13 Authors@R: c(person("Benjamin Leon", "Bodirsky", email = "bodirsky@pik-potsdam.de", role = "aut"), person("Kristine", "Karstens", role = "aut"), person("Lavinia", "Baumstark", role = "aut"), @@ -43,18 +43,17 @@ Depends: magclass (>= 3.17), madrat (>= 2.20.9), mrdrivers (>= 1.0.0), - mstools + mrfaocore (>= 1.0.0), + mrlandcore (>= 1.0.0), + mstools (>= 0.6.0) Imports: data.table, dplyr, hdf5r, GDPuc, - lpjclass, - lpjmlkit, luscale, magpiesets (>= 0.44.2), ncdf4, - nleqslv, openxlsx, purrr, quitte, @@ -63,7 +62,6 @@ Imports: readxl, reshape2, rlang, - SPEI, stringr, tidyr, tibble, diff --git a/NAMESPACE b/NAMESPACE index eb825120..5e5646df 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -6,7 +6,6 @@ export(calcBodyHeight) export(calcClimateClass) export(calcConstructionWoodDemand) export(calcDemography) -export(calcForestArea) export(calcGovernanceIndicator) export(calcGrowingStock) export(calcGrowingStockNRF) @@ -23,38 +22,19 @@ export(convertJRC_IDEES) export(convertMAgPIE) export(readEU_ReferenceScenario) export(readEurostat) -export(readFAO_FRA2015) -export(readFRA2020) export(readFeedEfficiencyReg) export(readFeedShareReg) export(readHoulton2018) export(readJRC_IDEES) -export(readLPJmLClimateInput) -export(toolAggregateCell2Country) export(toolCalcIEAfromStructureMappingPEFE) -export(toolCell2isoCell) -export(toolClimateInputVersion) -export(toolCoord2Isocell) -export(toolCoord2Isocoord) -export(toolCountryFillBilateral) -export(toolExtrapolateFodder) -export(toolFAOcombine) -export(toolForestRelocate) -export(toolFreezeEffect) -export(toolGetMappingCoord2Country) -export(toolHarmonize2Baseline) -export(toolHoldConstantBeyondEnd) -export(toolIso2CellCountries) -export(toolLPJmLVersion) export(toolPregnant) -export(toolSmooth) -export(toolSum2Country) import(madrat) import(magclass) import(mrdrivers) +import(mrfaocore) +import(mrlandcore) import(mstools) importFrom(GDPuc,convertGDP) -importFrom(SPEI,thornthwaite) importFrom(countrycode,countrycode) importFrom(data.table,":=") importFrom(data.table,as.data.table) @@ -84,16 +64,10 @@ importFrom(dplyr,rename) importFrom(dplyr,right_join) importFrom(dplyr,select) importFrom(dplyr,summarise) -importFrom(dplyr,ungroup) -importFrom(graphics,plot) importFrom(hdf5r,h5file) -importFrom(lpjclass,read.LPJ_input) -importFrom(lpjclass,readLPJ) -importFrom(lpjmlkit,read_io) importFrom(luscale,getAggregationMatrix) importFrom(luscale,groupAggregate) importFrom(luscale,rename_dimnames) -importFrom(luscale,speed_aggregate) importFrom(madrat,calcOutput) importFrom(madrat,getISOlist) importFrom(madrat,metadataGFZ) @@ -107,11 +81,8 @@ importFrom(madrat,toolFillWithRegionAvg) importFrom(madrat,toolFillYears) importFrom(madrat,toolGetMapping) importFrom(madrat,toolNAreplace) -importFrom(madrat,toolOrderCells) importFrom(madrat,toolSplitSubtype) importFrom(madrat,toolSubtypeSelect) -importFrom(madrat,toolTimeAverage) -importFrom(madrat,toolTimeSpline) importFrom(magclass,"getItems<-") importFrom(magclass,"getNames<-") importFrom(magclass,"getSets<-") @@ -123,14 +94,11 @@ importFrom(magclass,as.magpie) importFrom(magclass,clean_magpie) importFrom(magclass,collapseDim) importFrom(magclass,collapseNames) -importFrom(magclass,complete_magpie) importFrom(magclass,convergence) -importFrom(magclass,dimExists) importFrom(magclass,dimOrder) importFrom(magclass,dimReduce) importFrom(magclass,dimSums) importFrom(magclass,getCPR) -importFrom(magclass,getCells) importFrom(magclass,getCoords) importFrom(magclass,getItems) importFrom(magclass,getNames) @@ -140,13 +108,10 @@ importFrom(magclass,getYears) importFrom(magclass,hasCoords) importFrom(magclass,lowpass) importFrom(magclass,magpie_expand) -importFrom(magclass,magpiesort) importFrom(magclass,mbind) importFrom(magclass,mselect) -importFrom(magclass,ncells) importFrom(magclass,ndim) importFrom(magclass,new.magpie) -importFrom(magclass,nyears) importFrom(magclass,read.magpie) importFrom(magclass,read.report) importFrom(magclass,setCells) @@ -163,11 +128,9 @@ importFrom(magpiesets,reporthelper) importFrom(magpiesets,reportingnames) importFrom(methods,new) importFrom(mstools,toolFertilizerDistribution) -importFrom(mstools,toolHoldConstant) importFrom(ncdf4,nc_close) importFrom(ncdf4,nc_open) importFrom(ncdf4,ncvar_get) -importFrom(nleqslv,nleqslv) importFrom(openxlsx,read.xlsx) importFrom(purrr,map) importFrom(purrr,map2) @@ -206,21 +169,12 @@ importFrom(stats,na.omit) importFrom(stats,quantile) importFrom(stats,reshape) importFrom(stats,xtabs) -importFrom(stringr,str_count) importFrom(stringr,str_detect) importFrom(stringr,str_extract) -importFrom(stringr,str_match) -importFrom(stringr,str_split) importFrom(stringr,str_sub) -importFrom(stringr,str_subset) -importFrom(stringr,str_trim) -importFrom(terra,"ext<-") -importFrom(terra,aggregate) importFrom(terra,app) importFrom(terra,classify) -importFrom(terra,ext) importFrom(terra,focal) -importFrom(terra,project) importFrom(terra,rast) importFrom(terra,rasterize) importFrom(terra,segregate) @@ -240,17 +194,13 @@ importFrom(tidyr,replace_na) importFrom(tidyr,starts_with) importFrom(tidyr,unite) importFrom(tidyr,unnest) -importFrom(tools,file_ext) -importFrom(tools,file_path_sans_ext) importFrom(utils,bibentry) importFrom(utils,capture.output) importFrom(utils,download.file) importFrom(utils,head) -importFrom(utils,packageVersion) importFrom(utils,person) importFrom(utils,read.csv) importFrom(utils,read.csv2) -importFrom(utils,read.delim) importFrom(utils,read.table) importFrom(utils,tail) importFrom(utils,unzip) diff --git a/R/calcAttributes.R b/R/calcAttributes.R deleted file mode 100644 index 2622bd87..00000000 --- a/R/calcAttributes.R +++ /dev/null @@ -1,22 +0,0 @@ -#' @title calcAttributes -#' @description provides attributes of different products -#' -#' @param subtype subtype of readProductAttributes function. -#' -#' @return List of magpie objects with results on global level, empty weight, unit and description. -#' @author Benjamin Leon Bodirsky -#' @seealso -#' [readProductAttributes()] -#' @examples -#' \dontrun{ -#' calcOutput("Attributes") -#' } -#' -calcAttributes <- function(subtype = "Products") { - attributes <- readSource("ProductAttributes", subtype = subtype) - return(list(x = attributes, - weight = NULL, - unit = "t X per t dry matter (DM), except generalizable energy (ge), where it is PJ/Mt DM.", - description = paste("Values from literature survey. See SVN:", - "tools/Nutrients/crop_specifications.ods or livestock_specifications.ods"))) -} diff --git a/R/calcBodyHeight.R b/R/calcBodyHeight.R index 92424048..95d1f47c 100644 --- a/R/calcBodyHeight.R +++ b/R/calcBodyHeight.R @@ -8,8 +8,6 @@ #' Also,the year 1965 is extrapolatedusing the worldbank population #' data and sex, age, and education structure of 1970. #' @export -#' @importFrom luscale speed_aggregate - calcBodyHeight <- function(convert = TRUE) { demo <- calcOutput("Demography", education = FALSE, aggregate = FALSE) diff --git a/R/calcCroparea.R b/R/calcCroparea.R deleted file mode 100644 index 1a8f05d9..00000000 --- a/R/calcCroparea.R +++ /dev/null @@ -1,239 +0,0 @@ -#' @title calcCroparea -#' @description Returns harvested areas of individual crops from FAOSTAT. -#' Total harvested areas can be lower or higher than arable -#' land because of multicropping or fallow land. -#' Rice areas are distributed to flooded LUH areas. Additional FAOSTAT -#' rice areas are distributed based on country shares. -#' -#' @param sectoral "area_harvested" returns croparea aggregated to FAO products, -#' "ProductionItem" unaggregated ProdSTAT items, -#' "FoodBalanceItem" Food Balance Sheet categories, -#' "kcr" MAgPIE items, and "lpj" LPJmL items -#' @param physical if TRUE the sum over all crops agrees with the cropland area per country -#' @param cellular if TRUE: calculates cellular MAgPIE crop area for all magpie croptypes. -#' Crop area from LUH2 crop types (c3ann, c4ann, c3per, c4per, cnfx) -#' are mapped to MAgpIE crop types using mappingLUH2cropsToMAgPIEcrops.csv. -#' Harvested areas of FAO weight area within a specific LUH crop type -#' to divide into MAgPIE crop types. -#' @param cells Switch between "magpiecell" (59199) and "lpjcell" (67420) -#' @param irrigation If true: cellular areas are returned separated -#' into irrigated and rainfed (see setup in calcLUH2v2) -#' -#' @return areas of individual crops from FAOSTAT and weight -#' -#' @author Ulrich Kreidenweis, Kristine Karstens, Felicitas Beier -#' -#' @importFrom utils read.csv -#' @importFrom magclass setNames getCells collapseDim getItems -#' @importFrom magpiesets findset addLocation -#' @importFrom madrat toolAggregate toolGetMapping -#' @importFrom withr local_options - -calcCroparea <- function(sectoral = "kcr", physical = TRUE, cellular = FALSE, - cells = "lpjcell", irrigation = FALSE) { - - local_options(magclass_sizeLimit = 1e+10) - - if (!cellular) { - - if (irrigation) stop("Irrigation levels for country based data not yet implemented!") - - ################################# - ### Croparea on country level ### - ################################# - - if (!is.null(sectoral) && !(sectoral == "lpj")) { - - cropPrim <- readSource("FAO_online", "Crop")[, , "area_harvested"] - # use linear_interpolate - fodder <- readSource("FAO", "Fodder")[, , "area_harvested"] - fodder <- toolExtrapolateFodder(fodder, endyear = max(getYears(cropPrim, as.integer = TRUE))) - data <- toolFAOcombine(cropPrim, fodder) / 10^6 # convert to Mha - - if (sectoral %in% c("FoodBalanceItem", "kcr")) { - - aggregation <- toolGetMapping("FAOitems_online.csv", type = "sectoral", - where = "mappingfolder") - remove <- setdiff(getNames(data, dim = 1), aggregation$ProductionItem) - data <- data[, , remove, invert = TRUE] - data <- toolAggregate(data, rel = aggregation, from = "ProductionItem", - to = ifelse(sectoral == "kcr", "k", sectoral), - dim = 3.1, partrel = TRUE) - - if (sectoral == "kcr") { - - # add bioenergy with 0 values - data <- add_columns(x = data, addnm = c("betr", "begr"), dim = 3.1) - data[, , c("betr", "begr")] <- 0 - - # remove all non kcr items - kcr <- findset("kcr") - remove <- setdiff(getItems(data, dim = 3.1), kcr) - - if (length(remove) > 0) { - remainArea <- mean(dimSums(data[, , "remaining.area_harvested"], dim = 1) / - dimSums(dimSums(data[, , "area_harvested"], dim = 3), dim = 1)) - if (remainArea > 0.02) vcat(1, "Aggregation created a 'remaining' category. The area harvested is", - round(remainArea, digits = 3) * 100, "% of total \n") - vcat(2, paste0("Data for the following items removed: ", remove)) - data <- data[, , kcr] - } - } - - } else if (sectoral != "ProductionItem") { - stop("Sectoral aggregation not supported") - } - - } else if (sectoral == "lpj") { - - magCroparea <- calcOutput("Croparea", sectoral = "kcr", physical = physical, - cellular = FALSE, irrigation = FALSE, aggregate = FALSE) - - mag2lpj <- toolGetMapping(type = "sectoral", name = "MAgPIE_LPJmL.csv", - where = "mappingfolder") - mag2lpj <- mag2lpj[!(mag2lpj$MAgPIE == "pasture"), ] - - lpjCroparea <- toolAggregate(magCroparea, rel = mag2lpj, from = "MAgPIE", to = "LPJmL", dim = 3.1) - data <- lpjCroparea - - } else { - stop("Sectoral aggregation not supported") - } - - # use the share of the single crops to calculate their "physical" area - if (physical) { - # 6620 = (6620|Arable land and Permanent crops or 6620|Cropland) - cropland <- setNames(collapseNames(calcOutput("FAOLand", - aggregate = FALSE)[, , "6620", pmatch = TRUE]), "crop") - harvestedShare <- data / dimSums(data, dim = 3.1) - commonyears <- intersect(getYears(cropland), getYears(harvestedShare)) - data <- collapseNames(cropland[, commonyears, ] * harvestedShare[, commonyears, ]) - } - - data[is.na(data)] <- 0 - - } else { - - ################################## - ### Croparea on cellular level ### - ################################## - - if (sectoral == "kcr") { - - # LUH related data input on cell level - luhWeights <- calcOutput("LUH2MAgPIE", share = "MAGofLUH", - missing = "fill", rice = "non_flooded", aggregate = FALSE) - - luhCroptypes <- c("c3ann", "c4ann", "c3per", "c4per", "c3nfx") - - luhCroparea <- calcOutput("LUH2v2", landuse_types = "LUH2v2", - cells = cells, aggregate = FALSE, irrigation = irrigation, - cellular = TRUE, selectyears = "past") - if (cells == "magpiecell") { - luhCroparea <- toolCell2isoCell(luhCroparea, cells = cells) - } - - # Differentiation step that is necessary until full transition to 67k cells - if (cells == "magpiecell") { - commonCountries <- intersect(getItems(luhWeights, dim = "ISO"), getItems(luhCroparea, dim = "country")) - } else if (cells == "lpjcell") { - commonCountries <- intersect(getItems(luhWeights, dim = "ISO"), getItems(luhCroparea, dim = "iso")) - } else { - stop("Please select cellular data (mapgiecell or lpjcell) to be returned - by calcCroparea when selecting cellular = TRUE") - } - - # corrected rice area (in Mha) - ricearea <- calcOutput("Ricearea", cellular = TRUE, cells = cells, - share = FALSE, aggregate = FALSE) - - # irrigation - if (irrigation == TRUE) { - - # for check - luhCropareaTotal <- dimSums(luhCroparea[, , luhCroptypes][, , "total"], dim = 3) - - # calculate irrigation share for rice area correction - irrigShr <- new.magpie(cells_and_regions = getCells(luhCroparea), - years = getYears(luhCroparea), - names = getNames(luhCroparea), fill = NA) - irrigShr <- irrigShr[, , "total", invert = TRUE] - - irrigShr[, , "irrigated"] <- collapseNames(ifelse(luhCroparea[, , "total"] > 0, - luhCroparea[, , "irrigated"] / luhCroparea[, , "total"], 0)) - irrigShr[, , "rainfed"] <- 1 - collapseNames(irrigShr[, , "irrigated"]) - - # flooded rice areas - floodedRice <- collapseNames(ricearea[, , "flooded"] * irrigShr[, , "c3ann"]) - - # reduce object size (if "total" is also reported magpie object grows too big (>1.3GB)) - luhCroparea <- luhCroparea[, , "total", invert = TRUE] - - } else { - - # for check - luhCropareaTotal <- dimSums(luhCroparea[, , luhCroptypes], dim = 3) - - # flooded rice areas - floodedRice <- collapseNames(ricearea[, , "flooded"]) - - } - - # temporarily exclude flooded rice for distribution of other crops and aerobic rice areas - luhCroparea[, , "c3ann"] <- luhCroparea[, , "c3ann"] - floodedRice - - # correction of LUH cropareas with FAO country shares - luhCroparea <- luhCroparea[, , luhCroptypes] - luh2mag <- luhCroparea * luhWeights[commonCountries, , ] - magCroparea <- dimSums(luh2mag, dim = 3.1) - - # total rice area correction - magCroparea[, , "rice_pro"] <- magCroparea[, , "rice_pro"] + floodedRice - - # check sums - if (any(round(abs(dimSums(magCroparea, dim = 3) - luhCropareaTotal), digits = 6) > 1e-6)) { - stop("Sums after rice correction in calcCroparea don't match!") - } - - data <- collapseNames(magCroparea) - - } else if (sectoral == "lpj") { - - magCroparea <- calcOutput("Croparea", sectoral = "kcr", physical = physical, - cellular = TRUE, irrigation = irrigation, - cells = cells, aggregate = FALSE) - mag2lpj <- toolGetMapping(type = "sectoral", name = "MAgPIE_LPJmL.csv", - where = "mappingfolder") - mag2lpj <- mag2lpj[!(mag2lpj$MAgPIE == "pasture"), ] - lpjCroparea <- toolAggregate(magCroparea, rel = mag2lpj, from = "MAgPIE", to = "LPJmL", dim = "MAG") - data <- lpjCroparea - - } else { - stop("Not possible (for now) for the given item set (sectoral)!") - } - - if (!physical) { - - multiCropping <- calcOutput("Multicropping", aggregate = FALSE) - - if (cells == "magpiecell") { - commonCountries <- intersect(getItems(multiCropping, dim = "ISO"), getItems(data, dim = "country")) - } else if (cells == "lpjcell") { - commonCountries <- intersect(getItems(multiCropping, dim = "ISO"), getItems(data, dim = "iso")) - } - - data <- data * multiCropping[commonCountries, getYears(data), ] - } - } - - data <- collapseNames(data) - - # not more precision than 1 ha needed. very small areas can make problems in some weighting scripts - data <- round(data, 6) - - return(list(x = data, - weight = NULL, - unit = "million ha", - description = "harvested crop areas from FAOSTAT", - isocountries = !cellular)) -} diff --git a/R/calcCropareaLandInG.R b/R/calcCropareaLandInG.R deleted file mode 100644 index a6fa3ecb..00000000 --- a/R/calcCropareaLandInG.R +++ /dev/null @@ -1,304 +0,0 @@ -#' @title calcCropareaLandInG -#' @description This function uses total physical area and -#' crop-specific harvested area data from LandInG -#' to calculate crop-specific physical and harvested -#' areas considering special rules -#' for the allocation of perennial and annual crops. -#' -#' @param sectoral "kcr" MAgPIE items, and "lpj" LPJmL items -#' @param physical if TRUE the sum over all crops plus fallow land (of calcFallowLand) -#' agrees with the physical cropland of readLandInG(subtype = physical) -#' @param cellular if TRUE: calculates cellular crop area for all magpie croptypes. -#' Option FALSE is not (yet) available. -#' @param cells Switch between "magpiecell" (59199) and "lpjcell" (67420) -#' @param irrigation If true: cellular areas are returned separated -#' into irrigated and rainfed -#' @param selectyears extract certain years from the data -#' @param lpjml LPJmL version used to determine multiple cropping suitability -#' @param climatetype Climate scenario or historical baseline "GSWP3-W5E5:historical" -#' used to determine multiple cropping suitability -#' -#' @return MAgPIE object with cropareas -#' -#' @author David Hoetten, Felicitas Beier -#' -#' @importFrom madrat readSource toolConditionalReplace toolCountryFill toolAggregate -#' @importFrom magclass dimSums getItems -#' @importFrom mstools toolHoldConstant -#' -calcCropareaLandInG <- function(sectoral = "kcr", physical = TRUE, cellular = FALSE, - cells = "magpiecell", irrigation = FALSE, selectyears = "all", - lpjml = c(natveg = "LPJmL4_for_MAgPIE_44ac93de", - crop = "ggcmi_phase3_nchecks_bft_e511ac58"), - climatetype = "GSWP3-W5E5:historical") { - - if (climatetype != "GSWP3-W5E5:historical" || - lpjml[["crop"]] != "ggcmi_phase3_nchecks_bft_e511ac58") { - warning("Error in calcCropareaLandInG: The LPJmL version has been updated - since LandInG was run for the last time. - Please consider updating the LandInG data.") - # Kristine: How to include a mapping here? - } - - withr::local_options(magclass_sizeLimit = 1e+12) - - ### Read in data ### - # total physical area from LandInG (in Mha) - physicalArea <- readSource("LandInG", subtype = "physicalArea") - # crop-specific harvested area (in Mha) - harvestedArea <- readSource("LandInG", subtype = "harvestedArea") - - ### Calculations ### - # read in fallow land (for check below) - fallow <- calcOutput("FallowLand", aggregate = FALSE) - - # year selection - if (any(selectyears == "all")) { - selectyears <- getItems(physicalArea, dim = "year") - } - if (is.numeric(selectyears)) { - selectyears <- paste0("y", selectyears) - } - - # extrapolate years - if (!all(selectyears %in% getItems(physicalArea, dim = "year"))) { - physicalArea <- toolHoldConstant(physicalArea, selectyears) - harvestedArea <- toolHoldConstant(harvestedArea, selectyears) - fallow <- toolHoldConstant(fallow, selectyears) - } - - # reduce harvested area to crop area - nonCrops <- c("pasture") - harvestedArea <- harvestedArea[, , nonCrops, invert = TRUE] - - # croplists - crops <- getItems(harvestedArea, dim = "crop") - perennials <- c("sugr_cane", "oilpalm") - annuals <- crops[!crops %in% perennials] - - # Reduce to selected number of years - # and split calculation into single years for memory reasons - cropAreaList <- vector(mode = "list", length = length(selectyears)) - names(cropAreaList) <- selectyears - for (y in selectyears) { - # select year - physicalAreaYearly <- physicalArea[, y, ] - harvestedAreaYearly <- harvestedArea[, y, ] - - ################################## - ## Crop-specific physical areas ## - ################################## - # Total physical area (in Mha) - physicalAreaSum <- dimSums(physicalAreaYearly, dim = "irrigation") - - # Calculate the total harvested areas for different crop groups - # for perennial crops no multicropping is happening, so physical area = harvested area - perennialHarvestedA <- dimSums(harvestedAreaYearly[, , perennials], dim = c("crop", "irrigation")) - annualsHarvestedA <- dimSums(harvestedAreaYearly[, , annuals], dim = c("crop", "irrigation")) - totalHarvestedA <- perennialHarvestedA + annualsHarvestedA - - # Check how much physical area is remaining for the annuals after subtracting the perennial physical area - annualsPhysicalA <- physicalAreaSum - perennialHarvestedA - - # Calculate a factor by which the annuals should be scaled down so the sum does not exceed annualsPhysicalA - factorAnnuals <- ifelse(annualsPhysicalA > 0 & annualsHarvestedA > 0, - annualsPhysicalA / annualsHarvestedA, - 1) - - # Calculate a factor by which all crops in mismatch cells (i.e. no annualPhyiscalA left) should be scaled down - factorMismatches <- ifelse(annualsPhysicalA <= 0 & totalHarvestedA > 0, - physicalAreaSum / totalHarvestedA, - 1) - - # Only scale crops down not up (i.e. keep fallow land) - factorAnnuals[factorAnnuals > 1] <- 1 - factorMismatches[factorMismatches > 1] <- 1 - - # Apply the factors - physicalAreaYearly <- harvestedAreaYearly - physicalAreaYearly[, , annuals] <- harvestedAreaYearly[, , annuals] * factorAnnuals - physicalAreaYearly <- physicalAreaYearly * factorMismatches - - # Clean up for memory reasons - rm(factorMismatches, factorAnnuals) - - ################################### - ## Correction of harvested areas ## - ################################### - # Correction of perennial harvested area required - # due to above allocation of crops distinguishing - # annuals and perennials - - # Check whether more than 5% of harvested area would be lost - if (any(dimSums(harvestedAreaYearly[, , perennials] - physicalAreaYearly[, , perennials], - dim = c(1, 3.2)) / dimSums(harvestedAreaYearly, dim = c(1, 3.2)) * 100 > 5)) { - stop("More than 5% of global harvested area is lost through perennial area correction") - } - # Check whether more than 10% of harvested area would be lost in any country - # that has more than 100 000 ha total harvested area - if (any(dimSums(harvestedAreaYearly, - dim = c(1.1, 1.2, 3)) > 0.1 & - (dimSums(harvestedAreaYearly[, , perennials] - physicalAreaYearly[, , perennials], - dim = c(1.1, 1.2, 3)) / dimSums(harvestedAreaYearly, - dim = c(1.1, 1.2, 3)) * 100) > 10, - na.rm = TRUE)) { - stop(paste0("Some countries (with more than 100 000 ha harvested area) would loose more than 10% in year ", y)) - } - - # In the allocation of perennials to physical area, some harvested area is lost and needs to be corrected - harvestedAreaYearly[, , perennials] <- physicalAreaYearly[, , perennials] - - ########################################### - ## Correction of multiple cropping cases ## - ########################################### - # In the LandInG calculations, some rainfed harvested area is allocated to irrigated land. - # This leads to cases where areas are declared as "rainfed harvested area" resulting in - # cropping intensities > 1 for rainfed crops where not multiple cropping is possible - # according to the multiple cropping suitability. - # These areas are declared irrigated in the following correction. - - ### Read in data ### - # Crop-specific and irrigation-type specific multiple cropping suitability - mcSuit <- calcOutput("MulticroppingSuitability", selectyears = y, - lpjml = lpjml, climatetype = climatetype, - suitability = "endogenous", sectoral = "kcr", - aggregate = FALSE) - mcSuit <- dimOrder(mcSuit, c(2, 1), dim = 3) - mcSuit <- mcSuit[, , getItems(harvestedAreaYearly, dim = 3)] - - # Sanity checks - if (any(harvestedAreaYearly != 0 & physicalAreaYearly == 0)) { - stop("Please check calcCropareaLandInG. The following calculations area based on the - assumption that there is no harvested area where no physical area exists.") - } - # Crop- and irrigation-specific cropping intensity - fctMCwhereNonSuit <- function(physicalAreaYearly = physicalAreaYearly, - harvestedAreaYearly = harvestedAreaYearly, - mcSuit = mcSuit) { - ci <- ifelse(physicalAreaYearly > 0, harvestedAreaYearly / physicalAreaYearly, 1) - # Boolean: is there multiple cropping or not? - mcCurr <- ci - mcCurr[, , ] <- 0 - mcCurr[ci > (1 + 1e-3)] <- 1 - # Multiple cropping where non-suitable for multiple cropping - violation <- mcCurr == 1 & mcSuit == 0 - - return(violation) - } - - # Multiple cropping where non-suitable for multiple cropping - violation <- fctMCwhereNonSuit(physicalAreaYearly = physicalAreaYearly, - harvestedAreaYearly = harvestedAreaYearly, - mcSuit = mcSuit) - rfViolation <- collapseNames(violation[, , "rainfed"]) - - # Temporary objects with correct dimensionality - harvIR <- collapseNames(harvestedAreaYearly[, , "irrigated"]) - physIR <- collapseNames(physicalAreaYearly[, , "irrigated"]) - harvRF <- collapseNames(harvestedAreaYearly[, , "rainfed"]) - physRF <- collapseNames(physicalAreaYearly[, , "rainfed"]) - - # Add multiple cropped rainfed areas to harvested irrigated areas - harvIR[rfViolation] <- harvIR[rfViolation] + harvRF[rfViolation] - physRF[rfViolation] - # Reduce harvested rainfed areas where not suitable to physical rainfed: - harvRF[rfViolation] <- physRF[rfViolation] - - # Areas where no physical irrigated area was available, but now received harvested area - # have to be re-declared to irrigated physical areas. - # (Note: this can occur due to physical area correction) - noPhysical <- (harvIR != 0 & physIR == 0) - - # Allocate areas that are declared rainfed to these irrigated areas - physIR[noPhysical] <- physIR[noPhysical] + physRF[noPhysical] - harvIR[noPhysical] <- harvIR[noPhysical] + physRF[noPhysical] - physRF[noPhysical] <- 0 - harvRF[noPhysical] <- physRF[noPhysical] - - # Overwrite original object with corrected values - harvestedAreaYearly[, , "irrigated"] <- harvIR - harvestedAreaYearly[, , "rainfed"] <- harvRF - physicalAreaYearly[, , "irrigated"] <- physIR - physicalAreaYearly[, , "rainfed"] <- physRF - - rm(harvIR, harvRF, physIR, physRF) - - # Check whether multiple cropping has been corrected - violation <- fctMCwhereNonSuit(physicalAreaYearly = physicalAreaYearly, - harvestedAreaYearly = harvestedAreaYearly, - mcSuit = mcSuit) - if (any(violation)) { - stop("Not all cases where multiple cropping happens - despite not being suitable for multiple cropping have been corrected.") - } - - ################### - ## Select output ## - ################### - if (!physical) { - cropArea <- harvestedAreaYearly - } else { - cropArea <- physicalAreaYearly - } - - if (sectoral == "kcr") { - # this is already the format of cropArea - } else if (sectoral == "lpj") { - # crop mapping - mapMagToLpj <- toolGetMapping(type = "sectoral", name = "MAgPIE_LPJmL.csv", - where = "mappingfolder") - mapMagToLpj <- mapMagToLpj[!(mapMagToLpj$MAgPIE %in% nonCrops), ] - - cropArea <- toolAggregate(cropArea, rel = mapMagToLpj, - from = "MAgPIE", to = "LPJmL", dim = "crop") - } else { - stop("This sectoral aggregation is not available in calcCropareaLandInG") - } - - if (irrigation == TRUE) { - # this is already the format of cropArea - } else { - cropArea <- dimSums(cropArea, dim = "irrigation") - } - - # Check consistency with calcFallowLand - if (physical == TRUE) { - if (irrigation == TRUE) { - physicalCropSum <- dimSums(cropArea, dim = c("crop", "irrigation")) - } else { - physicalCropSum <- dimSums(cropArea, dim = c("crop")) - } - - if (any(abs(physicalCropSum + fallow[, y, ] - physicalAreaSum) > 10^-16)) { - stop("Sum of crops + fallow land doesn't match with total physical cropland.") - } - } - - # Aggregation to iso-level - if (!cellular) { - # aggregate to countries - cropArea <- dimSums(cropArea, dim = c("x", "y")) - # fill missing countries with 0 - cropArea <- toolConditionalReplace(x = toolCountryFill(cropArea), - conditions = "is.na()", replaceby = 0) - } else { - if (cells == "magpiecell") { - cropArea <- toolCoord2Isocell(cropArea) - } else if (cells == "lpjcell") { - # this is already the format of cropArea - } else { - stop("This value for the cell parameter is not supported, - choose between \"magpiecell\" and \"lpjcell\"") - } - } - cropAreaList[[y]] <- cropArea - } - - # bind years together - out <- mbind(cropAreaList) - - return(list(x = out, - weight = NULL, - description = "Croparea for different croptypes", - unit = "Mha", - isocountries = FALSE)) -} diff --git a/R/calcDemography.R b/R/calcDemography.R index 97886f0b..ef06b669 100644 --- a/R/calcDemography.R +++ b/R/calcDemography.R @@ -16,8 +16,8 @@ calcDemography <- function(convert = TRUE, education = TRUE) { lutz <- readSource("Lutz2014", convert = convert) mapping2 <- toolGetMapping(type = "sectoral", name = "lutz2hic2.csv", - where = "mappingfolder") - demo <- luscale::speed_aggregate(x = lutz, rel = mapping2, from = "lutz", to = "hic", dim = 3.2) + where = "mappingfolder") + demo <- toolAggregate(x = lutz, rel = mapping2, from = "lutz", to = "hic", dim = 3.2) demo <- demo[, , "B", invert = TRUE] demo <- demo[, , "All", invert = TRUE] @@ -39,8 +39,7 @@ calcDemography <- function(convert = TRUE, education = TRUE) { naming = "scenario", years = magpiesets::findset("time"), aggregate = FALSE) - diff <- dimSums(demo, dim = c("sex", "age", "education")) - population[, getYears(demo), - getNames(demo, dim = 1)] + diff <- dimSums(demo, dim = c("sex", "age", "education")) - population[, getYears(demo), getNames(demo, dim = 1)] diff[] <- abs(diff) if (sum(diff) > 100) { vcat(2, paste0( @@ -59,13 +58,13 @@ calcDemography <- function(convert = TRUE, education = TRUE) { } # recalibration to SSP population scenarios - # create SSP2EU and SDP scenarios columns based on SSP2 and SSP1 - if (any(c("SDP", "SDP_EI", "SDP_MC", "SDP_RC", "SSP2EU") %in% getNames(population))) { - demo <- add_columns(demo, addnm = c("SDP", "SDP_EI", "SDP_MC", "SDP_RC", "SSP2EU"), - dim = 3.1, fill = NA) - demo[, , "SSP2EU"] <- demo[, , "SSP2"] - demo[, , "SDP", pmatch = TRUE] <- demo[, , "SSP1"] - } + # create SSP2EU and SDP scenarios columns based on SSP2 and SSP1 + if (any(c("SDP", "SDP_EI", "SDP_MC", "SDP_RC", "SSP2EU") %in% getNames(population))) { + demo <- add_columns(demo, addnm = c("SDP", "SDP_EI", "SDP_MC", "SDP_RC", "SSP2EU"), + dim = 3.1, fill = NA) + demo[, , "SSP2EU"] <- demo[, , "SSP2"] + demo[, , "SDP", pmatch = TRUE] <- demo[, , "SSP1"] + } demoShr <- demo / dimSums(demo, dim = c("sex", "age", "education")) vcat(verbosity = 2, paste0("Year 1965 in demography data missing. Used values of 1970 instead")) diff --git a/R/calcFAOBilateralTrade.R b/R/calcFAOBilateralTrade.R deleted file mode 100644 index bd4a9623..00000000 --- a/R/calcFAOBilateralTrade.R +++ /dev/null @@ -1,149 +0,0 @@ -#' @title calcFAOBilateralTrade -#' @description Calculates bilateral trade values based on FAO trade matrix -#' @param output "value", "qty", or "price" -#' @param products "kcr", "kli", or "kothers" -#' @param prodAgg binary to keep FAO product level or magpie -#' @param fiveYear only 5 year steps due to memory load -#' @return List of magpie objects with results on bilateral country level, -#' weight on bilateral country level, unit and description. -#' @author David M Chen -#' @examples -#' \dontrun{ -#' calcOutput("FAOBilateralTrade", output = "qty", products = "kcr") -#' } -#' -calcFAOBilateralTrade <- function(output = "value", products = "kcr", prodAgg = TRUE, fiveYear = TRUE) { - #### harmonize export and import-based reporting based on reliability index (Gelhar 1996) - # importer and exporter datasets - - if (output %in% c("qty", "value")) { - - im <- collapseNames(readSource("FAOTradeMatrix", - subtype = paste("import", output, products, sep = "_"), convert = TRUE)) - im <- im[, c(min(getYears(im, as.integer = TRUE)):1994), invert = TRUE] # subset years for lighter load on mem - ex <- collapseNames(readSource("FAOTradeMatrix", - subtype = paste("export", output, products, sep = "_"), convert = TRUE)) - ex <- ex[, c(min(getYears(ex, as.integer = TRUE)):1994), invert = TRUE] - - if (fiveYear) { - im <- im[, seq(1995, 2020, 5), ] - ex <- ex[, seq(1995, 2020, 5), ] - } - - .harmBilat <- function(im, ex, value) { - - if (value) { - # imports generally reported on cif basis, use generic 12% (FAOSTAT) for now. - fobCvn <- 1.12 - # convert exporter values to cif - ex <- ex * fobCvn - } - # remove missing items from intersect for now - citems <- intersect(getNames(im), getNames(ex)) - im <- im[, , citems] - ex <- ex[, , citems] - - #### determine reliability index of countries imports and exports reporting (Gelhar 1996) - # re-order exports so it's reporter country second, so exports imports in same dim order - ex <- dimOrder(ex, perm = c(2, 1), dim = 1) - # create accuracy level of each commodity-countrypair - accLevel <- abs((im - ex) / im) - # total - gc() - imTot <- dimSums(im, dim = 1.2) - gc() - ## use reliability as Accuracy Level accLevel within =< 0.20 (Gelhar 1996) - # get all trades within 0.2 accuracy (0 most accurate) - imAcc <- im - imAcc[which(accLevel > 0.2)] <- 0 - gc() - imAcc <- dimSums(imAcc, dim = 1.2) - # reliability index - rim <- imAcc / imTot * 100 - gc() - - exTot <- dimSums(ex, dim = 1.1) - gc() - - exAcc <- ex - exAcc[which(accLevel > 0.2)] <- 0 - gc() - exAcc <- dimSums(exAcc, dim = 1.1) - gc() - # reliability index - rix <- exAcc / exTot * 100 - gc() - # make difference in reliability for all country combinations - getItems(rix, dim = 1) <- paste0(getItems(rix, dim = 1), "1") - imR <- exR <- rim - rix - getItems(imR, dim = 1) <- gsub("1", "", getItems(imR, dim = 1)) - getItems(imR, dim = 1, raw = TRUE) <- gsub("p", "\\.", getItems(imR, dim = 1)) - getItems(exR, dim = 1) <- gsub("1", "", getItems(exR, dim = 1)) - getItems(exR, dim = 1, raw = TRUE) <- gsub("p", "\\.", getItems(exR, dim = 1)) - gc() - exR[which(exR >= 0)] <- 0 - exR[which(exR < 0)] <- 1 - imR[which(imR >= 0)] <- 1 - imR[which(imR < 0)] <- 0 - gc() - - imROnly <- im * imR - exROnly <- ex * exR - gc() - - out <- imROnly + exROnly - return(out) - } - - if (output == "qty") { - value <- FALSE - } else if (output == "value") { - value <- TRUE - } - out <- .harmBilat(ex = ex, im = im, value = value) - weight <- NULL - - if (output == "qty") { - out <- out / 1e6 # convert million tonnes - unit <- "MtWM" - } else if (output == "value") { - out <- out / 1e3 # in millions - unit <- "million USD$05" - } - - } else if (output == "price") { - qty <- calcOutput("FAOBilateralTrade", output = "qty", products = products, prodAgg = FALSE, aggregate = FALSE) - value <- calcOutput("FAOBilateralTrade", output = "value", products = products, prodAgg = FALSE, aggregate = FALSE) - out <- value / qty - weight <- qty - unit <- "US$05/tDM" - } - - if (prodAgg) { - # aggregate to get a preliminary cif/fob ratio - out[is.na(out)] <- 0 - mapping <- toolGetMapping("newFAOitems_online_DRAFT.csv", type = "sectoral", where = "mrcommons") - out <- toolAggregate(out, rel = mapping, from = "new_FAOoriginalItem_fromWebsite", - to = "k", partrel = TRUE, dim = 3.1) - - if (output == "qty") { - attr <- calcOutput("Attributes", aggregate = FALSE) - out <- out / collapseNames(attr[, , "wm"][, , getNames(out)]) - unit <- "MtDM" - } else if (output == "price") { - attr <- calcOutput("Attributes", aggregate = FALSE) - out <- out * collapseNames(attr[, , "wm"][, , getNames(out)]) - unit <- "tDM" - } - } - - getSets(out)[c(1, 2)] <- c("im", "ex") - out[is.na(out)] <- 0 - out[is.infinite(out)] <- 0 - - return(list(x = out, - weight = weight, - unit = unit, - description = "Bilateral Trade values") - ) -} diff --git a/R/calcFAOLand.R b/R/calcFAOLand.R deleted file mode 100644 index 136feb9b..00000000 --- a/R/calcFAOLand.R +++ /dev/null @@ -1,17 +0,0 @@ -#' @title calcFAOLand -#' @description Returns physical land areas from FAOSTAT -#' -#' @return land areas from FAOSTAT and weight -#' @author Ulrich Kreidenweis, Kristine Karstens - -calcFAOLand <- function() { - - data <- readSource("FAO_online", "Land") - data <- data[, , "1000_ha", pmatch = TRUE] # subset all area information - data <- collapseDim(data / 10^3, dim = 3.2) # transform unit, drop unit statement - - return(list(x = data, - weight = NULL, - unit = "mio. ha", - description = "land-use categories from FAOSTAT")) -} diff --git a/R/calcFAOTradePrices.R b/R/calcFAOTradePrices.R deleted file mode 100644 index ae4cab30..00000000 --- a/R/calcFAOTradePrices.R +++ /dev/null @@ -1,69 +0,0 @@ -#' @title calcFAOTradePrices -#' -#' @description calculates USD per kg of FAOSTAT Trade data -#' for import and export prices -#' -#' @param aggregation "none", "k", "fbs" or "springmann" -#' for the last uses Marco Springmann's custom product mapping -#' -#' @return List of magpie objects with results on country level, -#' weight on country level, unit and description. -#' @author David M Chen -#' @examples -#' \dontrun{ -#' calcOutput("calcFAOTradePrices") -#' } -#' -calcFAOTradePrices <- function(aggregation = "k") { - - trade <- readSource("FAO_online", subtype = "Trade") - - # no conversion for heads or numbers of animals yet? - trade <- trade[, , c("export", "import", "import_US$MER05", "export_US$MER05")] - - # get mapping - mapping <- toolGetMapping("FAOitems_online.csv", type = "sectoral", where = "mappingfolder") - - if (aggregation == "k") { - tradeAgg <- toolAggregate(trade, rel = mapping, - from = "ProductionItem", to = "k", - partrel = TRUE, dim = 3.1) - } else if (aggregation == "fbs") { - tradeAgg <- toolAggregate(trade, rel = mapping, - from = "ProductionItem", to = "FoodBalanceItem", - partrel = TRUE, dim = 3.1) - } else if (aggregation == "springmann") { - tradeFBS <- toolAggregate(trade, rel = mapping, - from = "ProductionItem", to = "FoodBalanceItem", - partrel = TRUE, dim = 3.1) - mappingSpringmann <- toolGetMapping("springmann_fbs_mapping.csv", - type = "sectoral", where = "mappingfolder") - getNames(tradeFBS, dim = 1) <- gsub(".*\\|", "", getNames(tradeFBS, dim = 1)) - tradeAgg <- toolAggregate(tradeFBS, rel = mappingSpringmann, - from = "FBS.item", to = "Food.group", - partrel = TRUE, dim = 3.1) - } else if (aggregation == "none") { - tradeAgg <- trade - } else { - stop("Only none k fbs and springmann aggregations currently") - } - - importPrice <- collapseNames(tradeAgg[, , "import_kUS$"] / tradeAgg[, , "import"], - collapsedim = 3) - getNames(importPrice, dim = 2) <- "importPrice" - - exportPrice <- collapseNames(tradeAgg[, , "export_kUS$"] / tradeAgg[, , "export"], - collapsedim = 3) - getNames(exportPriceS, dim = 2) <- "exportPrice" - - out <- mbind(importPrice, exportPrice) - - weight <- tradeAgg[, , c("export", "import")] - getNames(weight, dim = 2) <- c("importPrice", "exportPrice") - - return(list(x = out, - weight = weight, - unit = "$/t", - description = "FAO Prices at Trade")) - -} diff --git a/R/calcFAOYield.R.orig b/R/calcFAOYield.R.orig deleted file mode 100644 index 36c77132..00000000 --- a/R/calcFAOYield.R.orig +++ /dev/null @@ -1,90 +0,0 @@ -#' @title calcFAOYield -#' -#' @description calculates the yield based on FAO data -#' @param physical physical area or havested area -#' @param attributes in dm, wm, ge, nr, p, k -#' @param cellular if TRUE value is calculate on cellular level -#' @param areaSource data source for croparea used in calculation: FAO or Toolbox -#' @param irrigation distinguish irrigation or not -#' @param cut FALSE (default) - do not cut off yields, -#' number between 0 and 1 to define percentile value for cut off -#' @param average averaging period in years (if NULL no averaging is used) -#' @return MAgPIE object of yields -#' @author Debbora Leip, Jan Philipp Dietrich, Kristine Karstens, Felicitas Beier -#' @importFrom stats quantile - -calcFAOYield <- function(physical = TRUE, attributes = "dm", irrigation = FALSE, - cellular = FALSE, cut = FALSE, average = 5, areaSource = "FAO") { - - production <- calcOutput("Production", products = "kcr", attributes = attributes, - irrigation = irrigation, cellular = cellular, - cells = "lpjcell", aggregate = FALSE) - selectyears <- getItems(production, dim = "year") - - if (areaSource == "FAO") { - - area <- calcOutput("Croparea", sectoral = "kcr", physical = physical, -<<<<<<< HEAD - cellular = cellular, cells = "lpjcell", - irrigation = irrigation, aggregate = FALSE) -======= - irrigation = irrigation, aggregate = FALSE, cellular = cellular) ->>>>>>> master - - } else if (areaSource == "LandInG") { - - area <- calcOutput("CropareaLandInG", sectoral = "kcr", physical = physical, - irrigation = irrigation, selectyears = selectyears, - cellular = cellular, cells = "lpjcell", aggregate = FALSE) - } else { - stop("Please specify which area should be used for calculation. - Note: LandInG should be FAO-consistent.") - } - - faoyears <- intersect(getYears(production), getYears(area)) - - yield <- collapseNames(production[, faoyears, ]) / area[, faoyears, ] - yield[yield == Inf | yield == -Inf | is.nan(yield) | yield == 0] <- NA - - # If cut!=FALSE, cut yields at 'cut'-percentile and hold constant from there on - if (cut != FALSE) { - for (k in getNames(yield)) { - # define cut off, depending on 'cut' value ('cut'-percentile) - cutK <- quantile(yield[, , k], cut, na.rm = TRUE) - # set all values above the threshold to cut off value - yield[, , k][yield[, , k] > cutK] <- cutK - } - } - - # if no data for begr and betr is available just take highest observed yields for the given country as replacement - max <- as.magpie(suppressWarnings(apply(yield, 1:2, max, na.rm = TRUE))) - max[max == -Inf] <- NA - for (b in c("begr", "betr")) { - if (all(is.na(yield[, , b]))) { - yield[, , b] <- max - } - } - - # use lower end yield values as replacements for missing data points - tmp <- as.magpie(apply(yield, 2:3, quantile, probs = 0.2, na.rm = TRUE)) - low <- yield - low[, , ] <- tmp - na <- which(is.na(yield), arr.ind = TRUE) - yield[na] <- low[na] - - if (!is.null(average)) { - area <- toolTimeAverage(area, average) - yield <- toolTimeAverage(yield, average) - } - - years <- findset("past") - yield <- yield[, years, ] - area <- area[, years, ] - - return(list(x = yield, - weight = area + 10^-10, - min = 0, - unit = "t/ha", - description = "Calculates the yield based on FAO", - isocountries = !cellular)) -} diff --git a/R/calcFAOharmonized.R b/R/calcFAOharmonized.R deleted file mode 100644 index ef5f703f..00000000 --- a/R/calcFAOharmonized.R +++ /dev/null @@ -1,97 +0,0 @@ -#' @title calcFAOharmonized -#' @description Calculate harmonized FAO Commodity Balance and Food Supply data based on CB, only harvested areas -#' are taken from ProdSTAT. This functions adds the CBCrop, CBLive, FSCrop and FSLive data together. -#' -#' @return FAO harmonized data, weight as NULL, and a description as as a list of MAgPIE objects -#' @author Ulrich Kreidenweis, David Chen, Kristine Karstens -#' @examples -#' \dontrun{ -#' a <- calcOutput("FAOharmonized") -#' } -#' @importFrom utils read.csv - -calcFAOharmonized <- function() { - - - # input data: Commodity Balance (Crops Primary + Livestock Primary), Food Supply (Crops Primary + Livestock Primary) - cbCrop <- readSource("FAO_online", "CBCrop") - cbLive <- readSource("FAO_online", "CBLive") - fsCrop <- readSource("FAO_online", "FSCrop") - fsLive <- readSource("FAO_online", "FSLive") - - cb <- toolFAOcombine(cbLive, cbCrop, combine = "Item") - fs <- toolFAOcombine(fsLive, fsCrop, combine = "Item") - rm(cbCrop, cbLive, fsCrop, fsLive) - - faoData <- mbind(cb, fs) - - ## in addition harvested area from Crops Primary - - prod <- readSource("FAO_online", "Crop", convert = TRUE) - - ## aggregate Prod to CB units - aggregation <- toolGetMapping("FAOitems_online.csv", type = "sectoral", where = "mappingfolder") - - # remove aggregate categories - remove <- setdiff(getNames(prod, dim = 1), aggregation$ProductionItem) - prod <- prod[, , remove, invert = TRUE] - - areaHarvested <- toolAggregate(prod, rel = aggregation, from = "ProductionItem", to = "FoodBalanceItem", - dim = 3.1, partrel = TRUE)[, , "area_harvested"] - - commonyears <- intersect(getYears(areaHarvested), getYears(faoData)) - - faoData <- mbind(faoData[, commonyears, ], areaHarvested[, commonyears, ]) - - rm(areaHarvested) - - - ### add Fodder data ### - - fodder <- readSource("FAO", "Fodder") - fodder <- toolExtrapolateFodder(fodder, endyear = max(getYears(faoData, as.integer = TRUE))) - fodder <- add_columns(x = fodder, addnm = "domestic_supply", dim = 3.2) - fodder[, , "domestic_supply"] <- fodder[, , "feed"] - fodderAggregated <- toolAggregate(fodder, rel = aggregation, from = "ProductionItem", - to = "FoodBalanceItem", dim = 3.1, partrel = TRUE) - cyears <- intersect(getYears(faoData), getYears(fodderAggregated)) - faoData <- mbind(faoData[, cyears, ], fodderAggregated[, cyears, ]) - rm(fodder, fodderAggregated) -gc() - - faoData[is.na(faoData)] <- 0 - - ## check if there is data without an element name - - ## what to do? In case there is data these dimensions should not be deleted - - if (any(getItems(faoData, dim = 3.1) == "")) { - if (sum(faoData[, , ""]) == 0) { - faoData <- faoData[, , "", invert = TRUE] - } else { - vcat(1, 'Aggregation created entries without name (""), but containing information. This should not be the case.') - } - } - - if (any(getNames(faoData) == "remaining.production")) { - remainProd <- mean(dimSums(faoData[, , "remaining.production"], dim = 1) / - dimSums(dimSums(faoData[, , "production"], dim = 3), dim = 1)) - if (remainProd > 0.02) vcat(1, "Aggregation created a 'remaining' category. Production is", - round(remainProd, digits = 3) * 100, "% of total \n") - } - if (any(getNames(faoData) == "remaining.area_harvested")) { - remainArea <- mean(dimSums(faoData[, , "remaining.area_harvested"], dim = 1) / - dimSums(dimSums(faoData[, , "area_harvested"], dim = 3), dim = 1)) - if (remainArea > 0.02) vcat(1, "Aggregation created a 'remaining' category. The area harvested is", - round(remainArea, digits = 3) * 100, "% of total \n") - } - - # conversion from tonnes to Mt, hectares to Mha and 10^6kcal to 10^12kcal. - faoData <- faoData / 10^6 - - return(list(x = faoData, - weight = NULL, - description = "FAO Commodity Balance and Food Supply data", - unit = "Unit in Mt/yr, for area Mha, calories in 10^12 kcal/yr", - note = "food_supply_kcal, protein_supply and fat_supply were calculated from per capita per day values")) -} diff --git a/R/calcFAOmassbalance_pre.R b/R/calcFAOmassbalance_pre.R deleted file mode 100644 index af97a412..00000000 --- a/R/calcFAOmassbalance_pre.R +++ /dev/null @@ -1,848 +0,0 @@ -#' @title calcFAOmassbalance_pre -#' @description Calculates an extended version of the Food Balance Sheets. Makes explicit the conversion processes that -#' convert one type of product into another. Includes processes like milling, distilling, extraction etc. Adds certain -#' byproducts like distillers grains or ethanol. -#' -#' @param years years to be estimated, if null, then all years in FAOharmonized are returned -#' -#' @return List of magpie objects with results on country level, weight on country level, unit and description. -#' This is an intermediary result, which is used e.g. for estimating the feed baskets. For most uses, it is more -#' appropriate to use the FAOmasbalance instead of the FAOmassbalance_pre. -#' @author Benjamin Leon Bodirsky -#' @seealso -#' [calcFAOmassbalance()] -#' @examples -#' \dontrun{ -#' calcOutput("FAOmassbalance_pre") -#' } -#' @importFrom graphics plot -#' @importFrom magclass getSets as.magpie complete_magpie -#' @importFrom utils read.csv -#' @importFrom withr local_options - -calcFAOmassbalance_pre <- function(years = NULL) { # nolint - #### Data input #### - - ### FAO Commodity Balance - cbc <- calcOutput(type = "FAOharmonized", aggregate = FALSE) - getSets(cbc) <- c("region", "year", "ItemCodeItem.ElementShort") - - if (any(duplicated(dimnames(cbc)[[3]]) == TRUE)) { - stop("The folowing dimnames are duplicated: ", paste(getNames(cbc)[duplicated(getNames(cbc))], collapse = "\", \"")) - } - - # determine years - if (is.null(years)) { - years <- getYears(cbc) - } - if (nchar(years[[1]]) < 5) { - years <- paste0("y", years) - } - - cbc <- cbc[, years, ] - - # remove double counting and add missing products - removethem <- c( - # crop commodity balance and Food Supply items aggregated - "2924|Alcoholic Beverages", - "2905|Cereals - Excluding Beer", - "2919|Fruits - Excluding Wine", - "2928|Miscellaneous", - "2913|Oilcrops", - "2911|Pulses", - "2923|Spices", - "2907|Starchy Roots", - "2922|Stimulants", - "2909|Sugar & Sweeteners", - "2908|Sugar Crops", - "2912|Treenuts", - "2914|Vegetable Oils", - "2918|Vegetables", - "2903|Vegetal Products", - "2901|Grand Total", - # livestock commodity balance and Food Supply items aggregated - "2941|Animal Products", - "2946|Animal fats", - "2961|Aquatic Products, Other", - "2949|Eggs", - "2960|Fish, Seafood", - "2943|Meat", - "2948|Milk - Excluding Butter", - "2738|Milk, Whole", - "2739|Milk, Skimmed", - "2945|Offals", - # others and equivalents - "2741|Cheese", - "2556|Groundnuts (Shelled Eq)", - "2562|Palm kernels", - "2805|Rice (Milled Equivalent)", - "2815|Roots & Tuber Dry Equiv", - "2672|Rubber", - "2747|Silk", - "2827|Sugar, Raw Equivalent", - "2542|Sugar (Raw Equivalent)", - "2671|Tobacco", - "2742|Whey" - ) - - cbc <- cbc[, , removethem, invert = TRUE] - - cbc <- complete_magpie(cbc, fill = 0) - - missingproducts <- c("X001|Ethanol", - "X002|Distillers_grain", - "X003|Palmoil_Kerneloil_Kernelcake", - "X004|Brewers_grain") - - cbc <- add_columns(cbc, addnm = missingproducts, dim = 3.1) - - - #### Product Attributes - prodAttributes <- calcOutput("Attributes", aggregate = FALSE) - attributeTypes <- getNames(prodAttributes, dim = 1) - removeProd <- c("betr", "begr", "pasture", "scp", "res_cereals", - "res_fibrous", "res_nonfibrous", "wood", "woodfuel") - prodAttributes <- prodAttributes[, , removeProd, invert = TRUE] - - # Sectoral mapping for FAO items - relationmatrix <- toolGetMapping("FAOitems_online.csv", type = "sectoral", where = "mappingfolder") - relationmatrix <- relationmatrix[, c("FoodBalanceItem", "k")] - relationmatrix <- relationmatrix[!duplicated(relationmatrix[, "FoodBalanceItem"]), ] - - .getFAOitems <- function(magpieItems) { - return(relationmatrix[relationmatrix$k %in% magpieItems, "FoodBalanceItem"]) - } - - # Map production attributes to FAO items - prodAttributes <- toolAggregate(x = prodAttributes, rel = relationmatrix, dim = 3.2, from = "k", - to = "FoodBalanceItem", partrel = TRUE) - getSets(prodAttributes) <- c("region", "year", "attributes", "ItemCodeItem") - - # change prod attributes from share of dm to share of wm - attributesWM <- (prodAttributes / dimSums(prodAttributes[, , "wm"], dim = "attributes")) - - cbcItemnames <- getNames(cbc, dim = "ItemCodeItem") - itemnamesAttributes <- getNames(attributesWM, dim = "ItemCodeItem") - if (!(all(cbcItemnames %in% itemnamesAttributes))) { - vcat(verbosity = 2, "The following items were removed from the dataset because of missing prodAttributes: ", - paste(cbcItemnames[!(cbcItemnames %in% itemnamesAttributes)], collapse = "\", \"")) - cbc <- cbc[, , cbcItemnames[cbcItemnames %in% itemnamesAttributes]] - relationmatrix <- relationmatrix[relationmatrix[, "FoodBalanceItem"] %in% - intersect(cbcItemnames, itemnamesAttributes), ] - } - if (!all(itemnamesAttributes %in% cbcItemnames)) { - stop("For the following items there were entries in prodAttributes but no respective data: ", - paste(itemnamesAttributes[!(itemnamesAttributes %in% cbcItemnames)], collapse = "\", \"")) - } - - - ### FAO items not relevant to processing, and processing dimensions that need to be added - noProcessing <- c("livst_rum", "livst_pig", "livst_milk", "livst_egg", "livst_chick", "foddr", "fish", "fibres") - noProcessingFAO <- .getFAOitems(noProcessing) - noProcessingFAO <- noProcessingFAO[noProcessingFAO %in% getNames(cbc, dim = 1)] - - namesProcessing <- c("production_estimated", - "milling", "brans1", "branoil1", "flour1", - "refining", "sugar1", "molasses1", "refiningloss", - "extracting", "oil1", "oil2", "oilcakes1", "extractionloss", - "fermentation", "alcohol1", "alcohol2", "alcohol3", "alcohol4", "brewers_grain1", "alcoholloss", - "distilling", "ethanol1", "distillers_grain1", "distillingloss", - "intermediate", - "households") - - #### Definition of subfunctions ##### - - # run massbalance checks and clear processed positions after calculating process - .checkAndClear <- function(object, - goodsIn, - from, - process, - reportAs, - residual, - relevantAttributes = attributeTypes, - goodsOut = NULL, - threshold = 1e-5) { - # perform massbalance tests: - # 1) input goods balanced? - diff <- (dimSums(object[, , list(goodsIn, c(reportAs, residual))], dim = c("ElementShort")) - - dimSums(object[, , list(goodsIn, from)], dim = c("ElementShort"))) - if (any(abs(diff) > threshold)) { - stop("NAs in dataset or function corrupt: process not balanced for ", - paste(goodsIn, collapse = ", "), " reported as ", paste(reportAs, collapse = ", ")) - } - - # 2) output goods balanced... - if (!is.null(goodsOut)) { - # ... with input goods? - diff <- (dimSums(object[, , list(goodsIn, from)], dim = c("ElementShort", "ItemCodeItem")) - - dimSums(object[, , list(goodsIn, residual)], dim = c("ElementShort", "ItemCodeItem")) - - dimSums(object[, , list(goodsOut, "production_estimated")], dim = c("ElementShort", "ItemCodeItem")) - ) - if (any(abs(diff) > threshold)) { - stop("NAs in dataset or function corrupt: goods not balanced for ", - paste(goodsOut, collapse = ", "), " from ", paste(goodsIn, collapse = ", ")) - } - - # ... in production? - diff <- (sum(object[, , list(goodsOut, "production_estimated")]) - - sum(object[, , list(goodsOut, "production")])) - if (any(abs(diff) > threshold)) { - stop("Global estimated production does not meet global production for ", - paste(goodsOut, collapse = ", ")) - } - } - - # 4) special case for cereal milling: branoils balanced? - if (residual == "flour1") { - diff <- (dimSums(object[, , list(goodsIn, "branoil1")], dim = c("ElementShort", "ItemCodeItem")) - - dimSums(object[, , list(c("2581|Ricebran Oil", "2582|Maize Germ Oil"), "production_estimated")], - dim = c("ElementShort", "ItemCodeItem"))) - if (any(abs(diff) > threshold)) { - stop("NAs in dataset or function corrupt: branoil1 not balanced") - } - } - - # negative value check - relValues <- object[, , list(goodsIn, c(reportAs, from, residual), relevantAttributes)] - if (any(relValues < -threshold)) { - warning("Massbalancing failed, negative values for ", - paste(unique(unname(where(relValues < -threshold)[[1]]$individual[, 3])), collapse = ", ")) - } - - # move from "from" to "process" and clear "from" - if (from != process) { - object[, , list(goodsIn, process)] <- object[, , list(goodsIn, from)] - } - object[, , list(goodsIn, from)] <- 0 # if from == process it is "intermediate" which is to be cleared as well - - gc() - return(object) - } - - # Different processes, e.g. ethanol production from cereals, are not specified - # in cbc (instead the general categories "other_util" and "processed" are used), - # but are required within MAgPIE. - # This function calculates the maximum amount out of a given product "goodIn.from" - # that can be used to produce a given output product "goodOut", depending on - # the "extractionQuantity" and the "extractionAttribute". This quantity is - # reported as "goodIn.reportAs", while any remaining quantity in "goodIn.from" - # is reported as "goodIn.residual". The full amount in "goodIn.from" is then - # moved to "goodIn.process" (i.e. "goodIn.from" will be empty after the function - # call). The variable "process" specifies the process leading to the output product - # "reportAs". - # The calculated production quantity is also added to "goodOut.production_estimated". - .extractGoodFromFlow <- function(object, - goodIn, # FAO-defined input product, e.g. "2536|Sugar cane" - from, # FAO-defined process, e.g. "other_util" - process, # MAgPIE-defined process, e.g. "distilling" - goodOut, # FAO-defined output product, e.g. "X001|Ethanol" - reportAs, # MAgPIE-defined output product, e.g. "ethanol1" - residual, # MAgPIE-defined residual, e.g. "distillingloss" - extractionQuantity, # e.g. 0.516006 - extractionAttribute, # e.g. "dm" - prodAttributes) { - - if (length(from) > 1 || length(reportAs) > 1 || length(goodIn) > 1 || length(goodOut) > 1) { - stop("please only use one item each for \"from\", \"reportAs\", \"goodIn\", and \"goodOut\"") - } - if (any(object[, , list(goodIn, c(reportAs, residual))] != 0)) { - warning("Output flows already exist!") - } - - # relevant attributes for extraction quantity - attrNoWM <- setdiff(attributeTypes, "wm") - - # calculating possible extraction quantity per attribute - attributesFrom <- dimSums(object[, , list(goodIn, from), drop = TRUE], dim = "region") / - dimSums(object[, , list(goodIn, from, extractionAttribute), drop = TRUE], dim = c("region")) - attributesTo <- prodAttributes[, , goodOut, drop = TRUE] / - prodAttributes[, , list(goodOut, extractionAttribute), drop = TRUE] - extractionFactor <- attributesFrom[, , attrNoWM] / attributesTo[, , attrNoWM] - - # maximum extraction quantity as minimum over the possible quantity per attribute - maxextract <- as.magpie(apply(X = extractionFactor, MARGIN = 2, FUN = min)) - if (extractionQuantity == "max") { - extractionQuantity <- maxextract - } else if (any(extractionQuantity > maxextract)) { - stop("too high extraction quantity") - } - - # calculate outputs - extracted <- object[, , list(goodIn, from, extractionAttribute), drop = TRUE] * extractionQuantity * attributesTo - losses <- dimSums(object[, , list(goodIn, from)], dim = "ElementShort") - extracted - - object[, , list(goodIn, reportAs)] <- extracted - object[, , list(goodIn, residual)] <- losses - - object[, , list(goodOut, "production_estimated")] <- object[, , list(goodOut, "production_estimated")] + extracted - - # check results and clear processed position - object <- .checkAndClear(object, goodIn, from, process, reportAs, residual, attrNoWM) - - return(object) - } - - # This function is similar to .extractGoodFromFlow, with the difference that - # multiple input goods can be given (which will then be added up before calculating - # the amount of "goodsOut" that can be produced), and that multiple output goods - # (and corresponding items in reportAs) can be given. The order of FAO categories - # in "goodsOut" and corresponding MAgPIE categories in "reportAs" needs to match! - # In contrast to .extractGoodFromFlow, this function calculates global - # conversion factors per attribute instead of using an "extractionQuantity" - # and "extractionAttribute" for calculations. - .processingGlobal <- function(object, - goodsIn, # e.g. c("2536|Sugar cane", "2537|Sugar beet") - from, # e.g. "processed" #nolint - process, # e.g. "refining" - goodsOut, # e.g. c("2818|Sugar, Refined Equiv", "2544|Molasses") - (the order matters!) - reportAs, # e.g. c("sugar1", "molasses1") - (the order matters!) - residual # e.g. "refiningloss" - ) { - if (any(object[, , list(goodsIn, c(reportAs, residual))] != 0)) { - stop("Output flows already exist.") - } - if (any(object[, , list(goodsOut, "production_estimated")] != 0)) { - stop("Output flows already exist.") - } - - # attributes relevant for checking massbalance and convFactor - relevantAttributes <- setdiff(attributeTypes, "wm") - - # calculate global conversion factor per attributes - convFactor <- (dimSums(object[, , list(goodsOut, "production")], dim = c("region", "ElementShort")) - / dimSums(object[, , list(goodsIn, from)], dim = c("region", "ItemCodeItem", "ElementShort"))) - - factors <- dimSums(convFactor[, , list(goodsOut, relevantAttributes)], dim = "ItemCodeItem") - if (any(factors > 1)) { - stop("conversion factors exceed 1. not suitable for a global conversion factor.", - paste(unique(unname(where(factors > 1)[[1]]$individual)), collapse = ", ")) - } - - # estimate outputs - for (j in seq_along(goodsOut)) { - object[, , list(goodsIn, reportAs[j])] <- dimSums(object[, , list(goodsIn, from)], dim = "ElementShort") * - convFactor[, , goodsOut[j], drop = TRUE] - object[, , list(goodsOut[j], "production_estimated")] <- dimSums(object[, , list(goodsIn, reportAs[j])], - dim = c("ElementShort", "ItemCodeItem")) - } - - # calculate refining losses as mass balance difference - object[, , list(goodsIn, residual)] <- (dimSums(object[, , list(goodsIn, from)], dim = c("ElementShort")) - - dimSums(object[, , list(goodsIn, reportAs)], dim = c("ElementShort"))) - - # check results and clear processed position - object <- .checkAndClear(object, goodsIn, from, process, reportAs, residual, relevantAttributes, goodsOut) - - return(object) - } - - - # processing of cereals (milled) to bran and flour: this is the only process that does - # not use the functions .extractGoodFromFlow and/or .processingGlobal, - # as the extraction quantity of bran is calculated in a specific way, using the - # ratio of bran to full cereal as given by Feedipedia. - .cerealMillingGlobal <- function(object) { - - cereals <- c("2511|Wheat and products", - "2513|Barley and products", - "2514|Maize and products", - "2515|Rye and products", - "2516|Oats", - "2517|Millet and products", - "2518|Sorghum and products", - "2520|Cereals, Other", - "2804|Rice (Paddy Equivalent)") - - brans <- .getFAOitems("brans") - milled <- "food" - flour <- "flour1" - process <- "milling" - - if (any(object[, , list(cereals, c(flour, "brans1", "branoil1"))] != 0)) { - stop("Output flows already exist.") - } - - milledGlobal <- dimSums(object[, , list(cereals, milled)], dim = c("region", "ElementShort")) - bransGlobal <- dimSums(object[, , list(brans, "production")], dim = c("region", "ElementShort", "ItemCodeItem")) - - # as share of dm instaed of wm - branAttributes <- bransGlobal / dimSums(bransGlobal[, , "dm"], dim = "attributes") - - # estimating bran based on simple factors (Feedipedia) - # rice: 10%, wheat: 25% - # we use 20% for wheat to account for some wholegrain meal - # own estimates to not violate massbalance: corn and trce get only 5% - branRatio <- new.magpie("GLO", getYears(object), cereals, fill = 0.20) - getSets(branRatio) <- c("region", "year", "ItemCodeItem") - branRatio[, , c("2804|Rice (Paddy Equivalent)", "2514|Maize and products")] <- 0.1 - branRatio[, , c("2518|Sorghum and products", "2517|Millet and products")] <- 0.05 - bransUncalibrated <- dimSums(branRatio * milledGlobal[, , "dm"], dim = "ItemCodeItem") - branRatio <- branRatio * dimSums(bransGlobal[, , "dm"], dim = "attributes") / bransUncalibrated - - # bran estimation - branEstimated <- branRatio * branAttributes * dimSums(object[, , list(cereals, milled)][, , "dm"], - dim = "attributes") - object[, , list(cereals, "brans1")] <- dimSums(branEstimated[, , cereals], dim = c("ElementShort")) - object[, , list(brans, "production_estimated")] <- dimSums(branEstimated[, , cereals], - dim = c("ItemCodeItem", "ElementShort")) - object[, , list(cereals, flour)] <- object[, , list(cereals, milled)] - branEstimated - - # branoil estimation - .branoil1Production <- function(object, branoilItem, cropItem) { - branoilRatio <- (dimSums(object[, , list(branoilItem, "production")], - dim = c("region", "ItemCodeItem", "ElementShort")) / dimSums(milledGlobal[, , cropItem], - dim = "ItemCodeItem")) - estimatedBranoil <- object[, , list(cropItem, milled)] * branoilRatio - object[, , list(cropItem, "branoil1")] <- dimSums(estimatedBranoil[, , cropItem], - dim = c("ElementShort")) - object[, , list(branoilItem, "production_estimated")] <- dimSums(estimatedBranoil[, , cropItem], - dim = c("ItemCodeItem", "ElementShort")) - object[, , list(cropItem, flour)] <- object[, , list(cropItem, flour)] - estimatedBranoil - return(object) - } - - object <- .branoil1Production(object, "2582|Maize Germ Oil", "2514|Maize and products") - object <- .branoil1Production(object, "2581|Ricebran Oil", "2804|Rice (Paddy Equivalent)") - - # check results and clear processed position - object <- .checkAndClear(object, goodsIn = cereals, from = milled, process = process, - reportAs = c("brans1", "branoil1"), residual = flour) - - ### Fooduse in brans is included in the commodity balance sheets, but not reflected in calories. - # We subtract bran consumption from cereal consumption in the respective countries. - # For simplicity, we distribute brans proportional to all cereal fooduse. - relAttributes <- c("wm", "ge", "nr") - branshr <- (dimSums(object[, , list(brans, milled, relAttributes)], dim = c(3.1, 3.2)) - / dimSums(object[, , list(cereals, "households", relAttributes)], dim = c(3.1, 3.2))) - branshr[is.nan(branshr)] <- 0 - if (any(branshr < 0)) { - vcat(1, "branshr should not be smaller than zero.") - } - object[, , list(cereals, "households", relAttributes)] <- (1 - branshr) * - object[, , list(cereals, "households", relAttributes)] - object[, , list(brans, "households", relAttributes)] <- object[, , list(brans, milled, relAttributes)] - - return(object) - } - - # processing of tece and maiz (other_util) to ethanol, distillers grain and distilling loss - .ethanolProcessing <- function(object) { - " - ethanol: - DDGS Handbook - U.S. Grains Council. 2013. A Guide to Distillers Dried Grains with Solubles (DDGS). - http://www.grains.org/buyingselling/ddgs/handbook/20140422/comparison-different-grain-ddgs-sources-nutrient-composition. # nolint - sugarcane: 654 l/t - barley: 399 l/t - corn: 408 l/t - oats: 262 l/t - wheat: 375 l/t - ethanol weight per l: 789g - similar numbers: - Balat M and Balat H 2009 Recent trends in global production and utilization of bio-ethanol fuel Applied Energy - 86 2273-82 - " - - # Wheat instead of tece would be more correct, but we need to have homogeneous products - tece <- .getFAOitems("tece") - teceMaize <- c(tece, "2514|Maize and products") - - # liter yield for different sources - ethanolYieldLiterPerTonTece <- 375 - ethanolYieldLiterPerTonMaize <- 408 - ethanolYieldLiterPerTonSugarcane <- 654 - - # liter yield converted to dm (-> extraction factor) - ethanolYieldLiterPerTonTeceMaize <- c(rep(ethanolYieldLiterPerTonTece, length(tece)), ethanolYieldLiterPerTonMaize) - extractionQuantityTeceMaize <- 0.789 * ethanolYieldLiterPerTonTeceMaize / 1000 - extractionQuantitySugarcane <- 0.789 * ethanolYieldLiterPerTonSugarcane / 1000 - - # ethanol processing from tece and maize (ethanol1, distillers_grain, and distillingloss) - for (j in seq_along(teceMaize)) { - object[, , c(teceMaize[j], "X001|Ethanol")] <- .extractGoodFromFlow( - object = object[, , c(teceMaize[j], "X001|Ethanol")], # nolint - goodIn = teceMaize[j], - from = "other_util", - process = "distilling", - goodOut = "X001|Ethanol", - reportAs = "ethanol1", - residual = "intermediate", - extractionQuantity = extractionQuantityTeceMaize[j], - extractionAttribute = "dm", - prodAttributes = prodAttributes) - - object[, , c(teceMaize[j], "X002|Distillers_grain")] <- .extractGoodFromFlow( - object = object[, , c(teceMaize[j], "X002|Distillers_grain")], # nolint - goodIn = teceMaize[j], - from = "intermediate", - process = "intermediate", - goodOut = "X002|Distillers_grain", - reportAs = "distillers_grain1", - residual = "distillingloss", - extractionQuantity = "max", - extractionAttribute = "nr", - prodAttributes = prodAttributes) - } - - # ethanol processing from sugarcane (only ethanol1 and distillingloss) - object[, , c("2536|Sugar cane", "X001|Ethanol")] <- .extractGoodFromFlow( - object = object[, , c("2536|Sugar cane", "X001|Ethanol")], # nolint - goodIn = "2536|Sugar cane", - from = "other_util", - process = "distilling", - goodOut = "X001|Ethanol", - reportAs = "ethanol1", - residual = "distillingloss", - extractionQuantity = extractionQuantitySugarcane, - extractionAttribute = "dm", - prodAttributes = prodAttributes) - - return(object) - } - - # processing of tece (processed) to alcohol1 and alcoholloss - .beerProcessing <- function(object) { - # Barley would be more correct, but we need to have homogenous products - beercereals <- .getFAOitems("tece") - - object[, , c(beercereals, "2656|Beer")] <- .processingGlobal(object = object[, , c(beercereals, "2656|Beer")], - goodsIn = beercereals, - from = "processed", - process = "fermentation", - goodsOut = "2656|Beer", - reportAs = "alcohol1", - residual = "intermediate") - - for (x in beercereals) { - object[, , c(x, "X004|Brewers_grain")] <- .extractGoodFromFlow(object = object[, , c(x, "X004|Brewers_grain")], - goodIn = x, - from = "intermediate", - process = "intermediate", - goodOut = "X004|Brewers_grain", - reportAs = "brewers_grain1", - residual = "alcoholloss", - extractionQuantity = "max", - extractionAttribute = "dm", - prodAttributes = prodAttributes) - } - - return(object) - } - - # processing of sugar cane and sugar beet (processed) to sugar1, molasses1 and refiningloss - .sugarProcessing <- function(object) { - - goodsIn <- c("2536|Sugar cane", "2537|Sugar beet") - goodsOut <- c("2818|Sugar, Refined Equiv", "2544|Molasses") - object[, , c(goodsIn, goodsOut)] <- .processingGlobal(object = object[, , c(goodsIn, goodsOut)], - goodsIn = goodsIn, - from = "processed", - process = "refining", - goodsOut = goodsOut, - reportAs = c("sugar1", "molasses1"), - residual = "refiningloss") - - goodsIn <- c("2514|Maize and products") - goodsOut <- c("2543|Sweeteners, Other") - object[, , c(goodsIn, goodsOut)] <- .processingGlobal(object = object[, , c(goodsIn, goodsOut)], - goodsIn = goodsIn, - from = "processed", - process = "refining", - goodsOut = goodsOut, - reportAs = c("sugar1"), - residual = "refiningloss") - - return(object) - } - - # processing of oil and oilcake from palm/palmkernel (processed) - .oilpalmProcessing <- function(object) { - # aggregate FAO products relating to oilpalm to a single raw product - faoProductsOilpalm <- c("2577|Palm Oil", "2576|Palmkernel Oil", "2595|Palmkernel Cake") - newproduct <- dimSums(object[, , list("production", faoProductsOilpalm, "dm")], - dim = c("ItemCodeItem", "ElementShort", "attributes")) - newproduct <- prodAttributes[, , "X003|Palmoil_Kerneloil_Kernelcake"] * newproduct - object[, , list("X003|Palmoil_Kerneloil_Kernelcake", c("production", "domestic_supply", "processed"))] <- newproduct - - # extract oil - goodIn <- "X003|Palmoil_Kerneloil_Kernelcake" - goodsOut1 <- c("2577|Palm Oil", "2576|Palmkernel Oil") - goodsOut2 <- "2595|Palmkernel Cake" - - object[, , c(goodIn, goodsOut1)] <- .processingGlobal(object = object[, , c(goodIn, goodsOut1)], - goodsIn = goodIn, - from = "processed", - process = "extracting", - goodsOut = goodsOut1, - reportAs = c("oil1", "oil2"), - residual = "intermediate") - - object[, , c(goodIn, goodsOut2)] <- .extractGoodFromFlow(object = object[, , c(goodIn, goodsOut2)], - goodIn = goodIn, - from = "intermediate", - process = "intermediate", - goodOut = goodsOut2, - reportAs = "oilcakes1", - residual = "extractionloss", - extractionQuantity = "max", - extractionAttribute = "dm", - prodAttributes = prodAttributes) - - return(object) - } - - # extraction of oil and oilcakes from oilcrops (processed) - .oilProcessing <- function(object) { - # orders must match! - cropsIn <- c("2555|Soyabeans", "2820|Groundnuts (in Shell Eq)", "2557|Sunflower seed", - "2559|Cottonseed", "2558|Rape and Mustardseed", "2560|Coconuts - Incl Copra", - "2561|Sesame seed") - oilOut <- c("2571|Soyabean Oil", "2572|Groundnut Oil", "2573|Sunflowerseed Oil", - "2575|Cottonseed Oil", "2574|Rape and Mustard Oil", "2578|Coconut Oil", - "2579|Sesameseed Oil") - cakeOut <- c("2590|Soyabean Cake", "2591|Groundnut Cake", "2592|Sunflowerseed Cake", - "2594|Cottonseed Cake", "2593|Rape and Mustard Cake", "2596|Copra Cake", - "2597|Sesameseed Cake") - - otherCropsIn <- c("2570|Oilcrops, Other", "2563|Olives (including preserved)") - otherOilOut <- "2586|Oilcrops Oil, Other" - otherCakeOut <- "2598|Oilseed Cakes, Other" - - # main oil crops - for (j in seq_along(cropsIn)) { - object[, , c(cropsIn[j], oilOut[j])] <- .processingGlobal(object = object[, , c(cropsIn[j], oilOut[j])], - goodsIn = cropsIn[j], - from = "processed", - process = "extracting", - goodsOut = oilOut[j], - reportAs = "oil1", - residual = "intermediate") - object[, , c(cropsIn[j], cakeOut[j])] <- .extractGoodFromFlow(object = object[, , c(cropsIn[j], cakeOut[j])], - goodIn = cropsIn[j], - from = "intermediate", - process = "intermediate", - goodOut = cakeOut[j], - reportAs = "oilcakes1", - residual = "extractionloss", - extractionQuantity = "max", - extractionAttribute = "dm", - prodAttributes = prodAttributes) - } - - - # other oil crops - object[, , c(otherCropsIn, otherOilOut)] <- .processingGlobal(object = object[, , c(otherCropsIn, otherOilOut)], - goodsIn = otherCropsIn, - from = "processed", - process = "extracting", - goodsOut = otherOilOut, - reportAs = "oil1", - residual = "intermediate") - - for (goodIn in otherCropsIn) { - object[, , c(goodIn, otherCakeOut)] <- .extractGoodFromFlow(object = object[, , c(goodIn, otherCakeOut)], - goodIn = goodIn, - from = "intermediate", - process = "intermediate", - goodOut = otherCakeOut, - reportAs = "oilcakes1", - residual = "extractionloss", - extractionQuantity = "max", - extractionAttribute = "dm", - prodAttributes = prodAttributes) - } - - return(object) - } - - # main function combining all processing functions - .massbalanceProcessing <- function(years) { - # preparing dataset for given years - cells <- getCells(cbc) - s1 <- getNames(cbc, dim = 1) - s2 <- c(getNames(cbc, dim = 2), namesProcessing) - s3 <- attributeTypes - flowsCBC <- array(dim = c(length(cells), length(years), length(s1), length(s2), length(s3)), - dimnames = list(cells, years, s1, s2, s3)) - flowsCBC <- as.magpie(flowsCBC) - getSets(flowsCBC) <- c("region", "year", "ItemCodeItem", "ElementShort", "attributes") - flowsCBC[, , getNames(cbc, dim = 2)] <- cbc[, years, ] * attributesWM[, , getNames(cbc, dim = 1)] - gc() - - # conversion from 10^12 kcal to PJ - flowsCBC[, , list("households", "ge")] <- cbc[, years, "food_supply_kcal"] * 4.184 - # conversion of protein to nitrogen using average N content - flowsCBC[, , list("households", "nr")] <- cbc[, years, "protein_supply"] / 6.25 - flowsCBC[, , list("households", "wm")] <- cbc[, years, "food_supply"] - - flowsCBC <- flowsCBC[, , setdiff(getNames(flowsCBC, dim = "ElementShort"), - c("food_supply_kcal", "protein_supply", "food_supply", "fat_supply"))] - flowsCBC[is.na(flowsCBC)] <- 0 - flowsCBC[is.nan(flowsCBC)] <- 0 - gc() - - # relevant processing dimensions - millingDimensions <- c("production", "production_estimated", "milling", "brans1", - "branoil1", "flour1", "food", "households") - distillingDimensions <- c("production", "production_estimated", "other_util", "distilling", - "ethanol1", "intermediate", "distillers_grain1", "distillingloss") - fermentationDimensions <- c("production", "production_estimated", "processed", "fermentation", - "alcohol1", "intermediate", "brewers_grain1", "alcoholloss") - refiningDimensions <- c("production", "production_estimated", "processed", "sugar1", - "molasses1", "refining", "refiningloss") - extractingDimensions <- c("production", "production_estimated", "domestic_supply", "processed", - "extracting", "oil1", "oil2", "intermediate", "oilcakes1", "extractionloss") - - # relevant processing products - millingProducts <- c(.getFAOitems(c("tece", "maiz", "rice_pro", "trce", "brans")), - "2582|Maize Germ Oil", "2581|Ricebran Oil") - distillingProducts <- c(.getFAOitems(c("tece", "maiz", "sugr_cane", "ethanol")), "X002|Distillers_grain") - fermentationProducts <- c(.getFAOitems("tece"), "2656|Beer", "X004|Brewers_grain") - refiningProducts <- c(.getFAOitems(c("sugr_cane", "sugr_beet", "maiz", "molasses")), - "2818|Sugar, Refined Equiv", "2543|Sweeteners, Other") - extractingProducts1 <- c("2577|Palm Oil", "2576|Palmkernel Oil", "2595|Palmkernel Cake", - "X003|Palmoil_Kerneloil_Kernelcake") - extractingProducts2 <- setdiff(.getFAOitems(c("soybean", "groundnut", "sunflower", "cottn_pro", - "rapeseed", "oils", "oilcakes")), - c(extractingProducts1, "2580|Olive Oil", "2581|Ricebran Oil", "2582|Maize Germ Oil")) - - - # Food processing calculations - flowsCBC[, , list(millingProducts, millingDimensions)] <- - .cerealMillingGlobal(flowsCBC[, , list(millingProducts, millingDimensions)]) - flowsCBC[, , list(distillingProducts, distillingDimensions)] <- - .ethanolProcessing(flowsCBC[, , list(distillingProducts, distillingDimensions)]) - flowsCBC[, , list(fermentationProducts, fermentationDimensions)] <- - .beerProcessing(flowsCBC[, , list(fermentationProducts, fermentationDimensions)]) - flowsCBC[, , list(refiningProducts, refiningDimensions)] <- - .sugarProcessing(flowsCBC[, , list(refiningProducts, refiningDimensions)]) - flowsCBC[, , list(extractingProducts1, extractingDimensions)] <- - .oilpalmProcessing(flowsCBC[, , list(extractingProducts1, extractingDimensions)]) - flowsCBC[, , list(extractingProducts2, extractingDimensions)] <- - .oilProcessing(flowsCBC[, , list(extractingProducts2, extractingDimensions)]) - - # harmonizing conversion factors within the rapeseed group - goodsIn <- list("2558|Rape and Mustardseed", "2560|Coconuts - Incl Copra", "2561|Sesame seed", - c("2570|Oilcrops, Other", "2563|Olives (including preserved)")) - oilsOut <- list("2574|Rape and Mustard Oil", "2578|Coconut Oil", "2579|Sesameseed Oil", "2586|Oilcrops Oil, Other") - cakesOut <- list("2593|Rape and Mustard Cake", "2596|Copra Cake", "2597|Sesameseed Cake", - "2598|Oilseed Cakes, Other") - - for (from in c("oil1", "oilcakes1", "extractionloss")) { - factor <- dimSums(flowsCBC[, , list(unlist(goodsIn), from)], dim = c(1, 3.1, 3.2)) / - dimSums(flowsCBC[, , list(unlist(goodsIn), "extracting")], dim = c(1, 3.1, 3.2)) - flowsCBC[, , list(unlist(goodsIn), from)] <- factor * dimSums(flowsCBC[, , list(unlist(goodsIn), "extracting")], - dim = 3.2) - gc() - } - - for (j in seq_along(oilsOut)) { - flowsCBC[, , list(oilsOut[[j]], "production_estimated")] <- dimSums(flowsCBC[, , list(goodsIn[[j]], "oil1")], - dim = c(3.1, 3.2)) - flowsCBC[, , list(cakesOut[[j]], "production_estimated")] <- dimSums(flowsCBC[, , list(goodsIn[[j]], - "oilcakes1")], - dim = c(3.1, 3.2)) - gc() - } - - # Alcohol production - cropsAlcohol <- .getFAOitems(c("others", "trce", "rice_pro", "potato", "cassav_sp", "sugar", "molasses", "brans")) - fermentationDimensions <- c("production", "production_estimated", "processed", "fermentation", "alcohol1", - "alcohol2", "alcohol3", "alcohol4", "intermediate", "brewers_grain1", "alcoholloss") - fermentationProducts <- .getFAOitems(c("tece", "others", "trce", "rice_pro", "potato", "cassav_sp", "sugar", - "molasses", "brans", "alcohol", "distillers_grain")) - flowsCBC[, , list(fermentationProducts, fermentationDimensions)] <- .processingGlobal( - flowsCBC[, , list(fermentationProducts, fermentationDimensions)], # nolint - goodsIn = cropsAlcohol, - from = "processed", - process = "fermentation", - goodsOut = c("2655|Wine", "2657|Beverages, Fermented", - "2658|Beverages, Alcoholic", "2659|Alcohol, Non-Food"), - reportAs = c("alcohol1", "alcohol2", "alcohol3", "alcohol4"), # nolint - residual = "alcoholloss") - - # Define use of products that are not existing in FAOSTAT - goods <- c("X002|Distillers_grain", "X004|Brewers_grain") - flowsCBC[, , list(goods, c("production", "domestic_supply", "feed"))] <- - flowsCBC[, , list(goods, "production_estimated"), drop = TRUE] - flowsCBC[, , list("X001|Ethanol", c("production", "domestic_supply", "other_util"))] <- - flowsCBC[, , list("X001|Ethanol", "production_estimated"), drop = TRUE] - gc() - - # add remaining 'processed' to 'other_util' and remove obsolete dimensions - flowsCBC[, , "other_util"] <- dimSums(flowsCBC[, , c("other_util", "processed")], dim = 3.2) - flowsCBC <- flowsCBC[, , c("processed", "intermediate"), invert = TRUE] - gc() - - # map to magpie categories - massbalanceProcessing <- toolAggregate(x = flowsCBC, - rel = relationmatrix, - dim = 3.1, - from = "FoodBalanceItem", - to = "k", - partrel = TRUE) - gc() - return(massbalanceProcessing) - } - - # function dealing with the non-processing aspects of cbc - .massbalanceNoProcessing <- function(years) { - # initializing magpie object - cells <- getCells(cbc) - s2 <- c(getNames(cbc, dim = 2), namesProcessing) - s3 <- attributeTypes - noProcessingCBC <- array(dim = c(length(cells), length(years), length(noProcessingFAO), length(s2), - length(s3)), dimnames = list(cells, years, noProcessingFAO, s2, s3)) - noProcessingCBC <- as.magpie(noProcessingCBC) - getSets(noProcessingCBC) <- c("region", "year", "ItemCodeItem", "ElementShort", "attributes") - - # adding attributes and filling household dimension - noProcessingCBC[, , getNames(cbc, dim = 2)] <- cbc[, years, noProcessingFAO] * attributesWM[, , noProcessingFAO] - # conversion from 10^12 kcal to PJ - noProcessingCBC[, , list("households", "ge")] <- noProcessingCBC[, , list("food_supply_kcal", "wm")] * 4.184 - # conversion of protein to nitrogen using average N content - noProcessingCBC[, , list("households", "nr")] <- noProcessingCBC[, , list("protein_supply", "wm")] / 6.25 - noProcessingCBC[, , list("households", "wm")] <- noProcessingCBC[, , list("food_supply", "wm")] - noProcessingCBC <- noProcessingCBC[, , setdiff(getNames(noProcessingCBC, dim = "ElementShort"), - c("food_supply_kcal", "protein_supply", - "food_supply", "fat_supply"))] - - # fill NAs and NaNs - noProcessingCBC[is.na(noProcessingCBC)] <- 0 - noProcessingCBC[is.nan(noProcessingCBC)] <- 0 - - # add 'processed' to 'other_util' and remove obsolete dimensions - noProcessingCBC[, , "other_util"] <- dimSums(noProcessingCBC[, , c("other_util", "processed")], dim = 3.2) - noProcessingCBC <- noProcessingCBC[, , c("processed", "intermediate"), invert = TRUE] - - # map to magpie categories - massbalanceNoProcessing <- toolAggregate(x = noProcessingCBC, - rel = relationmatrix, - dim = 3.1, - from = "FoodBalanceItem", - to = "k", - partrel = TRUE) - gc() - return(massbalanceNoProcessing) - } - - - #### Calculations #### - - # increase magclass sizelimit - local_options(magclass_sizeLimit = 2e8) - - # option without splitting years (in case of memory issues this can be done in year chunks) - massbalanceNoProcessing <- .massbalanceNoProcessing(years) - cbc <- cbc[, , noProcessingFAO, invert = TRUE] - massbalanceProcessing <- .massbalanceProcessing(years) - - # put results together - massbalance <- mbind(massbalanceProcessing, massbalanceNoProcessing) - - return(list(x = massbalance, - weight = NULL, - unit = "MT C, Mt DM, PJ, Mt K, Mt Nr, Mt P, Mt WM", - description = paste("FAO massbalance calculates all conversion processes within the FAO CBS/FBS and", - "makes them explict. More complete version can be found in calcFAOmassbalance"))) -} diff --git a/R/calcFallowLand.R b/R/calcFallowLand.R deleted file mode 100644 index 168460e8..00000000 --- a/R/calcFallowLand.R +++ /dev/null @@ -1,52 +0,0 @@ -#' @title calcFallowLand -#' @description -#' Calculates fallow land on grid cell level, -#' based on physical cropland extend and harvested area output -#' of LandInG data. -#' The formula -#' "fallow land are = max( physical cropland area - harvested cropland area, 0)" -#' is used. -#' Due to multiple cropping, harvested cropland area can be greater than non-fallow land area -#' and even greater than physical cropland area. -#' Thus, the results can only be considered a rough estimate of fallow land area. -#' @param cellular TRUE for cellular outputs. -#' @return MAgPIE object containing fallow land in Mha -#' @author David Hoetten, Felicitas Beier -#' @seealso -#' \code{\link{readLandInG}} -#' @examples -#' \dontrun{ -#' calcOutput("FallowLand") -#' } -#' @importFrom magclass dimSums mbind -#' @importFrom madrat toolConditionalReplace -#' -calcFallowLand <- function(cellular = TRUE) { - - harvestedArea <- readSource("LandInG", subtype = "harvestedArea") - - harvestedAreaCrops <- harvestedArea[, , c("pasture"), invert = TRUE] - - physicalArea <- readSource("LandInG", subtype = "physicalArea") - - fallowLand <- dimSums(physicalArea, "irrigation") - - dimSums(harvestedAreaCrops, c("irrigation", "crop")) - - fallowLand <- toolConditionalReplace(fallowLand, conditions = c("<0"), replaceby = 0) - - # Aggregation to iso-level - if (!cellular) { - # aggregate to countries - fallowLand <- dimSums(fallowLand, dim = c("x", "y")) - # fill missing countries with 0 - fallowLand <- toolConditionalReplace(x = toolCountryFill(fallowLand), - conditions = "is.na()", replaceby = 0) - } - - return(list(x = fallowLand, - weight = NULL, - description = "Fallow land", - unit = "Mha", - isocountries = FALSE)) - -} diff --git a/R/calcFertilizerPricesFAO.R b/R/calcFertilizerPricesFAO.R deleted file mode 100644 index dd03d2ff..00000000 --- a/R/calcFertilizerPricesFAO.R +++ /dev/null @@ -1,80 +0,0 @@ -#' @title calcFertilizerPricesFAO -#' @description calculates dataset of fertilizer prices in US$MER05/tonne (either referring to the amount of fertilizer -#' product, or to the amount of nutrients within the fertilizer) based on FAO data -#' @param subtype "N" for fertilizer containing nitrogen, "P" for fertilizer containing phosphorus -#' @param by "nutrient" if referring to price per amount of nutrients (N or P) within the fertilizer products, or -#' "product" if referring to price per amount of fertilizer product -#' @return List of magpie objects with results on country level, weight on country level, unit and description. -#' @author Debbora Leip -#' @examples -#' \dontrun{ -#' calcOutput("FertilizerPricesFAO", subtype = "N", by = "nutrient") -#' } -#' @importFrom GDPuc convertGDP - -calcFertilizerPricesFAO <- function(subtype = "N", by = "nutrient") { - - ## read FAO data on fertilizer by product and subset to relevant products - fertByProduct <- complete_magpie(readSource("FAO_online", "FertilizerProducts"), fill = 0) - mapping <- toolGetMapping("fertilizer_products.csv", type = "sectoral", where = "mappingfolder") - products <- mapping[mapping[, subtype] != "other", "product"] - fertByProduct <- fertByProduct[, , products] - - ## fertilizer price (per amount of fertilizer products) - # calculate prices for import and export - priceImport <- fertByProduct[, , "import_US$MER05", drop = TRUE] / fertByProduct[, , "import", drop = TRUE] - priceExport <- fertByProduct[, , "export_US$MER05", drop = TRUE] / fertByProduct[, , "export", drop = TRUE] - priceImport[!is.finite(priceImport)] <- 0 - priceExport[!is.finite(priceExport)] <- 0 - - # choose lower price - priceMin <- priceImport - priceMin[priceMin == 0] <- priceExport[priceMin == 0] - exportLower <- as.logical((priceImport > priceExport) * (priceExport != 0)) - priceMin[exportLower] <- priceExport[exportLower] - priceMin[priceMin > 500] <- 0 # set unreasonably high values to missing - - # fill gaps with world averages per year and fertilizer item - worldAvgPrice <- priceMin - worldAvgPrice[worldAvgPrice != 0] <- 1 - worldAvgPrice[, , ] <- dimSums(priceMin, dim = 1) / dimSums(worldAvgPrice, dim = 1) - worldAvgPrice[is.na(worldAvgPrice)] <- 0 - priceMin[priceMin == 0] <- worldAvgPrice[priceMin == 0] - # before 51739 values missing, now 8217 (cases where no country reports a price) - - # calculate average fertilizer price over all products using agricultural use as weight - weight <- fertByProduct[, , "Agricultural_Use_(tonnes)", drop = TRUE] - weight <- weight / dimSums(weight, dim = 3) - # assuming world-ratio between different fertilizer items for missing country weights - worldRatio <- fertByProduct[, , "Agricultural_Use_(tonnes)", drop = TRUE] - worldRatio[, , ] <- dimSums(worldRatio, dim = 1) / dimSums(worldRatio, dim = c(1, 3)) - weight[is.na(weight)] <- worldRatio[is.na(weight)] - - weight[priceMin[, , products] == 0] <- 0 - fertPrice <- toolAggregate(priceMin[, , products], - rel = mapping[mapping[, subtype] != "other", ], - weight = weight, - from = "product", to = subtype, dim = 3.1 - ) - - if (by == "product") { - totalUseProducts <- calcOutput("FertilizerUseFAO", subtype = subtype, by = "product", aggregate = FALSE) - res <- fertPrice - weight <- totalUseProducts - unit <- "US$MER05/tonne" - } else if (by == "nutrient") { - totalUseProducts <- calcOutput("FertilizerUseFAO", subtype = subtype, by = "product", aggregate = FALSE) - totalUseNutrients <- calcOutput("FertilizerUseFAO", subtype = subtype, by = "nutrient", aggregate = FALSE) - years <- intersect(getItems(totalUseNutrients, dim = 2), getItems(totalUseProducts, dim = 2)) - avgNutrientContent <- totalUseNutrients[, years, ] / totalUseProducts[, years, , drop = TRUE] - res <- fertPrice[, years, , drop = TRUE] / avgNutrientContent - res[!is.finite(res)] <- 0 - weight <- totalUseNutrients[, years, ] - unit <- "US$MER05/tonne" - } - - return(list(x = res, - unit = unit, - weight = weight, - description = "fertilizer prices per amount of nutrients or amount of fertilizer products")) -} diff --git a/R/calcFertilizerUseFAO.R b/R/calcFertilizerUseFAO.R deleted file mode 100644 index eac523c5..00000000 --- a/R/calcFertilizerUseFAO.R +++ /dev/null @@ -1,114 +0,0 @@ -#' @title calcFertilizerUseFAO -#' @description calculates dataset of fertilizer use in tonnes (either referring to the amount of fertilizer products -#' used, or to the amount of nutrients within the fertilizer used) based on FAO data -#' @param subtype "N" for fertilizer containing nitrogen, "P" for fertilizer containing phosphorus (note that there -#' is an overlap between those categories, as some fertilizers include both nutrients) -#' @param by "nutrient" if referring to amount of nutrients (N or P) in total used fertilizer, or "product" if -#' referring to total amount of fertilizer used -#' @return List of magpie objects with results on country level, weight on country level, unit and description. -#' @author Debbora Leip -#' @examples -#' \dontrun{ -#' calcOutput("FertilizerUseFAO", subtype = "N", by = "nutrient") -#' } -#' -calcFertilizerUseFAO <- function(subtype = "N", by = "nutrient") { - - ## read FAO data on fertilizer by nutrient - if (subtype == "N") { - nutrient <- "3102|Nutrient nitrogen N (total)" - } else if (subtype == "P") { - nutrient <- "3103|Nutrient phosphate P2O5 (total)" - } - fertByNutrient <- readSource("FAO_online", "FertilizerNutrients")[, , nutrient, drop = TRUE] - totalUseNutrients <- setNames(fertByNutrient[, , "Agricultural_Use_(tonnes)", drop = TRUE], subtype) - - # calculate nutrients per area (to fill in fertilizer by nutrient data later on) - cropland <- readSource("FAO_online", "Land")[, , list("6620|Cropland", "Area_(1000_ha)"), drop = TRUE] - years <- intersect(getItems(cropland, dim = 2), getItems(totalUseNutrients, dim = 2)) - totalUseNutrients <- totalUseNutrients[, years, ] - cropland <- cropland[, years, ] - nutrientPerArea <- totalUseNutrients / cropland - nutrientPerArea[!is.finite(nutrientPerArea)] <- 0 - - # fill gaps in nutrients per area data by using regional averages (scaled by a multiplicative correction coefficient - # to match first observation of each country) - regionMapping <- toolGetMapping("regionmappingH12.csv", type = "regional", where = "mappingfolder") - numCountries <- nutrientPerArea - numCountries[numCountries != 0] <- 1 - numCountries <- toolAggregate(numCountries, rel = regionMapping, weight = NULL, - from = "CountryCode", to = "RegionCode", dim = 1) - worldRegionNutrientPerArea <- toolAggregate(nutrientPerArea, rel = regionMapping, - weight = NULL, from = "CountryCode", to = "RegionCode", dim = 1) / numCountries - - .calibratingWorldAverages <- function(reg, x, data, glo = FALSE) { - tmp <- x[reg, , ] - if (isFALSE(glo)) { - wr <- regionMapping$RegionCode[regionMapping$CountryCode == reg] - data <- data[wr, , ] - } - yearsObs <- where(tmp != 0)$true$years - yearsMissing <- where(tmp == 0)$true$years - if (length(yearsObs) == 0) { - tmp[, , ] <- data[, , drop = TRUE] - } else if (length(yearsMissing) > 0) { - y <- min(yearsObs) - factor <- tmp[, y, ] / data[, y, , drop = TRUE] # multiplicative correction coefficient - tmp[, yearsMissing, ] <- data[, yearsMissing, , drop = TRUE] * factor - } - return(tmp) - } - nutrientPerArea <- mbind(lapply(getItems(nutrientPerArea, dim = 1), - .calibratingWorldAverages, nutrientPerArea, worldRegionNutrientPerArea)) - - # fill gaps in fertilizer use by nutrient based on cropland and nutrient per area - estFertByNutrient <- cropland * nutrientPerArea - totalUseNutrients[totalUseNutrients == 0] <- estFertByNutrient[totalUseNutrients == 0] - - - ## read FAO data on fertilizer by product and subset to fertilizer products of given nutrient type - fertByProduct <- complete_magpie(readSource("FAO_online", "FertilizerProducts"), fill = 0) - mapping <- toolGetMapping("fertilizer_products.csv", type = "sectoral", where = "mappingfolder") - products <- mapping[mapping[, subtype] != "other", "product"] - fertByProduct <- fertByProduct[, , products] - totalUseProducts <- setNames(dimSums(fertByProduct[, , "Agricultural_Use_(tonnes)"], dim = 3.1), - paste0(subtype, "_fertilizer")) - - years <- intersect(getItems(totalUseNutrients, dim = 2), getItems(totalUseProducts, dim = 2)) - totalUseProducts <- totalUseProducts[, years, ] - - ## average nutrient content in fertilizer products - avgNutrientContent <- totalUseNutrients[, years, , drop = TRUE] / totalUseProducts - avgNutrientContent[avgNutrientContent > 1] <- 0 - avgNutrientContent[!is.finite(avgNutrientContent)] <- 0 - - # fill gaps with world averages calibrated to countries - numCountries <- avgNutrientContent - numCountries[numCountries != 0] <- 1 - worldAvgNutrientContent <- dimSums(avgNutrientContent, dim = 1) / dimSums(numCountries, dim = 1) - - avgNutrientContent <- mbind(lapply(getItems(avgNutrientContent, dim = 1), - .calibratingWorldAverages, avgNutrientContent, worldAvgNutrientContent, TRUE)) - - ## fill gaps in total fertilizer use based on fertilizer by nutrient use - estTotalUseProducts <- totalUseNutrients[, years, , drop = TRUE] / avgNutrientContent - totalUseProducts[totalUseProducts == 0] <- estTotalUseProducts[totalUseProducts == 0] - - ## select output - if (by == "nutrient") { - res <- totalUseNutrients - weight <- NULL - unit <- "tonnes" - } else if (by == "product") { - res <- totalUseProducts - weight <- NULL - unit <- "tonnes" - } - - getSets(res) <- c("region", "year", by) - - return(list(x = res, - unit = unit, - weight = weight, - description = "total use of fertilizer either in amount of nutrients or amount of fertilizer products")) -} diff --git a/R/calcForestArea.R b/R/calcForestArea.R deleted file mode 100644 index cb89824e..00000000 --- a/R/calcForestArea.R +++ /dev/null @@ -1,126 +0,0 @@ -#' @title calcForestArea -#' @description Calculates consistent forest area and its subcategories based on FAO_FRA2015 -#' and LanduseInitialisation data. -#' -#' @param selectyears defaults to past -#' @return List of magpie object with results on country level, weight, unit and description. -#' @author Kristine Karstens, Jan Philipp Dietrich -#' @examples -#' \dontrun{ -#' calcOutput("ForestArea") -#' } -#' @export - -calcForestArea <- function(selectyears = "past") { - - years <- sort(findset(selectyears, noset = "original")) - - forest <- readSource("FAO_FRA2015", "fac")[, , c("Forest", "NatFor", "PrimFor", "NatRegFor", "PlantFor")] - - # Plantation data is bit strange in FRA2015, we update this with FRA2020 data (but only till 2015) - # We do this because FRA2020 has stopped reporting separately on primf and secdf - # but we can still use data for planted forest - - ## Overall FRA 2020 data - fra2020 <- readSource("FRA2020", "forest_area") - - ## Find which year is missing in FRA2020 data (which exisits in FRA2015) - missingYears <- setdiff(getYears(forest), getYears(fra2020)) - - ## Linear interpolation to missing year - fra2020 <- time_interpolate(dataset = fra2020, - interpolated_year = missingYears, - integrate_interpolated_years = TRUE, - extrapolation_type = "linear") - - ## Replace FRA2015 planted forest data with FRA 2020 data - forest[, , "PlantFor"] <- fra2020[, getYears(forest), "plantedForest"] - - # As planted forest data is now different, we need to update overall forest area - # (sum of nat.reg.forest and planted forest) - forest[, , "Forest"] <- forest[, , "NatFor"] + forest[, , "PlantFor"] - - forest <- time_interpolate(forest, interpolated_year = years, integrate_interpolated_years = TRUE, - extrapolation_type = "constant")[, years, ] - vcat(verbosity = 3, "Forest is interpolated for missing years and held constant for the period before FAO starts") - - ### fix know issues - - forest["HND", , "PlantFor"] <- forest["HND", , "Forest"] - forest["HND", , "NatFor"] - forest["IDN", , "Forest"] <- forest["IDN", , "NatFor"] + forest["IDN", , "PlantFor"] - forest["FIN", , "NatRegFor"] <- forest["FIN", , "NatFor"] - forest["FIN", , "PrimFor"] - forest["PSE", , "PlantFor"] <- 2 / 3 * forest["PSE", , "Forest"] - forest["PSE", , "NatRegFor"] <- 1 / 3 * forest["PSE", , "Forest"] - - ### fixing inconsistencies assuming total forest areas and shares of subcategories are reported correctly - - forestSumSub <- dimSums(forest[, , c("NatFor", "PlantFor")], dim = 3) - forest[, , "PlantFor"] <- toolNAreplace(forest[, , "PlantFor"] / - forestSumSub * setNames(forest[, , "Forest"], NULL))$x - forest[, , "NatFor"] <- toolNAreplace(forest[, , "NatFor"] / - forestSumSub * setNames(forest[, , "Forest"], NULL))$x - - forestSumSubSub <- dimSums(forest[, , c("PrimFor", "NatRegFor")], dim = 3) - forest[, , "PrimFor"] <- toolNAreplace(forest[, , "PrimFor"] / - forestSumSubSub * setNames(forest[, , "NatFor"], NULL))$x - forest[, , "NatRegFor"] <- toolNAreplace(forest[, , "NatRegFor"] / - forestSumSubSub * setNames(forest[, , "NatFor"], NULL))$x - - ########################### - # fixing missing data on split between PrimFor (primforest), NatRegFor (secdforest) - # and PlantFor (forestry) with LUH data - - luh <- calcOutput("LUH2v2", landuse_types = "LUH2v2", irrigation = FALSE, selectyears = selectyears, - cells = "lpjcell", cellular = FALSE, aggregate = FALSE)[, , c("primf", "secdf")] - - secondaryForest <- luh[, , "secdf"] - setNames(forest[, getYears(luh), "PlantFor"], NULL) - if (any(secondaryForest < 0)) { - tmp <- secondaryForest - tmp[tmp > 0] <- 0 - tmp <- dimSums(tmp, dim = 1) - vcat(verbosity = 2, paste("Mismatch of FAO forestry and Hurtt secondary forest:", - paste(paste(getYears(tmp), round(tmp, 0), "Mha, "), collapse = " "), ". cut off.")) - secondaryForest[secondaryForest < 0] <- 0 - } - forestry <- luh[, , "secdf"] - secondaryForest - - luhForest <- mbind(setNames(forestry, "PlantFor"), - setNames(luh[, , c("primf")], "PrimFor"), - setNames(secondaryForest, "NatRegFor")) + 10^-10 - # 10^-10 added to allow share estimation even under missing area information - luhForestShare <- luhForest / dimSums(luhForest, dim = 3) - luhNatForestShare <- luhForest[, , c("PrimFor", "NatRegFor")] / dimSums(luhForest[, , c("PrimFor", "NatRegFor")], - dim = 3) - - miss <- where(round(dimSums(forest[, , c("NatFor", "PlantFor")], dim = 3), 6) != - round(forest[, , "Forest"], 6))$true$regions - - if (length(miss) > 0) { - forest[miss, , c("PlantFor", "PrimFor", "NatRegFor")] <- luhForestShare[miss, , ] * - setNames(forest[miss, , "Forest"], NULL) - forest[miss, , "NatFor"] <- setNames(forest[miss, , "PrimFor"] + forest[miss, , "NatRegFor"], NULL) - } - - miss <- where(round(dimSums(forest[, , c("PrimFor", "NatRegFor")], dim = 3), 6) != - round(forest[, , "NatFor"], 6))$true$regions - if (length(miss > 0)) { - forest[miss, , c("PrimFor", "NatRegFor")] <- luhNatForestShare[miss, , ] * setNames(forest[miss, , "NatFor"], NULL) - } - - #################################### - - map <- data.frame(fao = c("Forest", "NatFor", "PrimFor", "NatRegFor", "PlantFor"), - magpie = c("forest", "natrforest", "primforest", "secdforest", "forestry")) - out <- toolAggregate(forest, map, from = "fao", to = "magpie", dim = 3) - - if (max(abs(dimSums(out[, , c("natrforest", "forestry")]) - out[, , "forest"])) > 10^-6 || - max(abs(dimSums(out[, , c("secdforest", "primforest")], dim = 3) - out[, , "natrforest"])) > 10^-6) { - warning("There are inconsistencies within the forest area data set.") - } - - return(list(x = out, - weight = NULL, - unit = "Mha", - description = "Forest are and its subcategories") - ) -} diff --git a/R/calcGrassGPP.R b/R/calcGrassGPP.R deleted file mode 100644 index b136714d..00000000 --- a/R/calcGrassGPP.R +++ /dev/null @@ -1,148 +0,0 @@ -#' @title calcGrassGPP -#' -#' @description Calculates gross primary production (GPP) of grassland -#' under irrigated and rainfed conditions based on LPJmL inputs. -#' -#' @param selectyears Years to be returned -#' @param lpjml LPJmL version required for respective inputs: natveg or crop -#' @param climatetype Switch between different climate scenarios or historical baseline "GSWP3-W5E5:historical" -#' @param season "wholeYear": grass GPP in the entire year (main + off season) -#' "mainSeason": grass GPPP in the crop-specific growing -#' period of LPJmL (main season) -#' -#' @return magpie object in cellular resolution -#' @author Felicitas Beier -#' -#' @examples -#' \dontrun{ -#' calcOutput("GrassGPP", aggregate = FALSE) -#' } -#' -#' @importFrom madrat calcOutput -#' @importFrom magclass dimSums getItems new.magpie getSets add_dimension -#' - -calcGrassGPP <- function(selectyears, lpjml, climatetype, season) { - - if (grepl("GSWP3-W5E5", climatetype)) { - stage <- "smoothed" - } else { - stage <- "harmonized2020" - } - - #################### - ### Read in data ### - #################### - - # monthly irrigated grass GPP (in tDM/ha) - monthlyIrrigated <- calcOutput("LPJmL_new", subtype = "mgpp_grass_ir", - years = selectyears, - stage = stage, - version = lpjml[["crop"]], climatetype = climatetype, - aggregate = FALSE) - # monthly irrigated grass GPP (in tDM/ha) - monthlyRainfed <- calcOutput("LPJmL_new", subtype = "mgpp_grass_rf", - years = selectyears, - stage = stage, - version = lpjml[["crop"]], climatetype = climatetype, - aggregate = FALSE) - - # irrigated grass GPP in irrigated growing period of crop (in tDM/ha) - grperIrrigated <- calcOutput("LPJmL_new", subtype = "cft_gpp_grass_ir", - years = selectyears, - stage = stage, - version = lpjml[["crop"]], climatetype = climatetype, - aggregate = FALSE) - # rainfed grass GPP in rainfed growing period of crop (in tDM/ha) - grperRainfed <- calcOutput("LPJmL_new", subtype = "cft_gpp_grass_rf", - years = selectyears, - stage = stage, - version = lpjml[["crop"]], climatetype = climatetype, - aggregate = FALSE) - - ######################## - ### Data preparation ### - ######################## - - # Empty objects to be filled - grassGPPannual <- grassGPPgrper <- new.magpie(cells_and_regions = getItems(grperIrrigated, dim = 1), - years = getItems(grperIrrigated, dim = 2), - names = getItems(grperIrrigated, dim = 3), - fill = NA) - # Name dimensions - getSets(grassGPPannual) <- c("x", "y", "iso", "year", "crop", "irrigation") - getSets(grassGPPgrper) <- c("x", "y", "iso", "year", "crop", "irrigation") - - # Extract rainfed grass GPP in rainfed growing period of crop - grassGPPgrper[, , "rainfed"] <- grperRainfed[, , "rainfed"] - # Extract irrigated grass GPP in irrigated growing period of crop - grassGPPgrper[, , "irrigated"] <- grperIrrigated[, , "irrigated"] - - - #################### - ### Calculations ### - #################### - - # Monthly grass GPP - monthlyRainfed <- add_dimension(monthlyRainfed, - add = "irrigation", nm = "rainfed") - monthlyIrrigated <- add_dimension(monthlyIrrigated, - add = "irrigation", nm = "irrigated") - - ############## - ### Return ### - ############## - unit <- "tDM per ha" - description <- "irrigated and rainfed gross primary production of grass" - - if (season == "mainSeason") { - - out <- grassGPPgrper - description <- paste0(description, " in growing season of LPJmL") - - } else if (season == "wholeYear") { - # read in months with favorable growing conditions (boolean: 1=growing month; 0=no growing month) - grperPOT <- calcOutput("GrowingPeriodMonths", - selectyears = selectyears, - lpjml = lpjml, climatetype = climatetype, - aggregate = FALSE) - - # Calculate "annual" rainfed grass GPP for potential growing period - # (i.e., months with favorable crop growth conditions) - grassGPPannual[, , "rainfed"] <- dimSums(monthlyRainfed * grperPOT[, , "rainfed"], - dim = 3) - # Calculate "annual" irrigated grass GPP for potential growing period - # (i.e., months with favorable crop growth conditions) - grassGPPannual[, , "irrigated"] <- dimSums(monthlyIrrigated * grperPOT[, , "irrigated"], - dim = 3) - - out <- grassGPPannual - description <- paste0(description, " in the entire year (when crop growth is possible)") - - } else if (season == "monthly") { - - out <- mbind(monthlyRainfed, monthlyIrrigated) - getSets(out)["d3.2"] <- "month" - description <- paste0(description, " per month") - - } else { - stop("Please specify output to be returned by function calcGrassGPP: - mainSeason or wholeYear or monthly") - } - - ############## - ### Checks ### - ############## - if (any(is.na(out))) { - stop("calcGrassGPP produced NA values") - } - if (any(out < 0)) { - stop("calcGrassGPP produced negative values") - } - - return(list(x = out, - weight = NULL, - unit = unit, - description = description, - isocountries = FALSE)) -} diff --git a/R/calcGrowingPeriodMonths.R b/R/calcGrowingPeriodMonths.R deleted file mode 100644 index b606239b..00000000 --- a/R/calcGrowingPeriodMonths.R +++ /dev/null @@ -1,85 +0,0 @@ -#' @title calcGrowingPeriodMonths -#' -#' @description Calculates which gridcell-specific months in which -#' growing conditions are favorable for crop growth -#' based on monthly grass GPP -#' -#' @param selectyears Years to be returned -#' @param lpjml LPJmL version required for respective inputs: natveg or crop -#' @param climatetype Switch between different climate scenarios or -#' historical baseline "GSWP3-W5E5:historical" -#' @param minThreshold Threshold of monthly grass GPP to be classified as -#' growing period month -#' Unit of the threshold is gC/m^2. -#' Default: 100gC/m^2 -#' Note: the default value is chosen based on LPJmL version 5 -#' to reflect multiple cropping suitability as shown in GAEZ-4. -#' An update of LPJmL5 with regards to grass management may -#' require an adjustment of the threshold. -#' -#' @return magpie object in cellular resolution -#' @author Felicitas Beier, Jens Heinke -#' -#' @examples -#' \dontrun{ -#' calcOutput("GrowingPeriodMonths", aggregate = FALSE) -#' } -#' -#' @importFrom madrat calcOutput -#' @importFrom magclass setYears getSets mbind getItems new.magpie -#' - -calcGrowingPeriodMonths <- function(selectyears, lpjml, climatetype, - minThreshold = 100) { - #################### - ### Definitions ### - #################### - # Transformation factor for grass (gC/m^2 -> tDM/ha) - yieldTransform <- 0.01 / 0.45 - - #################### - ### Read in data ### - #################### - # monthly grass GPP (in tDM/ha) - grassGPPmonth <- setYears(calcOutput("GrassGPP", season = "monthly", - lpjml = lpjml, climatetype = climatetype, - selectyears = selectyears, aggregate = FALSE), - selectyears) - - #################### - ### Calculations ### - #################### - # Calculate growing period - grper <- grassGPPmonth - grper[, , ] <- 0 - # Classification as growing period month when monthly grass GPP > 100gC/m^2 - thresholdLGP <- minThreshold * yieldTransform - grper[grassGPPmonth >= thresholdLGP] <- 1 - - ############## - ### Checks ### - ############## - if (any(is.na(grper))) { - stop("mrland::calcGrowingPeriodMonths produced NA values") - } - if (any(grper < 0)) { - stop("mrland::calcGrowingPeriodMonths produced negative values") - } - if (any(grper != 1 && grper != 0)) { - stop("Problem in mrland::calcGrowingPeriodMonths: Value should be 0 or 1!") - } - - ############## - ### Return ### - ############## - unit <- "boolean" - description <- paste0("Classification of months as growing period month ", - "under irrigated and rainfed conditions. ", - "1 = suitable for crop growth, 0 = not suitable for crop growth") - - return(list(x = grper, - weight = NULL, - unit = unit, - description = description, - isocountries = FALSE)) -} diff --git a/R/calcIntakeBodyweight.R b/R/calcIntakeBodyweight.R index d35d6dc4..acbeae32 100644 --- a/R/calcIntakeBodyweight.R +++ b/R/calcIntakeBodyweight.R @@ -37,8 +37,8 @@ calcIntakeBodyweight <- function(bodyweight, bodyheight = NULL, inactivity, tmea requirement <- readSource("HHS_USDA", convert = FALSE) weight <- requirement * 0 + 1 mapping <- toolGetMapping(type = "sectoral", name = "HHS_USDA2hic.csv", where = "mappingfolder") - requirement <- speed_aggregate(x = requirement, rel = mapping, weight = weight, - from = "HHS_USDA", to = "hic", dim = 3.2) + requirement <- toolAggregate(x = requirement, rel = mapping, weight = weight, + from = "HHS_USDA", to = "hic", dim = 3.2) standardizedRequirement <- (collapseNames(requirement[, , "Sedentary"]) * inactivity + collapseNames(requirement[, , "Active"]) * (1 - inactivity)) diff --git a/R/calcLPJmLClimateInput.R b/R/calcLPJmLClimateInput.R deleted file mode 100644 index a1e603b5..00000000 --- a/R/calcLPJmLClimateInput.R +++ /dev/null @@ -1,136 +0,0 @@ -#' @title calcLPJmLClimateInput -#' @description Handle LPJmL climate input data and its time behaviour -#' (smoothing and harmonizing to baseline) -#' -#' @param climatetype Switch between different climate scenario -#' @param variable Switch between different climate inputs and temporal resolution -#' @param stage Degree of processing: raw, smoothed - raw or smoothed data from 1930|1951 -#' raw1901, smoothed1901 - raw or smoothed data from 1901 -#' harmonized, harmonized2020 - based on toolLPJmLVersion -#' @param lpjmlVersion LPJmL Version hand over -#' -#' @return magpie object in cellular resolution -#' @author Marcos Alves, Kristine Karstens, Felicitas Beier -#' -#' @examples -#' \dontrun{ -#' calcOutput("LPJmLClimateInput", -#' climatetype = "MRI-ESM2-0:ssp370", -#' variable = "temperature:annualMean") -#' } -#' -#' @importFrom madrat toolSplitSubtype toolTimeAverage -#' @importFrom magclass getNames -#' @importFrom magpiesets findset -#' @importFrom mstools toolHoldConstant -#' @importFrom SPEI thornthwaite -#' - -calcLPJmLClimateInput <- function(climatetype = "MRI-ESM2-0:ssp370", - variable = "temperature:annualMean", - stage = "harmonized2020", - lpjmlVersion = "LPJmL4_for_MAgPIE_44ac93de") { - - # Create settings for LPJmL/GCM from version and climatetype argument - cfg <- toolClimateInputVersion(lpjmlVersion = lpjmlVersion, - climatetype = climatetype) - var <- toolSplitSubtype(variable, list(type = NULL, timeres = NULL)) - outtype <- ifelse(var$timeres != "wetDaysMonth", var$type, "wetDaysMonth") - - if (grepl("raw|smoothed", stage)) { - - ########## PLUG HIST + FUTURE ########## - - if (!grepl("historical", climatetype)) { - - .subtypeScen <- paste(cfg$versionScen, cfg$climatetype, var$type, sep = ":") - .subtypeHist <- gsub("ssp[0-9]{3}", "historical", .subtypeScen) - - # For climate scenarios historical GCM data has to be read in from a different file - x <- mbind(readSource("LPJmLClimateInput", subtype = .subtypeHist, - subset = var$timeres, convert = "onlycorrect"), - readSource("LPJmLClimateInput", subtype = .subtypeScen, - subset = var$timeres, convert = "onlycorrect")) - years <- getYears(x, as.integer = TRUE) - x <- x[, years[years >= 1951], ] - - } else { - - .subtypeHist <- paste(cfg$versionHist, cfg$climatetype, var$type, sep = ":") - x <- readSource("LPJmLClimateInput", subtype = .subtypeHist, - subset = var$timeres, convert = "onlycorrect") - years <- getYears(x, as.integer = TRUE) - if (!grepl("1901", stage)) x <- x[, years[years >= 1930], ] - } - ########## PLUG HIST + FUTURE ########## - - if (grepl("smoothed", stage)) { - out <- toolSmooth(x) - } else { - out <- x - } - - } else if (grepl("harmonized", stage)) { - - harmStyle <- switch(outtype, - "temperature" = "additive", - "precipitation" = "limited", - "longWaveNet" = stop(paste0("No harmonization available for: ", var$variable)), - "shortWave" = stop(paste0("No harmonization available for: ", var$variable)), - "wetDaysMonth" = stop(paste0("No harmonization available for: ", var$variable))) - - if (stage == "harmonized") { - # read in historical data for subtype - baseline <- calcOutput("LPJmLClimateInput", climatetype = cfg$baselineHist, - variable = variable, stage = "smoothed", - lpjmlVersion = lpjmlVersion, aggregate = FALSE) - x <- calcOutput("LPJmLClimateInput", climatetype = cfg$climatetype, - variable = variable, stage = "smoothed", - lpjmlVersion = lpjmlVersion, aggregate = FALSE) - out <- toolHarmonize2Baseline(x, baseline, ref_year = cfg$refYearHist, method = harmStyle) - - } else if (stage == "harmonized2020") { - # read in historical data for subtype - baseline2020 <- calcOutput("LPJmLClimateInput", climatetype = cfg$baselineGcm, - variable = variable, stage = "harmonized", - lpjmlVersion = lpjmlVersion, aggregate = FALSE) - - if (cfg$climatetype == cfg$baselineGcm) { - - out <- baseline2020 - - } else { - - x <- calcOutput("LPJmLClimateInput", climatetype = cfg$climatetype, - variable = variable, stage = "smoothed", - lpjmlVersion = lpjmlVersion, aggregate = FALSE) - out <- toolHarmonize2Baseline(x, baseline2020, ref_year = cfg$refYearGcm, method = harmStyle) - } - - } else { - stop("Stage argument not supported!") - } - } else { - stop("Stage argument not supported!") - } - - unit <- switch(outtype, - "temperature" = "Degree Celcius", - "precipitation" = "mm/day", - "longWaveNet" = "watt per m2", - "shortWave" = "watt per m2", - "wetDaysMonth" = "number of rainy days") - - description <- switch(outtype, - "temperature" = paste0("Average ", var$timeres, " air temperature"), - "precipitation" = paste0("Average ", var$timeres, " precipitation"), - "longWaveNet" = "Long wave radiation", - "ShortWave" = "Short wave radiation", - "wetDaysMonth" = "number of rainy days") - - return(list(x = out, - weight = NULL, - unit = unit, - description = description, - isocountries = FALSE)) -} diff --git a/R/calcLPJmL_new.R b/R/calcLPJmL_new.R deleted file mode 100644 index 1305cf61..00000000 --- a/R/calcLPJmL_new.R +++ /dev/null @@ -1,261 +0,0 @@ -#' @title calcLPJmL_new -#' @description Handle LPJmL data and its time behaviour (smoothing and harmonizing to baseline) -#' -#' @param version Switch between LPJmL versions (including addons for further version specification) -#' @param climatetype Switch between different climate scenarios -#' @param subtype Switch between different lpjml input as specified in readLPJmL -#' @param subdata Switch between data dimension subitems -#' @param stage Degree of processing: raw, smoothed - raw or smoothed data from 1930|1951 -#' raw1901, smoothed1901 - raw or smoothed data from 1901 -#' harmonized, harmonized2020 - based on toolLPJmLVersion -#' -#' @return List of magpie objects with results on cellular level, weight, unit and description. -#' -#' @author Kristine Karstens, Felicitas Beier -#' -#' @importFrom madrat calcOutput readSource toolSubtypeSelect toolSplitSubtype -#' @importFrom magclass dimSums getYears setYears -#' -#' @seealso -#' [readLPJmL()] -#' @examples -#' \dontrun{ -#' calcOutput("LPJmL_new", subtype = "soilc", aggregate = FALSE) -#' } -#' -calcLPJmL_new <- function(version = "LPJmL4_for_MAgPIE_44ac93de", # nolint - climatetype = "MRI-ESM2-0:ssp370", - subtype = "soilc", subdata = NULL, stage = "harmonized2020") { - - # Create settings for LPJmL from version and climatetype argument - cfg <- toolLPJmLVersion(version = version, climatetype = climatetype) - - if (grepl("raw|smoothed", stage)) { - - if (subtype %in% c("discharge", "runoff", "lake_evap", "input_lake")) { - # calcLPJmL subtypes (returned by calcLPJmL) that are calculated based on different original LPJmL subtypes - readinmap <- c(lake_evap = "mpet", # mpet_natveg lake_evap = pet * lake_shr * cell_area - input_lake = "aprec", # aprec_natveg input_lake = aprec * lake_shr * cell_area - discharge = "mdischarge", - runoff = "mrunoff") - - subtypeIn <- toolSubtypeSelect(subtype, readinmap) - - } else { - subtypeIn <- subtype - } - - if (grepl("\\+scen", cfg$readin_version)) { - scen <- gsub("(.+)\\+scen:([^\\+]*)(.*)", "_\\2", cfg$readin_version) - cfg$readin_version <- gsub("\\+scen:([^\\+]*)", "", cfg$readin_version) - cfg$climatetype <- paste0(cfg$climatetype, scen) - } - readinName <- paste(cfg$readin_version, cfg$climatetype, subtypeIn, sep = ":") - readinHist <- gsub("ssp[0-9]{3}", "historical", readinName) - - ########## PLUG HIST + FUTURE ########## - - if (!grepl("historical", cfg$climatetype)) { - - x <- mbind(readSource("LPJmL_new", subtype = readinHist, convert = FALSE), - readSource("LPJmL_new", subtype = readinName, convert = FALSE)) - years <- getYears(x, as.integer = TRUE) - x <- x[, years[years >= 1951], ] - - } else { - - x <- readSource("LPJmL_new", subtype = readinName, convert = FALSE) - years <- getYears(x, as.integer = TRUE) - if (!grepl("1901", stage)) x <- x[, years[years >= 1930], ] - - } - ########## PLUG HIST + FUTURE ########## - - if (!is.null(subdata)) { - if (!all(subdata %in% getNames(x))) { - stop(paste0("Subdata items '", subdata, "' are not part of selected LPJmL subtype!")) - } - x <- x[, , subdata] - } - - ########## UNIT TRANSFORMATION ############### - - if (grepl("soilc|soilc_layer|litc|vegc|alitfallc|alitter|vegc_grass|litc_grass|soilc_grass", subtype)) { - - unitTransform <- 0.01 - x <- x * unitTransform - unit <- "tC/ha" - - if (grepl("litc|soilc_layer", subtype)) x <- toolConditionalReplace(x, "<0", 0) - - } else if (grepl("*date*", subtype)) { - - unit <- "day of the year" - - } else if (grepl("aet|cft_transp_pft|discharge|runoff|lake_evap|input_lake", subtype)) { - # unit transformation - if (grepl("aet|cft_transp_pft", subtype)) { - # Annual evapotranspiration (evaporation + transpiration + interception) given in liter/m^2 - # Plant transpiration in liter/m^2 per season - # Transform units: liter/m^2 -> m^3/ha - unitTransform <- 10 - x <- x * unitTransform - - } else if (grepl("discharge", subtype)) { - # In LPJmL: (monthly) discharge given in hm3/d (= mio. m3/day) - # Transform units of discharge: mio. m^3/day -> mio. m^3/month - dayofmonths <- as.magpie(c(jan = 31, feb = 28, mar = 31, apr = 30, may = 31, jun = 30, - jul = 31, aug = 31, sep = 30, oct = 31, nov = 30, dec = 31)) - x <- x * dayofmonths - - # Annual value (total over all month) - if (!grepl("^m", subtype)) { - x <- dimSums(x, dim = 3) - } - - } else if (grepl("runoff", subtype)) { - ## In LPJmL: (monthly) runoff given in LPJmL: mm/month - # Transform units: liter/m^2 -> liter - # landarea in mio. ha - landarea <- setYears(collapseNames(dimSums(readSource("LUH2v2", subtype = "states", - convert = "onlycorrect")[, "y1995", ], - dim = 3)), NULL) - x <- x * landarea * 1e10 - # Transform units: liter -> mio. m^3 - x <- x / (1000 * 1000000) - - # Annual value (total over all month) - if (!grepl("^m", subtype)) { - x <- dimSums(x, dim = 3) - } - - } else if (grepl("lake_evap|input_lake", subtype)) { - ## In LPJmL: given in mm (=liter/m^2) - # Multiply by lake share - lakeShare <- readSource("LPJmLInputs", subtype = "lakeshare", convert = "onlycorrect") - x <- x * lakeShare - - # Transform units: liter/m^2 -> liter - cb <- toolGetMapping("LPJ_CellBelongingsToCountries.csv", - type = "cell", where = "mrcommons") - cellArea <- (111e3 * 0.5) * (111e3 * 0.5) * cos(cb$lat / 180 * pi) - x <- x * cellArea - - # Transform units: liter -> mio. m^3 - x <- x / (1000 * 1000000) - - # Annual value (total over all month) - if (grepl("lake_evap", subtype)) { - x <- dimSums(x, dim = 3) - } - } - - units <- c(aet = "m^3/ha", - cft_transp_pft = "m^3/ha", - discharge = "mio. m^3", - mdischarge = "mio. m^3", - lake_evap = "mio. m^3", - input_lake = "mio. m^3", - runoff = "mio. m^3", - mrunoff = "mio. m^3") - - unit <- toolSubtypeSelect(subtype, units) - - } else if (grepl("*harvest*|gpp_grass", subtype)) { - - yieldTransform <- 0.01 / 0.45 - x <- x * yieldTransform - unit <- "tDM/ha" - - } else if (grepl("irrig|cwater_b", subtype)) { - # Transform units: liter/m^2 (= mm) -> m^3/ha - irrigTransform <- 10 - # select only irrigated - x <- x[, , "irrigated"] * irrigTransform # units are now: m^3 per ha per year - unit <- "m^3/ha" - - } else if (grepl("et_grass", subtype)) { - # Transform units: liter/m^2 (= mm) -> m^3/ha - watTransform <- 10 - x <- x * watTransform - unit <- "m^3/ha" - - } else if (grepl("input_lake", subtype)) { - - unit <- "mio. m^3" - - } else if (grepl("cshift", subtype)) { - - unit <- "C/C" - - } else if (grepl("fpc", subtype)) { - - unit <- "ha/ha" - - } else if (grepl("mpet", subtype)) { - - unit <- "mm/month" - - } else { - stop(paste0("subtype ", subtype, " does not exist")) - } - - ########## UNIT TRANSFORMATION ############### - - if (grepl("smoothed", stage)) { - out <- toolSmooth(x) - } else { - out <- x - } - - } else if (stage == "harmonized") { - # read in historical data for subtype - baseline <- calcOutput("LPJmL_new", version = cfg$baseline_version, - climatetype = cfg$baseline_hist, subtype = subtype, - subdata = subdata, stage = "smoothed", - aggregate = FALSE, supplementary = TRUE) - - unit <- baseline$unit - baseline <- baseline$x - - x <- calcOutput("LPJmL_new", version = cfg$readin_version, - climatetype = cfg$climatetype, subtype = subtype, - subdata = subdata, stage = "smoothed", aggregate = FALSE) - out <- toolHarmonize2Baseline(x, baseline, ref_year = cfg$ref_year_hist) - - } else if (stage == "harmonized2020") { - # read in historical data for subtype - baseline2020 <- calcOutput("LPJmL_new", version = cfg$baseline_version, - climatetype = cfg$baseline_gcm, subtype = subtype, - subdata = subdata, stage = "harmonized", - aggregate = FALSE, supplementary = TRUE) - - unit <- baseline2020$unit - baseline2020 <- baseline2020$x - - if (cfg$climatetype == cfg$baseline_gcm && - cfg$readin_version == cfg$baseline_version) { - - out <- baseline2020 - - } else { - - x <- calcOutput("LPJmL_new", version = cfg$readin_version, - climatetype = cfg$climatetype, subtype = subtype, - subdata = subdata, stage = "smoothed", aggregate = FALSE) - out <- toolHarmonize2Baseline(x, baseline2020, ref_year = cfg$ref_year_gcm) - } - - } else { - stop("Stage argument not supported!") - } - - return(list( - x = out, - weight = NULL, - unit = unit, - min = 0, - description = paste0("Output from LPJmL (", subtype, ") for ", - version, " and ", climatetype, " at stage: ", stage, "."), - isocountries = FALSE)) -} diff --git a/R/calcLUH2MAgPIE.R b/R/calcLUH2MAgPIE.R deleted file mode 100644 index ba743b8a..00000000 --- a/R/calcLUH2MAgPIE.R +++ /dev/null @@ -1,145 +0,0 @@ -#' @title calcLUH2MAgPIE -#' @description Calculates the real aggregation of LUH croptypes to MAgPIE croptypes -#' out of LUH2FAO and FAO2MAgPIE mappings -#' -#' @param share total (for total numbers), LUHofMAG (for share of LUH within kcr types), -#' MAGofLUH (for share of kcr within LUH types) -#' @param bioenergy "ignore": 0 for share and totals, -#' "fix": fixes betr and begr shares in LUHofMAG to 1 for c3per and c4per -#' @param rice rice category: "non_flooded" or "total" -#' @param selectyears years to be returned (default: "past") -#' @param missing "ignore" will leave data as is, -#' "fill" will add proxy values for data gaps of FAO -#' @return List of magpie objects with results on country level, weight on country level, unit and description -#' @author Kristine Karstens, Felicitas Beier -#' @examples -#' \dontrun{ -#' calcOutput("LUH2MAgPIE") -#' } -#' -#' @importFrom magpiesets findset - -calcLUH2MAgPIE <- function(share = "total", bioenergy = "ignore", rice = "non_flooded", - selectyears = "past", missing = "ignore") { - past <- findset("past") - - if (share == "total") { - - if (missing == "fill") { - warning("No missing data for total numbers assumend.") - } - - FAOdata <- calcOutput("Croparea", sectoral = "ProductionItem", # nolint : object_name_linter. - physical = FALSE, aggregate = FALSE)[, past, ] - - if (rice == "non_flooded") { - # Rice areas are pre-determined by areas reported as flooded in LUH. - # All additional rice areas (according to FAO) are allocated using FAO data - nonfloodedShr <- calcOutput("Ricearea", cellular = FALSE, share = TRUE, aggregate = FALSE) - FAOdata[, , "27|Rice, paddy"] <- FAOdata[, , "27|Rice, paddy"] * nonfloodedShr # nolint : object_name_linter. - } - - kcr <- findset("kcr") - mapping <- toolGetMapping("FAO2LUH2MAG_croptypes.csv", type = "sectoral", where = "mrcommons") - - aggregation <- toolAggregate(FAOdata, rel = mapping, from = "ProductionItem", - to = "LUH2kcr", dim = 3.1, partrel = TRUE) - aggregation <- add_columns(aggregation, addnm = c("betr", "begr"), dim = 3.2) - aggregation <- aggregation[, , kcr] - aggregation <- complete_magpie(collapseNames(aggregation), fill = 0) - aggregation[which(is.na(aggregation))] <- 0 - getSets(aggregation, fulldim = FALSE) <- c("ISO", "Year", "LUH.MAG") - - x <- aggregation - unit <- "million ha" - - } else if (share == "LUHofMAG") { - - aggregation <- calcOutput("LUH2MAgPIE", aggregate = FALSE, selectyears = selectyears, rice = rice) - - MAG <- dimSums(aggregation, dim = "LUH") # nolint : object_name_linter. - x <- aggregation / MAG - x[which(is.na(x))] <- 0 - unit <- "share of area" - - if (bioenergy == "fix") { - - x[, , "c3per.betr"] <- 1 - x[, , "c4per.begr"] <- 1 - - } else if (bioenergy != "ignore") { - stop("2nd generation bioenergy setting not supported") - } - - if (missing == "fill") { - # check for countries/years where no data is reported from FAO and fill with proxy of similar country - noData <- where(dimSums(toolIso2CellCountries(x), dim = 3) == 0)$true$individual - proxyMapping <- c(ATF = "ISL", ESH = "MAR", FLK = "ISL", GRL = "ISL", - PSE = "ISR", SGS = "ISL", SJM = "NOR", - CIV = "GHA", GUF = "SUR", REU = "MUS", SSD = "CAF", SDN = "TCD") - - for (i in row(noData)[, 1]) { - x[noData[i, "ISO"], noData[i, "Year"], ] <- x[proxyMapping[noData[i, "ISO"]], noData[i, "Year"], ] - } - - # check for countries/years/croptypes where no data is reported from FAO and fill with default values - noData <- where(dimSums(x, dim = 3.1) == 0)$true$individual - meanValues <- dimSums(x * dimSums(aggregation, dim = "LUH"), dim = "ISO") / - dimSums(aggregation, dim = c("ISO", "LUH")) - meanValues[is.nan(meanValues)] <- 0 - for (i in row(noData)[, 1]) { - x[noData[i, "ISO"], noData[i, "Year"], noData[i, "MAG"]] <- meanValues[, noData[i, "Year"], noData[i, "MAG"]] - } - - # consistency check - if (any(round(dimSums(x, dim = 3.1), 4) != 1)) { - warning("Not all factors could been filled, even though 'missing' was set to 'fill'.") - } - } - - } else if (share == "MAGofLUH") { - - aggregation <- calcOutput("LUH2MAgPIE", aggregate = FALSE, selectyears = selectyears, rice = rice) - - LUH <- dimSums(aggregation, dim = "MAG") # nolint : object_name_linter. - x <- aggregation / LUH - x[which(is.na(x))] <- 0 - unit <- "share of area" - - if (bioenergy != "ignore") { - stop("2nd generation bioenergy setting not supported") - } - - if (missing == "fill") { - # check for countries/years where no data is reported from FAO and fill with proxy - noData <- where(dimSums(toolIso2CellCountries(x), dim = 3) == 0)$true$individual - proxyMapping <- c(ATF = "ISL", ESH = "MAR", FLK = "ISL", GRL = "ISL", - PSE = "ISR", SGS = "ISL", SJM = "NOR", - CIV = "GHA", GUF = "SUR", REU = "MUS", SSD = "CAF", SDN = "TCD") - for (i in row(noData)[, 1]) { - x[noData[i, "ISO"], noData[i, "Year"], ] <- x[proxyMapping[noData[i, "ISO"]], noData[i, "Year"], ] - } - - # check for countries/years/croptypes where no data is reported from FAO and fill with default values - noData <- where(dimSums(x, dim = 3.2) == 0)$true$individual - meanValues <- dimSums(x * dimSums(aggregation, dim = "MAG"), dim = "ISO") / - dimSums(aggregation, dim = c("ISO", "MAG")) - for (i in row(noData)[, 1]) { - x[noData[i, "ISO"], noData[i, "Year"], noData[i, "LUH"]] <- meanValues[, noData[i, "Year"], noData[i, "LUH"]] - } - - # consistency check - if (any(round(dimSums(x, dim = 3.2), 4) != 1)) { - warning("Not all factors could been filled, even though 'missing' was set to 'fill'.") - } - } - - } else { - stop("Share type not supported") - } - - return(list(x = x, - weight = NULL, - unit = unit, - description = "Relation matrix for LUH croptype and MAgPIE croptype areas")) -} diff --git a/R/calcLUH2v2.R b/R/calcLUH2v2.R deleted file mode 100644 index f30b7c7d..00000000 --- a/R/calcLUH2v2.R +++ /dev/null @@ -1,99 +0,0 @@ -#' @title calcLUH2v2 -#' @description Integrates the LUH2v2 landuse-dataset -#' -#' @param landuse_types magpie: magpie landuse classes, -#' LUH2v2: original landuse classes -#' flooded: flooded areas as reported by LUH -#' @param irrigation if true: areas are returned separated by irrigated and rainfed, -#' if false: total areas -#' @param cellular if true: dataset is returned on 0.5 degree resolution -#' @param cells Switch between "magpiecell" (59199) and "lpjcell" (67420) -#' NOTE: This setting also affects the sums on country level! -#' @param selectyears years to be returned (default: "past") -#' -#' @return List of magpie objects with results on country level, -#' weight on country level, unit and description -#' -#' @author Benjamin Leon Bodirsky, Florian Humpenoeder, Jens Heinke, Felicitas Beier -#' @seealso -#' [calcLanduseInitialisation()] -#' @examples -#' \dontrun{ -#' calcOutput("LUH2v2") -#' } -#' @importFrom magclass getNames -#' @importFrom magpiesets findset - -calcLUH2v2 <- function(landuse_types = "magpie", irrigation = FALSE, # nolint - cellular = FALSE, cells = "lpjcell", selectyears = "past") { - - selectyears <- sort(findset(selectyears, noset = "original")) - - if (!all(landuse_types %in% c("magpie", "LUH2v2", "flooded"))) { - stop("Unknown lanuses_types = \"", landuse_types, "\"") - } - - if (landuse_types == "flooded") { - x <- readSource("LUH2v2", subtype = "irrigation", convert = "onlycorrect")[, selectyears, "flood"] - } else { - x <- readSource("LUH2v2", subtype = "states", convert = "onlycorrect")[, selectyears, ] - getSets(x, fulldim = FALSE)[3] <- "landuse" - - if (isTRUE(irrigation)) { - - irrigLUH <- readSource("LUH2v2", subtype = "irrigation", convert = "onlycorrect")[, selectyears, ] - - if (is.null(selectyears)) { - vcat(verbosity = 3, "too many years may lead to memory problems if irrigation = TRUE") - } - - # irrigated areas (excluding flood) - irrigLUH <- irrigLUH[, , "flood", invert = TRUE] - getNames(irrigLUH) <- substring(getNames(irrigLUH), 7) - - x <- add_dimension(x, dim = 3.2, add = "irrigation", nm = "total") - x <- add_columns(x, dim = 3.2, addnm = c("irrigated", "rainfed")) - x[, , "irrigated"] <- 0 - - irrigLUH <- add_dimension(irrigLUH, dim = 3.2, add = "irrigation", nm = "irrigated") - x[, , paste(getNames(irrigLUH, dim = 1), "irrigated", sep = ".")] <- irrigLUH - - # rainfed areas - x[, , "rainfed"] <- collapseNames(x[, , "total"]) - collapseNames(x[, , "irrigated"]) - - if (any(x[, , "rainfed"] < 0)) { - vcat(verbosity = 2, "Flooded/irrigated area larger than rainfed area. - Irrigation limited to total cropland area.") - tmp <- collapseNames(x[, , "irrigated"]) - tmp[x[, , "rainfed"] < 0] <- collapseNames(x[, , "total"])[x[, , "rainfed"] < 0] - x[, , "irrigated"] <- tmp - x[, , "rainfed"] <- collapseNames(x[, , "total"]) - collapseNames(x[, , "irrigated"]) - } - - if (any(x[, , "rainfed"] < 0)) { - vcat(verbositiy = 1, "Flooded/irrigated area larger than rainfed area despite fix.") - } - } - if (landuse_types == "magpie") { - mapping <- toolGetMapping(type = "sectoral", name = "LUH2v2.csv", where = "mappingfolder") - x <- toolAggregate(x, mapping, dim = 3.1, from = "luh2v2", to = "land") - } - } - - # Return correct cell format for further calculations - # ATTENTION: depending on the settings this might remove some cells - # from the data set! - if (cellular) { - if (cells == "magpiecell") { - x <- toolCoord2Isocell(x, cells = cells) - } - } else { - x <- toolConv2CountryByCelltype(x, cells = cells) - } - - return(list(x = x, - weight = NULL, - unit = "Mha", - description = "land area for different land use types.", - isocountries = !cellular)) -} diff --git a/R/calcLanduseInitialisation.R b/R/calcLanduseInitialisation.R deleted file mode 100644 index 47173d1a..00000000 --- a/R/calcLanduseInitialisation.R +++ /dev/null @@ -1,72 +0,0 @@ -#' @title calcLanduseInitialisation -#' @description Calculates the cellular MAgPIE landuse initialisation area. -#' Data from FAO on forestry is used to split the secondary forest pool -#' of the LU2v2 dataset into forestry and secd_forest. -#' -#' @param cellular cellular (TRUE) or country-level/regional (FALSE) data? -#' For country-level vs regional data: remember to set "aggregate" to FALSE. -#' @param nclasses options are either "six", "seven" or "nine". -#' \itemize{ -#' \item "six" includes the original land use classes "crop", "past", "forestry", "forest", "urban" and "other" -#' \item "seven" separates primary and secondary forest and includes "crop", "past", "forestry", "primforest", -#' "secdforest", "urban" and "other" -#' \item "nine" adds the separation of pasture and rangelands, as well as a differentiation of primary -#' and secondary non-forest vegetation and therefore returns "crop", "past", "range", "forestry", "primforest", -#' "secdforest", "urban", "primother" and "secdother" -#' } -#' @param cells if cellular is TRUE: "magpiecell" for 59199 cells or "lpjcell" for 67420 cells -#' @param input_magpie applies area fix (set cells with zero area to minimal value to -#' not disturb aggregating to clusters) -#' @param selectyears default on "past" -#' @return List of magpie object with results on country or cellular level, weight on cellular level, -#' unit and description. -#' @author Jan Philipp Dietrich, Benjamin Leon Bodirsky, Kristine Karstens, Felcitas Beier, Patrick v. Jeetze -#' @examples -#' \dontrun{ -#' calcOutput("LanduseInitialisation") -#' } -#' @importFrom magclass setNames where - - -calcLanduseInitialisation <- function(cellular = FALSE, nclasses = "seven", - cells = "lpjcell", selectyears = "past", - input_magpie = FALSE) { # nolint - - if (isFALSE(cellular)) { - out <- calcOutput("LanduseInitialisationBase", cells = "lpjcell", - selectyears = selectyears, aggregate = FALSE) - out <- toolCountryFill(dimSums(out, - dim = c("x", "y")), - fill = 0, verbosity = 2) - } else { - out <- calcOutput("LanduseInitialisationBase", cells = cells, - selectyears = selectyears, aggregate = FALSE) - } - - if (isTRUE(input_magpie)) { - # add some small area to completely empty cells to avoid - # problems in the further processing - out <- round(out, 8) - cellArea <- dimSums(out, dim = 3) - out[, , "secdother"][cellArea == 0] <- 10^-6 - } - - if (nclasses != "nine") { - map <- data.frame(nine = c("crop", "past", "range", "forestry", "primforest", "secdforest", - "urban", "primother", "secdother"), - seven = c("crop", "past", "past", "forestry", "primforest", "secdforest", - "urban", "other", "other"), - six = c("crop", "past", "past", "forestry", "forest", "forest", - "urban", "other", "other")) - if (!(nclasses %in% names(map))) stop("unknown nclasses setting \"", nclasses, "\"") - out <- toolAggregate(out, rel = map, dim = 3, from = "nine", to = nclasses) - } - - return(list(x = out, - weight = NULL, - unit = "Mha", - min = 0, - max = 14900, ### global land area - description = "Land use initialisation data for different land pools", - isocountries = !cellular)) -} diff --git a/R/calcLanduseInitialisationBase.R b/R/calcLanduseInitialisationBase.R deleted file mode 100644 index 5ec68530..00000000 --- a/R/calcLanduseInitialisationBase.R +++ /dev/null @@ -1,132 +0,0 @@ -#' @title calcLanduseInitialisationBase -#' @description Calculates the cellular MAgPIE landuse initialisation area. Data from FAO on forestry is used -#' to split the secondary forest pool of the LU2v2 dataset into forestry and secd_forest. This function -#' returns the data set in a basic configuration. Use \code{\link{calcLanduseInitialisation}} for -#' more settings. -#' -#' @param cells "magpiecell" for 59199 cells or "lpjcell" for 67420 cells -#' @param selectyears Years to be computed (default on "past") -#' @return Cellular landuse initialisation in its base configuration -#' @author Jan Philipp Dietrich, Benjamin Leon Bodirsky, Kristine Karstens, Felcitas Beier, Patrick v. Jeetze -#' @examples -#' \dontrun{ -#' calcOutput("LanduseInitialisationBase") -#' } - -calcLanduseInitialisationBase <- function(cells = "lpjcell", selectyears = "past") { - - selectyears <- sort(findset(selectyears, noset = "original")) - - .luIni <- function(luh, forestArea) { - .shr <- function(x) { - x <- x + 10^-10 - return(x / dimSums(x, dim = 3)) - } - - .expand <- function(x, target) { - map <- data.frame(from = getItems(target, dim = "iso", full = TRUE), - to = getItems(target, dim = 1)) - return(toolAggregate(x[getItems(target, dim = "iso"), , ], map, from = "from", to = "to")) - } - map <- data.frame(luh = c("c3ann", "c4ann", "c3per", "c4per", "c3nfx", "pastr", "range", - "primf", "secdf", "secdf", "urban", "primn", "secdn"), - lu = c("crop", "crop", "crop", "crop", "crop", "past", "range", - "primforest", "secdforest", "forestry", "urban", "other", "other")) - lu <- toolAggregate(luh, map, dim = 3) - # Attention: mapping maps secdf on both: secdforest and forestry (both contain after aggregation the full secondary - # forest area)! Next step will calculate proper shares and multiply it to compute correct areas - secdf <- c("secdforest", "forestry") - forestShares <- .expand(x = .shr(forestArea[, , secdf]), target = lu) - lu[, , secdf] <- forestShares * lu[, , secdf] - return(lu) - } - - .natureTarget <- function(lu, forestArea) { - # compute target for primforest, secdforest and other (aggregate of primother and secdother) - forests <- c("primforest", "secdforest", "forestry") - nature <- c(forests, "other") - - # Correct for overflow effects (forestArea greater than forest and other land available in luInit) - overflow <- forestArea[, , "forest"] - dimSums(lu[, , nature], dim = 3) - overflow[overflow < 0] <- 0 - if (any((of <- dimSums(overflow, dim = 1)) > 0)) { - vcat(verbosity = 2, paste("Mismatch of FAO forest exceed LUH forest + other land by:", - paste0(paste(getYears(of), round(of, 0), "Mha"), collapse = ", "), - "- FAO forest data will be cut.")) - # corrected forest areas <- weight of forest subcategories * corrected total forest area - corr <- setNames((forestArea[, , "forest"] + 10e-10 - overflow) / (forestArea[, , "forest"] + 10e-10), NULL) - forestArea <- corr * forestArea - } - - # compute other land area (diff between total natural land and forest area) - otherArea <- setNames(dimSums(lu[, , nature], dim = 3) - forestArea[, , "forest"], "other") - if (any(otherArea < -10e-6)) { - warning("Other land area is partly negative. This should not be the case! values will be corrected to 0.") - } - # due to rounding there are always some very small values below 0 which is why it is always corrected to 0, but - # a warning is only triggered for values smaller than 10e-6 - otherArea[otherArea < 0] <- 0 - - return(mbind(forestArea[, , forests], otherArea)) - } - - # cellular landuse area - luh <- calcOutput("LUH2v2", landuse_types = "LUH2v2", irrigation = FALSE, cellular = TRUE, - selectyears = selectyears, cells = "lpjcell", aggregate = FALSE) - # country-level forest area - forestArea <- calcOutput("ForestArea", selectyears = selectyears, aggregate = FALSE) - # rename categories and split secondary forest into secondary forest and forestry - # based on forestArea information (area sizes kept as reported by luh) - lu <- .luIni(luh, forestArea) - - luCountry <- toolCountryFill(dimSums(lu, dim = c("x", "y")), - fill = 0, verbosity = 2) - natTarget <- .natureTarget(luCountry, forestArea) - - vegC <- calcOutput("LPJmL_new", subtype = "vegc", stage = "smoothed", - version = "LPJmL4_for_MAgPIE_44ac93de", climatetype = "GSWP3-W5E5:historical", - aggregate = FALSE)[, selectyears, ] - - lu2 <- toolForestRelocate(lu = lu, luCountry = luCountry, natTarget = natTarget, vegC = vegC) - - .splitOther <- function(lu, luh) { - # split other land in primary and secondary other land - # try to adjust only secondary other land and only touch primary other land - # if total other land is smaller than primary other land - other <- setNames(luh[, , c("primn", "secdn")], c("primother", "secdother")) - secdother <- setNames(lu[, , "other"] - other[, , "primother"], NULL) - # handle cases in which the above calculation became negative - if (any(secdother < 0)) { - remove <- -secdother - remove[remove < 0] <- 0 - secdother[secdother < 0] <- 0 - other[, , "primother"] <- other[, , "primother"] - remove - } - other[, , "secdother"] <- secdother - if (max(abs(dimSums(other, dim = 3) - lu[, , "other"])) > 10e-6) { - warning("splitted other land does not sum up to total other land!") - } - return(mbind(lu[, , "other", invert = TRUE], other)) - } - - out <- .splitOther(lu2, luh) - - if (cells == "magpiecell") { - out <- toolCoord2Isocell(out, cells = cells) - } - - if (any(out < 0)) { - if (min(out) < -10e-6) { - warning("Negative land values detected in LanduseInitialisationBase and replaced by 0.") - } - out[out < 0] <- 0 - } - - return(list(x = out, - weight = NULL, - unit = "Mha", - min = 0, - max = 14900, ### global land area - description = "Land use initialisation data for different land pools", - isocountries = FALSE)) -} diff --git a/R/calcMulticropping.R b/R/calcMulticropping.R deleted file mode 100644 index 0a5ef1a8..00000000 --- a/R/calcMulticropping.R +++ /dev/null @@ -1,70 +0,0 @@ -#' @title calcMulticropping -#' @description calculates a multiple cropping factor based on area harvested, -#' physical cropland area (and optionally fallow land). -#' -#' @param extend_future if TRUE -#' @param factortype CI: cropping intensity factor calculated as ratio of -#' harvested to physical area where values above one -#' indicate multicropping, below one fallow land (default) -#' MC: multiple cropping factor indicating areas that are -#' harvested more than once in one year calculated taking -#' fallow land into account explicitly: -#' harvestedArea / (physicalArea - fallowLand) -#' @return List of magpie objects with results on country level, weight on country level, unit and description. -#' @author Benjamin Leon Bodirsky, David Chen, Felicitas Beier -#' @seealso -#' [calcFAOLand()], -#' [calcCroparea()] -#' @examples -#' \dontrun{ -#' calcOutput("Multicropping") -#' } -#' -calcMulticropping <- function(extend_future = FALSE, factortype = "CI") { # nolint - - # physical cropland area ("6620|Cropland") - phys <- collapseNames(calcOutput("FAOLand", aggregate = FALSE)[, , "6620", pmatch = TRUE]) - # harvested area - harv <- collapseNames(dimSums(calcOutput("Croparea", physical = FALSE, - aggregate = FALSE, sectoral = "kcr"), - dim = 3.1)) - - # match year dimension - phys <- phys[, intersect(getYears(phys), getYears(harv)), ] - harv <- harv[, intersect(getYears(phys), getYears(harv)), ] - - if (factortype == "CI") { - - # Cropping intensity (>1: mulitple cropping dominates; <1: fallow land dominates) - out <- harv / phys - description <- "cropping intensity factor with values above one indicating multicropping, below one fallow land" - - } else if (factortype == "MC") { - - # fallow land ("6640|Land with temporary fallow") - fallow <- collapseNames(calcOutput("FAOLand", aggregate = FALSE)[, , "6640", pmatch = TRUE]) - - # match year dimension - fallow <- fallow[, intersect(getYears(fallow), getYears(harv)), ] - - # Multiple cropping factor accounting for land that is left fallow - out <- harv / (phys - fallow) - description <- "multiple cropping factor explicitly accounting for fallow land" - - } else { - stop("Please select calculation method via the type argument in calcMulticropping") - } - - out[is.na(out)] <- 0 - out[out == Inf] <- 0 - - if (extend_future == TRUE) { - out <- toolHoldConstantBeyondEnd(out) - phys <- toolHoldConstantBeyondEnd(phys) - } - - return(list(x = out, - weight = phys, - unit = "ratio", - description = description)) -} diff --git a/R/calcMulticroppingSuitability.R b/R/calcMulticroppingSuitability.R deleted file mode 100644 index b81108f9..00000000 --- a/R/calcMulticroppingSuitability.R +++ /dev/null @@ -1,150 +0,0 @@ -#' @title calcMulticroppingSuitability -#' -#' @description Calculates which grid cells are potentially suitable for -#' multiple cropping activities under rainfed and irrigated conditions. -#' Calculation is based on the length of the growing period determined by -#' monthly grassland gross primary production (GPP). -#' -#' @param selectyears Years to be returned -#' @param lpjml LPJmL version required for respective inputs: natveg or crop -#' @param climatetype Switch between different climate scenarios or -#' historical baseline "GSWP3-W5E5:historical" -#' @param suitability "endogenous": suitability for multiple cropping determined -#' by rules based on grass and crop productivity -#' "exogenous": suitability for multiple cropping given by -#' GAEZ data set -#' @param sectoral "kcr" MAgPIE crops, and "lpj" LPJmL crops -#' -#' @return magpie object in cellular resolution -#' @author Felicitas Beier, Jens Heinke -#' -#' @examples -#' \dontrun{ -#' calcOutput("MulticroppingSuitability", aggregate = FALSE) -#' } -#' -#' @importFrom madrat calcOutput -#' @importFrom magclass setYears getSets mbind getItems new.magpie -#' - -calcMulticroppingSuitability <- function(selectyears, lpjml, climatetype, - suitability = "endogenous", sectoral = "kcr") { - # mappings - lpj2mag <- toolGetMapping("MAgPIE_LPJmL.csv", - type = "sectoral", - where = "mappingfolder") - mapCell <- toolGetMappingCoord2Country() - - # crop selection - if (sectoral == "kcr") { - - # MAgPIE crops selected - croplist <- unique(lpj2mag$MAgPIE) - # remove pasture from croplist - croplist <- croplist[croplist != "pasture"] - # Crops that are not suitable for multiple cropping (either because they are a perennial - # crop that's grown over several years or because the growing period is too long to - # allow for another season - cropsNoMC <- c("sugr_cane", "oilpalm", "betr", "begr") - - } else if (sectoral == "lpj") { - - # LPJmL crops selected - croplist <- unique(lpj2mag$LPJmL) - # remove mgrass from croplist - croplist <- croplist[croplist != "mgrass"] - # Crops that are not suitable for multiple cropping (either because they are a perennial - # crop that's grown over several years or because the growing period is too long to - # allow for another season - cropsNoMC <- c("sugarcane", "betr", "begr") - - } - - # Prepare data structure as crop-specific object - # (While the chosen rule is not crop-specific, - # crops that are not multiple cropping suitable are marked as such. - # This is done further down in the code) - suitMC <- new.magpie(cells_and_regions = paste(mapCell$coords, mapCell$iso, sep = "."), - years = selectyears, - names = paste(croplist, - c(rep("rainfed", length(croplist)), - rep("irrigated", length(croplist))), - sep = "."), - fill = NA, - sets = c("x", "y", "iso", "year", "crop", "irrigation")) - - # Choose how multiple cropping suitability is determined - if (suitability == "endogenous") { - - # Read in growing period months - grper <- calcOutput("GrowingPeriodMonths", - selectyears = selectyears, - lpjml = lpjml, climatetype = climatetype, - aggregate = FALSE) - - # Calculate length of growing period - lgp <- dimSums(grper, dim = "month") - - ### Multicropping Mask ### - ## Rule: Minimum length of growing period of 9 months - suitMC[, , "rainfed"] <- (lgp >= 9)[, , "rainfed"] - suitMC[, , "irrigated"] <- (lgp >= 9)[, , "irrigated"] - - } else if (suitability == "exogenous") { - - #################### - ### Read in data ### - #################### - mcZones <- calcOutput("MultipleCroppingZones", layers = 2, aggregate = FALSE) - suitMC[, , "rainfed"] <- mcZones[, , "rainfed"] - suitMC[, , "irrigated"] <- mcZones[, , "irrigated"] - - } else { - stop("Please select whether endogenously calculated multiple cropping suitability - mask (endogenous) should be selected or whether - GAEZ Multiple Cropping Zones data set should be used (exogenous)") - } - - if (any(suitMC[, , sample(croplist, 1)] != suitMC[, , sample(croplist, 1)])) { - stop("Multiple cropping suitability is not defined as crop-specific - and should be the same for every crop. - This is not the case here. - Please double-check in mrland:calcMulticroppingSuitability") - } - - # Crops that cannot be multiple cropped have suitability of 0 - suitMC[, , cropsNoMC] <- 0 - - # If multiple cropping is possible under rainfed conditions, - # it is also possible under irrigated conditions - rfMC <- collapseNames(suitMC[, , "rainfed"]) - suitMC[, , "irrigated"][rfMC == 1] <- 1 - - ############## - ### Checks ### - ############## - if (any(is.na(suitMC))) { - stop("calcMulticroppingSuitability produced NA values") - } - if (any(suitMC < 0)) { - stop("calcMulticroppingSuitability produced negative values") - } - if (any(suitMC != 1 && suitMC != 0)) { - stop("Problem in calcMulticroppingSuitability: Value should be 0 or 1!") - } - - ############## - ### Return ### - ############## - out <- suitMC - unit <- "boolean" - description <- paste0("Suitability for multicropping ", - "under irrigated and rainfed conditions. ", - "1 = suitable, 0 = not suitable") - - return(list(x = out, - weight = NULL, - unit = unit, - description = description, - isocountries = FALSE)) -} diff --git a/R/calcMultipleCroppingZones.R b/R/calcMultipleCroppingZones.R deleted file mode 100644 index 03b6e6c5..00000000 --- a/R/calcMultipleCroppingZones.R +++ /dev/null @@ -1,82 +0,0 @@ -#' @title calcMultipleCroppingZones -#' @description This function returns multiple cropping zones at 0.5 degree resolution -#' -#' @param layers 8 for original GAEZ layers, -#' 3 for aggregated multiple cropping zones with -#' 1 = single cropping, 2 = double cropping, 3 = triple cropping -#' 2 for aggregated boolean multicropping potential with -#' 0 = no multicropping (single cropping), 1 = multiple cropping -#' -#' @return magpie object in cellular resolution -#' @author Felicitas Beier -#' -#' @examples -#' \dontrun{ -#' calcOutput("MultipleCroppingZones", layers = 3, aggregate = FALSE) -#' } -#' -#' @importFrom magclass new.magpie getYears getNames - -calcMultipleCroppingZones <- function(layers = 2) { - # Read in source - x <- readSource("GAEZv4", subtype = "MCzones", convert = "onlycorrect") - - if (layers == 8) { - - out <- x - - } else if (layers == 3) { - - mapping <- toolGetMappingCoord2Country(pretty = TRUE) - out <- new.magpie(cells_and_regions = paste(mapping$coords, mapping$iso, sep = "."), - years = getYears(x), - names = getNames(x), - sets = getSets(x), - fill = NA) - # Aggregation of multiple cropping zone categories - out[x == 0] <- 1 # where no data given single-cropping potential assumed - out[x == 1] <- 1 # where no cropping takes place yet single-cropping potential assumed - out[x == 2] <- 1 # single cropping -> single cropping - out[x == 3] <- 1 # limited double cropping -> single cropping - out[x == 4] <- 2 # double cropping -> double cropping - out[x == 5] <- 2 # double cropping with rice -> double cropping - out[x == 6] <- 2 # double rice cropping -> double cropping - out[x == 7] <- 3 # triple cropping -> triple cropping - out[x == 8] <- 3 # triple rice cropping -> triple cropping - - } else if (layers == 2) { - - mapping <- toolGetMappingCoord2Country(pretty = TRUE) - out <- new.magpie(cells_and_regions = paste(mapping$coords, mapping$iso, sep = "."), - years = getYears(x), - names = getNames(x), - sets = getSets(x), - fill = NA) - # Aggregation of multiple cropping zone categories - out[x == 0] <- 0 # where no data given single-cropping potential assumed - out[x == 1] <- 0 # where no cropping takes place yet single-cropping potential assumed - out[x == 2] <- 0 # single cropping -> single cropping - out[x == 3] <- 0 # limited double cropping -> single cropping - out[x == 4] <- 1 # double cropping -> double cropping - out[x == 5] <- 1 # double cropping with rice -> double cropping - out[x == 6] <- 1 # double rice cropping -> double cropping - out[x == 7] <- 1 # triple cropping -> triple cropping - out[x == 8] <- 1 # triple rice cropping -> triple cropping - - } else { - stop("Selected number of layers is not available. - Please select 8 for original GAEZ layers or 3 for reduced layers - or 2 for boolean whether multiple cropping is possible") - } - - # Checks - if (any(is.na(out))) { - stop("produced NA multiple cropping zones") - } - - return(list(x = out, - weight = NULL, - unit = "1", - description = "multiple cropping zones", - isocountries = FALSE)) -} diff --git a/R/calcRicearea.R b/R/calcRicearea.R deleted file mode 100644 index 9aa9a1d9..00000000 --- a/R/calcRicearea.R +++ /dev/null @@ -1,125 +0,0 @@ -#' @title calcRicearea -#' @description calculates rice area based on LUH flooded areas and -#' physical rice areas reported by FAOSTAT. -#' -#' @param cellular If TRUE: calculates cellular rice area -#' @param cells Switch between "magpiecell" (59199) and "lpjcell" (67420) -#' @param share If TRUE: non-flooded share is returned. -#' If FALSE: rice area (flooded, non-flooded, total) in Mha is returned -#' -#' @return rice areas or rice area shares of flooded and non-flooded category -#' -#' @author Felicitas Beier, Kristine Karstens -#' -#' @importFrom magpiesets findset -#' @importFrom withr local_options - -calcRicearea <- function(cellular = FALSE, cells = "lpjcell", share = TRUE) { - - local_options(magclass_sizeLimit = 1e+12) - - selectyears <- findset("past") - - ############################################ - ### Ricearea and shares on country level ### - ############################################ - - # Country-level LUH flooded areas - floodedLUHiso <- collapseNames(calcOutput("LUH2v2", landuse_types = "flooded", - cells = cells, aggregate = FALSE, irrigation = TRUE, - cellular = FALSE, selectyears = "past")) - - # FAO rice areas (physical to be comparable with LUH) - riceareaFAOiso <- collapseNames(calcOutput("Croparea", sectoral = "kcr", physical = TRUE, - cellular = FALSE, cells = "magpicell", irrigation = FALSE, - aggregate = FALSE)[, selectyears, "rice_pro"]) - - # Country-level rice area - ricearea <- floodedLUHiso - - # Correction for flooded non-rice areas (floodedLUHiso > riceareaFAOiso) - ricearea[floodedLUHiso > riceareaFAOiso] <- riceareaFAOiso[floodedLUHiso > riceareaFAOiso] - nonriceShr <- ifelse(floodedLUHiso > 0, - ricearea / floodedLUHiso, - 0) - - # Correction for aerobic (non-paddy) rice (floodedLUHiso < riceareaFAOiso) - floodedRicearea <- riceareaFAOiso - floodedRicearea[floodedLUHiso < riceareaFAOiso] <- floodedLUHiso[floodedLUHiso < riceareaFAOiso] - floodedShr <- ifelse(riceareaFAOiso > 0, - floodedRicearea / riceareaFAOiso, - 0) - - # Non-flooded rice area - nonfloodedRicearea <- ricearea * (1 - floodedShr) - - if (!cellular) { - - if (share) { - - out <- 1 - floodedShr - unit <- "Share" - description <- "Share of rice area that is non-flooded" - - } else { - - ricearea <- add_dimension(ricearea, dim = 3.1, - add = "type", nm = "total") - floodedRicearea <- add_dimension(floodedRicearea, dim = 3.1, - add = "type", nm = "flooded") - nonfloodedRicearea <- add_dimension(nonfloodedRicearea, dim = 3.1, - add = "type", nm = "nonflooded") - - out <- collapseNames(mbind(ricearea, floodedRicearea, nonfloodedRicearea)) - unit <- "Mha" - description <- "Physical rice area on country level" - - } - - } else { - ############################################ - ### Ricearea and shares on cellular level - ############################################ - - # Cellular LUH flooded areas - floodedLUH <- collapseNames(calcOutput("LUH2v2", landuse_types = "flooded", - cells = cells, cellular = TRUE, irrigation = TRUE, - selectyears = "past", aggregate = FALSE)) - - # Correction for flooded non-rice areas (floodedLUHiso > riceareaFAOiso) - if (cells == "magpiecell") { - commonCountries <- intersect(getItems(nonriceShr, dim = "country"), getItems(floodedLUH, dim = "country")) - ricearea <- floodedLUH * toolIso2CellCountries(nonriceShr, cells = cells) - } else if (cells == "lpjcell") { - commonCountries <- intersect(getItems(nonriceShr, dim = "country"), getItems(floodedLUH, dim = "iso")) - ricearea <- floodedLUH * nonriceShr[commonCountries, , ] - } else { - stop("When cellular==TRUE in calcRicearea: please select number of cells - (magpiecell or lpjcell) via cells argument") - } - - # Correction for aerobic (non-paddy) rice (floodedLUHiso < riceareaFAOiso) - floodedRicearea <- ricearea * floodedShr[commonCountries, , ] - nonfloodedRicearea <- ricearea * (1 - floodedShr)[commonCountries, , ] - ricearea <- floodedRicearea + nonfloodedRicearea - - ricearea <- add_dimension(ricearea, dim = 3.1, add = "type", nm = "total") - floodedRicearea <- add_dimension(floodedRicearea, dim = 3.1, add = "type", nm = "flooded") - nonfloodedRicearea <- add_dimension(nonfloodedRicearea, dim = 3.1, add = "type", nm = "nonflooded") - - out <- collapseNames(mbind(floodedRicearea, nonfloodedRicearea, ricearea)) - unit <- "Mha" - description <- "Physical rice area on cellular level" - - if (share) { - stop("Argument share = TRUE not supported with cellular = TRUE. - Please select cellular = FALSE to return flooded rice area share") - } - } - - return(list(x = out, - weight = NULL, - unit = unit, - description = description, - isocountries = !cellular)) -} diff --git a/R/calcTemperature.R b/R/calcTemperature.R index 9114aa80..f42a43bf 100644 --- a/R/calcTemperature.R +++ b/R/calcTemperature.R @@ -13,15 +13,15 @@ #' \dontrun{ #' calcOutput("Temperature") #' } -#' @importFrom magpiesets findset +#' @importFrom magpiesets findset addLocation calcTemperature <- function(landusetypes = "all", months = FALSE, convert = TRUE) { temp <- calcOutput("LPJmLClimateInput", lpjmlVersion = "LPJmL4_for_MAgPIE_44ac93de", - climatetype = "GSWP3-W5E5:historical", - variable = "temperature:monthlyMean", - stage = "smoothed", aggregate = FALSE) + climatetype = "GSWP3-W5E5:historical", + variable = "temperature:monthlyMean", + stage = "smoothed", aggregate = FALSE) if (!months) { temp <- dimSums(temp, dim = 3.1) / 12 diff --git a/R/convertFAO.R b/R/convertFAO.R deleted file mode 100644 index d7736d92..00000000 --- a/R/convertFAO.R +++ /dev/null @@ -1,297 +0,0 @@ -#' Convert FAO data -#' -#' Converts FAO data to fit to the common country list and removes or converts -#' relative values where possible. Yields (Hg/ha) are for instance removed -#' since they can later easily be calculated from production and area but might -#' be problematic in the spatial aggregation. Per capita demand values are -#' transformed into absolute values using population estimates from the -#' calcPopulationPast function. -#' -#' Update 23-Jan-2017 - Added FAO Forestry production and trade data (Abhi) -#' -#' @param x MAgPIE object containing original values -#' @param subtype The FAO file type, e.g.: CBCrop -#' @return Data as MAgPIE object with common country list -#' @author Ulrich Kreidenweis, Abhijeet Mishra, Mishko Stevanovic -#' @seealso [readFAO()], [readSource()], -#' @examples -#' \dontrun{ -#' a <- readSource("FAO", "Crop", convert = TRUE) -#' } -#' @importFrom magclass magpiesort getItems dimExists -#' - -## check why LivePrim has such strange Units such as (0_1Gr/An) and "Yield_(Hg)" - -convertFAO <- function(x, subtype) { # nolint: cyclocomp_linter. - ## datasets that have only absolute values - absolute <- c("CBCrop", "CBLive", "CropProc", "Fertilizer", "Land", "LiveHead", - "LiveProc", "Pop", "ValueOfProd", "ForestProdTrade", "Fbs") - - - - ## datasets that contain relative values that can be deleted because they can - ## be calculated again at a later point in time - ## and the dimensions that can be deleted - relativeDelete <- list() - relativeDelete[["Crop"]] <- "Yield_(Hg/Ha)" - relativeDelete[["Fodder"]] <- "Yield_(Hg/Ha)" - relativeDelete[["LivePrim"]] <- c("Yield_Carcass_Weight_(Hg/An)", - "Yield_(100Mg/An)", - "Yield_Carcass_Weight_(0_1Gr/An)", - "Yield_(Hg/An)", - "Yield_(Hg)") - - ## datasets that contain relative values: and define these dimensions - relative <- list() - relative[["FSCrop"]] <- c("food_supply_kg/cap/yr", - "food_supply_g/cap/day", - "food_supply_kcal/cap/day", - "protein_supply_g/cap/day", - "fat_supply_g/cap/day") - - relative[["FSLive"]] <- c("food_supply_kg/cap/yr", - "food_supply_g/cap/day", - "food_supply_kcal/cap/day", - "protein_supply_g/cap/day", - "fat_supply_g/cap/day") - - - ### Section for country specific treatment ### - - ## data for Eritrea ERI added with 0 if not existing in the dimensionality of - ## Ethiopia, to make toolISOhistorical work - if (all((c("XET", "ETH", "ERI") %in% getItems(x, dim = 1)) == c(TRUE, TRUE, FALSE))) { - xERI <- x["ETH", , ] - xERI[, , ] <- 0 - getItems(xERI, dim = 1) <- "ERI" - x <- magpiesort(mbind(x, xERI)) - } - - ## add additional mappings - additionalMapping <- list() - - # Eritrea ERI and Ethiopia ETH - if (all(c("XET", "ETH", "ERI") %in% getItems(x, dim = 1))) { - additionalMapping <- append(additionalMapping, list(c("XET", "ETH", "y1992"), c("XET", "ERI", "y1992"))) - } - - # Belgium-Luxemburg - if (all(c("XBL", "BEL", "LUX") %in% getItems(x, dim = 1))) { - additionalMapping <- append(additionalMapping, list(c("XBL", "BEL", "y1999"), c("XBL", "LUX", "y1999"))) - } else if (("XBL" %in% getItems(x, dim = 1)) && !("BEL" %in% getItems(x, dim = 1))) { - getItems(x, dim = 1)[getItems(x, dim = 1) == "XBL"] <- "BEL" - } - - # Sudan (former) to Sudan and Southern Sudan. If non of the latter two is in the data make Sudan (former) to Sudan - if (all(c("XSD", "SSD", "SDN") %in% getItems(x, dim = 1))) { - additionalMapping <- append(additionalMapping, list(c("XSD", "SSD", "y2010"), c("XSD", "SDN", "y2010"))) - } else if ("XSD" %in% getItems(x, dim = 1) && !any(c("SDD", "SDN") %in% getItems(x, dim = 1))) { - getItems(x, dim = 1)[getItems(x, dim = 1) == "XSD"] <- "SDN" - } - - ## if there is information for CHN: China, XCN: China, mainland and at least one of the regions - ## HKG: China, Hong Kong SAR, TWN: China, Taiwan Province of, MAC: China, Macao SAR - ## then replace CHN information by XCN, otherwise discard XCN - if (all(c("CHN", "XCN") %in% getItems(x, dim = 1)) && any(getItems(x, dim = 1) %in% c("HKG", "TWN", "MAC"))) { - chinaMainland <- x["XCN", , ] - getItems(chinaMainland, dim = 1) <- "CHN" - x["CHN", , ] <- chinaMainland - x <- x["XCN", , , invert = TRUE] - } else if (any(getItems(x, dim = 1) == "XCN")) { - x <- x["XCN", , , invert = TRUE] - } - - ## data for the Netherlands Antilles is currently removed because currently no - ## information for its successors SXM, CUW, ABW is available as input for toolISOhistorical - if ("ANT" %in% getItems(x, dim = 1)) { - x <- x["ANT", , , invert = TRUE] - } - - - ## data for PCI split up into: - # Marshall Islands (MH, MHL, 584) - # Micronesia, Federated States of (FM, FSM, 583) - # Northern Mariana Islands (MP, MNP, 580) - # Palau (PW, PLW, 585) - if (all(c("PCI", "MHL", "FSM", "MNP", "PLW") %in% getItems(x, dim = 1))) { - additionalMapping <- append(additionalMapping, list(c("PCI", "MHL", "y1991"), c("PCI", "FSM", "y1991"), - c("PCI", "MNP", "y1991"), c("PCI", "PLW", "y1991"))) - } else if ("PCI" %in% getItems(x, dim = 1)) { - x <- x["PCI", , invert = TRUE] - } - - - ### in the dataset EmisAgRiceCult certain follow up states of the Soviet Union are missing. Add them with values of 0 - if (subtype == "EmisAgRiceCult") { - isoHistorical <- read.csv2(system.file("extdata", "ISOhistorical.csv", package = "madrat"), - stringsAsFactors = FALSE) - former <- isoHistorical[isoHistorical$fromISO %in% c("SUN", "YUG", "SCG"), "toISO"] - missing <- former[!former %in% getItems(x, dim = 1)] - x2 <- new.magpie(cells_and_regions = missing, years = getYears(x), names = getNames(x)) - x2[, getYears(x2)[getYears(x2, as.integer = TRUE) >= 1992], ] <- 0 - x <- mbind(x, x2) - vcat(2, "Added the countries", missing, "with value of 0 from 1992 onwards") - } - - - - if (any(subtype == absolute)) { - x[is.na(x)] <- 0 - x <- toolISOhistorical(x, overwrite = TRUE, additional_mapping = additionalMapping) - x <- toolCountryFill(x, fill = 0, verbosity = 2) - if (any(grepl(pattern = "yield|Yield|/", getNames(x, fulldim = TRUE)[[2]]))) { - warning("The following elements could be relative: \n", - paste(grep(pattern = "yield|Yield|/", getNames(x, fulldim = TRUE)[[2]], value = TRUE), collapse = " "), - "\n", "and would need a different treatment of NAs in convertFAO") - } - - } else if (any(subtype == names(relativeDelete))) { - x[is.na(x)] <- 0 - x <- x[, , relativeDelete[[subtype]], invert = TRUE] - x <- toolISOhistorical(x, overwrite = TRUE, additional_mapping = additionalMapping) - x <- toolCountryFill(x, fill = 0, verbosity = 2) - if (any(grepl(pattern = "yield|Yield|/", getNames(x, fulldim = TRUE)[[2]]))) { - warning("The following elements could be relative: \n", - paste(grep(pattern = "yield|Yield|/", getNames(x, fulldim = TRUE)[[2]], value = TRUE), collapse = " "), - "\n", "and would need a different treatment of NAs in convertFAO") - } - } else if (any(subtype == c("FSCrop", "FSLive"))) { - - - xabs <- x[, , relative[[subtype]], invert = TRUE] - xrel <- x[, , relative[[subtype]], invert = FALSE] - - - # handling of relative values - # replaced toolISOhistorical by the following approach for disaggregation - mapping <- read.csv2(system.file("extdata", "ISOhistorical.csv", package = "madrat"), stringsAsFactors = FALSE) - for (elem in additionalMapping) { - mapping <- rbind(mapping, elem) - } - - adoptAggregatedAverage <- function(country, data, mapping) { - if (length(country) > 1) { - stop("only one transition per function call") - } - toISO <- mapping$toISO[mapping$fromISO == country] - lastyear <- unique(mapping$lastYear[mapping$fromISO == country]) - if (length(lastyear) > 1) { - stop("strange transition mapping") - } - allyears <- getYears(data, as.integer = TRUE) - years <- allyears[allyears <= as.integer(substring(lastyear, 2, 5))] - data[toISO, years, ] <- magclass::colSums(data[country, years]) - data <- data[country, , , invert = TRUE] - return(data) - } - xrel <- adoptAggregatedAverage(country = "SUN", data = xrel, mapping = mapping) - xrel <- adoptAggregatedAverage(country = "YUG", data = xrel, mapping = mapping) - xrel <- adoptAggregatedAverage(country = "CSK", data = xrel, mapping = mapping) - xrel <- adoptAggregatedAverage(country = "XET", data = xrel, mapping = mapping) - xrel <- adoptAggregatedAverage(country = "XBL", data = xrel, mapping = mapping) - xrel <- adoptAggregatedAverage(country = "SCG", data = xrel, mapping = mapping) - xrel <- adoptAggregatedAverage(country = "XSD", data = xrel, mapping = mapping) - - # transforming relative values into absolute values - pop <- calcOutput("PopulationPast", aggregate = FALSE) - xrel <- toolCountryFill(xrel, fill = 0, verbosity = 2) - commonyears <- intersect(getYears(pop), getYears(x)) - xrelpop <- collapseNames(complete_magpie(pop[, commonyears, ]) * complete_magpie(xrel[, commonyears, ])) - xrelpop <- xrelpop[, , c("food_supply_kcal/cap/day", "protein_supply_g/cap/day", "fat_supply_g/cap/day")] * 365 - getNames(xrelpop, dim = 2) <- c("food_supply_kcal", "protein_supply", "fat_supply") - xrelpop[is.na(xrelpop)] <- 0 - - # absolute values - xabs[is.na(xabs)] <- 0 - xabs[xabs < 0] <- 0 - xabs <- toolISOhistorical(xabs, overwrite = TRUE, additional_mapping = additionalMapping) - xabs <- toolCountryFill(xabs, fill = 0, verbosity = 2) - - x <- mbind(xabs, xrelpop) - x <- complete_magpie(x) - x <- toolCountryFill(x, fill = 0, verbosity = 2) - if (any(grepl(pattern = "yield|Yield|/", getNames(x, fulldim = TRUE)[[2]]))) { - warning("The following elements could be relative: \n", - paste(grep(pattern = "yield|Yield|/", getNames(x, fulldim = TRUE)[[2]], value = TRUE), collapse = " "), - "\n", "and would need a different treatment of NAs in convertFAO") - } - # automatically delete the "Implied emissions factor XXX" dimension for Emission datasets - } else if (substring(subtype, 1, 6) == "EmisAg" || substring(subtype, 1, 6) == "EmisLu") { - if (any(grepl("Implied_emission_factor", getItems(x, dim = 3.2)))) { - x <- x[, , "Implied_emission_factor", pmatch = TRUE, invert = TRUE] - } - x[is.na(x)] <- 0 - x <- toolISOhistorical(x, overwrite = TRUE, additional_mapping = additionalMapping) - x <- toolCountryFill(x, fill = 0, verbosity = 2) - - # Producer Prices Annual - } else if (subtype == "PricesProducerAnnual") { - x <- collapseNames(x[, , "Producer_Price_(US_$_tonne)_(USD)"]) - ## Serbia and Montenegro split - if (all(c("SCG", "SRB") %in% getItems(x, dim = 1)) && !("MNE" %in% getItems(x, dim = 1))) { - mne <- x["SRB", , ] - dimnames(mne)[[1]] <- "MNE" - x <- mbind(x, mne) - } - ## Adjust prices of live animal weight to the carcass weith - mapping <- toolGetMapping("FAO_livestock_carcass_price_factor.csv", type = "sectoral", where = "mrcommons") - for (item in mapping$FAO_carcass) { - litem <- mapping$FAO_live_weigth[grep(item, mapping$FAO_carcass)] - countries <- unique(rownames(which(!is.na(x[, , item]), arr.ind = TRUE))) - countries <- setdiff(getItems(x, dim = 1), countries) - x[countries, , item] <- x[countries, , litem] / mapping$Price_factor[grep(item, mapping$FAO_carcass)] - } - x[is.na(x)] <- 0 - x <- toolISOhistorical(x, overwrite = TRUE, additional_mapping = additionalMapping) - x <- toolCountryFill(x, fill = 0, verbosity = 2) - } else if (subtype == "PricesProducerAnnualLCU") { - x <- collapseNames(x[, , "Producer_Price_(Standard_local_Currency_tonne)_(SLC)"]) - ## Serbia and Montenegro split - if (all(c("SCG", "SRB") %in% getItems(x, dim = 1)) && !"MNE" %in% getItems(x, dim = 1)) { - mne <- x["SRB", , ] - dimnames(mne)[[1]] <- "MNE" - x <- mbind(x, mne) - } - ## Adjust prices of live animal weight to the carcass weith - mapping <- toolGetMapping("FAO_livestock_carcass_price_factor.csv", type = "sectoral", where = "mrcommons") - for (item in mapping$FAO_carcass) { - litem <- mapping$FAO_live_weigth[grep(item, mapping$FAO_carcass)] - countries <- unique(rownames(which(!is.na(x[, , item]), arr.ind = TRUE))) - countries <- setdiff(getItems(x, dim = 1), countries) - x[countries, , item] <- x[countries, , litem] / mapping$Price_factor[grep(item, mapping$FAO_carcass)] - } - x[is.na(x)] <- 0 - x <- toolISOhistorical(x, overwrite = TRUE, additional_mapping = additionalMapping) - x <- toolCountryFill(x, fill = 0, verbosity = 2) - } else { - cat("Specify whether dataset contains absolute or relative values in convertFAO") - } - - - ### set negative values (except stock variation) to 0 - - if (dimExists(3.2, x)) { - novar <- setdiff(getItems(x, dim = 3.2), "stock_variation") - x[, , novar][x[, , novar] < 0] <- 0 - } - - ## Unit conversion in case of FAO Forestry Trade and Production Data: - - if (subtype == "ForestProdTrade") { - x[, , "Import_Value_(1000_US$)"] <- x[, , "Import_Value_(1000_US$)"] / 1000 - x[, , "Export_Value_(1000_US$)"] <- x[, , "Export_Value_(1000_US$)"] / 1000 - x[, , "Production_(tonnes)"] <- x[, , "Production_(tonnes)"] / 1000000 - x[, , "Export_Quantity_(tonnes)"] <- x[, , "Export_Quantity_(tonnes)"] / 1000000 - x[, , "Import_Quantity_(tonnes)"] <- x[, , "Import_Quantity_(tonnes)"] / 1000000 - - getNames(x, dim = 2)[3] <- "Import_Value_(Mio_US$)" - getNames(x, dim = 2)[5] <- "Export_Value_(Mio_US$)" - getNames(x, dim = 2)[6] <- "Production_(Mio_tonnes)" - getNames(x, dim = 2)[7] <- "Import_Quantity_(Mio_tonnes)" - getNames(x, dim = 2)[8] <- "Export_Quantity_(Mio_tonnes)" - - getNames(x) <- gsub("^\\|", "", getNames(x)) - } - return(x) -} diff --git a/R/convertFAOTradeMatrix.R b/R/convertFAOTradeMatrix.R deleted file mode 100644 index 0484b16a..00000000 --- a/R/convertFAOTradeMatrix.R +++ /dev/null @@ -1,185 +0,0 @@ -#' Convert FAOTradeMatrix -#' -#' Convert FAOSTAT detailed trade matrix. -#' FAOSTAT does not balance or harmonize the import/export side reporting. -#' Furthermore, in terms of trade value, exporters are "usuallY" reporting FOB, -#' while importers report CIF. Difference in value, -#' given identical qty, is thus the transport margin mixed with unharmonized reporting. -#' @param x output from read function -#' @param subtype subsets of the detailed trade matrix to read in. Very large csv needs to be read in chunks -#' separated by export/import quantities and values, as well as kcr, kli and kothers (not in kcr nor kli) -#' Options are all combinations of c("import_value", -#' "import_qty", "export_value", "export_quantity") X c("kcr", "kli", "kothers")) -#' import is import side reporting while export is export-sde reporting -#' @return FAO data as MAgPIE object -#' @author David C -#' @seealso [readSource()] -#' @importFrom GDPuc convertGDP - - -convertFAOTradeMatrix <- function(x, subtype) { # nolint - - gc() - - # ---- Section for country specific treatment ---- - - # make a set name for dim 1.2 - getSets(x)[1] <- "ISO.Partner" - - ## data for Eritrea ERI and South Sudan SSD added with 0 if not existing after the split - ## to make toolISOhistorical work - if (any(getItems(x, dim = 1.1) == "XET") && any(getItems(x, dim = 1.1) == "ETH") && - !any(getItems(x, dim = 1.1) == "ERI")) { - xERI <- x[list("ISO" = c("ETH")), , ] - xERI[, , ] <- 0 - getItems(xERI, dim = 1.1) <- "ERI" - missingC <- paste0("ERI.", - setdiff(getItems(x, dim = 1.2), getItems(xERI, dim = 1.2))) - fillC <- new.magpie(cells_and_regions = missingC, years = getYears(x), names = getNames(x), fill = 0) - xERI <- mbind(xERI, fillC) - x <- magpiesort(mbind(x, xERI)) - } - if (any(getItems(x, dim = 1.2) == "XET") && any(getItems(x, dim = 1.2) == "ETH") && - !any(getItems(x, dim = 1.2) == "ERI")) { - xERI <- x[list("Partner" = c("ETH")), , ] - xERI[, , ] <- 0 - getItems(xERI, dim = 1.2) <- "ERI" - missingC <- paste0(".ERI", - setdiff(getItems(x, dim = 1.1), getItems(xERI, dim = 1.1))) - fillC <- new.magpie(cells_and_regions = missingC, years = getYears(x), names = getNames(x), fill = 0) - xERI <- mbind(xERI, fillC) - x <- magpiesort(mbind(x, xERI)) - } - - if (any(getItems(x, dim = 1.1) == "XSD") && any(getItems(x, dim = 1.1) == "SDN") && - !any(getItems(x, dim = 1.1) == "SSD")) { - xSSD <- x[list("ISO" = c("SDN")), , ] - xSSD[, , ] <- 0 - getItems(xSSD, dim = 1.1) <- "SSD" - missingC <- paste0("SSD.", - setdiff(getItems(x, dim = 1.2), getItems(xSSD, dim = 1.2))) - xSSD <- mbind(xSSD, fillC) - x <- magpiesort(mbind(x, xSSD)) - } - - if (any(getItems(x, dim = 1.2) == "XSD") && any(getItems(x, dim = 1.2) == "SDN") && - !any(getItems(x, dim = 1.2) == "SSD")) { - xSSD <- x[list("Partner" = c("SDN")), , ] - xSSD[, , ] <- 0 - getItems(xSSD, dim = 1.2) <- "SSD" - missingC <- paste0(".SSD", - setdiff(getItems(x, dim = 1.1), getItems(xSSD, dim = 1.1))) - xSSD <- mbind(xSSD, fillC) - x <- magpiesort(mbind(x, xSSD)) - } - - ## add additional mappings - additionalMapping <- list() - - # Eritrea ERI and Ethiopia ETH - if ((all(c("XET", "ETH", "ERI") %in% getItems(x, dim = 1.1))) || - (all(c("XET", "ETH", "ERI") %in% getItems(x, dim = 1.2)))) { - additionalMapping <- append(additionalMapping, - list(c("XET", "ETH", "y1992"), c("XET", "ERI", "y1992"))) - } - - # Belgium-Luxemburg - if ((all(c("XBL", "BEL", "LUX") %in% getItems(x, dim = 1.1))) || - (all(c("XBL", "BEL", "LUX") %in% getItems(x, dim = 1.2)))) { - additionalMapping <- append(additionalMapping, - list(c("XBL", "BEL", "y1999"), c("XBL", "LUX", "y1999"))) - } else if (("XBL" %in% getItems(x, dim = 1.1)) && !("BEL" %in% getItems(x, dim = 1.1))) { - getCells(x)[getItems(x, dim = 1.1) == "XBL"] <- "BEL" - } else if (("XBL" %in% getItems(x, dim = 1.2)) && !("BEL" %in% getItems(x, dim = 1.2))) { - getCells(x)[getItems(x, dim = 1.2) == "XBL"] <- "BEL" - } - - # Sudan (former) to Sudan and Southern Sudan. If non of the latter two is in the data make Sudan (former) to Sudan - if ((all(c("XSD", "SSD", "SDN") %in% getItems(x, dim = 1.1))) || - (all(c("XSD", "SSD", "SDN") %in% getItems(x, dim = 1.2)))) { - additionalMapping <- append(additionalMapping, - list(c("XSD", "SSD", "y2011"), c("XSD", "SDN", "y2011"))) - } else if ("XSD" %in% getItems(x, dim = 1.1) && !any(c("SSD", "SDN") %in% getItems(x, dim = 1.1))) { - getCells(x)[getItems(x, dim = 1.1) == "XSD"] <- "SDN" - } else if ("XSD" %in% getItems(x, dim = 1.2) && !any(c("SSD", "SDN") %in% getItems(x, dim = 1.2))) { - getCells(x)[getItems(x, dim = 1.2) == "XSD"] <- "SDN" - } - - ## if XCN exists, replace CHN with XCN. - if ("XCN" %in% getItems(x, dim = 1.1)) { - if ("CHN" %in% getItems(x, dim = 1.1)) x <- x[list("ISO" = c("CHN")), , , invert = TRUE] - getItems(x, dim = 1.1)[getItems(x, dim = 1.1) == "XCN"] <- "CHN" - } - if ("XCN" %in% getItems(x, dim = 1.2)) { - if ("CHN" %in% getItems(x, dim = 1.2)) x <- x[list("Partner" = c("CHN")), , , invert = TRUE] - getItems(x, dim = 1.2)[getItems(x, dim = 1.2) == "XCN"] <- "CHN" - } - ## data for the Netherlands Antilles is currently removed because currently no - ## information for its successors SXM, CUW, ABW is available as input for toolISOhistorical - if (any(getItems(x, dim = 1.1) == "ANT")) { - x <- x[list("ISO" = c("ANT")), , , invert = TRUE] - } - if (any(getItems(x, dim = 1.2) == "ANT")) { - x <- x[list("Partner" = c("ANT")), , , invert = TRUE] - } - ## data for PCI split up into: - # Marshall Islands (MH, MHL, 584) - # Micronesia, Federated States of (FM, FSM, 583) - # Northern Mariana Islands (MP, MNP, 580) - # Palau (PW, PLW, 585) - if (all(c("PCI", "MHL", "FSM", "MNP", "PLW") %in% getItems(x, dim = 1.1))) { - additionalMapping <- append(additionalMapping, - list(c("PCI", "MHL", "y1991"), c("PCI", "FSM", "y1991"), - c("PCI", "MNP", "y1991"), c("PCI", "PLW", "y1991"))) - } else if ("PCI" %in% getItems(x, dim = 1.1)) { - x <- x[list("ISO" = c("PCI")), , invert = TRUE] - } - if (all(c("PCI", "MHL", "FSM", "MNP", "PLW") %in% getItems(x, dim = 1.2))) { - additionalMapping <- append(additionalMapping, - list(c("PCI", "MHL", "y1991"), c("PCI", "FSM", "y1991"), - c("PCI", "MNP", "y1991"), c("PCI", "PLW", "y1991"))) - } else if ("PCI" %in% getItems(x, dim = 1.2)) { - x <- x[list("Partner" = c("PCI")), , invert = TRUE] - } - - # some of the follow up states of the Soviet Union (SUN), Yugoslavia (YUG), - # Serbia and Montenegro (SCG) are missing add them with values of 0 - isoHistorical <- read.csv2(system.file("extdata", "ISOhistorical.csv", - package = "madrat"), stringsAsFactors = FALSE) - former <- isoHistorical[isoHistorical$fromISO %in% c("SUN", "YUG", "SCG"), "toISO"] - - missing1 <- former[!former %in% getItems(x, dim = 1.1)] - if (length(missing1 > 0)) { - x2 <- new.magpie(cells_and_regions = missing1, years = getYears(x), names = getNames(x)) - x2 <- add_dimension(x2, dim = 1.2, add = "Partner", nm = getItems(x, dim = 1.2)) - x2[, getYears(x2)[getYears(x2, as.integer = TRUE) >= 1992], ] <- 0 - x <- mbind(x, x2) - } - missing2 <- former[!former %in% getItems(x, dim = 1.2)] - if (length(missing2 > 0)) { - x2 <- new.magpie(cells_and_regions = missing2, years = getYears(x), names = getNames(x)) - x2 <- add_dimension(x2, dim = 1.2, add = "ISO", nm = getItems(x, dim = 1.1)) - x2[, getYears(x2)[getYears(x2, as.integer = TRUE) >= 1992], ] <- 0 - x <- mbind(x, x2) - } - - x[is.na(x)] <- 0 - - ### do ISOhistorical - x <- toolISOhistorical(x, mapping = NULL, overwrite = TRUE, additional_mapping = additionalMapping) - - out <- toolCountryFillBilateral(x, fill = 0) - rm(x) - gc() - # currency convert values -# if (subtype %in% c("import_value_kcr", "import_value_kli", "import_value_kothers", #nolint -# "export_value_kcr", "export_value_kli", "export_value_kothers")) { #nolint -# out <- convertGDP(out, unit_in = "current US$MER", #nolint -# unit_out = "constant 2005 US$MER", #nolint -# replace_NAs = "no_conversion") #nolint -# } #nolint - - out <- magpiesort(out) - - return(out) -} diff --git a/R/convertFAO_FRA2015.R b/R/convertFAO_FRA2015.R deleted file mode 100644 index 95d4831c..00000000 --- a/R/convertFAO_FRA2015.R +++ /dev/null @@ -1,57 +0,0 @@ -#' Convert FRA 2015 data -#' Update dd-Jmm-jjjj - Please add comment if changes made here (Abhi) -#' -#' @param x MAgPIE object containing original values -#' @param subtype The FAO FRA 2015 file type, e.g.: fac, production, biodiversity or anndat. -#' @return Data as MAgPIE object with common country list -#' @author Abhijeet Mishra -#' @seealso [readSource()], -#' @examples -#' \dontrun{ -#' a <- readSource("FRA2015", "production", convert = TRUE) -#' } -#' @importFrom magclass magpiesort -#' - -convertFAO_FRA2015 <- function(x, subtype) { # nolint: object_name_linter. - if (any(c("fac", "production", "biodiversity", "anndat") %in% subtype)) { - x <- toolCountryFill(x, fill = 0) - if (any(c("ProdFor", "MulUseFor") %in% getNames(x))) { - x[, , "ProdFor"] <- x[, , "ProdFor"] / 1000 # conversion from 1000ha to Million ha - x[, , "MulUseFor"] <- x[, , "MulUseFor"] / 1000 # conversion from 1000ha to Million ha - } - if (any(c("NetAnnIncr", "IncrConif", "IncrBroa") %in% getNames(x))) { - x[, , "NetAnnIncr"] <- x[, , "NetAnnIncr"] * 1000 # conversion from m3/ha/yr to mil.m3/mil.ha/yr - x[, , "IncrConif"] <- x[, , "IncrConif"] * 1000 # conversion from m3/ha/yr to mil.m3/mil.ha/yr - x[, , "IncrBroa"] <- x[, , "IncrBroa"] * 1000 # conversion from m3/ha/yr to mil.m3/mil.ha/yr - } - if (any(c("Forest", "Forchange", "NatFor", "Nfchange", "OthWooLan", "OthLan", - "LanTreCov", "InWater", "Landarea", "PrimFor", "NatRegFor", "IntroSpp", - "NatzedSpp", "PlantFor", "Pfchange", "IntroSppPlant") %in% getNames(x))) { - namesToFix <- c("Forest", "Forchange", "NatFor", "Nfchange", "OthWooLan", "OthLan", - "LanTreCov", "InWater", "Landarea", "PrimFor", "NatRegFor", "IntroSpp", - "NatzedSpp", "PlantFor", "Pfchange", "IntroSppPlant", "Mangrove") - for (i in namesToFix) { - x[, , i] <- x[, , i] / 1000 # conversion from 1000ha to Million ha - } - } - if (any(c("ForExp", "Afforest", "NatForExp", "Deforest", "HumDef", "Reforest", "ArtRef") %in% getNames(x))) { - namesToFix <- c("ForExp", "Afforest", "NatForExp", "Deforest", "HumDef", "Reforest", "ArtRef") - for (i in namesToFix) { - x[, , i] <- x[, , i] / 1000000 # conversion from ha/yr to Mil.ha/yr - } - } - if (any(c("BioCons", "ProtArea") %in% getNames(x))) { - x[, , "BioCons"] <- x[, , "BioCons"] / 1000 # conversion from 1000ha to Million ha - x[, , "ProtArea"] <- x[, , "ProtArea"] / 1000 # conversion from 1000ha to Million ha - } - if (any(c("WooRemov", "WooFuel", "WooRW") %in% getNames(x))) { - x[, , "WooRemov"] <- x[, , "WooRemov"] / 1000 # conversion from 1000m3 to Million m3 - x[, , "WooFuel"] <- x[, , "WooFuel"] / 1000 # conversion from 1000m3 to Million m3 - x[, , "WooRW"] <- x[, , "WooRW"] / 1000 # conversion from 1000m3 to Million m3 - } - return(x) - } else { -stop("Invalid subtype ", subtype) -} -} diff --git a/R/convertFAO_online.R b/R/convertFAO_online.R deleted file mode 100644 index f16856ca..00000000 --- a/R/convertFAO_online.R +++ /dev/null @@ -1,392 +0,0 @@ -#' Convert FAO data -#' -#' Converts FAO data to fit to the common country list and removes or converts -#' relative values where possible. Yields (Hg/ha) are for instance removed -#' since they can later easily be calculated from production and area but might -#' be problematic in the spatial aggregation. Per capita demand values are -#' transformed into absolute values using population estimates from the -#' calcPopulationPast function. -#' -#' Update 23-Jan-2017 - Added FAO Forestry production and trade data (Abhi) -#' -#' @param x MAgPIE object containing original values -#' @param subtype The FAO file type, e.g.: CBCrop -#' @return Data as MAgPIE object with common country list -#' @author Ulrich Kreidenweis, Abhijeet Mishra, Mishko Stevanovic, David Klein, Daivd Chen, Edna Molina Bacca -#' @seealso [readFAO()], [readSource()], -#' @examples -#' \dontrun{ -#' a <- readSource("FAO_online", "Crop", convert = TRUE) -#' } -#' @importFrom magclass magpiesort dimExists getItems -#' @importFrom GDPuc convertGDP -#' - -## check why LivePrim has such strange Units such as (0_1Gr/An) and "Yield_(Hg)" - -convertFAO_online <- function(x, subtype) { # nolint: cyclocomp_linter, object_name_linter. - - # ---- Settings ---- - - ## datasets that have only absolute values - absolute <- c("CBCrop", "CBLive", "CropProc", "Fertilizer", "Land", "LiveHead", - "LiveProc", "Pop", "ValueOfProd", "ForestProdTrade", "Fbs", "FbsHistoric", - "FertilizerProducts", "FertilizerNutrients", "Trade", "TradeMatrix") - - ## datasets that contain relative values that can be deleted because they can - ## be calculated again at a later point in time - ## and the dimensions that can be deleted - relativeDelete <- list() - relativeDelete[["Crop"]] <- c("Yield_(hg/ha)", "Yield_(Hg/Ha)") - relativeDelete[["Fodder"]] <- "Yield_(Hg/Ha)" - relativeDelete[["LivePrim"]] <- c("Yield_Carcass_Weight_(Hg/An)", - "Yield_(100Mg/An)", - "Yield_Carcass_Weight_(0_1Gr/An)", - "Yield_(Hg/An)", - "Yield_(Hg)", - "Yield_(100mg/An)", # new FAO data - "Yield_(hg/An)", # new FAO data - "Yield_Carcass_Weight_(hg/An)", # new FAO data - "Yield_Carcass_Weight_(0_1g/An)", # new FAO data - "Yield_(hg)") # new FAO data - - # Relative and unused datasets for the Capital Stock database - relativeDelete[["CapitalStock"]] <- - c(paste0("22030|Gross Fixed Capital Formation (Agriculture, Forestry and Fishing).", - c("Value_Local_Currency_(millions)", - "Value_Local_Currency_2015_prices_(millions)", - "Value_USD_(millions)", - "Share_of_Value_Added_Local_Currency_(percentage)", - "Share_of_Value_Added_Local_Currency_2015_prices_(percentage)", - "Share_of_Value_Added_USD_(percentage)", - "Agriculture_orientation_index_Local_Currency_(index)", - "Agriculture_orientation_index_Local_Currency_2015_prices_(index)", - "Agriculture_orientation_index_USD_(index)", - "Agriculture_orientation_index_USD_2015_prices_(index)", - "Share_of_Gross_Fixed_Capital_Formation_USD_(percentage)", - "Share_of_Gross_Fixed_Capital_Formation_(percentage)", - "Share_of_Gross_Fixed_Capital_Formation_2015_prices_(percentage)")), - paste0("22031|Consumption of Fixed Capital (Agriculture, Forestry and Fishing).", - c("Value_Local_Currency_(millions)", - "Value_Local_Currency_2015_prices_(millions)", - "Value_USD_(millions)")), - paste0("22034|Net Capital Stocks (Agriculture, Forestry and Fishing).", - c("Value_Local_Currency_(millions)", - "Value_Local_Currency_2015_prices_(millions)", - "Value_USD_(millions)")), - "22033|Gross Capital Stocks (Agriculture, Forestry and Fishing).Value_Local_Currency_(millions)", - paste0("22030|Gross Fixed Capital Formation (Agriculture, Forestry and Fishing).", - c("Share_of_Value_Added_USD_2015_prices_(percentage)", - "Share_of_Gross_Fixed_Capital_Formation_USD_2015_prices_(percentage)", - "Value_Local_Currency_(millions)"))) - - if (subtype == "ValueShares") { - stop(paste("Too many missing countries in Value Shares dataset to convert.", - "Rather use as validation. Don't forget to currency convert manually.")) - } - - # select elements only if unit (dim=3.2) exists in x (otherwise magclass would complain when trying to remove - # non-existent elements with invert=TRUE). For capital stocks selects the complete name. The dot in the original - # dataset causes errors. - if ((subtype %in% names(relativeDelete)) && subtype != "CapitalStock") { - relativeDelete <- relativeDelete[[subtype]][relativeDelete[[subtype]] %in% getItems(x, dim = 3.2)] - } else if (subtype == "CapitalStock") { - relativeDelete <- relativeDelete[[subtype]][relativeDelete[[subtype]] %in% getItems(x, dim = 3)] - } else { - relativeDelete <- NULL - } - - if (identical(relativeDelete, character(0))) { - stop("For this subtype (", subtype, ") units are listed in 'convertFAO' whose entries should be deleted from the ", - "data, but none of the specified units could be found in the data.") - } - - ## datasets that contain relative values: and define these dimensions - relative <- list() - relative[["FSCrop"]] <- c("food_supply_kg/cap/yr", - "food_supply_g/cap/day", - "food_supply_kcal/cap/day", - "protein_supply_g/cap/day", - "fat_supply_g/cap/day") - - relative[["FSLive"]] <- c("food_supply_kg/cap/yr", - "food_supply_g/cap/day", - "food_supply_kcal/cap/day", - "protein_supply_g/cap/day", - "fat_supply_g/cap/day") - - # ---- Section for country specific treatment ---- - - ## data for Eritrea ERI and South Sudan SSD added with 0 if not existing after the split - ## to make toolISOhistorical work - if (any(getItems(x, dim = 1.1) == "XET") && - any(getItems(x, dim = 1.1) == "ETH") && - !any(getItems(x, dim = 1.1) == "ERI")) { - xERI <- x["ETH", , ] - xERI[, , ] <- 0 - getItems(xERI, dim = 1) <- "ERI" - x <- magpiesort(mbind(x, xERI)) - } - - if (any(getItems(x, dim = 1.1) == "XSD") && - any(getItems(x, dim = 1.1) == "SDN") && - !any(getItems(x, dim = 1.1) == "SSD")) { - xSSD <- x["SDN", , ] - xSSD[, , ] <- 0 - getItems(xSSD, dim = 1) <- "SSD" - x <- magpiesort(mbind(x, xSSD)) - } - - - ## add additional mappings - additionalMapping <- list() - - # Eritrea ERI and Ethiopia ETH - if (all(c("XET", "ETH", "ERI") %in% getItems(x, dim = 1.1))) { - additionalMapping <- append(additionalMapping, list(c("XET", "ETH", "y1992"), c("XET", "ERI", "y1992"))) - } - - # Belgium-Luxemburg - if (all(c("XBL", "BEL", "LUX") %in% getItems(x, dim = 1.1))) { - additionalMapping <- append(additionalMapping, list(c("XBL", "BEL", "y1999"), c("XBL", "LUX", "y1999"))) - } else if (("XBL" %in% getItems(x, dim = 1.1)) && !("BEL" %in% getItems(x, dim = 1.1))) { - getCells(x)[getItems(x, dim = 1.1) == "XBL"] <- "BEL" - } - - # Sudan (former) to Sudan and Southern Sudan. If non of the latter two is in the data make Sudan (former) to Sudan - if (all(c("XSD", "SSD", "SDN") %in% getItems(x, dim = 1.1))) { - additionalMapping <- append(additionalMapping, list(c("XSD", "SSD", "y2011"), c("XSD", "SDN", "y2011"))) - } else if ("XSD" %in% getItems(x, dim = 1.1) && !any(c("SSD", "SDN") %in% getItems(x, dim = 1.1))) { - getCells(x)[getItems(x, dim = 1.1) == "XSD"] <- "SDN" - } - - ## if XCN exists, replace CHN with XCN. - if ("XCN" %in% getItems(x, dim = 1.1)) { - if ("CHN" %in% getItems(x, dim = 1.1)) x <- x["CHN", , , invert = TRUE] - getItems(x, dim = 1)[getItems(x, dim = 1) == "XCN"] <- "CHN" - } - - ## data for the Netherlands Antilles is currently removed because currently no - ## information for its successors SXM, CUW, ABW is available as input for toolISOhistorical - if (any(getItems(x, dim = 1.1) == "ANT")) { - x <- x["ANT", , , invert = TRUE] - } - - ## data for PCI split up into: - # Marshall Islands (MH, MHL, 584) - # Micronesia, Federated States of (FM, FSM, 583) - # Northern Mariana Islands (MP, MNP, 580) - # Palau (PW, PLW, 585) - if (all(c("PCI", "MHL", "FSM", "MNP", "PLW") %in% getItems(x, dim = 1.1))) { - additionalMapping <- append(additionalMapping, list(c("PCI", "MHL", "y1991"), - c("PCI", "FSM", "y1991"), - c("PCI", "MNP", "y1991"), - c("PCI", "PLW", "y1991"))) - } else if ("PCI" %in% getItems(x, dim = 1.1)) { - x <- x["PCI", , invert = TRUE] - } - - - ### For certain subtypes: if some of the follow up states of the Soviet Union (SUN), Yugoslavia (YUG), Serbia and - # Montenegro (SCG) are missing add them with values of 0 - if (subtype %in% c("EmisAgRiceCult", "Fertilizer", "FertilizerNutrients", "EmisAgCultOrgSoil", "EmisLuCrop", - "EmisLuGrass", "EmisAgSynthFerti")) { - isoHistorical <- read.csv2(system.file("extdata", "ISOhistorical.csv", package = "madrat"), - stringsAsFactors = FALSE) - former <- isoHistorical[isoHistorical$fromISO %in% c("SUN", "YUG", "SCG"), "toISO"] - missing <- former[!former %in% getItems(x, dim = 1.1)] - x2 <- new.magpie(cells_and_regions = missing, years = getYears(x), names = getNames(x)) - x2[, getYears(x2)[getYears(x2, as.integer = TRUE) >= 1992], ] <- 0 - x <- mbind(x, x2) - } - - # ---- Treatment of absolute or relative values ---- - - if (any(subtype == absolute)) { - x[is.na(x)] <- 0 - if (subtype != "Fbs") { - x <- toolISOhistorical(x, overwrite = TRUE, additional_mapping = additionalMapping) - } - x <- toolCountryFill(x, fill = 0, verbosity = 2) - if (any(grepl(pattern = "yield|Yield|/", getNames(x, fulldim = TRUE)[[2]]))) { - warning("The following elements could be relative: \n", - paste(grep(pattern = "yield|Yield|/", getNames(x, fulldim = TRUE)[[2]], value = TRUE), collapse = " "), - "\n", "and would need a different treatment of NAs in convertFAO") - } - - } else if (!is.null(relativeDelete)) { - x[is.na(x)] <- 0 - x <- x[, , relativeDelete, invert = TRUE] - if (subtype != "CapitalStock") { - # Capital Stock available starting from 1995 (no need for transitions) - x <- toolISOhistorical(x, overwrite = TRUE, additional_mapping = additionalMapping) - } - x <- toolCountryFill(x, fill = 0, verbosity = 2) - if (subtype != "CapitalStock") { - if (any(grepl(pattern = "yield|Yield|/", getNames(x, fulldim = TRUE)[[2]]))) { - warning("The following elements could be relative: \n", - paste(grep(pattern = "yield|Yield|/", getNames(x, fulldim = TRUE)[[2]], value = TRUE), collapse = " "), - "\n", "and would need a different treatment of NAs in convertFAO") - } - } - - } else if (any(subtype == c("FSCrop", "FSLive"))) { - - xabs <- x[, , relative[[subtype]], invert = TRUE] - xrel <- x[, , relative[[subtype]], invert = FALSE] - - # handling of relative values - # replaced toolISOhistorical by the following approach for disaggregation - mapping <- read.csv2(system.file("extdata", "ISOhistorical.csv", package = "madrat"), stringsAsFactors = FALSE) - for (elem in additionalMapping) { - mapping <- rbind(mapping, elem) - } - - .adoptAggregatedAverage <- function(country, data, mapping) { - if (length(country) > 1) stop("only one transition per function call") - - toISO <- mapping$toISO[mapping$fromISO == country] - lastyear <- unique(mapping$lastYear[mapping$fromISO == country]) - - if (length(lastyear) > 1) stop("strange transition mapping") - - allyears <- getYears(data, as.integer = TRUE) - years <- allyears[allyears <= as.integer(substring(lastyear, 2, 5))] - data[toISO, years, ] <- magclass::colSums(data[country, years]) - data <- data[country, , , invert = TRUE] - return(data) - } - - xrel <- .adoptAggregatedAverage(country = "SUN", data = xrel, mapping = mapping) - xrel <- .adoptAggregatedAverage(country = "YUG", data = xrel, mapping = mapping) - xrel <- .adoptAggregatedAverage(country = "CSK", data = xrel, mapping = mapping) - xrel <- .adoptAggregatedAverage(country = "XET", data = xrel, mapping = mapping) - xrel <- .adoptAggregatedAverage(country = "XBL", data = xrel, mapping = mapping) - xrel <- .adoptAggregatedAverage(country = "SCG", data = xrel, mapping = mapping) - xrel <- .adoptAggregatedAverage(country = "XSD", data = xrel, mapping = mapping) - - # transforming relative values into absolute values - pop <- calcOutput("PopulationPast", aggregate = FALSE) - xrel <- toolCountryFill(xrel, fill = 0, verbosity = 2) - commonyears <- intersect(getYears(pop), getYears(x)) - xrelpop <- collapseNames(complete_magpie(pop[, commonyears, ]) * complete_magpie(xrel[, commonyears, ])) - xrelpop <- xrelpop[, , c("food_supply_kcal/cap/day", "protein_supply_g/cap/day", "fat_supply_g/cap/day")] * 365 - getNames(xrelpop, dim = 2) <- c("food_supply_kcal", "protein_supply", "fat_supply") - xrelpop[is.na(xrelpop)] <- 0 - - # absolute values - xabs[is.na(xabs)] <- 0 - xabs[xabs < 0] <- 0 - xabs <- toolISOhistorical(xabs, overwrite = TRUE, additional_mapping = additionalMapping) - xabs <- toolCountryFill(xabs, fill = 0, verbosity = 2) - - x <- mbind(xabs, xrelpop) - x <- complete_magpie(x) - x <- toolCountryFill(x, fill = 0, verbosity = 2) - if (any(grepl(pattern = "yield|Yield|/", getNames(x, fulldim = TRUE)[[2]]))) { - warning("The following elements could be relative: \n", - paste(grep(pattern = "yield|Yield|/", getNames(x, fulldim = TRUE)[[2]], value = TRUE), collapse = " "), - "\n", "and would need a different treatment of NAs in convertFAO") - } - - # automatically delete the "Implied emissions factor XXX" dimension for Emission datasets - } else if (substring(subtype, 1, 6) == "EmisAg" || substring(subtype, 1, 6) == "EmisLu") { - if (any(grepl("Implied_emission_factor", getItems(x, dim = 3.2)))) { - x <- x[, , "Implied_emission_factor", pmatch = TRUE, invert = TRUE] - } - x[is.na(x)] <- 0 - x <- toolISOhistorical(x, overwrite = TRUE, additional_mapping = additionalMapping) - x <- toolCountryFill(x, fill = 0, verbosity = 2) - - # Producer Prices Annual - } else if (subtype %in% c("PricesProducerAnnual", "PricesProducerAnnualLCU")) { - # FAO changed the unit. Look for all possible names and select only existing ones from the magpie object - possibleNames <- list(PricesProducerAnnual = c("Producer_Price_(US_$_tonne)_(USD)", - "Producer_Price_(USD_tonne)_(USD)"), - PricesProducerAnnualLCU = c("Producer_Price_(Standard_local_Currency_tonne)_(SLC)", - "Producer_Price_(SLC_tonne)_(SLC)")) - possibleNames <- toolSubtypeSelect(subtype, possibleNames) - x <- collapseNames(x[, , possibleNames[possibleNames %in% getItems(x, dim = 3.2)]]) - ## Serbia and Montenegro split - if (all(c("SCG", "SRB") %in% getItems(x, dim = 1.1)) && !"MNE" %in% getItems(x, dim = 1.1)) { - mne <- x["SRB", , ] - dimnames(mne)[[1]] <- "MNE" - x <- mbind(x, mne) - } - ## Adjust prices of live animal weight to the carcass weight - mapping <- toolGetMapping("FAO_livestock_carcass_price_factor.csv", type = "sectoral", where = "mrcommons") - for (item in mapping$FAO_carcass) { - itemn <- gsub("([0-9]+).*$", "\\1", item) - litem <- mapping$FAO_live_weigth[grep(item, mapping$FAO_carcass)] - litemn <- gsub("([0-9]+).*$", "\\1", litem) - countries <- unique(rownames(which(!is.na(x[, , itemn, pmatch = TRUE]), arr.ind = TRUE))) - countries <- setdiff(getItems(x, dim = 1.1), countries) - x[countries, , itemn, pmatch = TRUE] <- x[countries, , litemn, pmatch = TRUE] / - mapping$Price_factor[grep(item, mapping$FAO_carcass)] - } - x[is.na(x)] <- 0 - x <- toolISOhistorical(x, overwrite = TRUE, additional_mapping = additionalMapping) - x <- toolCountryFill(x, fill = 0, verbosity = 2) - - if (subtype == "PricesProducerAnnual") { - x <- convertGDP(x, unit_in = "current US$MER", - unit_out = "constant 2005 US$MER", - replace_NAs = "no_conversion") - - } else if (subtype == "PricesProducerAnnualLCU") { - x <- convertGDP(x, unit_in = "current LCU", - unit_out = "constant 2005 LCU", - replace_NAs = "no_conversion") - } - - } else { - cat("Specify in convertFAO whether dataset contains absolute or relative values!") - } - - if (subtype == "ValueOfProd") { - x2 <- x[, , "Gross_Production_Value_(current_thousand_US$)_(1000_US$)"] - x2 <- convertGDP(x2, unit_in = "current US$MER", - unit_out = "constant 2005 US$MER", - replace_NAs = "no_conversion") - getNames(x2, dim = 2) <- "Gross_Production_Value_(USDMER05)_(1000_US$)" - x <- mbind(x, x2) - } - - if (subtype == "FertilizerProducts") { - currencyDims <- c("import_kUS$", "export_kUS$") - xCurrentUSD <- x # nolint - x[, , currencyDims] <- convertGDP(x[, , currencyDims], - unit_in = "current US$MER", - unit_out = "constant 2005 US$MER", - replace_NAs = "no_conversion") * 1000 - # for countries with missing conversion factors we assume no inflation: - x[is.na(x)] <- xCurrentUSD[is.na(x)] - - getNames(x, dim = 2)[getNames(x, dim = 2) == "import_kUS$"] <- "import_US$MER05" - getNames(x, dim = 2)[getNames(x, dim = 2) == "export_kUS$"] <- "export_US$MER05" - - } - - if (subtype == "Trade") { - currencyDims <- c("import_kUS$", "export_kUS$") - xCurrentUSD <- x # nolint - x[, , currencyDims] <- convertGDP(x[, , currencyDims], - unit_in = "current US$MER", - unit_out = "constant 2005 US$MER", - replace_NAs = "no_conversion") * 1000 - # for countries with missing conversion factors we assume no inflation: - x[is.na(x)] <- xCurrentUSD[is.na(x)] - - getNames(x, dim = 2)[getNames(x, dim = 2) == "import_kUS$"] <- "import_US$MER05" - getNames(x, dim = 2)[getNames(x, dim = 2) == "export_kUS$"] <- "export_US$MER05" - - } - # ---- Set negative values to 0 (except stock variation) ---- - - if (dimExists(3.2, x)) { - novar <- setdiff(getItems(x, dim = 3.2), "stock_variation") - x[, , novar][x[, , novar] < 0] <- 0 - } - - return(x) -} diff --git a/R/convertFRA2020.R b/R/convertFRA2020.R deleted file mode 100644 index ec3b2189..00000000 --- a/R/convertFRA2020.R +++ /dev/null @@ -1,33 +0,0 @@ -#' Convert FRA 2020 data -#' -#' @param x MAgPIE object containing original values -#' @param subtype The FAO FRA 2020 subtype. -#' @return Data as MAgPIE object with common country list -#' @author Abhijeet Mishra -#' @seealso [readSource()], -#' @examples -#' \dontrun{ -#' a <- readSource("FRA2020", "growing_stock", convert = TRUE) -#' } -#' @importFrom madrat toolCountryFill -#' - -convertFRA2020 <- function(x, subtype) { - if (subtype %in% c("forest_area", "deforestation", "growing_stock", "management", "disturbance", "forest_fire")) { - x <- toolCountryFill(x, fill = 0) - - if (any(getNames(x) %in% grep(pattern = "gs_ha", x = getNames(x), value = TRUE))) { - ## This is done because gs_ha variables are already in m3/ha - out <- x - } else { - out <- x / 1000 ## Conversion from 000 units to million units - } - return(out) - } else if (subtype %in% c("biomass_stock", "carbon_stock")) { - x <- toolCountryFill(x, fill = 0) - out <- x - return(out) - } else { - stop("Invalid subtype ", subtype) - } -} diff --git a/R/convertIEA_EEI.R b/R/convertIEA_EEI.R new file mode 100644 index 00000000..2b7ef70f --- /dev/null +++ b/R/convertIEA_EEI.R @@ -0,0 +1,21 @@ +#' Convert IEA End Uses and Efficiency Indicators data to data on ISO country level. +#' +#' @author Falk Benke +#' @param x MAgPIE object to be converted +#' @importFrom madrat toolCountry2isocode toolCountryFill +#' @importFrom magclass getItems getItems<- +convertIEA_EEI <- function(x) { #nolint object_name_linter + x <- x["IEATOT", , , invert = TRUE] + + getItems(x, dim = 1) <- toolCountry2isocode(getItems(x, dim = 1), warn = TRUE) + + # aggregate Kosovo to Serbia + x1 <- x["KOS", , ] + getItems(x1, dim = 1) <- c("SRB") + x["SRB", , ] <- x["SRB", , ] + x1 + x <- x[c("KOS"), , , invert = TRUE] + + x <- toolCountryFill(x, 0, verbosity = 2) + + return(x) +} diff --git a/R/convertLPJmL.R b/R/convertLPJmL.R deleted file mode 100644 index ed0e001e..00000000 --- a/R/convertLPJmL.R +++ /dev/null @@ -1,16 +0,0 @@ -#' @title convertLPJmL -#' @description Convert LPJmL content -#' @param x magpie object provided by the read function -#' @return List of magpie objects with results on cellular level, weight, unit and description. -#' @author Kristine Karstens -#' @seealso -#' [readLPJmL()] -#' @examples -#' \dontrun{ -#' readSource("LPJmL", subtype = "soilc", convert = TRUE) -#' } -#' -convertLPJmL <- function(x) { - - return(x) -} diff --git a/R/convertLUH2v2.R b/R/convertLUH2v2.R deleted file mode 100644 index 74ceead4..00000000 --- a/R/convertLUH2v2.R +++ /dev/null @@ -1,6 +0,0 @@ -#' @importFrom magclass ncells setItems -#' @importFrom luscale groupAggregate - -convertLUH2v2 <- function(x, subtype) { - return(toolConv2CountryByCelltype(x, cells = "lpjcell")) -} diff --git a/R/convertLutz2014.R b/R/convertLutz2014.R index d7ec36c0..88695ca1 100644 --- a/R/convertLutz2014.R +++ b/R/convertLutz2014.R @@ -22,7 +22,7 @@ convertLutz2014 <- function(x) { x <- toolCountryFill(x, fill = NA, no_remove_warning = "ANT") - # BB: use of speed_aggregate with an external mapping could replace the following function and speed it up + # BB: use of toolAggregate with an external mapping could replace the following function and speed it up fillCountryByAverageOfRegion <- function(x, country, region) { vcat(2, paste0("interpolating country: ", country)) values <- x[region, , ] diff --git a/R/correctFAO.R b/R/correctFAO.R deleted file mode 100644 index 382cac32..00000000 --- a/R/correctFAO.R +++ /dev/null @@ -1,22 +0,0 @@ -#' @title correctFAO -#' -#' @description Corrects FAO data for known mismatches or insufficiencies -#' -#' @param x MAgPIE object containing original values -#' @param subtype The FAO file type, e.g.: CBCrop -#' -#' @return Data as MAgPIE object -#' @author Kristine Karstens -#' -#' @seealso [readFAO()], [readSource()], -#' @examples -#' \dontrun{ -#' a <- readSource("FAO", "Crop", convert = TRUE) -#' } -#' -correctFAO <- function(x, subtype) { - - if (subtype == "Fodder") x <- x[, , "645|Pumpkins for Fodder", invert = TRUE] # smashing some pumpkins here - - return(x) -} diff --git a/R/correctFAO_online.R b/R/correctFAO_online.R deleted file mode 100644 index 53f6d4c7..00000000 --- a/R/correctFAO_online.R +++ /dev/null @@ -1,22 +0,0 @@ -#' @title correctFAO_online -#' -#' @description Corrects FAO data for known mismatches or insufficiencies -#' -#' @param x MAgPIE object containing original values -#' @param subtype The FAO file type, e.g.: CBCrop -#' -#' @return Data as MAgPIE object -#' @author Kristine Karstens -#' -#' @seealso [readFAO()], [readSource()], -#' @examples -#' \dontrun{ -#' a <- readSource("FAO_online", "Crop", convert = TRUE) -#' } -#' -correctFAO_online <- function(x, subtype) { # nolint: object_name_linter. - - if (subtype == "Fodder") x <- x[, , "645|Pumpkins for Fodder", invert = TRUE] # smashing some pumpkins here - - return(x) -} diff --git a/R/correctGAEZv4.R b/R/correctGAEZv4.R deleted file mode 100644 index 8f3be928..00000000 --- a/R/correctGAEZv4.R +++ /dev/null @@ -1,32 +0,0 @@ -#' @title correctGAEZv4 -#' @description Correct Global Agro-ecological Zones (GAEZ) data -#' @param x MAgPIE object provided by readGAEZv4 function -#' @return MAgPIE object at 0.5 cellular level -#' @author Felicitas Beier -#' -#' @examples -#' \dontrun{ -#' readSource("GAEZv4", convert = "onlycorrect") -#' } -#' -#' @importFrom madrat toolConditionalReplace -#' @importFrom magclass getYears getNames new.magpie mbind - -correctGAEZv4 <- function(x) { - - mapping <- toolGetMappingCoord2Country(pretty = TRUE) - tmp <- new.magpie(cells_and_regions = setdiff(mapping$coords, getCells(x)), - years = getYears(x), names = getNames(x), fill = NA) - - x <- mbind(x, tmp) - - # NAs are set to 0 - x <- toolConditionalReplace(x, conditions = c("is.na()", "<0"), replaceby = 0) - - # Sort cells correctly and rename - x <- x[mapping$coords, , ] - getCells(x) <- paste(mapping$coords, mapping$iso, sep = ".") - getSets(x) <- c("x", "y", "iso", "year", "MCzones") - - return(x) -} diff --git a/R/correctLPJmL.R b/R/correctLPJmL.R deleted file mode 100644 index 4d869234..00000000 --- a/R/correctLPJmL.R +++ /dev/null @@ -1,25 +0,0 @@ -#' @title correctLPJmL -#' @description Correct LPJmL content -#' -#' @param x magpie object provided by the read function -#' @return List of magpie objects with results on cellular level, weight, unit and description. -#' @author Kristine Karstens, Felicitas Beier -#' @seealso -#' [correctLPJmL()] -#' -#' @examples -#' \dontrun{ -#' readSource("LPJmL", subtype = "soilc", convert = "onlycorrect") -#' } -#' -#' @importFrom lpjclass readLPJ - -correctLPJmL <- function(x) { - - x <- toolConditionalReplace(x, conditions = c("is.na()", "<0"), replaceby = 0) - if (length(getCells(x)) == 59199) { - x <- toolCell2isoCell(x) - } - - return(x) -} diff --git a/R/correctLPJmLClimateInput.R b/R/correctLPJmLClimateInput.R deleted file mode 100644 index d2946b22..00000000 --- a/R/correctLPJmLClimateInput.R +++ /dev/null @@ -1,27 +0,0 @@ -#' @title correctLPJmLClimateInput -#' @description Correct LPJmL climate input variables -#' -#' @param x magpie object provided by the read function -#' -#' @return Magpie objects with results on cellular level, weight, unit and description. -#' @author Marcos Alves, Felicitas Beier -#' -#' @seealso -#' \code{\link{readLPJmLClimateInput}} -#' @examples -#' -#' \dontrun{ -#' readSource("LPJmLClimateInput", subtype, convert="onlycorrect") -#' } -#' -#' @import magclass -#' @importFrom madrat toolConditionalReplace - -correctLPJmLClimateInput <- function(x) { # nolint - - x <- toolConditionalReplace(x, - conditions = c("is.na()"), - replaceby = 0) - - return(x) -} diff --git a/R/correctLPJmLInputs.R b/R/correctLPJmLInputs.R deleted file mode 100644 index c34b2c39..00000000 --- a/R/correctLPJmLInputs.R +++ /dev/null @@ -1,21 +0,0 @@ -#' @title correctLPJmLInputs -#' @description correct LPJmLInputs content (dummy function) -#' -#' @param x magpie object provided by the read function -#' -#' @author Felicitas Beier -#' -#' @examples -#' \dontrun{ -#' readSource("LPJmLInputs", convert = "onlycorrect") -#' } -#' -#' @importFrom madrat toolConditionalReplace -#' - -correctLPJmLInputs <- function(x) { - - x <- toolConditionalReplace(x, conditions = c("is.na()", "<0"), replaceby = 0) - - return(x) -} diff --git a/R/correctLPJmL_new.R b/R/correctLPJmL_new.R deleted file mode 100644 index d5417d11..00000000 --- a/R/correctLPJmL_new.R +++ /dev/null @@ -1,21 +0,0 @@ -#' @title correctLPJmL_new -#' @description Convert LPJmL content (dummy function) -#' @param x magpie object provided by the read function -#' -#' @author Kristine Karstens -#' @seealso -#' [readLPJmL()] -#' @examples -#' \dontrun{ -#' readSource("LPJmL", convert = "onlycorrect") -#' } -#' -#' @importFrom madrat toolConditionalReplace -#' - -correctLPJmL_new <- function(x) { # nolint: object_name_linter. - - x <- toolConditionalReplace(x, conditions = c("is.na()", "<0"), replaceby = 0) - - return(x) -} diff --git a/R/correctLUH2v2.R b/R/correctLUH2v2.R deleted file mode 100644 index 5706d807..00000000 --- a/R/correctLUH2v2.R +++ /dev/null @@ -1,62 +0,0 @@ -#' @title correctLUH2v2 -#' @description Correct LUH2v2 content -#' -#' @param x magpie object provided by the read function -#' @param subtype switch between different inputs -#' -#' @return List of magpie object with results on cellular level -#' -#' @author Florian Humpenoeder, Stephen Wirth, Kristine Karstens, Felicitas Beier, Jan Philipp Dietrich, -#' Edna J. Molina Bacca -#' -#' @importFrom magclass getCells -#' -correctLUH2v2 <- function(x, subtype) { - - if (any(is.na(x))) { - vcat(verbosity = 1, paste(sum(is.na(x)) / length(x) * 100, "% of data points with NAs in LUH2. set to 0.")) - x[is.na(x)] <- 0 - } - if (any(x < 0)) { - vcat(verbosity = 1, paste(sum(x < 0) / length(x) * 100, "% of data points with negative values in LUH2. set to 0.")) - x[x < 0] <- 0 - } - - years <- getYears(x, as.integer = TRUE) - - if (grepl("states", subtype) && - length(intersect(2001:2015, years)) > 0 && - 2000 %in% years && - 2005 %in% years) { - - # check, if in JPN pasture+rangeland is unnaturally low - if (sum(x["JPN", "y2005", c("pastr", "range")]) < 0.01) { - - # if so correct all years since 2001 (first year of buggy data) - # using secondary forest area as buffer - buggedYears <- intersect(2001:2015, years) - pasture <- setYears(x["JPN", "y2000", c("pastr", "range")], NULL) - x["JPN", buggedYears, "secdf"] <- x["JPN", buggedYears, "secdf"] - dimSums(pasture, dim = 3) - x["JPN", buggedYears, c("pastr", "range")] <- x["JPN", buggedYears, c("pastr", "range")] + pasture - - # correct for negative values if secondary forest is exceeded - secdfNegative <- (x["JPN", buggedYears, "secdf"] < 0) - x["JPN", buggedYears, "pastr"][secdfNegative] <- x["JPN", buggedYears, "pastr"][secdfNegative] + - x["JPN", buggedYears, "secdf"][secdfNegative] - x["JPN", buggedYears, "secdf"][secdfNegative] <- 0 - - # correct potentially newly introduced negative values in rangelands - pastrNegative <- (x["JPN", buggedYears, "pastr"] < 0) - x["JPN", buggedYears, "range"][pastrNegative] <- x["JPN", buggedYears, "range"][pastrNegative] + - x["JPN", buggedYears, "pastr"][pastrNegative] - x["JPN", buggedYears, "pastr"][pastrNegative] <- 0 - x["JPN", buggedYears, "range"][x["JPN", buggedYears, "range"] < 0] <- 0 - - } else { - stop("it seems the Japan bug in LUH2v2 has been removed. - Please remove the bugfix in correct LUH2v2 before proceeding!") - } - } - - return(x) -} diff --git a/R/correctLandInG.R b/R/correctLandInG.R deleted file mode 100644 index db6094ef..00000000 --- a/R/correctLandInG.R +++ /dev/null @@ -1,24 +0,0 @@ -#' @title correctLandInG -#' @description correct LandInG data. Convert unit from ha to mio ha -#' @return corrected magpie object -#' @param x magpie object provided by the read function -#' @author David Hoetten, Felicitas Beier -#' @seealso -#' \code{\link{readLandInG}} -#' @examples -#' \dontrun{ -#' a <- readSource("LandInG", convert = "onlycorrect") -#' } -#' -#' @importFrom madrat toolConditionalReplace - -correctLandInG <- function(x) { - - # replace NAs and negatives with 0 - x <- toolConditionalReplace(x, conditions = c("is.na()", "<0"), replaceby = 0) - # convert from ha to Mha - x <- x * 1e-06 - - return(x) - -} diff --git a/R/downloadFAO_online.R b/R/downloadFAO_online.R deleted file mode 100644 index 487ca71a..00000000 --- a/R/downloadFAO_online.R +++ /dev/null @@ -1,95 +0,0 @@ -#' Download FAO data -#' -#' Downloads the latest data and meta data form the FAOStat website. -#' -#' @param subtype Type of FAO data that should be read. -#' -#' @importFrom utils download.file unzip person - -downloadFAO_online <- function(subtype) { # nolint: object_name_linter. - - if (!requireNamespace("XML", quietly = TRUE)) { - stop("The 'XML' package is required to download data from FAO. Please install it.") - } - - # Additional information not accessed by this function but potentially interesting. - # DEFINITION AND CLASSIFICATION OF COMMODITIES - # http://www.fao.org/es/faodef/fdef11e.htm - # LICENSING INFORMATION - # http://www.fao.org/3/ca7570en/ca7570en.pdf - # META DATA PRINTED AS TABLE - # http://fenixservices.fao.org/faostat/static/releasecalendar/Default.aspx - - files <- c( - CapitalStock = "Investment_CapitalStock_E_All_Data_(Normalized).zip", - CBCrop = "CommodityBalances_Crops_E_All_Data_(Normalized).zip", - CBLive = "CommodityBalances_LivestockFish_E_All_Data_(Normalized).zip", - Crop = "Production_Crops_E_All_Data_(Normalized).zip", - CropProc = "Production_CropsProcessed_E_All_Data_(Normalized).zip", - EmisAgBurnCropResid = "Emissions_Agriculture_Burning_crop_residues_E_All_Data_(Normalized).zip", - EmisAgBurnSavanna = "Emissions_Agriculture_Burning_Savanna_E_All_Data_(Normalized).zip", - EmisAgCropResid = "Emissions_Agriculture_Crop_Residues_E_All_Data_(Normalized).zip", - EmisAgCultOrgSoil = "Emissions_Agriculture_Cultivated_Organic_Soils_E_All_Data_(Normalized).zip", - EmisAgEnergy = "Emissions_Agriculture_Energy_E_All_Data_(Normalized).zip", - EmisAgEntericFerment = "Emissions_Agriculture_Enteric_Fermentation_E_All_Data_(Normalized).zip", - EmisAgManureManag = "Emissions_Agriculture_Manure_Management_E_All_Data_(Normalized).zip", - EmisAgManurePasture = "Emissions_Agriculture_Manure_left_on_pasture_E_All_Data_(Normalized).zip", - EmisAgManureSoil = "Emissions_Agriculture_Manure_applied_to_soils_E_All_Data_(Normalized).zip", - EmisAgRiceCult = "Emissions_Agriculture_Rice_Cultivation_E_All_Data_(Normalized).zip", - EmisAgSynthFerti = "Emissions_Agriculture_Synthetic_Fertilizers_E_All_Data_(Normalized).zip", - EmisAgTotal = "Emissions_Agriculture_Agriculture_total_E_All_Data_(Normalized).zip", - EmisLuBurnBiomass = "Emissions_Land_Use_Burning_Biomass_E_All_Data_(Normalized).zip", - EmisLuCrop = "Emissions_Land_Use_Cropland_E_All_Data_(Normalized).zip", - EmisLuForest = "Emissions_Land_Use_Forest_Land_E_All_Data_(Normalized).zip", - EmisLuGrass = "Emissions_Land_Use_Grassland_E_All_Data_(Normalized).zip", - EmisLuTotal = "Emissions_Land_Use_Land_Use_Total_E_All_Data_(Normalized).zip", - FSCrop = "FoodSupply_Crops_E_All_Data_(Normalized).zip", - FSLive = "FoodSupply_LivestockFish_E_All_Data_(Normalized).zip", - FbsHistoric = "FoodBalanceSheetsHistoric_E_All_Data_(Normalized).zip", - Fbs = "FoodBalanceSheets_E_All_Data_(Normalized).zip", - Fertilizer = "Environment_Fertilizers_E_All_Data_(Normalized).zip", - FertilizerNutrients = "Inputs_FertilizersNutrient_E_All_Data_(Normalized).zip", - FertilizerProducts = "Inputs_FertilizersProduct_E_All_Data_(Normalized).zip", - FoodSecurity = "Food_Security_Data_E_All_Data_(Normalized).zip", - ForestProdTrade = "Forestry_E_All_Data_(Normalized).zip", - Land = "Inputs_LandUse_E_All_Data_(Normalized).zip", - LiveHead = "Production_Livestock_E_All_Data_(Normalized).zip", - LivePrim = "Production_LivestockPrimary_E_All_Data_(Normalized).zip", - LiveProc = "Production_LivestockProcessed_E_All_Data_(Normalized).zip", - Pop = "Population_E_All_Data_(Normalized).zip", - PricesProducerAnnual = "Prices_E_All_Data_(Normalized).zip", - PricesProducerAnnualLCU = "Prices_E_All_Data_(Normalized).zip", - Trade = "Trade_CropsLivestock_E_All_Data_(Normalized).zip", - TradeMatrix = "Trade_DetailedTradeMatrix_E_All_Data_(Normalized).zip", - ValueOfProd = "Value_of_Production_E_All_Data_(Normalized).zip", - ValueShares = "Value_shares_industry_primary_factors_E_All_Data_(Normalized).zip" - ) - - file <- toolSubtypeSelect(subtype, files) - - # Download meta data (e.g. name, description, release date, file path) for all FAO data sets currently available - faoMetaXmlFile <- "FAO_datasets_E.xml" - download.file(url = "http://fenixservices.fao.org/faostat/static/bulkdownloads/datasets_E.xml", - destfile = faoMetaXmlFile) - faoMeta <- XML::xmlToDataFrame(faoMetaXmlFile, stringsAsFactors = FALSE) - unlink(faoMetaXmlFile) - - # extract the data set for the selected subtype by searching for the file name - faoMeta <- faoMeta[grepl(pattern = file, faoMeta$FileLocation, fixed = TRUE), ] - - # download the data - download.file(faoMeta$FileLocation, destfile = file, mode = "wb") - - # Compose meta data - return(list(url = faoMeta$FileLocation, - doi = "not available", - title = faoMeta$DatasetName, - author = person(faoMeta$Contact, email = faoMeta$Email), - version = "not available", - release_date = faoMeta$DateUpdate, - description = faoMeta$DatasetDescription, - license = "Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO)", - reference = "not available") - ) - -} diff --git a/R/downloadLPJmLClimateInput.R b/R/downloadLPJmLClimateInput.R deleted file mode 100644 index 7d31ebd1..00000000 --- a/R/downloadLPJmLClimateInput.R +++ /dev/null @@ -1,68 +0,0 @@ -#' @title downloadLPJmLClimateInput -#' @description Download GCM climate input used for LPJmL runs -#' -#' @param subtype Switch between different inputs (e.g. "ISIMIP3b:IPSL-CM6A-LR:historical:1850-2014:temperature") -#' Argument consists of GCM version, climate model, scenario and variable, -#' separated by ":" -#' -#' @return metadata entry -#' @author Marcos Alves, Kristine Karstens -#' @examples -#' \dontrun{ -#' readSource("LPJmLClimateInput", convert = "onlycorrect") -#' } -#' -downloadLPJmLClimateInput <- function(subtype = "ISIMIP3bv2:MRI-ESM2-0:ssp370:temperature") { # nolint - - x <- toolSplitSubtype(subtype, list(version = NULL, climatemodel = NULL, - scenario = NULL, variable = NULL)) - - varList <- c(temperature = "tas", - precipitation = "pr", - longWaveNet = "lwnet", - shortWave = "rsds", - temperatureMin = "tasmin", - temperatureMax = "tasmax") - shortVar <- toolSubtypeSelect(x$variable, varList) - - if (x$climatemodel == "GSWP3-W5E5") { - storage <- "/p/projects/lpjml/input/historical/" # nolint: absolute_path_linter. - } else { - storage <- "/p/projects/lpjml/input/scenarios" # nolint: absolute_path_linter. - } - - path <- file.path(storage, # historical or scenarios - x$version, # version: ISIMIP3a or b(v2) - gsub("_", "/", x$scenario), # obsclim e.g. ssp119 - x$climatemodel) # GCMs or GSWP3-W5E5 - - if (!dir.exists(path)) { - path <- file.path(storage, - x$version, - gsub("_", "/", x$scenario), - gsub("_", "-", x$climatemodel)) - } - - fileList <- list.files(path) - file <- grep(paste0(shortVar, "_"), - fileList, value = TRUE) - filePath <- file.path(path, file) - - if (file.exists(filePath)) { - file.copy(filePath, file) - } else { - stop("Data is not available so far!") - } - - # Compose meta data - return(list(url = paste0(storage, filePath), - doi = NULL, - title = x$version, - author = NULL, - version = x$version, - release_date = NULL, - description = NULL, - license = NULL, - reference = NULL) - ) -} diff --git a/R/downloadLPJmL_new.R b/R/downloadLPJmL_new.R deleted file mode 100644 index da31ce93..00000000 --- a/R/downloadLPJmL_new.R +++ /dev/null @@ -1,117 +0,0 @@ -#' @title downloadLPJmL_new -#' @description Download LPJmL content by version, climate model and scenario -#' -#' @param subtype Switch between different input -#' It consists of LPJmL version, climate model, scenario and variable. -#' For pasture lpjml runs, the scenario variable is used to navigate the output folder structure -#' (e.g. 'LPJmL4_for_MAgPIE_3dda0615:GSWP3-W5E5:historical:soilc' or -#' "LPJmL5.2_Pasture:IPSL_CM6A_LR:ssp126_co2_limN_00:soilc_past_hist") -#' @return metadata entry -#' @author Kristine Karstens, Marcos Alves, Felicitas Beier -#' @examples -#' \dontrun{ -#' readSource("LPJmL_new", convert = FALSE) -#' } -#' @importFrom utils head -#' @importFrom stringr str_detect -#' @importFrom madrat toolSplitSubtype - -downloadLPJmL_new <- function(subtype = "LPJmL4_for_MAgPIE_44ac93de:GSWP3-W5E5:historical:soilc") { # nolint - - x <- toolSplitSubtype(subtype, - list(version = NULL, - climatemodel = NULL, - scenario = NULL, - variable = NULL)) - - files <- c(soilc = "soilc_natveg", - soilc_layer = "soilc_layer_natveg", - litc = "litc_natveg", - vegc = "vegc_natveg", - alitfallc = "alitfallc_natveg", - alitterfallc = "alitterfallc_natveg", - alitterfallc_wood = "alitterfallc_wood_natveg", - alitterburnc = "alitterburnc_natveg", - alitterburnc_wood = "alitterburnc_wood_natveg", - harvest = "pft_harvest.pft", - irrig = "cft_airrig.pft", - cwater_b = "cft_consump_water_b.pft", - sdate = "sdate", - hdate = "hdate", - mpet = "mpet_natveg", - met_grass_ir = "met_grass_ir", - met_grass_rf = "met_grass_rf", - cft_et_grass_ir = "cft_et_grass_ir", - cft_et_grass_rf = "cft_et_grass_rf", - aprec = "aprec_natveg", - aet = "aet_natveg", - mdischarge = "mdischarge_natveg", - mrunoff = "mrunoff_natveg", - mgpp_grass_ir = "mgpp_grass_ir", - mgpp_grass_rf = "mgpp_grass_rf", - cft_gpp_grass_ir = "cft_gpp_grass_ir", - cft_gpp_grass_rf = "cft_gpp_grass_rf", - vegc_grass = "mean_vegc_mangrass", - litc_grass = "litc_mangrass", - soilc_grass = "soilc_mangrass", - soilc_past_hist = "soilc_hist", - soilc_past_scen = "soilc_scen", - grass_pft_hist = "pft_harvest_hist.pft", - grass_pft_scen = "pft_harvest_scen.pft", - cshift_fast = "cshift_fast_natveg", - cshift_slow = "cshift_slow_natveg", - fpc = "fpc.clm") - - # handling the separate sources of grass runs - if (!grepl("Pasture", x$version, ignore.case = TRUE)) { - storage <- "/p/projects/landuse/users/cmueller/" # nolint: absolute_path_linter. - } else { - storage <- "/p/projects/rd3mod/inputdata/sources/LPJmL/" # nolint: absolute_path_linter. - } - - path <- paste(x$version, x$climatemodel, x$scenario, sep = "/") - if (!dir.exists(file.path(storage, path))) { - path <- paste(x$version, gsub("-", "_", x$climatemodel), x$scenario, sep = "/") - } - - listFiles <- list.files(paste0(storage, path)) - file <- grep(toolSubtypeSelect(x$variable, files), listFiles, value = TRUE) - filePath <- paste0(storage, path, "/", file) - - .findFile <- function(storage, path, listFiles, file) { - outputFiles <- grep(".out", listFiles, value = TRUE) - filesOut <- file.path(storage, path, outputFiles) - order <- order(file.info(filesOut)$ctime, decreasing = TRUE) - filesOut <- filesOut[order] - outputFiles <- outputFiles[order] - x <- sapply(filesOut, function(x) list(readLines(x))) # nolint - out <- sapply(x, function(x) any(stringr::str_detect(x, file))) # nolint - return(outputFiles[out][1]) - } - - if (file.exists(filePath)) { - file.copy(filePath, file) - if (grepl("Pasture", x$version, ignore.case = TRUE)) { - files2copy <- .findFile(storage, path, listFiles, file) - file.copy(file.path(storage, path, files2copy), files2copy, overwrite = TRUE) - } else { - file.copy(paste0(storage, path, "/lpjml_log.out"), "lpjml_log.out") - } - } else { - stop("Data is not available so far!") - } - - # Compose meta data - return(list(url = paste0(storage, filePath), - doi = NULL, - title = x$version, - author = list(person("Christoph", "Mueller", email = "cmueller@pik-potsdam.de"), - person("Jens", "Heinke", email = "heinke@pik-potsdam.de"), - person("Stephen", "Writh", email = "wirth@pik-potsdam.de")), - version = x$version, - release_date = NULL, - description = NULL, - license = "Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0)", - reference = NULL) - ) -} diff --git a/R/downloadLUH2v2.R b/R/downloadLUH2v2.R deleted file mode 100644 index 80ad0dbd..00000000 --- a/R/downloadLUH2v2.R +++ /dev/null @@ -1,12 +0,0 @@ -#' @importFrom utils download.file tail - -downloadLUH2v2 <- function(subtype = NULL) { - links <- c("https://luh.umd.edu/LUH2/LUH2_v2h/states.nc", - "https://luh.umd.edu/LUH2/LUH2_v2h/transitions.nc", - "https://luh.umd.edu/LUH2/LUH2_v2h/management.nc", - "https://luh.umd.edu/LUH2/LUH2_v2h/staticData_quarterdeg.nc") - for (link in links) { - fname <- tail(strsplit(link, split = "/")[[1]], 1) - download.file(link, destfile = fname, mode = "wb") - } -} diff --git a/R/imports.R b/R/imports.R index e2ab6b87..54997f32 100644 --- a/R/imports.R +++ b/R/imports.R @@ -1,4 +1,4 @@ # Generated by lucode2: do not edit by hand -#' @import magclass madrat mrdrivers mstools +#' @import magclass madrat mrdrivers mrfaocore mrlandcore mstools NULL diff --git a/R/readEurostatLivestock.R b/R/readEurostatLivestock.R index 1049e1b1..8d689600 100644 --- a/R/readEurostatLivestock.R +++ b/R/readEurostatLivestock.R @@ -22,7 +22,7 @@ #' \dontrun{ #' a <- readSource("EurostatLivestock", "MeatProd") #' } -#' @importFrom utils read.table +#' @importFrom utils read.table head #' @importFrom dplyr mutate mutate_all filter `%>%` across rename #' @importFrom tidyr pivot_longer starts_with matches #' @importFrom rlang .data diff --git a/R/readFAO.R b/R/readFAO.R deleted file mode 100644 index d01f3575..00000000 --- a/R/readFAO.R +++ /dev/null @@ -1,238 +0,0 @@ -#' Read FAO -#' -#' Read in FAO data that has been bulk downloaded from the FAOSTAT website. -#' Files with exception of fodder.csv are aquired from: -#' http://faostat.fao.org/Portals/_Faostat/Downloads/zip_files/ -#' -#' Update 23-Jan-2017 - Added FAO Forestry production and trade data (Abhi) -#' -#' -#' @param subtype Type of FAO data that should be read. Available types are: -#' \itemize{ -#' \item `CBCrop`: Commodity Balance Crop (CommodityBalances_Crops_E_All_Data.zip) -#' \item `CBLive`: Commoditiy Balance Livestock (CommodityBalances_LivestockFish_E_All_Data.zip) -#' \item `Crop`: Production Crops ("Production_Crops_E_All_Data.zip") -#' \item `CropProc`: Production Crops Processed ("Production_CropsProcessed_E_All_Data.zip") -#' \item `Fbs`: Food Balance Sheet ("FoodBalanceSheets_E_All_Data.zip") -#' \item `Fertilizer`: Fertilizer ("Resources_Fertilizers_E_All_Data.zip") -#' \item `Fodder`: Fodder (data that has been manually downloaded from the FAOSTAT website as -#' seperate .xls files via a search for "forage" and "fodder" withing -#' Production-Crops. These datasets have been added together to a "Fodder.csv" file) -#' \item `FoodSecurity`: Food Security Data ("Food_Security_Data_E_All_Data.zip") -#' \item `FSCrop`: Food Supply Crops ("FoodSupply_Crops_E_All_Data.zip") -#' \item `FSLive`: Food Supply Livestock ("FoodSupply_LivestockFish_E_All_Data.zip") -#' \item `Land`: Land ("Resources_Land_E_All_Data.zip") -#' \item `LiveHead`: Production Live Animals ("Production_Livestock_E_All_Data.zip") -#' \item `LivePrim`: Production Livestock Primary ("Production_LivestockPrimary_E_All_Data.zip") -#' \item `LiveProc`: Production Livestock Processed ("Production_LivestockProcessed_E_All_Data.zip") -#' \item `Pop`: Population ("Population_E_All_Data.zip") -#' \item `ForestProdTrade`: Forestry Production and Trade ("Forestry_E_All_Data_(Normalized).zip") -#' \item `PricesProducerAnnual`: Producer Prices - Annual ("Prices_E_All_Data.zip") -#' \item `PricesProducerAnnualLCU`: Producer Prices - Annual in LCU ("Prices_E_All_Data.zip") -#' \item `ValueOfProd`: Value of Agricultural Production ("Value_of_Production_E_All_Data.zip") -#' } -#' @return FAO data as MAgPIE object -#' @author Ulrich Kreidenweis, Abhijeet Mishra, Mishko Stevanovic -#' @seealso [readSource()] -#' @examples -#' \dontrun{ -#' a <- readSource("FAO", "Crop") -#' } -#' @importFrom data.table fread -#' @importFrom tools file_path_sans_ext file_ext -#' @importFrom utils unzip -#' @importFrom withr local_tempdir - -readFAO <- function(subtype) { - files <- c( - CBCrop = "CommodityBalances_Crops_E_All_Data.zip", - CBLive = "CommodityBalances_LivestockFish_E_All_Data.zip", - Crop = "Production_Crops_E_All_Data.zip", - CropProc = "Production_CropsProcessed_E_All_Data.zip", - EmisAgBurnCropResid = "Emissions_Agriculture_Burning_crop_residues_E_All_Data.zip", - EmisAgBurnSavanna = "Emissions_Agriculture_Burning_Savanna_E_All_Data.zip", - EmisAgCropResid = "Emissions_Agriculture_Crop_Residues_E_All_Data.zip", - EmisAgCultOrgSoil = "Emissions_Agriculture_Cultivated_Organic_Soils_E_All_Data.zip", - EmisAgEnergy = "Emissions_Agriculture_Energy_E_All_Data.zip", - EmisAgEntericFerment = "Emissions_Agriculture_Enteric_Fermentation_E_All_Data.zip", - EmisAgManureManag = "Emissions_Agriculture_Manure_Management_E_All_Data.zip", - EmisAgManurePasture = "Emissions_Agriculture_Manure_left_on_pasture_E_All_Data.zip", - EmisAgManureSoil = "Emissions_Agriculture_Manure_applied_to_soils_E_All_Data.zip", - EmisAgRiceCult = "Emissions_Agriculture_Rice_Cultivation_E_All_Data.zip", - EmisAgSynthFerti = "Emissions_Agriculture_Synthetic_Fertilizers_E_All_Data.zip", - EmisAgTotal = "Emissions_Agriculture_Agriculture_total_E_All_Data.zip", - EmisLuBurnBiomass = "Emissions_Land_Use_Burning_Biomass_E_All_Data.zip", - EmisLuCrop = "Emissions_Land_Use_Cropland_E_All_Data.zip", - EmisLuForest = "Emissions_Land_Use_Forest_Land_E_All_Data.zip", - EmisLuGrass = "Emissions_Land_Use_Grassland_E_All_Data.zip", - EmisLuTotal = "Emissions_Land_Use_Land_Use_Total_E_All_Data.zip", - FSCrop = "FoodSupply_Crops_E_All_Data.zip", - FSLive = "FoodSupply_LivestockFish_E_All_Data.zip", - # Fbs should not be used, use CB and FS or calcFAOharmonized() instead - Fbs = "FoodBalanceSheets_E_All_Data.zip", - Fertilizer = "Environment_Fertilizers_E_All_Data.zip", - Fodder = "Fodder.csv", - FoodSecurity = "Food_Security_Data_E_All_Data.zip", - ForestProdTrade = "Forestry_E_All_Data_(Normalized).zip", - Land = "Resources_Land_E_All_Data.zip", - LiveHead = "Production_Livestock_E_All_Data.zip", - LivePrim = "Production_LivestockPrimary_E_All_Data.zip", - LiveProc = "Production_LivestockProcessed_E_All_Data.zip", - Pop = "Population_E_All_Data.zip", - PricesProducerAnnual = "Prices_E_All_Data.zip", - PricesProducerAnnualLCU = "Prices_E_All_Data.zip", - ValueOfProd = "Value_of_Production_E_All_Data.zip" - ) - - - file <- toolSubtypeSelect(subtype, files) - - ## if file is .zip uncompress - extension <- file_ext(basename(file)) - csvName <- paste0(file_path_sans_ext(file), ".csv") - if (file.exists(csvName)) { - file <- csvName - } else if (extension == "zip" && !file.exists(csvName)) { - tempfolder <- local_tempdir() - unzip(file, exdir = tempfolder) # use the absolute path to the file in order to unzip when working in the function - file <- file.path(tempfolder, csvName) - } - - ## efficient reading of csv file: read only needed columns in the needed type (codes as factor) - csvcolnames <- colnames(read.table(file, header = TRUE, nrows = 1, sep = ",")) - - ## in case data with the years as columns has to be read in start the differentiation here - - if (subtype == "ForestProdTrade") { - readcolClass <- rep("NULL", length(csvcolnames)) - readcolClass[csvcolnames == "Area.Code" | csvcolnames == "Item.Code" | csvcolnames == "Element.Code"] <- "factor" - readcolClass[csvcolnames == "Area" | csvcolnames == "Element" | - csvcolnames == "Item" | csvcolnames == "Unit"] <- "character" - readcolClass[csvcolnames == "Value" | csvcolnames == "Year"] <- NA - fao <- read.table(file, - header = FALSE, - skip = 1, - sep = ",", - colClasses = readcolClass, - col.names = csvcolnames, - quote = "\"", - encoding = "latin1") - names(fao)[names(fao) == "Area.Code"] <- "CountryCode" - names(fao)[names(fao) == "Area"] <- "Country" - ## list countries where no respective ISO code is available in a message - countryandcode <- unique(fao[, c("CountryCode", "Country")]) - } else { - readcolClass <- rep("NULL", length(csvcolnames)) - readcolClass[csvcolnames == "CountryCode" | csvcolnames == "ItemCode" | csvcolnames == "ElementCode"] <- "factor" - readcolClass[csvcolnames == "Country" | csvcolnames == "Element" | - csvcolnames == "Item" | csvcolnames == "Unit"] <- "character" - - if (subtype == "EmisLuTotal") { - readcolClass[csvcolnames == "Flag" | csvcolnames == "ElementGroup"] <- NA - readcolClass[csvcolnames == "Value" | csvcolnames == "Year"] <- NA - } else if (subtype == "Fodder") { - readcolClass[csvcolnames == "Value" | csvcolnames == "Year"] <- "character" - csvcolnames <- csvcolnames[-grep("NULL", readcolClass)] - } else { - readcolClass[csvcolnames == "Value" | csvcolnames == "Year"] <- NA - csvcolnames <- csvcolnames[-grep("NULL", readcolClass)] - } - - fao <- fread(input = file, - header = FALSE, - skip = 1, - sep = ",", - colClasses = readcolClass, - col.names = csvcolnames, - quote = "\"", - encoding = "Latin-1", - showProgress = FALSE) - if (all(!is.factor(fao$CountryCode))) fao$CountryCode <- as.factor(fao$CountryCode) - fao$Value <- as.numeric(fao$Value) - # list countries where no respective ISO code is available in a message - countryandcode <- unique(fao[, c("CountryCode", "Country")]) - } - - # collect the countries that do not exist in the data - faoIsoFaoCode <- toolGetMapping("FAOiso_faocode.csv", where = "mrcommons") - notIncl <- countryandcode$Country[!countryandcode$CountryCode %in% faoIsoFaoCode$CountryCode] - notInclCoun <- notIncl[!grepl("(Total)", notIncl)] - if (length(notInclCoun) > 0) { - vcat(1, "The following countries were not included due to missing ISO codes:", - "\n", paste(notInclCoun, "\n"), "-> Consider an update of FAOiso_faocode.csv", "\n") - } - fao <- fao[fao$CountryCode %in% faoIsoFaoCode$CountryCode, ] - gc() - fao$ISO <- fao$CountryCode - # becomes necessary because data is now loaded as .csv - rownames(faoIsoFaoCode) <- as.character(faoIsoFaoCode$CountryCode) - levels(fao$ISO) <- as.character(faoIsoFaoCode[levels(fao$CountryCode), "ISO3"]) - - - ### convert some units - replace <- fao$Unit == "1000 tonnes" - if (any(replace)) { - fao$Value[replace] <- fao$Value[replace] * 1000 - fao$Unit[replace] <- "tonnes" - } - - replace <- fao$Unit == "1000 Head" - if (any(replace)) { - fao$Value[replace] <- fao$Value[replace] * 1000 - fao$Unit[replace] <- "Head" - } - - replace <- fao$Unit == "1000" - if (any(replace)) { - fao$Value[replace] <- fao$Value[replace] * 1000 - fao$Unit[replace] <- "number" - } - - replace <- fao$Unit == "1000 Ha" - if (any(replace)) { - fao$Value[replace] <- fao$Value[replace] * 1000 - fao$Unit[replace] <- "ha" - } - - ### use ElementShort or a combination of Element and Unit instead of ElementCode - faoElementShort <- toolGetMapping("FAOelementShort.csv", where = "mrcommons") - - elementShort <- faoElementShort - - ## make ElementShort a combination of Element and Unit, replace special characters, and substitute several _ by one - fao$ElementShort <- gsub("_{1,}", "_", - paste0(gsub("[\\.,;?\\+& \\/\\-]", "_", fao$Element, perl = TRUE), - "_(", gsub("[\\.,;\\+& \\-]", "_", fao$Unit, perl = TRUE), ")"), perl = TRUE) - ### replace ElementShort with the entries from ElementShort if the Unit is the same - elementShort <- elementShort[elementShort$ElementCode %in% fao$ElementCode, ] - - if (length(elementShort) > 0) { - for (i in seq_len(nrow(elementShort))) { - fao$ElementShort[fao$ElementCode == elementShort[i, "ElementCode"] - & fao$Unit == elementShort[i, "Unit"]] <- as.character(elementShort[i, "ElementShort"]) - } - } - - # remove accent in Mate to avoid problems - # remove other strange names - fao$Item <- gsub("\u00E9", "e", fao$Item, perl = TRUE) - fao$Item <- gsub("\n + (Total)", " + (Total)", fao$Item, fixed = TRUE) - fao$ItemCodeItem <- paste0(fao$ItemCode, "|", gsub("\\.", "", fao$Item, perl = TRUE)) - - gc() - - faoMag <- as.magpie(fao[, c("Year", "ISO", "ItemCodeItem", "ElementShort", "Value")], - temporal = 1, - spatial = 2, - datacol = 5) - if (subtype == "EmisAgBurnCropResid" || subtype == "EmisAgCropResid" || subtype == "EmisLuForest") { - getNames(faoMag, dim = 1) <- gsub("\\r", "", getNames(faoMag, dim = 1)) - } - - rm(fao) - gc() - - faoMag <- magpiesort(faoMag) - - return(faoMag) -} diff --git a/R/readFAOTradeMatrix.R b/R/readFAOTradeMatrix.R deleted file mode 100644 index 72159345..00000000 --- a/R/readFAOTradeMatrix.R +++ /dev/null @@ -1,165 +0,0 @@ -#' Read FAOTradeMatrix -#' -#' Read in FAOSTAT detail trade matrix. -#' FAOSTAT does not balance or harmonize the import/export side reporting. -#' Furthermore, in terms of trade value, exporters are "usuallY" reporting FOB, while importers report CIF. -#' Difference in value, given identical qty, -#' is thus the transport margin and any unharmonized reporting combined. -#' @param subtype subsets of the detailed trade matrix to read in. Very large csv needs to be read in chunks -#' separated by export/import quantities and values, as well as kcr, kli and kothers (not in kcr nor kli) -#' Options are all combinations of c("import_value", "import_qty", "export_value", -#' "export_quantity" X c("kcr", "kli", "kothers")) -#' import is import side reporting while export is export-sde reporting -#' @return FAO data as MAgPIE object -#' @author David C -#' @seealso [readSource()] -#' @examples -#' \dontrun{ -#' a <- readSource("FAOTradeMatrix", "import_value_kcr") -#' } -#' @importFrom data.table fread -#' @importFrom tidyr pivot_longer starts_with -#' @importFrom dplyr summarise filter group_by ungroup %>% distinct -#' @importFrom magpiesets findset - -readFAOTradeMatrix <- function(subtype) { # nolint - -file <- "Trade_DetailedTradeMatrix_E_All_Data_(Normalized).csv" - - # ---- Select columns to be read from file and read file ---- - - ## efficient reading of csv file: read only needed columns in the needed type (codes as factor) - csvcolnames <- colnames(read.table(file, header = TRUE, nrows = 1, sep = ",")) - - # check if data is in long or wide format - long <- ifelse("Year" %in% csvcolnames, TRUE, FALSE) - - # define vector with types corresponding to the columns in the file - readcolClass <- rep("NULL", length(csvcolnames)) - factorCols <- c("Reporter.Country.Code", "Partner.Country.Code", "Item.Code", "Element.Code", "Element") - readcolClass[csvcolnames %in% factorCols] <- "factor" - readcolClass[csvcolnames %in% c("Area", "Country", "Element", "Item", "Unit", - "Months", "Reporter.Countries", "Partner.Countries")] <- "character" - readcolClass[csvcolnames %in% c("Value", "Year")] <- NA - if (!long) readcolClass[grepl("Y[0-9]{4}$", csvcolnames)] <- NA - - fao <- suppressWarnings( - fread(input = file, header = FALSE, skip = 1, sep = ",", - colClasses = readcolClass, - col.names = csvcolnames[is.na(readcolClass) | readcolClass != "NULL"], - quote = "\"", - encoding = "Latin-1", showProgress = FALSE, - )) - fao <- as.data.frame(fao) - # from wide to long (move years from individual columns into one column) - if (!long) { - fao <- pivot_longer(fao, cols = starts_with("Y"), names_to = "Year", names_pattern = "Y(.*)", - names_transform = list("Year" = as.integer), values_to = "Value") - } - - names(fao)[names(fao) == "Reporter.Country.Code"] <- "ReporterCountryCode" - names(fao)[names(fao) == "Partner.Country.Code"] <- "PartnerCountryCode" - names(fao) <- gsub("\\.", "", names(fao)) - - # ---- Assigning the ISO codes to countries ---- - - # Load FAO specific countries (not included in country2iso.csv in madrat) - faoIsoFaoCode <- toolGetMapping("FAOiso_faocode_online.csv", where = "mrcommons") - # convert data frame into named vector as required by toolCountry2isocode - faoIsoFaoCode <- structure(as.character(faoIsoFaoCode$ISO), names = as.character(faoIsoFaoCode$Country)) - - fao$ReporterISO <- toolCountry2isocode(fao$ReporterCountries, mapping = faoIsoFaoCode) - fao$PartnerISO <- toolCountry2isocode(fao$PartnerCountries, mapping = faoIsoFaoCode) - - # remove countries with missing ISO code - fao <- fao[!is.na(fao$ReporterISO), ] - fao <- fao[!is.na(fao$PartnerISO), ] - - - # ---- Reformat elements ---- - - elementShort <- toolGetMapping("FAOelementShort.csv", where = "mrcommons") - # keep relevant rows only - elementShort <- elementShort[elementShort$ElementCode %in% fao$ElementCode, ] - - # make ElementShort a combination of Element and Unit, replace special characters, and replace multiple _ by one - tmpElement <- gsub("[\\.,;?\\+& \\/\\-]", "_", fao$Element, perl = TRUE) - tmpUnit <- gsub("[\\.,;\\+& \\-]", "_", fao$Unit, perl = TRUE) - tmpElementShort <- paste0(tmpElement, "_(", tmpUnit, ")") - fao$ElementShort <- gsub("_{1,}", "_", tmpElementShort, perl = TRUE) # nolint - - ### replace ElementShort with the entries from ElementShort if the Unit is the same - if (length(elementShort) > 0) { - for (i in seq_len(nrow(elementShort))) { - j <- (fao$ElementCode == elementShort[i, "ElementCode"] & fao$Unit == elementShort[i, "Unit"]) - fao$ElementShort[j] <- as.character(elementShort[i, "ElementShort"]) - } - } - - # remove accent in Mate to avoid problems and remove other strange names - fao$Item <- gsub("\u00E9", "e", fao$Item, perl = TRUE) - fao$Item <- gsub("\n + (Total)", " + (Total)", fao$Item, fixed = TRUE) - fao$ItemCodeItem <- paste0(fao$ItemCode, "|", gsub("\\.", "", fao$Item, perl = TRUE)) - - # some small islands correspond to the same ISO3code, just remove them for now - fao <- filter(fao, !.data$ReporterCountries %in% c("Johnston Island", "Midway Island", - "Canton and Enderbury Islands", "Wake Island"), - !.data$PartnerCountries %in% c("Johnston Island", "Midway Island", - "Canton and Enderbury Islands", "Wake Island")) - -fao <- unite(fao, col = "ISO", c(.data$ReporterISO, .data$PartnerISO), sep = ".", remove = FALSE) - -# subset by both trade column and product column -mapping <- toolGetMapping("newFAOitems_online_DRAFT.csv", type = "sectoral", where = "mrcommons") -mapping <- mapping[, c("new_FAOoriginalItem_fromWebsite", "k")] -colnames(mapping)[1] <- "ItemCodeItem" -mapping <- distinct(mapping) - - -fao <- inner_join(fao, mapping) - -kcr <- findset("kcr") -kli <- findset("kli") -kothers <- setdiff(findset("kall"), c(kcr, kli)) - - elements <- list( - import_value_kcr = list(trade = "import_kUS$", product = kcr), - import_value_kli = list(trade = "import_kUS$", product = kli), - import_value_kothers = list(trade = "import_kUS$", product = kothers), - import_qty_kcr = list(trade = c("import", "Import_Quantity_(1000_Head)", - "Import_Quantity_(Head)", "Import_Quantity_(no)"), - product = kcr), - import_qty_kli = list(trade = c("import", "Import_Quantity_(1000_Head)", - "Import_Quantity_(Head)", "Import_Quantity_(no)"), - product = kli), - import_qty_kothers = list(trade = c("import", "Import_Quantity_(1000_Head)", - "Import_Quantity_(Head)", "Import_Quantity_(no)"), - product = kothers), - export_value_kcr = list(trade = "export_kUS$", product = kcr), - export_value_kli = list(trade = "export_kUS$", product = kli), - export_value_kothers = list(trade = "export_kUS$", product = kothers), - export_qty_kcr = list(trade = c("export", "Export_Quantity_(1000_Head)", - "Export_Quantity_(Head)", "Export_Quantity_(no)"), - product = kcr), - export_qty_kli = list(trade = c("export", "Export_Quantity_(1000_Head)", - "Export_Quantity_(Head)", "Export_Quantity_(no)"), - product = kli), - export_qty_kothers = list(trade = c("export", "Export_Quantity_(1000_Head)", - "Export_Quantity_(Head)", "Export_Quantity_(no)"), - product = kothers) - ) - -element <- toolSubtypeSelect(subtype, elements) - -out <- filter(fao, .data$ElementShort %in% element$trade, .data$k %in% element$product) - -out <- as.magpie(out[, c("Year", "ISO", "ItemCodeItem", "ElementShort", "Value")], - temporal = 1, spatial = 2, datacol = 5) # import/export unit is in tonnes -getItems(out, dim = 1, raw = TRUE) <- gsub("_", ".", getItems(out, dim = 1)) -gc() - - -out <- magpiesort(out) - - return(out) -} diff --git a/R/readFAO_FRA2015.R b/R/readFAO_FRA2015.R deleted file mode 100644 index 26580791..00000000 --- a/R/readFAO_FRA2015.R +++ /dev/null @@ -1,323 +0,0 @@ -#' Read FAO_FRA2015 -#' -#' Read-in an FRA data from 2015 (forest resource assessment) -#' -#' -#' @param subtype data subtype. Either "production" or "fac" (forest area and characteristics) -#' or "biodiversity" or "anndat" (Annual Data) -#' @return magpie object of the FRA 2015 data -#' @author Abhijeet Mishra -#' @seealso [readSource()] -#' @examples -#' \dontrun{ -#' a <- readSource("FAO_FRA2015", "production") -#' } -#' -#' @importFrom magclass as.magpie -#' @importFrom madrat toolSubtypeSelect -#' @importFrom tools file_ext -#' @importFrom data.table fread -#' @importFrom utils unzip -#' @importFrom tools file_path_sans_ext -#' @export - - -readFAO_FRA2015 <- function(subtype) { # nolint - - is.nan.data.frame <- function(x) { # nolint - do.call(cbind, lapply(x, is.nan)) - } - - if (subtype == "production") { - csvtoread <- "2.PRODUCTION.csv" - } - if (subtype == "fac") { - csvtoread <- "1.FOREST AREA AND CHARACTERISTICS.csv" - } - if (subtype == "biodiversity") { - csvtoread <- "5.BIODIVERSITY AND CONSERVATION.csv" - } - if (subtype == "anndat") { - csvtoread <- "9. ANNUAL DATA.csv" - } # Notice the sapce in name. That's how it is in data received from FRA2015 - - if (subtype == "fac") { - # read the data - data <- read.csv(unz("BULK.zip", csvtoread), header = TRUE, sep = ",", dec = ".", stringsAsFactors = FALSE) - - # Subset the data by removig the year "9999". No eoutplanation in FRA 2015 reoprt. - data <- subset(data, data$Year != 9999) - - # Keep only the variables we need - data <- data[, c("Country", "Year", "Forest", "Forchange", "ForPerc", "PerCapFor", "NatFor", - "Nfchange", "OthWooLan", "OthLan", "LanTreCov", "InWater", "Landarea", "ForExp", - "Afforest", "NatForExp", "Deforest", "HumDef", "Reforest", "ArtRef", "PrimFor", - "NatRegFor", "IntroSpp", "NatzedSpp", "PlantFor", "Pfchange", "IntroSppPlant", - "Mangrove")] - - # More data cleaning - data$Forchange <- suppressWarnings(as.numeric(data$Forchange)) # nolint - data$Forchange[which(is.na(data$Forchange))] <- 0 - data$Landarea <- as.numeric(gsub(",", "", data$Landarea)) # nolint - - # Make country as a "char" - data$Country <- as.character(data$Country) # nolint - - ## Now we want to make sure that the mapping we have can be applied to the dataset in hand - mapping <- toolGetMapping(name = "CountryToCellMapping.rds", where = "mrcommons") - - # See how many countries are available in the mapping file - mapIso <- sort(unique(mapping$iso)) - - # find out which countries are missing from our dataset of FRA 2015 (as compared to mapping countries) - absent <- setdiff(mapIso, unique(data$Country)) # Missing countries to add - yeardiff <- subset(unique(data$Year), !is.na(unique(data$Year))) # Missing years to add - - # Add empty rows to data frame which contains missing countries from our dataset - # create a one-row matrix the same length as data - temprow <- matrix(c(rep.int(NA, length(data))), - nrow = length(yeardiff), - ncol = length(data)) - - # make it a data.frame and give cols the same names as data - newrow <- data.frame(temprow) - colnames(newrow) <- colnames(data) - rm(temprow) - - # Add these new dataset for each new country to original dataset - for (i in seq_along(absent)) { - for (j in seq_along(yeardiff)) { - newrow[j, "Year"] <- yeardiff[j] - } - newrow[, "Country"] <- absent[i] - data <- rbind(data, newrow) - } - - # Subset to countries which are in our updated dataset as compared to all - # countries for which ISO code is available in the mapping file - for (i in seq_along(mapIso)) { - for (j in seq_len(nrow(data))) { - if (data[j, "Country"] == mapIso[i]) { - data[j, "logic"] <- 1 - } - } - } - data <- subset(data, data$logic == 1) - data <- data[, -ncol(data)] - - # Now we deal with NAs - for (i in which(vapply(data, is.numeric, logical(1)))) { - for (j in which(is.na(data[, i]))) { - data[j, i] <- mean(data[data[, "Country"] == data[j, "Country"], i], na.rm = TRUE) - } - } - - data[is.nan(data)] <- 0 - data <- as.magpie(data) - return(data) - } else if (subtype == "production") { - # read the data - data <- read.csv(unz("BULK.zip", csvtoread), header = TRUE, sep = ",", dec = ".", stringsAsFactors = FALSE) - - # Subset the data by removig the year "9999". No eoutplanation in FRA 2015 reoprt. - data <- subset(data, data$Year != 9999) - - # Keep only the variables we need - data <- data[, c("Country", "Year", "ForGrow", "ConifGrow", "BroaGrow", "OthWooGrow", "OthConifGrow", - "OthBroaGrow", "NetAnnIncr", "IncrConif", "IncrBroa", "ForAbovCarb", "OthAbovCarb", - "ForBelCarb", "OthBelCarb", "ForSubLiv", "OthSubLiv", "ForSoilCarb", "OthSoilCarb", - "ProdFor", "MulUseFor")] - - # Make country as a "char" - data$Country <- as.character(data$Country) # nolint - - ## Now we want to make sure that the mapping we have can be applied to the dataset in hand - mapping <- toolGetMapping(name = "CountryToCellMapping.rds", where = "mrcommons") - - # See how many countries are available in the mapping file - mapIso <- sort(unique(mapping$iso)) - - # find out which countries are missing from our dataset of FRA 2015 (as compared to mapping countries) - absent <- setdiff(mapIso, unique(data$Country)) # Missing countries to add - yeardiff <- subset(unique(data$Year), !is.na(unique(data$Year))) # Missing years to add - - # Add empty rows to data frame which contains missing countries from our dataset - # create a one-row matrix the same length as data - temprow <- matrix(c(rep.int(NA, length(data))), - nrow = length(yeardiff), - ncol = length(data)) - - # make it a data.frame and give cols the same names as data - newrow <- data.frame(temprow) - colnames(newrow) <- colnames(data) - rm(temprow) - - # Add these new dataset for each new country to original dataset - for (i in seq_along(absent)) { - for (j in seq_along(yeardiff)) { - newrow[j, "Year"] <- yeardiff[j] - } - newrow[, "Country"] <- absent[i] - data <- rbind(data, newrow) - } - - # Subset to countries which are in our updated dataset as compared to all - # countries for which ISO code is available in the mapping file - for (i in seq_along(mapIso)) { - for (j in seq_len(nrow(data))) { - if (data[j, "Country"] == mapIso[i]) { - data[j, "logic"] <- 1 - } - } - } - data <- subset(data, data$logic == 1) - data <- data[, -ncol(data)] - - # Now we deal with NAs - for (i in which(vapply(data, is.numeric, logical(1)))) { - for (j in which(is.na(data[, i]))) { - data[j, i] <- mean(data[data[, "Country"] == data[j, "Country"], i], na.rm = TRUE) - } - } - - data[is.nan(data)] <- 0 - data <- as.magpie(data) - return(data) - } else if (subtype == "biodiversity") { - # read the data - data <- read.csv(unz("BULK.zip", csvtoread), header = TRUE, sep = ",", dec = ".", stringsAsFactors = FALSE) - - # Subset the data by removig the year "9999". No eoutplanation in FRA 2015 reoprt. - data <- subset(data, data$Year != 9999) - - # Keep only the variables we need - data <- data[, c("Country", "Year", "BioCons", "ProtArea")] - - # Make country as a "char" - data$Country <- as.character(data$Country) # nolint - - ## Now we want to make sure that the mapping we have can be applied to the dataset in hand - mapping <- toolGetMapping(name = "CountryToCellMapping.rds", where = "mrcommons") - - # See how many countries are available in the mapping file - mapIso <- sort(unique(mapping$iso)) - - # find out which countries are missing from our dataset of FRA 2015 (as compared to mapping countries) - absent <- setdiff(mapIso, unique(data$Country)) # Missing countries to add - yeardiff <- subset(unique(data$Year), !is.na(unique(data$Year))) # Missing years to add - - # Add empty rows to data frame which contains missing countries from our dataset - # create a one-row matrix the same length as data - temprow <- matrix(c(rep.int(NA, length(data))), - nrow = length(yeardiff), - ncol = length(data)) - - # make it a data.frame and give cols the same names as data - newrow <- data.frame(temprow) - colnames(newrow) <- colnames(data) - rm(temprow) - - # Add these new dataset for each new country to original dataset - for (i in seq_along(absent)) { - for (j in seq_along(yeardiff)) { - newrow[j, "Year"] <- yeardiff[j] - } - newrow[, "Country"] <- absent[i] - data <- rbind(data, newrow) - } - - # Subset to countries which are in our updated dataset as compared to all - # countries for which ISO code is available in the mapping file - for (i in seq_along(mapIso)) { - for (j in seq_len(nrow(data))) { - if (data[j, "Country"] == mapIso[i]) { - data[j, "logic"] <- 1 - } - } - } - data <- subset(data, data$logic == 1) - data <- data[, -ncol(data)] - - # Now we deal with NAs - for (i in which(vapply(data, is.numeric, logical(1)))) { - for (j in which(is.na(data[, i]))) { - data[j, i] <- mean(data[data[, "Country"] == data[j, "Country"], i], na.rm = TRUE) - } - } - - data[is.nan(data)] <- 0 - data <- as.magpie(data) - return(data) - } else if (subtype == "anndat") { - # read the data - # Notice that the separator in this particular csv file is ; (not ,) and decimal is , (not .) - data <- read.csv(unz("BULK.zip", csvtoread), header = TRUE, sep = ";", - dec = ",", stringsAsFactors = FALSE) - - # Subset the data by removig the year "9999". No eoutplanation in FRA 2015 reoprt. - data <- subset(data, data$Year != 9999) - - # Keep only the variables we need - data <- data[, c("Country", "Year", "WooRemov", "WooFuel")] - - # Make country as a "char" - data$Country <- as.character(data$Country) # nolint - - ## Now we want to make sure that the mapping we have can be applied to the dataset in hand - mapping <- toolGetMapping(name = "CountryToCellMapping.rds", where = "mrcommons") - - # See how many countries are available in the mapping file - mapIso <- sort(unique(mapping$iso)) - - # find out which countries are missing from our dataset of FRA 2015 (as compared to mapping countries) - absent <- setdiff(mapIso, unique(data$Country)) # Missing countries to add - yeardiff <- subset(unique(data$Year), !is.na(unique(data$Year))) # Missing years to add - - # Add empty rows to data frame which contains missing countries from our dataset - # create a one-row matrix the same length as data - temprow <- matrix(c(rep.int(NA, length(data))), - nrow = length(yeardiff), - ncol = length(data)) - - # make it a data.frame and give cols the same names as data - newrow <- data.frame(temprow) - colnames(newrow) <- colnames(data) - rm(temprow) - - # Add these new dataset for each new country to original dataset - for (i in seq_along(absent)) { - for (j in seq_along(yeardiff)) { - newrow[j, "Year"] <- yeardiff[j] - } - newrow[, "Country"] <- absent[i] - data <- rbind(data, newrow) - } - - # Subset to countries which are in our updated dataset as compared to all - # countries for which ISO code is available in the mapping file - for (i in seq_along(mapIso)) { - for (j in seq_len(nrow(data))) { - if (data[j, "Country"] == mapIso[i]) { - data[j, "logic"] <- 1 - } - } - } - data <- subset(data, data$logic == 1) - data <- data[, -ncol(data)] - - # Now we deal with NAs - for (i in which(vapply(data, is.numeric, logical(1)))) { - for (j in which(is.na(data[, i]))) { - data[j, i] <- mean(data[data[, "Country"] == data[j, "Country"], i], na.rm = TRUE) - } - } - - data[is.nan(data)] <- 0 - data <- as.magpie(data) - data <- add_columns(data, addnm = "WooRW", dim = 3.1) - data[, , "WooRW"] <- data[, , "WooRemov"] - data[, , "WooFuel"] - return(data) - } else { - stop("Invalid subtype ", subtype) - } -} diff --git a/R/readFAO_WHO_UNU1985.R b/R/readFAO_WHO_UNU1985.R deleted file mode 100644 index ede0d408..00000000 --- a/R/readFAO_WHO_UNU1985.R +++ /dev/null @@ -1,19 +0,0 @@ -#' Read parameters of Schofield equations -#' -#' Food and Agriculture Organization of the United Nations, World Health Organization, and United Nations University. -#' 1985. "Energy and protein requirements." http://www.who.int/iris/handle/10665/39527. -#' -#' @return MAgPIE object -#' @author Benjamin Bodirsky -#' @seealso [readSource()] -#' @examples -#' \dontrun{ -#' a <- readSource("Schofield") -#' } -#' -readFAO_WHO_UNU1985 <- function() { # nolint: object_name_linter. - file <- "energy_requirements.csv" - file2 <- read.csv(file, sep = ",", dec = ".", header = 2) - file2 <- as.magpie(file2, spatial = 0, temporal = 0, datacol = 3) - return(file2) -} diff --git a/R/readFAO_online.R b/R/readFAO_online.R deleted file mode 100644 index 3306897a..00000000 --- a/R/readFAO_online.R +++ /dev/null @@ -1,319 +0,0 @@ -#' Read FAO_online -#' -#' Read in FAO data that has been downloaded from the FAOSTAT website. -#' Files with exception of fodder.csv are aquired according to downloadFAO. -#' -#' Update 23-Jan-2017 - Added FAO Forestry production and trade data (Abhi) -#' -#' -#' @param subtype Type of FAO data that should be read. Available types are: -#' \itemize{ -#' \item `CBCrop`: Commodity Balance Crop (CommodityBalances_Crops_E_All_Data.zip) -#' \item `CBLive`: Commoditiy Balance Livestock (CommodityBalances_LivestockFish_E_All_Data.zip) -#' \item `Crop`: Production Crops ("Production_Crops_E_All_Data.zip") -#' \item `CropProc`: Production Crops Processed ("Production_CropsProcessed_E_All_Data.zip") -#' \item `Fbs`: Food Balance Sheet ("FoodBalanceSheets_E_All_Data.zip") -#' \item `Fertilizer`: Fertilizer ("Resources_Fertilizers_E_All_Data.zip") -#' \item `FertilizerProducts`: Fertilizer by product ("Inputs_FertilizersProduct_E_All_Data_(Normalized).zip") -#' \item `FertilizerNutrients`: Fertilizer by nutrient ("Inputs_FertilizersNutrient_E_All_Data_(Normalized).zip") -#' \item `Fodder`: Fodder (data that has been manually downloaded from the FAOSTAT website as -#' seperate .xls files via a search for "forage" and "fodder" withing -#' Production-Crops. These datasets have been added together to a "Fodder.csv" file) -#' \item `FoodSecurity`: Food Security Data ("Food_Security_Data_E_All_Data.zip") -#' \item `FSCrop`: Food Supply Crops ("FoodSupply_Crops_E_All_Data.zip") -#' \item `FSLive`: Food Supply Livestock ("FoodSupply_LivestockFish_E_All_Data.zip") -#' \item `Land`: Land ("Resources_Land_E_All_Data.zip") -#' \item `LiveHead`: Production Live Animals ("Production_Livestock_E_All_Data.zip") -#' \item `LivePrim`: Production Livestock Primary ("Production_LivestockPrimary_E_All_Data.zip") -#' \item `LiveProc`: Production Livestock Processed ("Production_LivestockProcessed_E_All_Data.zip") -#' \item `Pop`: Population ("Population_E_All_Data.zip") -#' \item `ForestProdTrade`: Forestry Production and Trade ("Forestry_E_All_Data_(Normalized).zip") -#' \item `PricesProducerAnnual`: Producer Prices - Annual ("Prices_E_All_Data.zip") -#' \item `PricesProducerAnnualLCU`: Producer Prices - Annual in LCU ("Prices_E_All_Data.zip") -#' \item `ValueOfProd`: Value of Agricultural Production ("Value_of_Production_E_All_Data.zip") -#' \item `ValueShares`: Value shares by industry and primary factors -#' \item `Trade`: Trade quantities and values -#' } -#' @return FAO data as MAgPIE object -#' @author Ulrich Kreidenweis, Abhijeet Mishra, Mishko Stevanovic, David Klein, Edna Molina Bacca -#' @seealso [readSource()] -#' @examples -#' \dontrun{ -#' a <- readSource("FAO_online", "Crop") -#' } -#' @importFrom data.table fread -#' @importFrom dplyr summarise filter group_by ungroup %>% -#' @importFrom tidyr pivot_longer starts_with -#' @importFrom tools file_ext file_path_sans_ext -#' @importFrom utils unzip -#' @importFrom withr local_tempdir -readFAO_online <- function(subtype) { # nolint - - # ---- Define subtypes and corresponding files ---- - - files <- list( - CapitalStock = c("Investment_CapitalStock_E_All_Data_(Normalized).zip"), - CBCrop = c("CommodityBalances_Crops_E_All_Data.zip"), - CBLive = c("CommodityBalances_LivestockFish_E_All_Data.zip"), - Crop = c("Production_Crops_E_All_Data.zip"), - CropProc = c("Production_CropsProcessed_E_All_Data.zip"), - EmisAgBurnCropResid = c("Emissions_Agriculture_Burning_crop_residues_E_All_Data.zip"), - EmisAgBurnSavanna = c("Emissions_Agriculture_Burning_Savanna_E_All_Data.zip"), - EmisAgCropResid = c("Emissions_Agriculture_Crop_Residues_E_All_Data.zip"), - EmisAgCultOrgSoil = c("Emissions_Agriculture_Cultivated_Organic_Soils_E_All_Data.zip"), - EmisAgEnergy = c("Emissions_Agriculture_Energy_E_All_Data.zip"), - EmisAgEntericFerment = c("Emissions_Agriculture_Enteric_Fermentation_E_All_Data.zip"), - EmisAgManureManag = c("Emissions_Agriculture_Manure_Management_E_All_Data.zip"), - EmisAgManurePasture = c("Emissions_Agriculture_Manure_left_on_pasture_E_All_Data.zip"), - EmisAgManureSoil = c("Emissions_Agriculture_Manure_applied_to_soils_E_All_Data.zip"), - EmisAgRiceCult = c("Emissions_Agriculture_Rice_Cultivation_E_All_Data.zip"), - EmisAgSynthFerti = c("Emissions_Agriculture_Synthetic_Fertilizers_E_All_Data.zip"), - EmisAgTotal = c("Emissions_Agriculture_Agriculture_total_E_All_Data.zip"), - EmisLuBurnBiomass = c("Emissions_Land_Use_Burning_Biomass_E_All_Data.zip"), - EmisLuCrop = c("Emissions_Land_Use_Cropland_E_All_Data.zip"), - EmisLuForest = c("Emissions_Land_Use_Forest_Land_E_All_Data.zip"), - EmisLuGrass = c("Emissions_Land_Use_Grassland_E_All_Data.zip"), - EmisLuTotal = c("Emissions_Land_Use_Land_Use_Total_E_All_Data.zip"), - FSCrop = c("FoodSupply_Crops_E_All_Data.zip"), - FSLive = c("FoodSupply_LivestockFish_E_All_Data.zip"), - FbsHistoric = c("FoodBalanceSheetsHistoric_E_All_Data.zip"), - Fbs = c("FoodBalanceSheets_E_All_Data_(Normalized).zip"), # old and new FBS - # should not be used, use CB and FS or calcFAOharmonized() instead - Fertilizer = c("Environment_Fertilizers_E_All_Data.zip"), - FertilizerNutrients = c("Inputs_FertilizersNutrient_E_All_Data_(Normalized).zip"), - FertilizerProducts = c("Inputs_FertilizersProduct_E_All_Data_(Normalized).zip"), - Fodder = c("Fodder.csv"), - FoodSecurity = c("Food_Security_Data_E_All_Data.zip"), - ForestProdTrade = c("Forestry_E_All_Data_(Normalized).zip"), - # old source file: Resources_Land_E_All_Data.zip - Land = c("Resources_Land_E_All_Data.zip", "Inputs_LandUse_E_All_Data_(Normalized).zip"), - LiveHead = c("Production_Livestock_E_All_Data.zip"), - LivePrim = c("Production_LivestockPrimary_E_All_Data.zip"), - LiveProc = c("Production_LivestockProcessed_E_All_Data.zip"), - Pop = c("Population_E_All_Data.zip"), - PricesProducerAnnual = c("Prices_E_All_Data.zip"), - PricesProducerAnnualLCU = c("Prices_E_All_Data.zip"), - Trade = c("Trade_CropsLivestock_E_All_Data_(Normalized).zip"), - ValueOfProd = c("Value_of_Production_E_All_Data.zip"), - ValueShares = c("Value_shares_industry_primary_factors_E_All_Data_(Normalized).zip") - ) - - - file <- toolSubtypeSelect(subtype, files) - - # ---- Read the first file you find, prefer normalized format ---- - - tryFiles <- NULL - - # Add an entry with "Normalized" in front of the current entry in the file list - # if the current entry does not contain "Normalized". - for (fi in file) { - extension <- file_ext(basename(fi)) - if (grepl("Normalized", fi)) { - tryFiles <- c(tryFiles, fi) - } else { - - tryFiles <- c(paste0(file_path_sans_ext(fi), "_(Normalized).", extension), fi) - } - } - - # look for data in normalized (i.e. long) format first before looking for the wide format - # decompress if it is zipped - for (file in tryFiles) { - extension <- file_ext(basename(file)) - csvName <- paste0(file_path_sans_ext(file), ".csv") - if (file.exists(csvName)) { - file <- csvName - break - } else if (extension == "zip" && file.exists(file)) { - tempfolder <- local_tempdir() - unzip(file, exdir = tempfolder) - file <- file.path(tempfolder, csvName) - break - } - } - - # ---- Select columns to be read from file and read file ---- - - ## efficient reading of csv file: read only needed columns in the needed type (codes as factor) - csvcolnames <- colnames(read.table(file, header = TRUE, nrows = 1, sep = ",")) - - # check if data is in long or wide format - long <- ifelse("Year" %in% csvcolnames, TRUE, FALSE) - - # define vector with types corresponding to the columns in the file - readcolClass <- rep("NULL", length(csvcolnames)) - factorCols <- c("Area.Code", "Country.Code", "CountryCode", "Food.Value.Code", - "Industry.Code", "Factor.Code", "Item.Code", "ItemCode", - "Element.Code", "ElementCode") - readcolClass[csvcolnames %in% factorCols] <- "factor" - readcolClass[csvcolnames %in% c("Area", "Country", "Factor", "Food.Value", - "Industry", "Element", "Item", "Unit", "Months")] <- "character" - readcolClass[csvcolnames %in% c("Value", "Year")] <- NA - if (!long) readcolClass[grepl("Y[0-9]{4}$", csvcolnames)] <- NA - - fao <- fread(input = file, header = FALSE, skip = 1, sep = ",", colClasses = readcolClass, - col.names = csvcolnames[is.na(readcolClass) | readcolClass != "NULL"], quote = "\"", - encoding = "Latin-1", showProgress = FALSE) - fao <- as.data.frame(fao) - # from wide to long (move years from individual columns into one column) - if (!long) { - fao <- pivot_longer(fao, cols = starts_with("Y"), names_to = "Year", names_pattern = "Y(.*)", - names_transform = list("Year" = as.integer), values_to = "Value") - } - # subtype 'PricesProducerAnnual' contains annual and seasonal data. Select annual data only - # and delete 'Months' column afterwards - if ("Months" %in% names(fao)) { - fao <- fao[fao$Months == "Annual value", ] - fao <- fao[, !names(fao) %in% "Months"] - } - - names(fao)[names(fao) == "Area.Code"] <- "CountryCode" - names(fao)[names(fao) == "Area"] <- "Country" - names(fao) <- gsub("\\.", "", names(fao)) - - # ---- Assigning the ISO codes to countries ---- - - # Load FAO specific countries (not included in country2iso.csv in madrat) - faoIsoFaoCode <- toolGetMapping("FAOiso_faocode_online.csv", where = "mrcommons") - # convert data frame into named vector as required by toolCountry2isocode - faoIsoFaoCode <- structure(as.character(faoIsoFaoCode$ISO), names = as.character(faoIsoFaoCode$Country)) - # look up ISO codes using central definition and extra FAO mapping from line above - # ignore warnings from FAO aggregate and other irrelevant regions - ignoreRegions <- c("Africa", "Americas", "Asia", "Australia & New Zealand", "Caribbean", - "Central America", "Central Asia", "Eastern Africa", "Eastern Asia", - "Eastern Europe", "Europe", "European Union", "Land Locked Developing Countries", - "Least Developed Countries", "Low Income Food Deficit Countries", - "Melanesia", "Middle Africa", "Net Food Importing Developing Countries", - "Netherlands Antilles (former)", "Northern Africa", "Northern America", - "Northern Europe", "Oceania", "Small Island Developing States", - "South America", "South-Eastern Asia", "Southern Africa", "Southern Asia", - "Southern Europe", "Western Africa", "Western Asia", "Western Europe", - "World", "Australia and New Zealand", "European Union (27)", "Antarctic Region", - "European Union (28)", "South-eastern Asia", "East/South Asia and Pacific", - "Annex I countries", "Non-Annex I countries", "OECD", " Africa (excluding intra-trade) ", - "Americas (excluding intra-trade)", "Asia (excluding intra-trade)", - "Australia and New Zealand (excluding intra-trade)", - "Caribbean (excluding intra-trade)", "Central America (excluding intra-trade)", - "Central Asia (excluding intra-trade)", "China (excluding intra-trade)", - "Eastern Africa (excluding intra-trade)", "Eastern Asia (excluding intra-trade)", - "Eastern Europe (excluding intra-trade)", "Europe (excluding intra-trade)", - "European Union (12) (excluding intra-trade)", "European Union (15) (excluding intra-trade)", - "European Union (25) (excluding intra-trade)", - "European Union (27) (excluding Croatia) (excluding intra-trade)", - "European Union (27) (excluding intra-trade)", - "European Union (28) (excluding intra-trade)", - "Land Locked Developing Countries (excluding intra-trade)", - "Least Developed Countries (excluding intra-trade)", - "Africa (excluding intra-trade)", "Low Income Food Deficit Countries (excluding intra-trade)", - "Melanesia (excluding intra-trade)", "Micronesia (excluding intra-trade)", - "Middle Africa (excluding intra-trade)", - "Net Food Importing Developing Countries (excluding intra-trade)", - "Northern Africa (excluding intra-trade)", "Northern America (excluding intra-trade)", - "Northern Europe (excluding intra-trade)", "Oceania (excluding intra-trade)", - "Polynesia (excluding intra-trade)", - "Small Island Developing States (excluding intra-trade)", - "South-Eastern Asia (excluding intra-trade)", - "South America (excluding intra-trade)", "Southern Africa (excluding intra-trade)", - "Southern Asia (excluding intra-trade)", - "Southern Europe (excluding intra-trade)", "Western Africa (excluding intra-trade)", - "Western Asia (excluding intra-trade)", - "Western Europe (excluding intra-trade)") - - fao$ISO <- toolCountry2isocode(fao$Country, mapping = faoIsoFaoCode, ignoreCountries = ignoreRegions) # nolint - # remove country aggregates (CountryCode >= 5000, formerly had '(Total)' in their name) - fao <- fao[as.integer(levels(fao$CountryCode)[fao$CountryCode]) < 5000, ] - # remove countries with missing ISO code - fao <- fao[!is.na(fao$ISO), ] - - # ---- Convert units ---- - - # define helper function for unit conversion - .convertUnit <- function(x, oldUnit, newUnit, factor) { - replace <- x$Unit == oldUnit - if (any(replace)) { - x$Value[replace] <- x$Value[replace] * factor - x$Unit[replace] <- newUnit - } - return(x) - } - - ### convert some units - fao <- .convertUnit(x = fao, oldUnit = "1000 tonnes", newUnit = "tonnes", factor = 1000) - fao <- .convertUnit(x = fao, oldUnit = "1000 Head", newUnit = "Head", factor = 1000) - fao <- .convertUnit(x = fao, oldUnit = "1000 number", newUnit = "number", factor = 1000) - fao <- .convertUnit(x = fao, oldUnit = "1000", newUnit = "number", factor = 1000) - fao <- .convertUnit(x = fao, oldUnit = "1000 Ha", newUnit = "ha", factor = 1000) - fao <- .convertUnit(x = fao, oldUnit = "1000 persons", newUnit = "persons", factor = 1000) - - - # ---- Reformat elements ---- - - elementShort <- toolGetMapping("FAOelementShort.csv", where = "mrcommons") - # keep relevant rows only - elementShort <- elementShort[elementShort$ElementCode %in% fao$ElementCode, ] - - # make ElementShort a combination of Element and Unit, replace special characters, and replace multiple _ by one - tmpElement <- gsub("[\\.,;?\\+& \\/\\-]", "_", fao$Element, perl = TRUE) - tmpUnit <- gsub("[\\.,;\\+& \\-]", "_", fao$Unit, perl = TRUE) - tmpElementShort <- paste0(tmpElement, "_(", tmpUnit, ")") - fao$ElementShort <- gsub("_{1,}", "_", tmpElementShort, perl = TRUE) # nolint - - ### replace ElementShort with the entries from ElementShort if the Unit is the same - if (length(elementShort) > 0) { - for (i in seq_len(nrow(elementShort))) { - j <- (fao$ElementCode == elementShort[i, "ElementCode"] & fao$Unit == elementShort[i, "Unit"]) - fao$ElementShort[j] <- as.character(elementShort[i, "ElementShort"]) - } - } - - if ("Item" %in% colnames(fao)) { - # remove accent in Mate to avoid problems and remove other strange names - fao$Item <- gsub("\u00E9", "e", fao$Item, perl = TRUE) # nolint - fao$Item <- gsub("\n + (Total)", " + (Total)", fao$Item, fixed = TRUE) # nolint - fao$ItemCodeItem <- paste0(fao$ItemCode, "|", gsub("\\.", "", fao$Item, perl = TRUE)) # nolint - } - - # trade data has element codes 5608 5609 for "Import_Quantity_(Head)" - # and codes 5908 5909 for "Export_Quantity_(Head)" for the "Other food" product, - # despite all other characteristics being the same - # this leads to duplicate rows when converting to magclass, sum these up first below - if (subtype == "Trade") { - tmp <- fao %>% - filter(.data$ItemCodeItem == "1848|Other food") %>% - group_by(.data$Year, .data$ISO, .data$ItemCodeItem, .data$ElementShort) %>% - summarise("Value" = sum(.data$Value, na.rm = TRUE)) %>% - ungroup() - fao <- fao[which(fao[, "ItemCodeItem"] != "1848|Other food"), - c("Year", "ISO", "ItemCodeItem", "ElementShort", "Value")] - fao <- rbind(tmp, fao) - } - - # Value Shares has no items, but rather food values, industries, and factor dimensions - if (subtype == "ValueShares") { - fao$FoodValueCodeFoodValue <- paste0(fao$FoodValueCode, "|", gsub("\\.", "", fao$FoodValue, perl = TRUE)) # nolint - fao$IndustryCodeIndustry <- paste0(fao$IndustryCode, "|", gsub("\\.", "", fao$Industry, perl = TRUE)) # nolint - fao$FactorCodeFactor <- paste0(fao$FactorCode, "|", gsub("\\.", "", fao$Factor, perl = TRUE)) # nolint - - fao <- as.magpie(fao[, c("Year", "ISO", "FoodValueCodeFoodValue", "IndustryCodeIndustry", - "FactorCodeFactor", "ElementShort", "Value")], - temporal = 1, spatial = 2, datacol = 7) - - } else { - - fao <- as.magpie(fao[, c("Year", "ISO", "ItemCodeItem", "ElementShort", "Value")], - temporal = 1, spatial = 2, datacol = 5) - - } - if (subtype %in% c("EmisAgBurnCropResid", "EmisAgCropResid", "EmisLuForest")) { - getNames(fao, dim = 1) <- gsub("\\r", "", getNames(fao, dim = 1)) - } - if (subtype %in% c("CapitalStock")) getNames(fao) <- gsub("[\\%]", "percentage", getNames(fao)) - if (subtype %in% c("CapitalStock")) getNames(fao) <- gsub("[\\$]", "D", getNames(fao)) - - gc() - - fao <- magpiesort(fao) - - return(fao) -} diff --git a/R/readFRA2020.R b/R/readFRA2020.R deleted file mode 100644 index 35e93ef2..00000000 --- a/R/readFRA2020.R +++ /dev/null @@ -1,201 +0,0 @@ -#' Read FRA2020 -#' -#' Read-in an FRA (forest resource assessment) dataset from 2020. -#' -#' @param subtype data subtype. Available subtypes: forest_area, deforestation, growing_stock, biomass_stock, -#' carbon_stock, management, disturbance, forest_fire -#' @return Magpie object of the FRA 2020 data -#' @author Abhijeet Mishra -#' @seealso [readSource()] -#' @examples -#' \dontrun{ -#' a <- readSource("FRA2020", "growing_stock") -#' } -#' -#' @importFrom magclass as.magpie getItems -#' @importFrom madrat toolCountry2isocode -#' @export -readFRA2020 <- function(subtype) { # nolint - # Capture source data - fraData <- read.csv("FRA_Years_2020_12_01.csv", header = TRUE, dec = ".", na.strings = c("", " ", "NA", ".")) - colnames(fraData) <- gsub(pattern = "X", replacement = "", x = colnames(fraData), ignore.case = FALSE) - allVariables <- colnames(fraData) - id <- c("iso3", "year") - - subtypeList <- c("forest_area", "deforestation", "growing_stock", "biomass_stock", "carbon_stock", "management", - "disturbance", "forest_fire") - if (!(subtype %in% subtypeList)) { - stop("Invalid or unsupported subtype ", subtype, ". Accepted subtypes are ", - paste(subtypeList, collapse = ", "), ". Choose one of the accepted subtype.") - } - - # Some cleanup in read data may be necessary in case of missing or unreported data - cleanData <- function(x) { - # Read the csv source file, First row is info not needed by us - data <- x - - # If data is not reported in any of the years, set it to 0, - # if partial data is reported, set it to mean values of known data in that country - varCount <- length(getNames(data)) - yrCount <- length(getYears(data)) - missingData <- 0 - partialData <- 0 - for (j in getItems(data, dim = 1.1)) { - for (i in seq_len(varCount)) { - naCount <- as.numeric(apply(data[j, , i], 1, function(x) sum(is.na(x)))) - if (naCount == yrCount) { - missingData <- missingData + 1 - data[j, , i] <- 0 - missingData <- missingData - } else if (naCount > 0 && naCount < yrCount) { - partialData <- partialData + 1 - data[j, , i][is.na(data[j, , i])] <- mean(as.numeric(as.vector(data[j, , i])), na.rm = TRUE) - partialData <- partialData - } - } - } - - if (missingData > 0) { - message(missingData, " missing data points.", " Such data will be set to 0.") - } - if (partialData > 0) { - message(partialData, " partial data points.", " Such data will be set to mean value of reported data.") - } - - # Replace X in colnames with y to make sure as.magpie recognizes this column as temporal dimension later - colnames(data) <- gsub(pattern = "X", replacement = "y", x = colnames(data)) - - # Return the cleaned data frame - return(data) - } - - switch(subtype, - forest_area = { - # Unit is 1000 ha - identifiers <- c("1a_|1b_|1c_|1d_|1e_|1f_") - variables <- grep(pattern = identifiers, x = allVariables, value = TRUE) - data <- fraData[, allVariables %in% c(id, variables)] - colnames(data) <- gsub(pattern = identifiers, replacement = "", x = colnames(data)) - out <- cleanData(as.magpie(data, spatial = "iso3")) - }, - growing_stock = { - # Unit is m3/ha or Mm3 - identifiers <- c("2a_") - variables <- grep(pattern = identifiers, x = allVariables, value = TRUE) - data <- fraData[, allVariables %in% c(id, variables)] - colnames(data) <- gsub(pattern = identifiers, replacement = "", x = colnames(data)) - out <- cleanData(as.magpie(data, spatial = "iso3")) - }, - biomass_stock = { - # Unit is tDM/ha - identifiers <- c("2c_") - variables <- grep(pattern = identifiers, x = allVariables, value = TRUE) - data <- fraData[, allVariables %in% c(id, variables)] - colnames(data) <- gsub(pattern = identifiers, replacement = "", x = colnames(data)) - out <- cleanData(as.magpie(data, spatial = "iso3")) - }, - carbon_stock = { - # Unit is tC/ha - identifiers <- c("2d_") - variables <- grep(pattern = identifiers, x = allVariables, value = TRUE) - - # "2d_soil_depth_cm" not needed hence dropped - data <- fraData[, allVariables %in% c(id, variables[-length(variables)])] - - colnames(data) <- gsub(pattern = identifiers, replacement = "", x = colnames(data)) - out <- cleanData(as.magpie(data, spatial = "iso3")) - }, - management = { - # Unit is 1000 ha - identifiers <- c("3a_tot_") - variables <- grep(pattern = identifiers, x = allVariables, value = TRUE) - data <- fraData[, allVariables %in% c(id, variables)] - colnames(data) <- gsub(pattern = identifiers, replacement = "", x = colnames(data)) - out <- cleanData(as.magpie(data, spatial = "iso3")) - - # Suriname has misreporting? The bad value is empty in 2020 from original file but - # when it is read in R it takes bizarre values - out["SUR", "y2020", ] <- out["SUR", "y2020", ] / 1000 - }) - - # Some data does not belong to the bulk download and has to be downloaded manually - # This data also needs some cleaning - - # Manual function to cleanup the data. Takes in source file as input - processData <- function(x) { - # Read the csv source file, First row is info not needed by us - data <- read.csv(x, header = TRUE, skip = 1) - - # Manually rename first column - colnames(data)[1] <- "Country" - - # Cleanup additional name info - data$Country <- # nolint - gsub(pattern = " \\(French Part\\)| \\(Desk study\\)", replacement = "", x = data$Country) - - # Convert from country names to ISO codes - data$Country <- # nolint - suppressWarnings(toolCountry2isocode(data$Country, - warn = TRUE, - mapping = c("Saint-Martin" = "MAF"), - ignoreCountries = c("2020-12-22", "2021-09-10", - "\u00A9 FRA 2020", "\u00A9 FRA 2021") - )) - - # Cleanup rows with NA in country names - Rows with no matching ISO code will be dropped - data <- data[!is.na(data$Country), ] - - # If data is not reported in any of the years, set it to 0, - # if partial data is reported, set it to mean values of known data in that country - yrCount <- ncol(data) - missingData <- 0 - partialData <- 0 - for (i in seq_len(nrow(data))) { - naCount <- as.numeric(apply(data[i, ], 1, function(x) sum(is.na(x)))) - if (naCount == yrCount - 1) { - missingData <- missingData + 1 - data[i, -1] <- 0 - missingData <- missingData - } else if (naCount > 0 && naCount < yrCount - 1) { - partialData <- partialData + 1 - data[i, is.na(data[i, ])] <- mean(as.numeric(as.vector(data[i, -1])), na.rm = TRUE) - partialData <- partialData - } - } - if (missingData > 0) { - cat(missingData, " missing data points.", " Such data will be set to 0.") - } - if (partialData > 0) { - cat(partialData, " partial data points.", " Such data will be set to mean value of reported data.") - } - - # Replace X in colnames with y to make sure as.magpie recognizes this column as temporal dimension later - colnames(data) <- gsub(pattern = "X", replacement = "y", x = colnames(data)) - - # Return the cleaned data frame - return(data) - } - - switch(subtype, - disturbance = { - # Unit is 1000 ha - out <- as.magpie(processData("fra2020-disturbances.csv")) - }, - forest_fire = { - # Unit is 1000 ha - out <- as.magpie(processData("fra2020-areaAffectedByFire.csv")) - }, - deforestation = { - # Capture source data for deforestation -- Not available in the bulk download - # Unit is 1000 ha/yr - tempDat <- processData("fra2020-forestAreaChange.csv") - colnames(tempDat) <- c("Country", "y1990", "y2000", "y2010", "2015") - out <- as.magpie(tempDat) - out2 <- out[, "y2010", , invert = TRUE] - getYears(out2) <- c("y1995", "y2005", "y2020") - out <- mbind(out, out2) - out <- out[, sort(getYears(out)), ] - }) - out <- out[grep(pattern = "X0|X1|X2", x = getItems(out, dim = 1.1), value = TRUE, invert = TRUE), , ] - return(out) -} diff --git a/R/readFishstatJ_FAO.R b/R/readFishstatJ_FAO.R deleted file mode 100644 index 00143f9d..00000000 --- a/R/readFishstatJ_FAO.R +++ /dev/null @@ -1,84 +0,0 @@ -#' @title readFishstatJ_FAO -#' @description Reads data of fisheries generated using the FishstatJ app of FAO. -#' Read-in specifically, exports_value, exports_quantity, and/or overall production of fish/aquatic products. -#' -#' -#' @param subtype data subtype needed. Either "exportsValue", "exportsQuantity", or "Production" -#' @return magpie object of either tonnes of liveweight or 1000 current USD -#' @author Edna J. Molina Bacca -#' @importFrom stats reshape -#' @seealso [readSource()] -#' @examples -#' \dontrun{ -#' a <- readSource("FishstatJ_FAO", "Production") -#' a <- readSource("FishstatJ_FAO", "exportsQuantity") -#' a <- readSource("FishstatJ_FAO", "exportsValue") -#' } -#' -readFishstatJ_FAO <- function(subtype = "Production") { # nolint: object_name_linter. - # Files generated using the FishstatJ app - files <- c(exportsValue = "FAOSTAT_data_1-26-2021_FishesTradeUSD.csv", - exportsQuantity = "FAOSTAT_data_1-26-2021_FishesTradeTonns.csv", - Production = "FAOSTAT_data_1-26-2021_FishesProduction.csv") - - # Subsetting based on type of requested output - file <- toolSubtypeSelect(subtype, files) - isocodeFAO <- toolGetMapping("FAOiso_faocode.csv", where = "mrcommons") - - # Reads data - data <- read.csv(file = paste(path.package("mrcommons"), paste0("extdata/sectoral/", file), sep = "/")) - - # Function to clean-up the data - faoCleaning <- function(data = data, mapping = isocodeFAO, - subsetvar = "Unit..Name.", unitVar = "Tonnes - live weight", value = "Production") { - - yearsStats <- paste0("X.", 1984:2018, ".") # wide format - # select needed columns - if (value == "Production") { - data <- data[, c("Country..Name.", "Unit..Name.", yearsStats)] - } else if (value %in% c("exportsValue", "exportsQuantity")) { - data <- data[, c("Country..Name.", "Trade.flow..Name.", "Unit..Name.", yearsStats)] - } - yearsStats <- as.character(1984:2018) - if (value == "Production") { - colnames(data) <- c("Country", "Variable", yearsStats) - } else if (value %in% c("exportsValue", "exportsQuantity")) { - colnames(data) <- c("Country", "Variable", "Unit", yearsStats) - } - data <- data[data$Variable == unitVar, ] # read only "Tonnes - live weight","Export" - # from wide to long format - data <- reshape(data, varying = yearsStats, direction = "long", - idvar = c("Country", "Variable"), v.names = "Value", timevar = "Year", times = yearsStats) - rownames(data) <- seq_len(nrow(data)) # fix names of rows - data <- merge(data, mapping, by = "Country") - data <- data[, c("ISO3", "Year", "Value")] - data[, "Year"] <- as.numeric(data[, "Year"]) - # converts to magpie object tonnes - live weight, current 1000 USD - x <- magpiesort(as.magpie(data, temporal = 2, spatial = 1, datacol = 3)) - - # remove historical countries; a more adequate solution is work in progress - x <- x[c("ANT", "CSK", "SCG", "XET", "XSD"), , , invert = TRUE] - - x <- toolCountryFill(x = x, fill = 0) # fill with zeros - getNames(x) <- value - - return(x) - } - - - # Cleaning based on output subtype selected - if (subtype == "Production") { - x <- faoCleaning(data = data, mapping = isocodeFAO, subsetvar = "Unit..Name.", - unitVar = "Tonnes - live weight", value = "Production") - - } else if (subtype == "exportsQuantity") { - x <- faoCleaning(data = data, mapping = isocodeFAO, subsetvar = "Trade.flow..Name.", - unitVar = "Export", value = "exportsQuantity") - - } else if (subtype == "exportsValue") { - x <- faoCleaning(data = data, mapping = isocodeFAO, subsetvar = "Trade.flow..Name.", - unitVar = "Export", value = "exportsValue") - } - - return(x) -} diff --git a/R/readGAEZv4.R b/R/readGAEZv4.R deleted file mode 100644 index 89584668..00000000 --- a/R/readGAEZv4.R +++ /dev/null @@ -1,59 +0,0 @@ -#' @title readGAEZv4 -#' @description Read in data from the Global Agro-ecological Zones (GAEZ) data set version 4 -#' @param subtype Subtype to be read -#' @return MAgPIE object at 0.5 cellular level -#' @author Felicitas Beier -#' -#' @examples -#' \dontrun{ -#' readSource("GAEZv4", convert = "onlycorrect") -#' } -#' -#' @importFrom magclass as.magpie mbind getNames -#' @importFrom raster brick raster projectRaster - -readGAEZv4 <- function(subtype = "MCzones") { - - # Transform from 0.08 to 0.5 spatial resolution and convert to magpie object - .transformObject <- function(x) { - x <- brick(projectRaster(from = x, to = raster(res = 0.5), method = "ngb", over = TRUE)) - x <- as.magpie(x) - return(x) - } - - if (subtype == "MCzones") { - ### Multiple cropping zones data - ## Legend - # 0: 0 - # 1: no cropping - # 2: single cropping - # 3: limited double cropping - # 4: double cropping - # 5: double cropping with rice - # 6: double rice cropping - # 7: triple cropping - # 8: triple rice cropping - - ### Rainfed - mcr <- brick(paste(subtype, "mcr_CRUTS32_Hist_0010.tif", sep = "/")) - mcr <- .transformObject(x = mcr) - getNames(mcr) <- "rainfed" - - ### Irrigated - mci <- brick(paste(subtype, "mci_CRUTS32_Hist_0010.tif", sep = "/")) - mci <- .transformObject(x = mci) - getNames(mci) <- "irrigated" - - x <- mbind(mci, mcr) - - } else { - stop("This GAEZ subtype is not available yet. - Please select available subtype, e.g. MCzones for multiple cropping zones") - } - - if (any(is.na(x))) { - stop("produced NA multiple cropping zones") - } - - return(x) -} diff --git a/R/readIEA_EEI.R b/R/readIEA_EEI.R new file mode 100644 index 00000000..c823b447 --- /dev/null +++ b/R/readIEA_EEI.R @@ -0,0 +1,27 @@ +#' Read-in data from IEA End Uses and Efficiency Indicators Database +#' +#' @author Falk Benke +#' @importFrom data.table fread +#' @importFrom dplyr %>% filter mutate distinct +#' @importFrom magclass as.magpie +readIEA_EEI <- function() { #nolint object_name_linter + + data <- NULL + + for (domain in c("INDUSTRY", "TRANSPORT", "RESIDENTIAL", "SERVICES")) { + data <- rbind(data, fread( + file = file.path("2023", paste0("IEA - EEI ", domain, ".TXT")), + col.names = c("ITEM", "ENDUSE", "TIME", "COUNTRY", "VALUE"), + colClasses = c("character", "character", "numeric", "character", "character"), + sep = " ", stringsAsFactors = FALSE, na.strings = c("x", "..", "c"), skip = 0, showProgress = FALSE + )) + } + + data <- filter(data, !is.na(.data$VALUE)) %>% + mutate("VALUE" = as.numeric(.data$VALUE)) %>% + distinct() + + x <- as.magpie(data, spatial = "COUNTRY", temporal = "TIME") + + return(x) +} diff --git a/R/readLPJmL.R b/R/readLPJmL.R deleted file mode 100644 index dd5cc881..00000000 --- a/R/readLPJmL.R +++ /dev/null @@ -1,376 +0,0 @@ -#' @title readLPJmL -#' @description Read LPJmL content -#' @param subtype Switch between different input -#' @return List of magpie objects with results on cellular level, weight, unit and description. -#' @author Kristine Karstens, Abhijeet Mishra, Felicitas Beier -#' @seealso -#' [readLPJ()] -#' @examples -#' \dontrun{ -#' readSource("LPJmL", subtype = "LPJmL5:CRU4p02.soilc", convert = "onlycorrect") -#' } -#' -#' @importFrom lpjclass readLPJ - -readLPJmL <- function(subtype = "LPJmL5:CRU4p02.soilc") { # nolint: cyclocomp_linter. - - if (grepl("\\.", subtype)) { - - subtype <- strsplit(gsub(":", "/", subtype), split = "\\.") - folder <- unlist(subtype)[1] - subtype <- unlist(subtype)[2] - - } else { - stop("readLPJmL needs version and climatetype information") - } - - files <- c(soilc = "soilc_natveg.bin", - soilc_layer = "soilc_layer_natveg.bin", - litc = "litc_natveg.bin", - vegc = "vegc_natveg.bin", - vegc_lpjcell = "vegc_natveg.bin", - alitfallc = "alitfallc_natveg.bin", - alitterfallc = "alitterfallc_natveg.bin", - alitfalln = "alitfalln_natveg.bin", - harvest = "pft_harvest.pft.bin", - irrig = "cft_airrig.pft.bin", - irrig_lpjcell = "cft_airrig.pft.bin", - cwater_b = "cft_consump_water_b.pft.bin", - cwater_b_lpjcell = "cft_consump_water_b.pft.bin", - sdate = "sdate.bin", - hdate = "hdate.bin", - transpiration = "mtransp_natveg.bin", - discharge = "mdischarge_natveg.bin", - discharge_lpjcell = "mdischarge_natveg.bin", - runoff = "mrunoff_natveg.bin", - runoff_lpjcell = "mrunoff_natveg.bin", - evaporation = "mevap_natveg.bin", - evap_lake = "mevap_lake.bin", - evap_lake_lpjcell = "mevap_lake.bin", - mevap_lake = "mevap_lake.bin", - mevap_lake_lpjcell = "mevap_lake.bin", - input_lake = "input_lake.bin", - input_lake_lpjcell = "input_lake.bin", - mtranspiration = "mtransp_natveg.bin", - mdischarge = "mdischarge_natveg.bin", - mdischarge_lpjcell = "mdischarge_natveg.bin", - mrunoff = "mrunoff_natveg.bin", - mrunoff_lpjcell = "mrunoff_natveg.bin", - mevaporation = "mevap_natveg.bin", - vegc_grass = "mean_vegc_mangrass.bin", - litc_grass = "litc_mangrass.bin", - soilc_grass = "soilc_mangrass.bin" - ) - - filename <- toolSubtypeSelect(subtype, files) - - if (tmp <- file.exists(file.path(folder, "tmp.out"))) { - - tmp <- readLines(file.path(folder, "tmp.out")) - years <- as.numeric(unlist(regmatches(tmp, gregexpr("\\d{4}", tmp)))) - startYear <- years[1] - years <- seq(years[1], years[2], 1) - - } else { - # default - startYear <- 1901 - years <- seq(startYear, 2017, 1) - } - - unitTrans <- 0.01 # Transformation factor gC/m^2 --> t/ha - - if (grepl("soilc|litc|vegc|alitfallc|alitterfallc|alitfalln|vegc_grass|litc_grass|soilc_grass", - subtype) && subtype != "soilc_layer") { - startYear <- startYear # Start year of data set - years <- years # Vector of years that should be exported - nbands <- 1 # Number of bands in the .bin file - avgRange <- 1 # Number of years used for averaging - - if (grepl("_lpjcell", subtype)) { - x <- readLPJ( - file_name = file.path(folder, filename), - wyears = years, - syear = startYear, - averaging_range = avgRange, - ncells = 67420, - bands = nbands, - soilcells = FALSE) - } else { - x <- readLPJ( - file_name = file.path(folder, filename), - wyears = years, - syear = startYear, - averaging_range = avgRange, - bands = nbands, - soilcells = TRUE) - } - - # Transform to MAgPIE object - if (grepl("_lpjcell", subtype)) { - - class(x) <- "array" - x <- collapseNames(as.magpie(x, spatial = 1)) - mapLPJcell <- toolGetMapping("LPJ_CellBelongingsToCountries.csv", - type = "cell", where = "mrcommons") - getCells(x) <- paste(mapLPJcell$ISO, 1:67420, sep = ".") - names(dimnames(x))[1] <- paste0(names(dimnames(x))[1], ".region") - - } else { - x <- collapseNames(as.magpie(x)) - } - - x <- x * unitTrans - getNames(x) <- subtype - - } else if (grepl("*date*", subtype)) { - - startYear <- startYear # Start year of data set - years <- years # Vector of years that should be exported - nbands <- 24 # Number of bands in the .bin file - avgRange <- 1 # Number of years used for averaging - - x <- readLPJ(file_name = file.path(folder, filename), - wyears = years, - syear = startYear, - averaging_range = avgRange, - bands = nbands, - datatype = integer(), - bytes = 2, - soilcells = TRUE, - ncells = 67420) - - x <- collapseNames(as.magpie(x)) - - } else if (subtype %in% c("soilc_layer")) { - - startYear <- startYear # Start year of data set - years <- years # Vector of years that should be exported - nbands <- 5 # Number of bands in the .bin file - avgRange <- 1 # Number of years used for averaging - - x <- readLPJ( - file_name = file.path(folder, filename), - wyears = years, - syear = startYear, - averaging_range = avgRange, - bands = nbands, - soilcells = TRUE) - - x <- collapseNames(as.magpie(x)) - x <- x * unitTrans - - getNames(x) <- paste0("soilc.", getNames(x)) - getSets(x)[4:5] <- c("data", "layer") - - } else if (grepl("transpiration|discharge|runoff|evaporation|evap_lake", subtype)) { - - startYear <- startYear # Start year of data set - years <- years # Vector of years that should be exported - nbands <- 1 # Number of bands in the .bin file - avgRange <- 1 # Number of years used for averaging - - # monthly values - if (grepl("_lpjcell", subtype)) { - x <- readLPJ( - file_name = file.path(folder, filename), - wyears = years, - syear = startYear, - averaging_range = avgRange, - monthly = TRUE, - ncells = 67420, - soilcells = FALSE) - } else { - x <- readLPJ( - file_name = file.path(folder, filename), - wyears = years, - syear = startYear, - averaging_range = avgRange, - monthly = TRUE, - soilcells = TRUE) - } - - # unit transformation - if (grepl("transpiration", subtype)) { - # Transform units: liter/m^2 -> m^3/ha - unitTransTRANSP <- 10 - x <- x * unitTransTRANSP - - } else if (grepl("discharge", subtype)) { - # In LPJmL: (monthly) discharge given in hm3/d (= mio. m3/day) - # Transform units of discharge: mio. m^3/day -> mio. m^3/month - monthDays <- c(31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31) - names(monthDays) <- dimnames(x)[[3]] - for (month in names(monthDays)) { - x[, , month, ] <- x[, , month, ] * monthDays[month] - } - - } else if (grepl("runoff|evap_lake", subtype)) { - # In LPJmL: (monthly) runoff given in LPJmL: mm/month - if (grepl("_lpjcell", subtype)) { - cb <- toolGetMapping("LPJ_CellBelongingsToCountries.csv", - type = "cell", where = "mrcommons") - cellArea <- (111e3 * 0.5) * (111e3 * 0.5) * cos(cb$lat / 180 * pi) - class(x) <- "array" - x <- as.magpie(x, spatial = 1) - # Transform units: liter/m^2 -> liter - x <- x * cellArea - } else { - # Get cellular coordinate information and calculate cell area - cb <- as.data.frame(magpie_coord) - cellArea <- (111e3 * 0.5) * (111e3 * 0.5) * cos(cb$lat / 180 * pi) - # Transform units: liter/m^2 -> liter - x <- as.magpie(x) * cellArea - } - # Transform units: liter -> mio. m^3 - x <- x / (1000 * 1000000) - - } else if (grepl("evaporation", subtype)) { - # Transform units: liter/m^2 -> m^3/ha - unitTransEVAP <- 10 - x <- x * unitTransEVAP - - } - - # Transform to MAgPIE object - if (grepl("_lpjcell", subtype)) { - - class(x) <- "array" - x <- collapseNames(as.magpie(x, spatial = 1)) - mapLPJcell <- toolGetMapping("LPJ_CellBelongingsToCountries.csv", - type = "cell", where = "mrcommons") - getCells(x) <- paste(mapLPJcell$ISO, 1:67420, sep = ".") - names(dimnames(x))[1] <- paste0(names(dimnames(x))[1], ".region") - - } else { - x <- collapseNames(as.magpie(x)) - } - - if (grepl("layer", subtype)) { - - subtype <- gsub("_", "\\.", subtype) # Expand dimension to layers - getNames(x) <- paste0(subtype, ".", getNames(x)) - getSets(x)[4:6] <- c("data", "layer", "month") - - } else { - getNames(x) <- paste0(subtype, ".", getNames(x)) - getSets(x)[4:5] <- c("data", "month") - } - - # Annual value (total over all month) - if (!grepl("^m", subtype)) { - x <- dimSums(x, dim = "month") - } - - } else if (grepl("*harvest*", subtype)) { - - startYear <- startYear # Start year of data set - years <- years # Vector of years that should be exported - nbands <- 32 # Number of bands in the .bin file - avgRange <- 1 # Number of years used for averaging - - x <- readLPJ( - file_name = file.path(folder, filename), - wyears = years, - syear = startYear, - averaging_range = avgRange, - bands = nbands, - soilcells = TRUE) - - # Transformation factor gC/m^2 --> t/ha - yieldTrans <- 0.01 / 0.45 - x <- collapseNames(as.magpie(x)) - x <- x * yieldTrans - - } else if (grepl("irrig|cwater_b", subtype)) { - - startYear <- startYear # Start year of data set - years <- years # Vector of years that should be exported - nbands <- 32 # Number of bands in the .bin file - avgRange <- 1 # Number of years used for averaging - - if (grepl("_lpjcell", subtype)) { - x <- readLPJ( - file_name = file.path(folder, filename), - wyears = years, - syear = startYear, - averaging_range = avgRange, - bands = nbands, - ncells = 67420, - soilcells = FALSE) - } else { - x <- readLPJ( - file_name = file.path(folder, filename), - wyears = years, - syear = startYear, - averaging_range = avgRange, - bands = nbands, - soilcells = TRUE) - } - - if (grepl("_lpjcell", subtype)) { - - class(x) <- "array" - x <- collapseNames(as.magpie(x, spatial = 1)) - mapLPJcell <- toolGetMapping("LPJ_CellBelongingsToCountries.csv", - type = "cell", where = "mrcommons") - getCells(x) <- paste(mapLPJcell$ISO, 1:67420, sep = ".") - names(dimnames(x))[1] <- paste0(names(dimnames(x))[1], ".region") - - } else { - x <- collapseNames(as.magpie(x)) - } - # Transform units (transform from: mm per year = liter per m^2 transform to: m^3 per ha) - # 1 000 liter = 1 m^3 - # 10 000 m^2 = 1 ha - # 1 liter/m^2 = 10 m^3/ha - # -> mm/yr * 10 = m^3/ha - irrigTransform <- 10 - x[, , "irrigated"] <- x[, , "irrigated"] * irrigTransform # units are now: m^3 per ha per year - - } else if (grepl("input_lake", subtype)) { - - startYear <- startYear # Start year of data set - years <- years # Vector of years that should be exported - nbands <- 1 # Number of bands in the .bin file - avgRange <- 1 # Number of years used for averaging - - if (grepl("_lpjcell", subtype)) { - x <- readLPJ( - file_name = file.path(folder, filename), - wyears = years, - syear = startYear, - averaging_range = avgRange, - bands = nbands, - ncells = 67420, - soilcells = FALSE) - } else { - x <- readLPJ( - file_name = file.path(folder, filename), - wyears = years, - syear = startYear, - averaging_range = avgRange, - bands = nbands, - soilcells = TRUE) - } - - if (grepl("_lpjcell", subtype)) { - - class(x) <- "array" - x <- collapseNames(as.magpie(x, spatial = 1)) - mapLPJcell <- toolGetMapping("LPJ_CellBelongingsToCountries.csv", - type = "cell", where = "mrcommons") - getCells(x) <- paste(mapLPJcell$ISO, 1:67420, sep = ".") - names(dimnames(x))[1] <- paste0(names(dimnames(x))[1], ".region") - - } else { - x <- collapseNames(as.magpie(x)) - } - getNames(x) <- subtype - - } else { - stop(paste0("subtype ", subtype, " is not existing")) - } - - return(x) - -} diff --git a/R/readLPJmLClimateInput.R b/R/readLPJmLClimateInput.R deleted file mode 100644 index 74e3dc7f..00000000 --- a/R/readLPJmLClimateInput.R +++ /dev/null @@ -1,195 +0,0 @@ -#' @title readLPJmLClimateInput -#' @description Read Climate data used as LPJmL inputs into MAgPIE objects -#' @param subtype Switch between different inputs, -#' e.g. "ISIMIP3bv2:MRI-ESM2-0:ssp370:1850-2014:tas" -#' Available variables are: * tas - -#' * wet - -#' * per - -#' @param subset Switch between different subsets of the same subtype -#' Available options are: "annualMean", "annualSum", -#' "monthlyMean", "monthlySum", -#' "wetDaysMonth" -#' Note that not all subtype-subset combinations make sense -#' @return MAgPIE objects with results on cellular level. -#' @author Marcos Alves, Kristine Karstens, Felicitas Beier -#' @seealso -#' \code{\link{readLPJmLClimateInput}} -#' @examples -#' \dontrun{ -#' readSource("LPJmLClimateInput", subtype, convert = "onlycorrect") -#' } -#' -#' @importFrom lpjclass read.LPJ_input -#' @importFrom madrat toolSplitSubtype -#' @importFrom magpiesets findset addLocation -#' @importFrom magclass collapseNames collapseDim as.magpie clean_magpie -#' @export - -readLPJmLClimateInput <- function(subtype = "ISIMIP3bv2:MRI-ESM2-0:ssp370:temperature", # nolint - subset = "annualMean") { - - nCells <- 67420 # number of cells in lpjml - subtype <- toolSplitSubtype(subtype, - list(version = NULL, - climatemodel = NULL, - scenario = NULL, - variable = NULL)) - - subsetTypes <- c("annualMean", "annualSum", "monthlyMean", - "monthlySum", "wetDaysMonth", "\\d{4}:\\d{4}") - subsetTypesMean <- c(grep("Mean", subsetTypes, value = TRUE), "\\d{4}:\\d{4}") - - allowedCombos <- list(temperature = subsetTypesMean, - precipitation = subsetTypes, - longWaveNet = subsetTypesMean, - shortWave = subsetTypesMean, - temperatureMin = subsetTypesMean, - temperatureMax = subsetTypesMean) - isAllowed <- any(vapply(allowedCombos[[subtype$variable]], - grepl, x = subset, - FUN.VALUE = logical(1))) - if (!isAllowed) stop("Subtype-subset combination not allowed") - - .prepareLPJinput <- function(subset = NULL) { - - filename <- Sys.glob(c("*.bin", "*.clm")) - filetype <- tail(unlist(strsplit(filename, "\\.")), 1) - - if (filetype == "clm") { - - filedata <- file(description = filename, open = "rb", - blocking = TRUE, encoding = getOption("encoding")) - seek(filedata, where = 15, origin = "start") - inHeader <- as.numeric(readBin(filedata, what = integer(), size = 4, - n = 5, endian = .Platform$endian)) - startyear <- inHeader[1] - nyear <- inHeader[2] - noAnnualPredictions <- inHeader[5] - years <- seq(startyear, startyear + nyear - 1, 1) - close(filedata) - - } else { - stop("File format of LPJmLClimateInput data unknown. Please provide .clm file format.") - } - - if (subset == "wetDaysMonth") { - - if (subtype$variable != "precipitation") stop("Subset 'wetDaysMonth' is only - available for 'precipitation'") - x <- lpjclass::read.LPJ_input(file_name = filename, - out_years = paste0("y", years), - namesum = TRUE, - ncells = nCells, - rule4binary = ">0") / noAnnualPredictions - - class(x) <- "array" - x <- collapseNames(as.magpie(x, spatial = 1)) - - - } else if (subset == "annualMean") { - - x <- lpjclass::read.LPJ_input(file_name = filename, - out_years = paste0("y", years), - namesum = TRUE, - ncells = nCells) / noAnnualPredictions - - class(x) <- "array" - x <- collapseNames(as.magpie(x, spatial = 1)) - - - } else if (subset == "annualSum") { - - x <- lpjclass::read.LPJ_input(file_name = filename, - out_years = paste0("y", years), - namesum = TRUE, - ncells = nCells) - - class(x) <- "array" - x <- collapseNames(as.magpie(x, spatial = 1)) - - - } else if (subset %in% c("monthlyMean", "monthlySum")) { - # define year sets (cut it in bunches) - bunchLength <- 1 - yearsets <- split(years, ceiling(seq_along(years) / bunchLength)) - - # define month mapping - monthLength <- c(jan = 31, feb = 28, mar = 31, apr = 30, - may = 31, jun = 30, jul = 31, aug = 31, - sep = 30, oct = 31, nov = 30, dec = 31) - daysMonth <- NULL - for (m in 1:12) { - daysMonth <- c(daysMonth, rep(names(monthLength[m]), - monthLength[m])) - } - month2day <- cbind(day = 1:sum(monthLength), - month = daysMonth) - monthLength <- as.magpie(monthLength) - - # create output object - x <- NULL - - # loop over bunches - for (b in seq_along(yearsets)) { - # read in a bunch of years - tmp <- lpjclass::read.LPJ_input(file_name = filename, - out_years = paste0("y", yearsets[[b]]), - namesum = FALSE, - ncells = nCells) - - tmp <- array(tmp, dim = dim(tmp)[1:3], dimnames = dimnames(tmp)[1:3]) - tmp <- as.magpie(tmp, spatial = 1) - getSets(tmp) <- c("fake", "year", "day") - # KRISTINE: Please double-check whether following line makes sense - # (introduced because toolAggregate doesn't work without dimension names) - getNames(tmp) <- as.character(seq(1, 365, 1)) - - # aggregate days to month - tmp <- toolAggregate(tmp, - rel = month2day, - from = "day", - to = "month", - dim = 3) - - if (subset == "monthlyMean") { - tmp <- tmp / monthLength - } - - x <- mbind(x, tmp) - getSets(x) <- c("fake", "year", "month") - } - - } else if (grepl("\\d{4}:\\d{4}", subset)) { - - subYears <- eval(parse(text = subset)) - years <- intersect(years, subYears) - if (any(!(subYears %in% years))) { - warning(paste0("Some subsetted years (subset = ", subset, - ") are not availabl\n in the original data.\n", - "Years set to:", years)) - } - - x <- lpjclass::read.LPJ_input(file_name = filename, - out_years = paste0("y", years), - namesum = FALSE, - ncells = nCells) - - class(x) <- "array" - x <- collapseNames(as.magpie(x, spatial = 1)) - - - } else { - stop("Subset argument unknown. Please check function help.") - } - - return(x) - } - - x <- .prepareLPJinput(subset) - - # Add location based on LPJmL cell ordering where fist cell is FJI, second RUS, etc - x <- collapseDim(addLocation(x), dim = c("N", "region")) - x <- clean_magpie(x) - - return(x) -} diff --git a/R/readLPJmLInputs.R b/R/readLPJmLInputs.R deleted file mode 100644 index 5128a175..00000000 --- a/R/readLPJmLInputs.R +++ /dev/null @@ -1,62 +0,0 @@ -#' @title readLPJmLInputs -#' @description This function reads in LPJmL inputs (inputs to LPJmL) -#' -#' @param subtype Switch between different inputs -#' -#' @return List of magpie objects with results on cellular level, weight, unit and description. -#' -#' @author Felicitas Beier -#' -#' @examples -#' \dontrun{ -#' readSource("LPJmLInputs", subtype = "lakeshare", convert = FALSE) -#' } -#' -#' @importFrom magclass as.magpie collapseNames -#' @importFrom lpjclass readLPJ -#' @importFrom magpiesets addLocation - -readLPJmLInputs <- function(subtype = "lakeshare") { - - files <- c(lakeshare = "glwd_lakes_and_rivers.bin") - file <- toolSubtypeSelect(subtype, files) - - # Data settings - if (subtype %in% c("lakeshare")) { - - unitTrans <- 0.01 - ncells <- 67420 - wyears <- 1 - syear <- 1 - avgRange <- NULL - filetype <- "bin" - bands <- 1 - datatype <- integer() - bytes <- 1 - monthly <- FALSE - } - - # Read in the data - x <- readLPJ(file_name = file, - wyears = wyears, - syear = syear, - averaging_range = avgRange, - ncells = ncells, - file_type = filetype, - bands = bands, - datatype = datatype, - bytes = bytes, - monthly = monthly) - - # Unit transformation - x <- x * unitTrans - - # Transform to magpie object and add dimension details - class(x) <- "array" - x <- collapseNames(as.magpie(x, spatial = 1)) - x <- addLocation(x) - x <- collapseDim(x, dim = "N") - x <- clean_magpie(x) - - return(x) -} diff --git a/R/readLPJmL_new.R b/R/readLPJmL_new.R deleted file mode 100644 index 530bcfce..00000000 --- a/R/readLPJmL_new.R +++ /dev/null @@ -1,123 +0,0 @@ -#' @title readLPJmL_new -#' -#' @description Read in LPJmL outputs -#' -#' @param subtype Switch between different inputs -#' (eg. "LPJmL5.2_Pasture:IPSL_CM6A_LR:ssp126_co2_limN_00:soilc_past_hist") -#' -#' @return List of magpie objects with results on cellular level, weight, unit and description. -#' -#' @author Kristine Karstens, Abhijeet Mishra, Felicitas Beier, Marcos Alves -#' -#' @seealso -#' [readLPJ()] -#' @examples -#' \dontrun{ -#' readSource("LPJmL_new", convert = FALSE) -#' } -#' -#' @importFrom madrat toolSplitSubtype -#' @importFrom magpiesets addLocation -#' @importFrom lpjclass readLPJ -#' @importFrom stringr str_subset str_trim str_split - -readLPJmL_new <- function(subtype = "LPJmL4_for_MAgPIE_44ac93de:GSWP3-W5E5:historical:soilc") { # nolint - - subtype <- toolSplitSubtype(subtype, - list(version = NULL, - climatemodel = NULL, - scenario = NULL, - variable = NULL))$variable - - .prepareLPJ <- function(datatype = numeric(), - bytes = 4, - monthly = FALSE, - nbands = NULL) { # nbands will be overwritten for clm data - - filename <- Sys.glob(c("*.bin", "*.clm")) - filetype <- tail(unlist(strsplit(filename, "\\.")), 1) - - if (filetype == "clm") { - filedata <- file(description = filename, - open = "rb", - blocking = TRUE, - encoding = getOption("encoding")) - seek(filedata, where = 15, origin = "start") - inHeader <- as.numeric(readBin(filedata, - what = integer(), - size = 4, - n = 5, - endian = .Platform$endian)) - startYear <- inHeader[1] - nyear <- inHeader[2] - nbands <- inHeader[5] # nbands will be overwritten for clm data - years <- seq(startYear, startYear + nyear - 1, 1) - headlines <- 51 # generation clm 3 - close(filedata) - - } else if (filetype == "bin") { - - outfile <- grep(".out", list.files(), value = TRUE) %>% head(1) - out <- readLines(outfile) - startYear <- out %>% - str_subset("Output written in year:") %>% - str_split(":") %>% - unlist() %>% - str_trim() %>% - subset(c(FALSE, TRUE)) %>% - as.numeric() - endYear <- out %>% - str_subset("Last year:") %>% - str_split(":") %>% - unlist() %>% - str_trim() %>% - subset(c(FALSE, TRUE)) %>% - as.numeric() - years <- seq(startYear, endYear, 1) - headlines <- 0 - - } else { - stop("File format of LPJmL input data unknown. Please provide .clm or .bin file format.") - } - - x <- readLPJ(file_name = filename, - wyears = years, - syear = startYear, - headlines = headlines, - averaging_range = 1, - ncells = 67420, - file_type = "bin", - bands = nbands, - datatype = datatype, - bytes = bytes, - monthly = monthly) - - class(x) <- "array" - x <- collapseNames(as.magpie(x, spatial = 1)) - x <- collapseDim(addLocation(x), dim = "N") - x <- clean_magpie(x) - - return(x) - } - - if (subtype %in% c("soilc", "litc", "vegc", "alitfallc", "aet", - "vegc_grass", "litc_grass", "soilc_grass", - "aprec", "soilc_past_hist", "soilc_past_scen") || grepl("alitter", subtype)) { - x <- .prepareLPJ(nbands = 1) - } else if (grepl("*date*", subtype)) { - x <- .prepareLPJ(nbands = 24, datatype = integer(), bytes = 2) - } else if (subtype %in% c("soilc_layer", "cshift_slow", "cshift_fast")) { - x <- .prepareLPJ(nbands = 5) - } else if (grepl("mdischarge|mrunoff|mpet|mgpp_grass_ir|mgpp_grass_rf|met_grass_ir|met_grass_rf", subtype)) { - x <- .prepareLPJ(monthly = TRUE) - } else if (grepl("harvest|irrig|cwater_b|grass_pft|cft_gpp_grass_rf|cft_gpp_grass_ir|cft_et_grass_rf|cft_et_grass_ir|cft_transp_pft", # nolint - subtype)) { - x <- .prepareLPJ(nbands = 32) - } else if (grepl("fpc", subtype)) { - x <- .prepareLPJ(nbands = 12) - } else { - stop(paste0("subtype ", subtype, " is not existing")) - } - - return(round(x, digits = 10)) -} diff --git a/R/readLUH2v2.R b/R/readLUH2v2.R deleted file mode 100644 index c731dcbb..00000000 --- a/R/readLUH2v2.R +++ /dev/null @@ -1,186 +0,0 @@ -#' @title readLUH2v2 -#' @description read LUH inputs -#' -#' @param subtype switch between different inputs -#' -#' @return List of magpie objects with results on cellular level, weight, unit and description -#' @author Florian Humpenoeder, Stephen Wirth, Kristine Karstens, Felicitas Beier, -#' Jan Philipp Dietrich, Patrick v. Jeetze -#' -#' @importFrom ncdf4 nc_open -#' @importFrom terra rast ext subset aggregate project ext<- -#' @importFrom magclass as.magpie mbind -#' @importFrom withr local_tempdir defer -#' @importFrom stringr str_match str_count str_subset - -readLUH2v2 <- function(subtype) { - - # set terra options and temporary directory - terraOptions(tempdir = local_tempdir(tmpdir = getConfig("tmpfolder")), todisk = TRUE, memfrac = 0.25) - defer(terraOptions(tempdir = tempdir())) - - # basic settings - timeSel <- seq(1901, 2015, by = 1) - offset <- 849 # year 850=1, year 1900=1051, year 2015=1166 - # grep years to set other than default years, if subtypes ends with '_850to1901' like time span expression - timeSpan <- str_match(subtype, "_(\\d+)to(\\d+)")[2:3] - if (all(!is.na(timeSpan))) { - timeSel <- seq(timeSpan[1], timeSpan[2], by = 1) - subtype <- gsub("_(\\d+)to(\\d+)", "", subtype) - } - - # File to process - fStates <- "states.nc" - fMan <- "management.nc" - fTrans <- "transitions.nc" - - ### Define dimensions - map <- toolGetMappingCoord2Country(pretty = TRUE) - - if (grepl("states", subtype)) { - - # Open file and process information - ncFile <- nc_open(fStates) - data <- setdiff(names(ncFile$var), c("secma", "secmb", "lat_bounds", "lon_bounds")) - # Land area - carea <- suppressWarnings(rast("staticData_quarterdeg.nc", subds = "carea")) - ext(carea) <- c(-180, 180, -90, 90) - - x <- NULL - for (item in data) { - # read in share of land type - shr <- suppressWarnings(subset(rast(fStates, subds = item), timeSel - offset)) - checkSum <- terra::global(shr * carea, sum, na.rm = TRUE) - # aggregate from 0.25 degree to 0.5 degree - mag <- terra::aggregate(shr * carea, fact = 2, fun = sum, na.rm = TRUE) - # Check whether sum before and after aggregation is the same. - # Note: unit is km^2, so only rounded to first digit - if (any(round(checkSum - terra::global(mag, sum, na.rm = TRUE), digits = 1) != 0)) { - stop("There is an issue with the aggregation. Please check mrcommons::readLUH2v2") - } - # transform to MAgPIE object and clean up - mag <- as.magpie(terra::extract(mag, map[c("lon", "lat")])[, -1], spatial = 1, temporal = 2) - getNames(mag) <- item - getCells(mag) <- paste(map$coords, map$iso, sep = ".") - getYears(mag) <- timeSel - getSets(mag) <- c("x.y.iso", "t", "data") - x <- mbind(x, mag) - } - - # Convert from km^2 to Mha - x <- x / 10000 - - } else if (grepl("transition", subtype)) { - - # Open file and process information - ncFile <- nc_open(fTrans) - luTrans <- setdiff(names(ncFile$var), c("secma", "secmb", "lat_bounds", "lon_bounds")) - luTrans <- grep("to", luTrans, value = TRUE) - - lu <- list(crop = c("c3ann", "c3per", "c4ann", "c4per", "c3nfx"), - past = c("pastr", "range"), - nat = c("primf", "primn", "secdf", "secdn"), - urban = c("urban")) - - luTransReduced <- luTrans - for (i in seq_along(lu)) { - luTransReduced <- gsub(paste(lu[[i]], collapse = "|"), names(lu[i]), luTransReduced) - } - - zeroTrans <- grepl(paste(paste(names(lu), names(lu), sep = "_to_"), - collapse = "|"), luTransReduced) - # Land area - carea <- suppressWarnings(rast("staticData_quarterdeg.nc", subds = "carea")) - ext(carea) <- c(-180, 180, -90, 90) - - x <- new.magpie(map$coords, timeSel, unique(luTransReduced[!zeroTrans]), fill = 0) - - for (item in seq_along(luTrans)) { - - # This attributes LUC to the year resulting from it - print(luTrans[item]) - if (!zeroTrans[item]) { - shr <- suppressWarnings(subset(rast(fTrans, subds = luTrans[item]), timeSel - offset - 1)) - checkSum <- terra::global(shr * carea, sum, na.rm = TRUE) - # aggregate from 0.25 degree to 0.5 degree - mag <- terra::aggregate(shr * carea, fact = 2, fun = sum, na.rm = TRUE) - # Check whether sum before and after aggregation is the same. - # Note: unit is km^2, so only rounded to first digit - if (any(round(checkSum - terra::global(mag, sum, na.rm = TRUE), digits = 1) != 0)) { - stop("There is an issue with the aggregation. Please check mrcommons::readLUH2v2") - } - # Transform to MAgPIE object - mag <- as.magpie(terra::extract(mag, map[c("lon", "lat")])[, -1], spatial = 1, temporal = 2) - getNames(mag) <- luTransReduced[item] - getCells(mag) <- paste(map$coords, map$iso, sep = ".") - getYears(mag) <- timeSel - getSets(mag) <- c("x.y.iso", "t", "data") - x[, , luTransReduced[item]] <- collapseNames(x[, , luTransReduced[item]] + mag) - } - } - - getCells(x) <- getCells(mag) - getSets(x) <- getSets(mag) - - # Convert from km^2 to Mha - x <- x / 10000 - - } else if (grepl("irrigation", subtype)) { - - # Mapping between states and management_irrigation - dataMan <- c("irrig_c3ann", "irrig_c3per", "irrig_c4ann", "irrig_c4per", "irrig_c3nfx", "flood") - dataStates <- c("c3ann", "c3per", "c4ann", "c4per", "c3nfx", "c3ann") - data <- matrix(data = c(dataMan, dataStates), ncol = 2) - - # Land area - carea <- suppressWarnings(rast("staticData_quarterdeg.nc", subds = "carea")) - ext(carea) <- c(-180, 180, -90, 90) - - x <- NULL - for (item in dataMan) { - shr <- suppressWarnings(subset(rast(fStates, subds = data[data[, 1] == item, 2]), timeSel - offset)) - irShr <- suppressWarnings(subset(rast(fMan, subds = item), timeSel - offset)) - # grid cell fraction of crop area x grid cell area x irrigated fraction of crop area - tmp <- shr - for (i in seq_len(dim(tmp)[3])) { - tmp[[i]] <- shr[[i]] * carea * irShr[[i]] - } - checkSum <- terra::global(tmp, sum, na.rm = TRUE) - # aggregate from 0.25 degree to 0.5 degree - mag <- terra::aggregate(tmp, fact = 2, fun = sum, na.rm = TRUE) - # Check whether sum before and after aggregation is the same. - # Note: unit is km^2, so only rounded to first digit - if (any(round(checkSum - terra::global(mag, sum, na.rm = TRUE), digits = 1) != 0)) { - stop("There is an issue with the aggregation. Please check mrcommons::readLUH2v2") - } - # Transform to MAgPIE object - mag <- as.magpie(terra::extract(mag, map[c("lon", "lat")])[, -1], spatial = 1, temporal = 2) - getNames(mag) <- item - getYears(mag) <- timeSel - getCells(mag) <- paste(map$coords, map$iso, sep = ".") - getSets(mag) <- c("x.y.iso", "t", "data") - x <- mbind(x, mag) - } - - # Convert from km^2 to Mha - x <- x / 10000 - - } else if (grepl("ccode", subtype)) { - - # Load raster data on 0.25° and extend to full grid - ccode25 <- suppressWarnings(rast("staticData_quarterdeg.nc", subds = "ccode")) - ext(ccode25) <- c(-180, 180, -90, 90) - - # Create new raster object on 0.5° and re-project 0.25°-raster on 0.5°-raster - r50 <- rast(res = 0.5) - ccode50 <- project(ccode25, r50, method = "near") # re-project to regular grid - - x <- as.magpie(terra::extract(ccode50, map[c("lon", "lat")])[, -1], spatial = 1) - getYears(x) <- 2000 - getNames(x) <- "ccode" - getCells(x) <- paste(map$coords, map$iso, sep = ".") - getSets(x) <- c("x.y.iso", "t", "ccode") - } - - return(clean_magpie(x)) -} diff --git a/R/readLandInG.R b/R/readLandInG.R deleted file mode 100644 index d9fa8d0f..00000000 --- a/R/readLandInG.R +++ /dev/null @@ -1,116 +0,0 @@ -#' @title readLandInG -#' -#' @description Reads in LandInG data -#' -#' @param subtype Type of LandInG data that should be read: -#' \itemize{ -#' \item \code{physicalArea}: Cropland extend/ physical cropping area separated in irrigated and rainfed -#' \item \code{harvestedArea}: Harvested area separated in different crop types -#' } -#' -#' @return magpie object -#' -#' @importFrom magclass as.magpie collapseNames collapseDim getItems getNames getSets -#' @importFrom magpiesets addLocation -#' @importFrom lpjmlkit read_io -#' @importFrom utils read.delim -#' -#' @author Felicitas Beier -#' @seealso \code{\link{readSource}} -#' @examples -#' \dontrun{ -#' A <- readSource("LandInG", subtype = "harvestedArea", aggregate = FALSE) -#' } -#' -readLandInG <- function(subtype = "physicalArea") { - - if (subtype == "physicalArea") { - - bands <- c("rainfed", "irrigated") - - # filename for irrigated and rainfed physical area - physicalAreaName <- paste0("OutputForMAgPIE_2023-10-20/", - "cft_cropland_MAgPIE_cft_aggregation_20200417_20200127_madrat_", - "multicropping_LUH2v2_disaggregated_30min_1960-2015.bin") - # unit: ha - - # read in data and transform to MAgPIE object - x <- as.magpie(read_io(filename = physicalAreaName, - band_names = bands, - nstep = 1, timestep = 1)) - # add coordinates - x <- collapseDim(addLocation(x), dim = "N") - # rename dimensions - years <- paste0("y", gsub("-12-31", "", getItems(x, dim = "time"))) - getItems(x, dim = "time") <- years - x <- clean_magpie(x) - getSets(x) <- c("x", "y", "iso", "year", "irrigation") - - } else if (subtype == "harvestedArea") { - # Ordered list of band names - # Note: This hard-coded list can be removed as soon as output - # is provided as json file. - bands <- c("rainfed tece", - "rainfed maiz", - "rainfed trce", - "rainfed rice_pro", - "rainfed soybean", - "rainfed rapeseed", - "rainfed groundnut", - "rainfed sunflower", - "rainfed oilpalm", - "rainfed puls_pro", - "rainfed potato", - "rainfed cassav_sp", - "rainfed sugr_cane", - "rainfed sugr_beet", - "rainfed others", - "rainfed cottn_pro", - "rainfed foddr", - "rainfed pasture", - "rainfed begr", - "rainfed betr", - "irrigated tece", - "irrigated maiz", - "irrigated trce", - "irrigated rice_pro", - "irrigated soybean", - "irrigated rapeseed", - "irrigated groundnut", - "irrigated sunflower", - "irrigated oilpalm", - "irrigated puls_pro", - "irrigated potato", - "irrigated cassav_sp", - "irrigated sugr_cane", - "irrigated sugr_beet", - "irrigated others", - "irrigated cottn_pro", - "irrigated foddr", - "irrigated pasture", - "irrigated begr", - "irrigated betr") - - # filename - harvestedAreaName <- paste0("OutputForMAgPIE_2023-10-20/", - "cft_MAgPIE_cft_aggregation_20200417_20200127_madrat_", - "multicropping_LUH2v2_disaggregated_30min_1960-2015.bin") - # unit: ha - - # read in data and transform to MAgPIE object - x <- as.magpie(read_io(filename = harvestedAreaName, - band_names = bands, - nstep = 1, timestep = 1)) - # add coordinates - x <- collapseDim(addLocation(x), dim = "N") - # rename dimensions - years <- paste0("y", gsub("-12-31", "", getItems(x, dim = "time"))) - getItems(x, dim = "time") <- years - getItems(x, dim = 3, raw = TRUE) <- gsub(" ", ".", getItems(x, dim = 3)) - x <- clean_magpie(x) - getSets(x) <- c("x", "y", "iso", "year", "irrigation", "crop") - - } - - return(x) -} diff --git a/R/readProductAttributes.R b/R/readProductAttributes.R deleted file mode 100644 index 0e2e14c7..00000000 --- a/R/readProductAttributes.R +++ /dev/null @@ -1,52 +0,0 @@ -#' Read product attributes -#' -#' Read-in a file containing the attributes of MAgPIE products. Currently -#' Covers dry matter (DM), reactive nitrogen (Nr), Phosphorus (P), -#' Generalizable Energy (GE) and wet matter (WM). Values are assembled from -#' various literature sources, and the weighting and allocation is done in the -#' spreadsheet crop_specifications_06_2011.ods and -#' livestock_specifications_2012_06_14.ods in the svn folder /tools/Nutrients . -#' Values standardized on DM. -#' -#' -#' @param subtype Available subtypes: "Products", MAgPIE products "AgResidues" -#' Aboveground crop residues and "BgResidues" Belowground crop residues -#' @return magpie object with the dimension crops and attributes -#' @author Benjamin Leon Bodrisky -#' @seealso [readSource()] -#' @examples -#' \dontrun{ -#' a <- readSource("ProductAttributes") -#' } -#' @importFrom magclass read.magpie - -readProductAttributes <- function(subtype = "Products") { - - folder <- "Version_2021_08_25/" - - if (!is.null(tmp <- getOption("prodatt_folder"))) folder <- tmp - - files <- c(Products = "product_attributes.csv", - AgResidues = "f_attributes_residue_ag.csv", - BgResidues = "nr_residue_bg.csv", - LivingAnimals = "attributes_living_animals.csv", - SlaughterFactor = "SlaughterFactor.csv") - - - file <- toolSubtypeSelect(subtype, files) - - if (subtype %in% c("SlaughterFactor", "LivingAnimals")) { - vcat(3, "Living Animal attributes and slaughter factor not yet parametrized for fish.") - } - - output <- read.magpie(paste0(folder, file)) - getSets(output) <- c("region", "year", "attributes", "products") - - if (subtype %in% c("Products", "AgResidues", "BgResidues", "LivingAnimals")) { - output <- output / collapseNames(output[, , "dm"]) - } - - output <- collapseNames(output) - - return(output) -} diff --git a/R/toolAggregateCell2Country.R b/R/toolAggregateCell2Country.R deleted file mode 100644 index a71b52a3..00000000 --- a/R/toolAggregateCell2Country.R +++ /dev/null @@ -1,28 +0,0 @@ -#' toolAggregateCell2Country -#' -#' Aggregate cellular data (with coordinate information) to countries and perform consistency checks -#' @param x cellular magpie object with coordinates -#' @param weight aggregation weight -#' @param ... additional options forwarded to `toolCountryFill` -#' @return return country ISO level data -#' @author Jan Philipp Dietrich -#' @importFrom magclass getItems -#' @export - -toolAggregateCell2Country <- function(x, weight = NULL, ...) { - - map <- toolGetMappingCoord2Country(extended = TRUE) - - unknown <- which(!(getItems(x, dim = 1) %in% map$coords)) - if (length(unknown) > 0) { - warning(length(unknown), " entries of x could not be mapped to a country and will be ignored!") - x <- x[-unknown, , ] - } - - out <- toolAggregate(x, map, from = 2, partrel = TRUE, weight = weight[getItems(x, dim = 1), , ]) - - # island states are NAs: will be set to 0 - out <- toolCountryFill(out, ...) - - return(out) -} diff --git a/R/toolCell2isoCell.R b/R/toolCell2isoCell.R deleted file mode 100644 index 7b56668c..00000000 --- a/R/toolCell2isoCell.R +++ /dev/null @@ -1,20 +0,0 @@ -#' toolCell2isoCell -#' -#' Sets cell names to "iso country code"."cell number" -#' @param x magpie object on cellular level -#' @param cells switch between magpie cells (59199) and lpj cells (67420) -#' @return return changed input data -#' @author Kristine Karstens -#' -#' @importFrom utils read.csv -#' @export - -toolCell2isoCell <- function(x, cells = "magpiecell") { - - if (cells == "magpiecell") { - cellToCellIso <- toolGetMapping(name = "CountryToCellMapping.rds", where = "mrcommons") - getCells(x) <- cellToCellIso$celliso - } - - return(x) -} diff --git a/R/toolClimateInputVersion.R b/R/toolClimateInputVersion.R deleted file mode 100644 index 74ad463a..00000000 --- a/R/toolClimateInputVersion.R +++ /dev/null @@ -1,34 +0,0 @@ -#' @title toolClimateInputVersion -#' -#' @description Specify default settings for LPJmL climate input version and baseline settings -#' @param lpjmlVersion Add-ons (+*) for further version specification for LPJmL version -#' @param climatetype Switch between different climate scenarios -#' -#' @return configuration as list -#' @author Kristine Karstens -#' -#' @importFrom stringr str_split -#' -#' @export - -toolClimateInputVersion <- function(lpjmlVersion, climatetype) { - - cfgLPJmL <- toolLPJmLVersion(lpjmlVersion, climatetype) - cfg <- NULL - - ##### DEFAULT CLIMATE CONFIG ##### - cfg$versionScen <- "ISIMIP3bv2" - cfg$versionHist <- "ISIMIP3av2" - cfg$baselineHist <- cfgLPJmL$baseline_hist - cfg$refYearHist <- cfgLPJmL$ref_year_hist - cfg$baselineGcm <- cfgLPJmL$baseline_gcm - cfg$refYearGcm <- cfgLPJmL$ref_year_gcm - cfg$climatetype <- climatetype - ##### DEFAULT CLIMATE CONFIG ##### - - if (cfg$climatetype == "GSWP3-W5E5:historical") { - cfg$climatetype <- "GSWP3-W5E5:obsclim" - } - - return(cfg) -} diff --git a/R/toolConv2CountryByCelltype.R b/R/toolConv2CountryByCelltype.R deleted file mode 100644 index c4c2696e..00000000 --- a/R/toolConv2CountryByCelltype.R +++ /dev/null @@ -1,15 +0,0 @@ -#' toolConv2CountryByCelltype -#' -#' Aggregates cellular data to ISO country level after conversion of cellular -#' data to a specific cell setup (this type is relevant as some settings, -#' such as "magpiecell" remove some cells and therby affect country sums) -#' @param x magpie object on cellular level -#' @param cells switch between 59199 ("magpiecell") and 67420 ("lpjcell") cells -#' @return return selected input data on ISO country level -#' @author Jan Philipp Dietrich - -toolConv2CountryByCelltype <- function(x, cells) { - getSets(x, fulldim = FALSE)[1] <- "x.y.iso" - out <- toolCoord2Isocell(x, cells = cells) - return(toolSum2Country(out)) -} diff --git a/R/toolCoord2Isocell.R b/R/toolCoord2Isocell.R deleted file mode 100644 index 10a4578b..00000000 --- a/R/toolCoord2Isocell.R +++ /dev/null @@ -1,37 +0,0 @@ -#' @title toolCoord2Isocell -#' @description Transforms an object with coordinate spatial data (on half-degree) -#' to isocell (59199) standard -#' -#' @param x Object to be transformed from coordinates to (old) magpie isocell standard -#' @param cells Switch between "magpiecell" (59199) and "lpjcell" (67420) -#' -#' @return magpie object with 59199 cells in isocell naming -#' @author Kristine Karstens, Felicitas Beier, Jan Philipp Dietrich -#' -#' @param cells Switch between "magpiecell" (59199) and "lpjcell" (67420) -#' @param fillMissing if NULL cells missing from the total 59199 are just being ignore. If set to a value -#' missing cells will be added with this value (e.g. all set to 0 if fillMissing is 0) -#' @param warnMissing Switch which controls whether missing cells should trigger a warning or not -#' @importFrom magpiesets addLocation -#' @importFrom madrat toolOrderCells -#' @importFrom magclass collapseDim -#' @importFrom utils packageVersion -#' -#' @export - -toolCoord2Isocell <- function(x, cells = "magpiecell", fillMissing = NULL, warnMissing = TRUE) { - if (cells == "magpiecell") { - removedim <- setdiff(unlist(strsplit(names(getItems(x))[1], "\\.")), c("x", "y")) - x <- collapseDim(x, dim = removedim) - x <- addLocation(x, fillMissing = fillMissing, naCellNumber = "NA") - x <- collapseDim(x, dim = c("x", "y")) - x <- toolOrderCells(x, na.rm = TRUE) - if (warnMissing && length(getCells(x)) != 59199) warning("Some cells out of the 59199 standard cells are missing.") - } else if (cells == "lpjcell") { - getItems(x, dim = "cell", maindim = 1) <- 1:67420 - x <- collapseDim(x, dim = c("x", "y")) - } else { - stop("Unknown cells argument.") - } - return(x) -} diff --git a/R/toolCoord2Isocoord.R b/R/toolCoord2Isocoord.R deleted file mode 100644 index 9fdc60f6..00000000 --- a/R/toolCoord2Isocoord.R +++ /dev/null @@ -1,31 +0,0 @@ -#' @title toolCoord2Isocoord -#' @description Transforms an object with coordinate spatial data (on half-degree) -#' to object with 67420 cells and coordinate and iso country information -#' -#' @param x object to be transformed from coordinates to iso-coordinate object -#' -#' @return magpie object with 67420 cells in x.y.iso naming -#' @author Felicitas Beier -#' -#' -#' @export - -toolCoord2Isocoord <- function(x) { - - # coordinate to country mapping for 67420 cells - mapping <- toolGetMappingCoord2Country() - mapping$coordiso <- paste(mapping$coords, - mapping$iso, - sep = ".") - - # sort first dimension as provided by mapping - x <- x[mapping$coords, , ] - # rename first dimension - getItems(x, dim = 1, raw = TRUE) <- mapping$coordiso - # set names - getSets(x)["d1.1"] <- "x" - getSets(x)["d1.2"] <- "y" - getSets(x)["d1.3"] <- "iso" - - return(x) -} diff --git a/R/toolCountryFillBilateral.R b/R/toolCountryFillBilateral.R deleted file mode 100644 index 43ec7e83..00000000 --- a/R/toolCountryFillBilateral.R +++ /dev/null @@ -1,19 +0,0 @@ -#' @title toolCountryFillBilateral -#' @description Fills bilateral iso-level magpie objects to 249 x 249 countries -#' @param x input variable, a bilateral magclass object -#' @param fill fill value, default NA -#' @export - -toolCountryFillBilateral <- function(x, fill = NA) { - isoCountry <- read.csv2(system.file("extdata", "iso_country.csv", package = "madrat"), row.names = NULL) - countrylist <- as.vector(isoCountry[, "x"]) - names(countrylist) <- isoCountry[, "X"] - full <- expand.grid(countrylist, countrylist) - full <- paste0(as.character(full[[1]]), ".", as.character(full[[2]])) - missing <- setdiff(full, getItems(x, dim = 1)) - if (length(missing) > 0) { - x <- mbind(x, new.magpie(cells_and_regions = missing, - years = getYears(x), names = getNames(x), fill = fill)) - } - return(x) -} diff --git a/R/toolExtrapolateFodder.R b/R/toolExtrapolateFodder.R deleted file mode 100644 index be717214..00000000 --- a/R/toolExtrapolateFodder.R +++ /dev/null @@ -1,25 +0,0 @@ -#' @title toolExtrapolateFodder -#' @description Extrapolate fodder data, based on two time steps (5-averages around this years) -#' @param x input data -#' @param exyears two years -#' @param average the averaging_range in toolTimeInterpolate -#' @param endyear year till when it should be extrapolated -#' @return magpie object including extrapolated years -#' @author Kristine Karstens -#' -#' @export - -toolExtrapolateFodder <- function(x, exyears = c(2004, 2009), average = 5, endyear = 2015) { - - if (endyear <= max(getYears(x, as.integer = TRUE))) return(x) - - dt <- floor(average / 2) - tmp <- time_interpolate(mbind(toolTimeAverage(x[, seq(exyears[1] - dt, exyears[1] + dt), ], average), - toolTimeAverage(x[, seq(exyears[2] - dt, exyears[2] + dt), ], average)), - c(2012:endyear), extrapolation_type = "linear") - - tmp <- toolConditionalReplace(tmp, "<0", 0) - x <- mbind(x, tmp) - - return(x) -} diff --git a/R/toolFAOcombine.R b/R/toolFAOcombine.R deleted file mode 100644 index 9a4ea0a8..00000000 --- a/R/toolFAOcombine.R +++ /dev/null @@ -1,88 +0,0 @@ -#' Combine FAO datasets -#' -#' Allows to combine two similar FAO datasets with dublicates being removed. -#' For instance combine Production:Crops and Production: Crops Processed to one -#' magpie object -#' -#' -#' @param ... two magpie objects with FAO data -#' @param combine "Item" to combine datasets that for instance both contain -#' palm oil data -#' @return MAgPIE object with data from both inputs but dublicates removed -#' @author Ulrich Kreidenweis -#' @seealso [readSource()] -#' @examples -#' \dontrun{ -#' a <- toolFAOcombine(Crop, CropPro, combine = "Item") -#' } -#' -#' @importFrom magclass getNames getYears dimSums -#' @export -#' -toolFAOcombine <- function(..., combine = "Item") { - x <- list(...) - dotnames <- names(x) - - if (length(x) < 2) stop("At least two files have to be provided") - if (length(x) > 2) stop("Function currently only working for two files") - - # first programmed for only two files (change to infinte number later) - - if (combine == "Item") { - - x1 <- x[[1]] - x1[is.na(x1)] <- 0 - x2 <- x[[2]] - x2[is.na(x2)] <- 0 - - ## match temporal dimension - years <- intersect(getYears(x1), getYears(x2)) - if (length(setdiff(getYears(x1), getYears(x2))) > 0) { - vcat(1, "No data for year", gsub("y", "", setdiff(getYears(x1), getYears(x2))), "in", dotnames[2], - "dataset. All data of this year removed. \n") - } - if (length(setdiff(getYears(x2), getYears(x1))) > 0) { - vcat(1, "No data for year", gsub("y", "", setdiff(getYears(x2), getYears(x1))), "in", dotnames[1], - "dataset. All data of this year removed. \n") - } - x1 <- x1[, years, ] - x2 <- x2[, years, ] - - - # items that occur in both datasets - items1 <- getNames(x1) - items2 <- getNames(x2) - inboth <- intersect(items1, items2) - - - if (length(inboth) > 0) { - vcat(2, "For the following items there were values in both datasets:", inboth, "Only values from", - dotnames[1], "dataset were considered") - - x1GLO <- dimSums(x1[, , inboth], dim = 1) - x2GLO <- dimSums(x2[, , inboth], dim = 1) - - # short check if global sums of the values are the same. - for (item in inboth) { - avg <- mean((x1GLO[, , item] + 10^-8) / (x2GLO[, , item] + 10^-8)) - if (avg > 1.01 || avg < 0.99) { - cat(0, "For", item, "the values in the two datasets seem to differ. Manual check recommended.") - } - } - } - - # data2 without data already in data1 (CHECK!) - x2rm <- x2[, , items2[!items2 %in% items1]] - - } - - if (combine == "Element") { - - stop("This option is not available yet", "\n") - - } - - bothtogether <- mbind(x1, x2rm) - - return(bothtogether) -} diff --git a/R/toolForestRelocate.R b/R/toolForestRelocate.R deleted file mode 100644 index 3fbf87b4..00000000 --- a/R/toolForestRelocate.R +++ /dev/null @@ -1,236 +0,0 @@ -#' @title toolForestRelocate -#' @description Reallocates cellular forest information from LUH2 -#' to better match FAO forest information -#' -#' @param lu uncorrected landuse initialisation data set (cell level) -#' @param luCountry uncorrected landuse initialisation on country level -#' @param natTarget target natural land allocation on country level -#' @param vegC vegetation carbon data used as reallocation weight -#' @return List of magpie object with results on cellular level -#' @author Kristine Karstens, Jan Philipp Dietrich, Felicitas Beier, Patrick v. Jeetze -#' @importFrom magclass setNames setItems new.magpie nyears -#' @importFrom nleqslv nleqslv -#' -#' @export - -toolForestRelocate <- function(lu, luCountry, natTarget, vegC) { # nolint - - .arrayReduce <- function(x) { - # drop dimensions but keep time dimension - if (dim(x)[3] != 1) stop("array2D only works with a single data dimension!") - if (dim(x)[1] == 1) return(array(x, dim = dim(x)[2], dimnames = dimnames(x)[2])) - return(array(x, dim = dim(x)[1:2], dimnames = dimnames(x)[1:2])) - } - - forests <- c("primforest", "secdforest", "forestry") - nature <- c(forests, "other") - - if (round(sum(lu) - sum(luCountry), 4) != 0) warning("lu and luCountry differ in total land area") - if (round(sum(lu[, , nature]) - sum(natTarget), 4) != 0) warning("lu and natTarget differ in total land area") - - # store cell area to check later that it remains constant - luCellArea <- setItems(dimSums(lu[, 1, ], dim = 3), dim = 2, NULL) - - # reduce, if necessary to FAO - reduce <- increase <- round(natTarget - luCountry[, , nature], 8) - reduce[reduce > 0] <- 0 - increase[increase < 0] <- 0 - - # grep land areas dependent on vegetation carbon density - if (is.null(getYears(vegC))) getYears(vegC) <- getYears(natTarget) - - # weight function to determine correct cellweights for area removal - findweight <- function(p, cellarea, isoreduction, cellweight) { - rowSums(cellarea * (1 - (1 - cellweight)^p)) + isoreduction + 10^-10 - } - - # loop over countries - countries <- getItems(lu, dim = "iso") - l <- list() - for (iso in countries) { - - l[[iso]] <- lu[iso, , ] - allocate <- setNames(l[[iso]][, , 1] * 0, NULL) - - vegCIso <- vegC[iso, , ] - - # normalized vegetation carbon (with small correction to ensure values between [0,1)) - vegCN <- t(.arrayReduce(vegCIso / (as.magpie(apply(vegCIso, 2, max)) + 10^-10))) - - ########################### - ### Reduction procedure ### - ########################### - - # loop over all land use categories, that have to be reallocated - for (cat in nature) { - - catreduce <- .arrayReduce(reduce[iso, , cat]) - - # check if area has to be cleared - if (any(catreduce != 0)) { - - # check for one cell countries - if (dim(l[[iso]])[1] == 1) { - # trivial case of one cell countries - remove <- -as.magpie(catreduce) - } else if (all(dimSums(l[[iso]][, , cat] != 0, dim = 1) == 1)) { - # trivial case in which in each year exactly one cell contains land in the category to be reduced - remove <- setNames(-1 * (l[[iso]][, , cat] != 0) * as.magpie(catreduce), NULL) - } else { - # for other land cell with highest vegc and for all forest categories lowest vegc should be cleared first - if (cat == "other") { - cellweight <- vegCN - } else { - cellweight <- (1 - 10^-16 - vegCN) - } - - # check for edge case in which all land of that category must be removed and treat it separately - fullremoval <- (round(dimSums(l[[iso]], dim = 1)[, , cat] + as.magpie(catreduce), 3) == 0) - if (any(fullremoval)) { - allocate[, fullremoval, ] <- (allocate[, fullremoval, ] - + setNames(l[[iso]][, fullremoval, cat], NULL)) - l[[iso]][, fullremoval, cat] <- 0 - catreduce[fullremoval] <- 0 - } - - t <- (catreduce != 0) - if (any(t)) { - # determine correct parameter for weights for multiple cell countries - # (weights below zero indicate an error) - # only determine them for cases where something has to be removed - p <- rep(1, nyears(l[[iso]])) - names(p) <- rownames(cellweight) - - for (ti in getYears(l[[iso]][, t, ])) { - - sol <- nleqslv(rep(1, nyears(l[[iso]][, ti, ])), findweight, - cellarea = t(.arrayReduce(l[[iso]][, ti, cat])), - isoreduction = catreduce[ti], cellweight = cellweight[ti, ], - control = list(allowSingular = TRUE)) - p[ti] <- sol$x - msg <- sol$message - criticalWarnings <- c("Jacobian is singular (1/condition=0.0e+00) (see allowSingular option)", - "Jacobian is completely unusable (all zero entries?)", - "Iteration limit exceeded") - - if (msg %in% criticalWarnings) { - - vcat(2, paste0("No solution for ", iso, ", ", cat, ", ", msg, ".", - "Restart from higher intial guess.")) - - sol <- nleqslv(rep(10^10, nyears(l[[iso]][, ti, ])), findweight, - cellarea = t(.arrayReduce(l[[iso]][, ti, cat])), - isoreduction = catreduce[ti], cellweight = cellweight[ti, ], - control = list(allowSingular = TRUE)) - p[ti] <- sol$x - msg <- sol$message - if (msg %in% criticalWarnings) warning("No solution for ", iso, ", ", cat, ", ", msg, ".") - - } - } - - if (any(p[t] < 0)) vcat(1, "Negative weight of p=", p, " for: ", cat, " ", iso, " ", t) - remove <- l[[iso]][, , cat] * (1 - (1 - as.magpie(cellweight, spatial = 2))^as.magpie(p)) - remove[, !t, ] <- 0 - } else { - remove <- 0 - } - } - - # remove area from cells and put to "allocate" area - l[[iso]][, , cat] <- l[[iso]][, , cat] - remove - allocate <- allocate + remove - } - } - - ############################ - ### Allocation procedure ### - ############################ - - catincrease <- .arrayReduce(increase[iso, , "other"]) - - # relocate other land to areas with low vegetation carbon density - # check if other land has to be filled - if (any(catincrease != 0)) { - - t <- (catincrease != 0) - - cellweight <- (1 - 10^-16 - vegCN) - - # check for one cell countries - if (dim(l[[iso]])[1] == 1) { - # trivial case of one cell countries - add <- as.magpie(catincrease) - } else if (all(dimSums(allocate != 0, dim = 1) == 1)) { - # trivial case in which in each year exactly one cell contains land in the category to be reduced - add <- setNames((allocate != 0) * as.magpie(catincrease), NULL) - } else { - # determine correct parameter for weights for multiple cell countries (weights below zero indicate an error) - - p <- rep(1, nyears(l[[iso]])) - names(p) <- rownames(cellweight) - - for (ti in getYears(l[[iso]][, t, ])) { - - sol <- nleqslv(rep(1, nyears(l[[iso]][, ti, ])), findweight, - cellarea = t(.arrayReduce(allocate[, ti, ])), - isoreduction = -catincrease[ti], cellweight = cellweight[ti, ]) - p[ti] <- sol$x - } - - if (any(p[t] < 0)) vcat(1, "Negative weight of p=", p, " for: ", cat, " ", iso, " ", t) - add <- allocate * (1 - (1 - as.magpie(cellweight, spatial = 2))^as.magpie(p)) - } - add[, !t, ] <- 0 - - # move area from "allocate" area to other land - l[[iso]][, , "other"] <- l[[iso]][, , "other"] + add - allocate <- allocate - add - } - - # relocate forest land to remaining "allocate" area - # check if forests has to be filled - - catincrease <- increase[iso, , forests] - - if (any(catincrease != 0)) { - # move area from "allocate" area to forests - forestsShare <- catincrease / (setNames(dimSums(catincrease, dim = 3), NULL) + 10^-10) - l[[iso]][, , forests] <- (l[[iso]][, , forests] + setCells(forestsShare, "GLO") * allocate) - allocate[, , ] <- 0 - } - - ############################ - ### Check reallocation ### - ############################ - - error <- abs(dimSums(l[[iso]][, , nature], dim = 1) - natTarget[iso, , ]) - if (max(error) >= 0.001) { - landuse <- getItems(error, dim = 3) - luMissmatches <- paste(landuse[unique(which(error >= 0.001, arr.ind = TRUE)[, 3])], collapse = ", ") - warning("Missmatch (", round(max(error), 3), " Mha) in ", iso, " for ", luMissmatches) - } - - } - - lu[names(l), , ] <- mbind(l) - - .checkCellArea <- function(lu, luCellArea) { - map <- data.frame(from = getItems(lu, dim = 3), to = "sum") - error <- abs(toolAggregate(lu, map, dim = 3) - luCellArea) - cell <- rownames(which(error == max(error), arr.ind = TRUE)) - if (max(error) > 10e-4) { - warning("Total cell areas differ (max diff = ", max(error), " in ", cell, ")!") - } - } - .checkCellArea(lu, luCellArea) - - error <- abs(toolCountryFill(dimSums(lu[, , nature], dim = c("x", "y")), - fill = 0, verbosity = 2) - natTarget) - if (max(error) > 10e-4) { - country <- rownames(which(error == max(error), arr.ind = TRUE)) - warning("Missmatch between computed and target land use (max error = ", max(error), " in ", country, ")") - } - getComment(lu) <- NULL - return(lu) -} diff --git a/R/toolFreezeEffect.R b/R/toolFreezeEffect.R deleted file mode 100644 index 46be8c9b..00000000 --- a/R/toolFreezeEffect.R +++ /dev/null @@ -1,44 +0,0 @@ -#' @title toolFreezeEffect -#' @description This function freeze values given a specific year and optionally additionally at the first -#' non-zero value -#' -#' @param x data set to freeze -#' @param year year to hold constant (onwards) -#' @param constrain if FALSE, no constrain. Other options: 'first_use' (freeze from 'first use' ( <=> !=0 )) -#' -#' @return magpie object with global parameters -#' @author Kristine Karstens -#' -#' @export - -toolFreezeEffect <- function(x, year, constrain = FALSE) { - - out <- x - resetYears <- getYears(x, as.integer = TRUE) >= year - out[, resetYears, ] <- setYears(x[, rep(year, sum(resetYears)), ], getYears(x[, resetYears, ])) - - if (constrain == "first_use") { - # determine year of first use (as index in year dim (1 <=> first year)) - firstValue <- firstUse <- toolConditionalReplace( - magpply(x[, resetYears, ], - function(x) { - return(which(x != 0)[1]) - }, - c(1, 3)), - "is.na()", - 1) - firstUse <- firstUse + length(which(getYears(x, as.integer = TRUE) < year)) - - # determine value of first use - ncells <- length(getCells(x)) - ndata <- length(getNames(x)) - nyears <- length(getYears(x)) - firstValue[] <- x[as.array((ncells * nyears) * (rep(1:ndata, each = ncells) - 1) - + ncells * (firstUse - 1) + rep(1:ncells, times = ndata))] - - # set value of first usage for all later appearing later non-zero values - out[as.array(out == 0 & x != 0)] <- firstValue[, rep(1, nyears(x)), ][as.array(out == 0 & x != 0)] - } - - return(out) -} diff --git a/R/toolGetMappingCoord2Country.R b/R/toolGetMappingCoord2Country.R deleted file mode 100644 index 3c12bad1..00000000 --- a/R/toolGetMappingCoord2Country.R +++ /dev/null @@ -1,35 +0,0 @@ -#' @title toolGetMappingCoord2Country -#' @description loads mapping of cellular coordinate data (67420 halfdegree cells) to country iso codes -#' -#' @param pretty If TRUE, coordinate data is returned as numeric 'lon' and 'lat' columns -#' @param extended If TRUE, additional cells missing in the original 67420 data set will be -#' returned as well. -#' -#' @return data frame of mapping -#' -#' @author Felicitas Beier, Kristine Karstens -#' -#' @importFrom stringr str_split -#' -#' @export - -toolGetMappingCoord2Country <- function(pretty = FALSE, extended = FALSE) { - out <- toolGetMapping("mapCoords2Country.rds", where = "mrcommons") - - if (!extended) { - out <- out[1:67420, ] - } - - if (pretty) { - tmp <- gsub("p", "\\.", str_split(out$coords, "\\.", simplify = TRUE)) - tmp <- as.data.frame( - matrix(apply(tmp, 2, as.numeric), dim(tmp)[1], dim(tmp)[2], - dimnames = list(NULL, c("lon", "lat")) - ), - stringsAsFactors = FALSE - ) - out <- data.frame(out, tmp) - } - - return(out) -} diff --git a/R/toolHarmonize2Baseline.R b/R/toolHarmonize2Baseline.R deleted file mode 100644 index 64e781c3..00000000 --- a/R/toolHarmonize2Baseline.R +++ /dev/null @@ -1,112 +0,0 @@ -#' toolHarmonize2Baseline -#' -#' @param x magclass object that should be set on baseline -#' @param base magclass object for baseline -#' @param ref_year Reference year -#' @param method additive: x is harmonized to base by additive factor -#' multiplicative: x is harmonized to base by multiplicative factor -#' limited: multiplicative harmonization, -#' but for an underestimated baseline the signal is -#' limited to the additive term rather than the multiplicative factor -#' @param hard_cut Switch to TRUE for data that can not be harmonized, but have to be glued together -#' -#' @return the averaged data in magclass format -#' @author Kristine Karstens, Felicitas Beier -#' -#' @export - -toolHarmonize2Baseline <- function(x, - base, - ref_year = "y2015", # nolint: object_name_linter - method = "limited", - hard_cut = FALSE # nolint: object_name_linter -) { - if (!is.magpie(x) || !is.magpie(base)) stop("Input is not a MAgPIE object, x has to be a MAgPIE object!") - - # check for negative range of values - negative <- (any(x < 0) | any(base < 0)) - - # check if years are overlapping and refs is part of both time horizons - if (!ref_year %in% intersect(getYears(x), getYears(base))) { - stop("Overlapping time period of baseline and data is not including the reference year!") - } - - # set years - years <- sort(union(getYears(base), getYears(x))) - tillRef <- getYears(base, as.integer = TRUE) - tillRef <- paste0("y", tillRef[tillRef <= as.numeric(substring(ref_year, 2))]) - afterRef <- getYears(x, as.integer = TRUE) - afterRef <- paste0("y", afterRef[afterRef > as.numeric(substring(ref_year, 2))]) - - - # check if x and base are identical in dimension except time - if (!setequal(getCells(x), getCells(base)) || !setequal(getNames(x), getNames(base))) { - stop("Dimensions of the MAgPIE objects do not match!") - } - - # create new magpie object with full time horizon - full <- new.magpie(getCells(x), years, getNames(x), sets = getSets(x)) - - full <- as.array(full) - x <- as.array(x) - base <- as.array(base) - - # from start until ref_year, use the corresponding ref value - full[, tillRef, ] <- base[, tillRef, ] - - repRefYear <- rep(ref_year, length(afterRef)) - - if (hard_cut) { - ########################################### - ### Use GCM data after historical data ### - ### from reference year +1 on ### - ########################################### - - full[, afterRef, ] <- x[, afterRef, ] - } else if (method == "multiplicative") { - full[, afterRef, ] <- x[, afterRef, ] * (base[, repRefYear, ] / x[, repRefYear, ]) - - # correct NAs and infinite - fullNotFinite <- !is.finite(full[, afterRef, ]) - # does this make sense? - full[, afterRef, ][fullNotFinite] <- (base[, repRefYear, ] + x[, afterRef, ])[fullNotFinite] - } else if (method == "additive") { - full[, afterRef, ] <- x[, afterRef, ] + (base[, repRefYear, ] - x[, repRefYear, ]) - } else if (method == "limited") { - ########################################### - ### Use DELTA-approach to put signal of ### - ### GCM data on historical observation ### - ### data from reference year +1 on ### - ########################################### - - lambda <- sqrt(x[, ref_year, , drop = FALSE] / base[, ref_year, , drop = FALSE]) - lambda[base[, ref_year, ] <= x[, ref_year, ]] <- 1 - lambda[is.nan(lambda)] <- 1 - lambda <- lambda[, repRefYear, ] - - full[, afterRef, ] <- - base[, repRefYear, ] + - (x[, afterRef, ] - x[, repRefYear, ]) * (base[, repRefYear, ] / x[, repRefYear, ])**lambda - - full[, afterRef, ][is.na(full[, afterRef, ])] <- 0 - } else { - stop("Please select harmonization method (additive, multiplicative, limited (default))") - } - - # check for nans and more - if (any(is.infinite(full) | is.nan(full) | is.na(full))) { - warning("Data containing inconsistencies.") - } - if (!negative && any(full < 0)) { - vcat(2, paste0( - "toolHarmonize2Baseline created unwanted negativities in the range of ", - range(full[which(full < 0)]), - ". They will be set to zero." - )) - full[full < 0] <- 0 - } - - out <- as.magpie(full, spatial = 1) - - return(out) -} diff --git a/R/toolHoldConstantBeyondEnd.R b/R/toolHoldConstantBeyondEnd.R deleted file mode 100644 index 041c9f16..00000000 --- a/R/toolHoldConstantBeyondEnd.R +++ /dev/null @@ -1,13 +0,0 @@ -#' @title toolHoldConstantBeyondEnd -#' @description Holds a historical dataset constant for the entire simulation period "time". -#' -#' @param x MAgPIE object to be continued. -#' @return MAgPIE object with completed time dimensionality. -#' @author Benjamin Leon Bodirsky -#' @importFrom magpiesets findset -#' @importFrom mstools toolHoldConstant -#' @export - -toolHoldConstantBeyondEnd <- function(x) { - return(toolHoldConstant(x, years = findset("time"))) -} diff --git a/R/toolIso2CellCountries.R b/R/toolIso2CellCountries.R deleted file mode 100644 index bfa3fffd..00000000 --- a/R/toolIso2CellCountries.R +++ /dev/null @@ -1,34 +0,0 @@ -#' toolIso2CellCountries -#' -#' Select country names of countries which are present on cellular level -#' @param x magpie object on iso country level -#' @param cells switch between 59199 ("magpiecell") and 67420 ("lpjcell") cells -#' @param absolute switch declaring the values as absolute (TRUE) or relative (FALSE) -#' for additional (type-specific) diagnostic information. If not defined (NULL) additional diagnostics -#' will not be shown. -#' @return return selected input data -#' @author Kristine Karstens, Felicitas Beier, Jan Philipp Dietrich -#' -#' @importFrom utils read.csv -#' @export - - -toolIso2CellCountries <- function(x, cells = "magpiecell", absolute = NULL) { - if (cells == "magpiecell") { - cellToCellIso <- toolGetMapping(name = "CountryToCellMapping.rds", where = "mrcommons") - isoCellCountry <- unique(cellToCellIso$iso) - } else if (cells == "lpjcell") { - map <- toolGetMappingCoord2Country() - isoCellCountry <- unique(map$iso) - } - y <- x[isoCellCountry, , ] - - if (isTRUE(absolute)) { - total <- dimSums(dimSums(x, dim = c(2, 3)), dim = 1) - returned <- dimSums(dimSums(y, dim = c(2, 3)), dim = 1) - lost <- round((1 - (returned / total)) * 100, 2) - if (lost != 0) vcat(1, paste0(lost, "% of the summed up values of the data set got lost.")) - } - - return(y) -} diff --git a/R/toolLPJmLVersion.R b/R/toolLPJmLVersion.R deleted file mode 100644 index d1d81492..00000000 --- a/R/toolLPJmLVersion.R +++ /dev/null @@ -1,63 +0,0 @@ -#' @title toolLPJmLVersion -#' -#' @description Specify default settings for LPJmL version and baseline settings -#' -#' @param version Switch between LPJmL versions (including add-ons (+*) for further version specification) -#' @param climatetype Switch between different climate scenarios -#' -#' @return configuration as list -#' @author Kristine Karstens -#' -#' @importFrom stringr str_split -#' -#' @export - -toolLPJmLVersion <- function(version, climatetype) { - cfg <- NULL - - ##### DEFAULT CONFIG ##### - cfg$baseline_hist <- "GSWP3-W5E5:historical" - cfg$ref_year_hist <- "y2010" - cfg$baseline_gcm <- "MRI-ESM2-0:ssp370" - cfg$ref_year_gcm <- "y2020" - cfg$readin_version <- version - cfg$baseline_version <- version - cfg$climatetype <- climatetype - ##### DEFAULT CONFIG ##### - - - ##### ADDON CONFIG ##### - # overwrite default settings and LPJmL version for - # (1) add-on tag in version argument - implemented add-ons: - # * `+baseline_gcm` - use another baseline for 2010--2020 - # * `+scen:` - implemented scenario will be handled automatically for - # the readLPJmL call, no additional changes needed here - # * `gsadapt2020` - Specific if in case the gsadapt scenario want to be - # harmonized to "standard" 2020 historical values and - # not with its own historical patterns - - ### version addon - if (grepl("\\+", version)) { - tmp <- unlist(str_split(version, "\\+")) - - if (any(grepl("baseline_gcm", tmp))) { - i <- grep("baseline_gcm", tmp) - cfg$baseline_gcm <- gsub("baseline_gcm", "", tmp[i]) - cfg$readin_version <- tmp[1] - } - - if (any(grepl("gsadapt2020", tmp))) { - - if (cfg$climatetype != cfg$baseline_hist) { - cfg$readin_version <- paste0(tmp[1], "+scen:gsadapt") - } else { - cfg$readin_version <- tmp[1] - } - - cfg$baseline_version <- tmp[1] - } - } - ##### ADDON CONFIG ##### - - return(cfg) -} diff --git a/R/toolSmooth.R b/R/toolSmooth.R deleted file mode 100644 index e9c7b4c7..00000000 --- a/R/toolSmooth.R +++ /dev/null @@ -1,29 +0,0 @@ -#' @title toolSmooth -#' -#' @description Smooth a time series using a given method and its default settings -#' -#' @param x magclass object that should be smoothed -#' @param method spline, average or more (See default argument for current default setting) -#' -#' @return smoothed data in magclass format -#' @author Kristine Karstens -#' -#' @importFrom madrat toolTimeSpline toolTimeAverage -#' @export - -toolSmooth <- function(x, method = "spline") { - - if (!is.magpie(x)) stop("Input is not a MAgPIE object, x has to be a MAgPIE object!") - - if (method == "spline") { - # current default is spline with 4 degrees of freedom per 100 years - out <- toolTimeSpline(x, dof = 4) - } else if (method == "average") { - # backup and old default of 8-year averages - out <- toolTimeAverage(x, averaging_range = 8, cut = FALSE) - } else { - stop("This method is not supported.") - } - - return(out) -} diff --git a/R/toolSum2Country.R b/R/toolSum2Country.R deleted file mode 100644 index 14685d54..00000000 --- a/R/toolSum2Country.R +++ /dev/null @@ -1,16 +0,0 @@ -#' toolSum2Country -#' -#' Efficient method to sum cellular data with country dimension as first -#' sub-dimension to country level -#' @param x magpie object on cellular level with countries in dim 1.1 -#' @return return selected input data on ISO country level -#' @author Jan Philipp Dietrich -#' @export - -toolSum2Country <- function(x) { - map <- data.frame(from = getItems(x, dim = 1), - to = getItems(x, dim = 1.1, full = TRUE)) - x <- toolAggregate(x, map) - getSets(x, fulldim = FALSE)[1] <- "country" - return(toolCountryFill(x, fill = 0, verbosity = 2)) -} diff --git a/README.md b/README.md index 1912661e..7b4f8b2c 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # MadRat commons Input Data Library -R package **mrcommons**, version **1.40.2** +R package **mrcommons**, version **1.41.0** [![CRAN status](https://www.r-pkg.org/badges/version/mrcommons)](https://cran.r-project.org/package=mrcommons) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3822009.svg)](https://doi.org/10.5281/zenodo.3822009) [![R build status](https://github.com/pik-piam/mrcommons/workflows/check/badge.svg)](https://github.com/pik-piam/mrcommons/actions) [![codecov](https://codecov.io/gh/pik-piam/mrcommons/branch/master/graph/badge.svg)](https://app.codecov.io/gh/pik-piam/mrcommons) [![r-universe](https://pik-piam.r-universe.dev/badges/mrcommons)](https://pik-piam.r-universe.dev/builds) @@ -39,7 +39,7 @@ In case of questions / problems please contact Jan Philipp Dietrich , R package version 1.40.2, . +Bodirsky B, Karstens K, Baumstark L, Weindl I, Wang X, Mishra A, Wirth S, Stevanovic M, Steinmetz N, Kreidenweis U, Rodrigues R, Popov R, Humpenoeder F, Giannousakis A, Levesque A, Klein D, Araujo E, Beier F, Oeser J, Pehl M, Leip D, Crawford M, Molina Bacca E, von Jeetze P, Martinelli E, Schreyer F, Soergel B, Sauer P, Hötten D, Hasse R, Abrahão G, Weigmann P, Dietrich J (2024). _mrcommons: MadRat commons Input Data Library_. doi:10.5281/zenodo.3822009 , R package version 1.41.0, . A BibTeX entry for LaTeX users is @@ -48,8 +48,8 @@ A BibTeX entry for LaTeX users is title = {mrcommons: MadRat commons Input Data Library}, author = {Benjamin Leon Bodirsky and Kristine Karstens and Lavinia Baumstark and Isabelle Weindl and Xiaoxi Wang and Abhijeet Mishra and Stephen Wirth and Mishko Stevanovic and Nele Steinmetz and Ulrich Kreidenweis and Renato Rodrigues and Roman Popov and Florian Humpenoeder and Anastasis Giannousakis and Antoine Levesque and David Klein and Ewerton Araujo and Felicitas Beier and Julian Oeser and Michaja Pehl and Debbora Leip and Michael Crawford and Edna {Molina Bacca} and Patrick {von Jeetze} and Eleonora Martinelli and Felix Schreyer and Bjoern Soergel and Pascal Sauer and David Hötten and Robin Hasse and Gabriel Abrahão and Pascal Weigmann and Jan Philipp Dietrich}, year = {2024}, - note = {R package version 1.40.2}, - doi = {10.5281/zenodo.3822009}, + note = {R package version 1.41.0}, url = {https://github.com/pik-piam/mrcommons}, + doi = {10.5281/zenodo.3822009}, } ``` diff --git a/inst/extdata/FAOelementShort.csv b/inst/extdata/FAOelementShort.csv deleted file mode 100644 index 4479f828..00000000 --- a/inst/extdata/FAOelementShort.csv +++ /dev/null @@ -1,42 +0,0 @@ -ElementCode;Element;ElementShort;Unit;Unit_original -511;Total Population - Both sexes;population;number;1000 -641;Food supply quantity (tonnes);food_supply;tonnes;tonnes -645;Food supply quantity (kg/capita/yr);food_supply_kg/cap/yr;Kg;Kg -645;Food supply quantity (kg/capita/yr);food_supply_kg/cap/yr;kg;kg -646;Food supply quantity (g/capita/day);food_supply_g/cap/day;g/capita/day;g/capita/day -664;Food supply (kcal/capita/day);food_supply_kcal/cap/day;kcal/capita/day;kcal/capita/day -674;Protein supply quantity (g/capita/day);protein_supply_g/cap/day;g/capita/day;g/capita/day -684;Fat supply quantity (g/capita/day);fat_supply_g/cap/day;g/capita/day;g/capita/day -5009;Area;area_1000_ha/yr;1000 Ha/year;1000 Ha/year -5071;Stock Variation;stock_variation;tonnes;tonnes -5072;Stock Variation;stock_variation;tonnes;1000 tonnes -5110;Area;area;ha;1000 Ha -5111;Stocks;stock;Head;Head -5112;Stocks;stock;Head;1000 Head -5120;Waste;waste;tonnes;tonnes -5123;Waste;waste;tonnes;1000 tonnes -5130;Processed;processed;tonnes;tonnes -5131;Processed;processed;tonnes;1000 tonnes -5141;Food;food;tonnes;tonnes -5142;Food;food;tonnes;1000 tonnes -5153;Other Util;other_util;tonnes;tonnes -5154;Other Util;other_util;tonnes;1000 tonnes -5300;Domestic supply quantity;domestic_supply;tonnes;tonnes -5301;Domestic supply quantity;domestic_supply;tonnes;1000 tonnes -5312;Area harvested;area_harvested;Ha;Ha -5312;Area harvested;area_harvested;ha;Ha -5510;Production;production;tonnes;tonnes -5511;Production Quantity;production;tonnes;1000 tonnes -5516;Production;production;m3;m3 -5520;Feed;feed;tonnes;tonnes -5521;Feed;feed;tonnes;1000 tonnes -5525;Seed;seed;tonnes;tonnes -5527;Seed;seed;tonnes;1000 tonnes -5610;Import Quantity;import;tonnes;tonnes -5611;Import Quantity;import;tonnes;1000 tonnes -5616;Import Quantity;import_m3;m3;m3 -5622;Import Value;import_kUS$;1000 US$;1000 US$ -5910;Export Quantity;export;tonnes;tonnes -5911;Export Quantity;export;tonnes;1000 tonnes -5916;Export Quantity;export_m3;m3;m3 -5922;Export Value;export_kUS$;1000 US$;1000 US$ diff --git a/inst/extdata/FAOiso_faocode.csv b/inst/extdata/FAOiso_faocode.csv deleted file mode 100644 index de709f84..00000000 --- a/inst/extdata/FAOiso_faocode.csv +++ /dev/null @@ -1,244 +0,0 @@ -CountryCode;Country;ISO3;manuallyadded -2;Afghanistan;AFG;FALSE -3;Albania;ALB;FALSE -4;Algeria;DZA;FALSE -6;Andorra;AND;FALSE -7;Angola;AGO;FALSE -8;Antigua and Barbuda;ATG;FALSE -9;Argentina;ARG;FALSE -1;Armenia;ARM;FALSE -10;Australia;AUS;FALSE -11;Austria;AUT;FALSE -52;Azerbaijan;AZE;FALSE -12;Bahamas;BHS;FALSE -13;Bahrain;BHR;FALSE -16;Bangladesh;BGD;FALSE -14;Barbados;BRB;FALSE -57;Belarus;BLR;FALSE -255;Belgium;BEL;FALSE -23;Belize;BLZ;FALSE -53;Benin;BEN;FALSE -18;Bhutan;BTN;FALSE -19;Bolivia (Plurinational State of);BOL;FALSE -80;Bosnia and Herzegovina;BIH;FALSE -20;Botswana;BWA;FALSE -21;Brazil;BRA;FALSE -26;Brunei Darussalam;BRN;FALSE -27;Bulgaria;BGR;FALSE -233;Burkina Faso;BFA;FALSE -29;Burundi;BDI;FALSE -35;Cabo Verde;CPV;FALSE -115;Cambodia;KHM;FALSE -32;Cameroon;CMR;FALSE -33;Canada;CAN;FALSE -37;Central African Republic;CAF;FALSE -39;Chad;TCD;FALSE -40;Chile;CHL;FALSE -351;China;CHN;FALSE -44;Colombia;COL;FALSE -45;Comoros;COM;FALSE -46;Congo;COG;FALSE -47;Cook Islands;COK;FALSE -48;Costa Rica;CRI;FALSE -107;Cote d'Ivoire;CIV;FALSE -98;Croatia;HRV;FALSE -49;Cuba;CUB;FALSE -50;Cyprus;CYP;FALSE -167;Czech Republic;CZE;FALSE -116;Democratic People's Republic of Korea;PRK;FALSE -250;Democratic Republic of the Congo;COD;FALSE -54;Denmark;DNK;FALSE -72;Djibouti;DJI;FALSE -55;Dominica;DMA;FALSE -56;Dominican Republic;DOM;FALSE -58;Ecuador;ECU;FALSE -59;Egypt;EGY;FALSE -60;El Salvador;SLV;FALSE -61;Equatorial Guinea;GNQ;FALSE -178;Eritrea;ERI;FALSE -63;Estonia;EST;FALSE -238;Ethiopia;ETH;FALSE -66;Fiji;FJI;FALSE -67;Finland;FIN;FALSE -68;France;FRA;FALSE -74;Gabon;GAB;FALSE -75;Gambia;GMB;FALSE -73;Georgia;GEO;FALSE -79;Germany;DEU;FALSE -81;Ghana;GHA;FALSE -84;Greece;GRC;FALSE -86;Grenada;GRD;FALSE -89;Guatemala;GTM;FALSE -90;Guinea;GIN;FALSE -175;Guinea-Bissau;GNB;FALSE -91;Guyana;GUY;FALSE -93;Haiti;HTI;FALSE -95;Honduras;HND;FALSE -97;Hungary;HUN;FALSE -99;Iceland;ISL;FALSE -100;India;IND;FALSE -101;Indonesia;IDN;FALSE -102;Iran (Islamic Republic of);IRN;FALSE -103;Iraq;IRQ;FALSE -104;Ireland;IRL;FALSE -105;Israel;ISR;FALSE -106;Italy;ITA;FALSE -109;Jamaica;JAM;FALSE -110;Japan;JPN;FALSE -112;Jordan;JOR;FALSE -108;Kazakhstan;KAZ;FALSE -114;Kenya;KEN;FALSE -83;Kiribati;KIR;FALSE -118;Kuwait;KWT;FALSE -113;Kyrgyzstan;KGZ;FALSE -120;Lao People's Democratic Republic;LAO;FALSE -119;Latvia;LVA;FALSE -121;Lebanon;LBN;FALSE -122;Lesotho;LSO;FALSE -123;Liberia;LBR;FALSE -124;Libya;LBY;FALSE -126;Lithuania;LTU;FALSE -256;Luxembourg;LUX;FALSE -129;Madagascar;MDG;FALSE -130;Malawi;MWI;FALSE -131;Malaysia;MYS;FALSE -132;Maldives;MDV;FALSE -133;Mali;MLI;FALSE -134;Malta;MLT;FALSE -127;Marshall Islands;MHL;FALSE -136;Mauritania;MRT;FALSE -137;Mauritius;MUS;FALSE -138;Mexico;MEX;FALSE -145;Micronesia (Federated States of);FSM;FALSE -140;Monaco;MCO;FALSE -141;Mongolia;MNG;FALSE -273;Montenegro;MNE;FALSE -143;Morocco;MAR;FALSE -144;Mozambique;MOZ;FALSE -28;Myanmar;MMR;FALSE -147;Namibia;NAM;FALSE -148;Nauru;NRU;FALSE -149;Nepal;NPL;FALSE -150;Netherlands;NLD;FALSE -156;New Zealand;NZL;FALSE -157;Nicaragua;NIC;FALSE -158;Niger;NER;FALSE -159;Nigeria;NGA;FALSE -160;Niue;NIU;FALSE -162;Norway;NOR;FALSE -221;Oman;OMN;FALSE -165;Pakistan;PAK;FALSE -180;Palau;PLW;FALSE -166;Panama;PAN;FALSE -168;Papua New Guinea;PNG;FALSE -169;Paraguay;PRY;FALSE -170;Peru;PER;FALSE -171;Philippines;PHL;FALSE -173;Poland;POL;FALSE -174;Portugal;PRT;FALSE -179;Qatar;QAT;FALSE -117;Republic of Korea;KOR;FALSE -146;Republic of Moldova;MDA;FALSE -183;Romania;ROU;FALSE -185;Russian Federation;RUS;FALSE -184;Rwanda;RWA;FALSE -188;Saint Kitts and Nevis;KNA;FALSE -189;Saint Lucia;LCA;FALSE -191;Saint Vincent and the Grenadines;VCT;FALSE -244;Samoa;WSM;FALSE -192;San Marino;SMR;FALSE -193;Sao Tome and Principe;STP;FALSE -194;Saudi Arabia;SAU;FALSE -195;Senegal;SEN;FALSE -272;Serbia;SRB;FALSE -196;Seychelles;SYC;FALSE -197;Sierra Leone;SLE;FALSE -200;Singapore;SGP;FALSE -199;Slovakia;SVK;FALSE -198;Slovenia;SVN;FALSE -25;Solomon Islands;SLB;FALSE -201;Somalia;SOM;FALSE -202;South Africa;ZAF;FALSE -277;South Sudan;SSD;FALSE -203;Spain;ESP;FALSE -38;Sri Lanka;LKA;FALSE -276;Sudan;SDN;FALSE -207;Suriname;SUR;FALSE -209;Swaziland;SWZ;FALSE -210;Sweden;SWE;FALSE -211;Switzerland;CHE;FALSE -212;Syrian Arab Republic;SYR;FALSE -208;Tajikistan;TJK;FALSE -216;Thailand;THA;FALSE -154;The former Yugoslav Republic of Macedonia;MKD;FALSE -176;Timor-Leste;TLS;FALSE -217;Togo;TGO;FALSE -219;Tonga;TON;FALSE -220;Trinidad and Tobago;TTO;FALSE -222;Tunisia;TUN;FALSE -223;Turkey;TUR;FALSE -213;Turkmenistan;TKM;FALSE -227;Tuvalu;TUV;FALSE -226;Uganda;UGA;FALSE -230;Ukraine;UKR;FALSE -225;United Arab Emirates;ARE;FALSE -229;United Kingdom;GBR;FALSE -215;United Republic of Tanzania;TZA;FALSE -231;United States of America;USA;FALSE -234;Uruguay;URY;FALSE -235;Uzbekistan;UZB;FALSE -155;Vanuatu;VUT;FALSE -236;Venezuela (Bolivarian Republic of);VEN;FALSE -237;Viet Nam;VNM;FALSE -249;Yemen;YEM;FALSE -251;Zambia;ZMB;FALSE -181;Zimbabwe;ZWE;FALSE -5;American Samoa;ASM;TRUE -258;Anguilla;AIA;TRUE -22;Aruba;ABW;TRUE -15;Belgium-Luxembourg;XBL;TRUE -17;Bermuda;BMU;TRUE -239;British Virgin Islands;VGB;TRUE -36;Cayman Islands;CYM;TRUE -96;China, Hong Kong SAR;HKG;TRUE -128;China, Macao SAR;MAC;TRUE -41;China, mainland;XCN;TRUE -214;China, Taiwan Province of;TWN;TRUE -51;Czechoslovakia;CSK;TRUE -62;Ethiopia PDR;XET;TRUE -65;Falkland Islands (Malvinas);FLK;TRUE -64;Faroe Islands;FRO;TRUE -69;French Guiana;GUF;TRUE -70;French Polynesia;PYF;TRUE -82;Gibraltar;GIB;TRUE -85;Greenland;GRL;TRUE -87;Guadeloupe;GLP;TRUE -88;Guam;GUM;TRUE -94;Holy See;VAT;TRUE -125;Liechtenstein;LIE;TRUE -135;Martinique;MTQ;TRUE -270;Mayotte;MYT;TRUE -142;Montserrat;MSR;TRUE -151;Netherlands Antilles;ANT;TRUE -153;New Caledonia;NCL;TRUE -163;Northern Mariana Islands;MNP;TRUE -299;Occupied Palestinian Territory;PSE;TRUE -164;Pacific Islands Trust Territory;PCI;TRUE -177;Puerto Rico;PRI;TRUE -182;Reunion;REU;TRUE -187;Saint Helena, Ascension and Tristan da Cunha;SHN;TRUE -190;Saint Pierre and Miquelon;SPM;TRUE -186;Serbia and Montenegro;SCG;TRUE -206;Sudan (former);XSD;TRUE -218;Tokelau;TKL;TRUE -224;Turks and Caicos Islands;TCA;TRUE -240;United States Virgin Islands;VIR;TRUE -228;USSR;SUN;TRUE -243;Wallis and Futuna Islands;WLF;TRUE -205;Western Sahara;ESH;TRUE -248;Yugoslav SFR;YUG;TRUE -264;Isle of Man;IMN;TRUE -161;Norfolk Island;NFK;TRUE -172;Pitcairn Islands;PCN;TRUE -260;Svalbard and Jan Mayen Islands;SJM;TRUE -259;Channel Islands;JEY;TRUE diff --git a/inst/extdata/FAOiso_faocode_online.csv b/inst/extdata/FAOiso_faocode_online.csv deleted file mode 100644 index d4b7eef4..00000000 --- a/inst/extdata/FAOiso_faocode_online.csv +++ /dev/null @@ -1,15 +0,0 @@ -Country,ISO -Belgium-Luxembourg,XBL -"China, mainland",XCN -Pacific Islands Trust Territory,PCI -Sudan (former),XSD -Channel Islands,JEY -French Guyana,GUF -Saint Barthélemy,BLM -Saint-Martin (French part),MAF -Wake Island,UMI -Türkiye,TUR -Heard and McDonald Islands,HMD -Johnston Island,UMI -Canton and Enderbury Islands,KIR -Midway Island,UMI \ No newline at end of file diff --git a/inst/extdata/sectoral/FAOSTAT_data_1-26-2021_FishesProduction.csv b/inst/extdata/sectoral/FAOSTAT_data_1-26-2021_FishesProduction.csv deleted file mode 100644 index 416a78b1..00000000 --- a/inst/extdata/sectoral/FAOSTAT_data_1-26-2021_FishesProduction.csv +++ /dev/null @@ -1,340 +0,0 @@ -Country (Name),ASFIS species (Name),FAO major fishing area (Name),Detailed production source (Name),Unit (Name),[1950],S,[1951],S,[1952],S,[1953],S,[1954],S,[1955],S,[1956],S,[1957],S,[1958],S,[1959],S,[1960],S,[1961],S,[1962],S,[1963],S,[1964],S,[1965],S,[1966],S,[1967],S,[1968],S,[1969],S,[1970],S,[1971],S,[1972],S,[1973],S,[1974],S,[1975],S,[1976],S,[1977],S,[1978],S,[1979],S,[1980],S,[1981],S,[1982],S,[1983],S,[1984],S,[1985],S,[1986],S,[1987],S,[1988],S,[1989],S,[1990],S,[1991],S,[1992],S,[1993],S,[1994],S,[1995],S,[1996],S,[1997],S,[1998],S,[1999],S,[2000],S,[2001],S,[2002],S,[2003],S,[2004],S,[2005],S,[2006],S,[2007],S,[2008],S,[2009],S,[2010],S,[2011],S,[2012],S,[2013],S,[2014],S,[2015],S,[2016],S,[2017],S,[2018],S -Afghanistan,All,All,All,Tonnes - live weight,100,,100,,100,,100,,100,,200,,200,,200,,200,,200,,200,,300,,300,,300,,300,,300,,300,,400,,400,,460,,460,F,560,F,560,F,560,F,560,F,660,F,770,F,770,F,770,F,770,F,870,F,870,F,870,F,970,F,970,F,970,F,970,F,1170,F,1170,F,1170,F,1400,F,1400,F,1500,F,1500,F,1600,F,1600,F,1600,F,1550,F,1500,F,1500,F,1300,F,1250,F,1350,F,1350,F,1450,F,1450,F,1450,F,2050,F,2050,F,2650,F,3250,F,4000,F,4780,F,5560,F,6360,F,7200,F,8050,F,9000,F,10000,F -Albania,All,All,All,Tonnes - live weight,1500,,1600,,2100,,2500,,2400,,2600,,2500,,2700,,2600,,2700,,2600,,3200,,2800,,3000,,3000,,4000,,4000,,5000,,6000,,7000,,8000,F,8000,F,8000,F,8000,F,8500,F,8500,F,8500,F,8500,F,9000,F,9000,F,9000,F,9376,,9511,,9815,,8006,,11399,,11922.2,,13134.4,,14559.3,,11961.4,,15015.6,,3871,,3093.2,,2889,F,2495,F,1720.3,,2449.2,,1110.8,,2807.5,,3057.9,,3635,,3597.2,,4516.8,,4274.6,,6118.5,,6473,,7709.3,,7505.4,,7365,,8128,,7652.2,,7150.2,,12055.2,,12673.5,,12705.5,,11350.5,,12611.7,,12994.2,,14906.3, -Algeria,All,All,All,Tonnes - live weight,27300,,23100,,29100,,22798,,20962,,25898,,21956,,21952,,18578,,22100,,25500,,30400,,21500,,16900,,17300,,18300,,20350,,21150,,18250,,23150,,24234,,23715,,28313,,31243,,35758,,37693,,35122,,43475,,34143,,38678,,48000,F,56000,F,64500,F,65002,F,65595.9,F,66141.6,F,65511.8,,94367.8,,106741.3,,99555.42,,90603.24,F,79852,F,95420.91,F,102199.97,F,135798.86,F,106247.67,,82317.8,,91907.4,,92620,,102649,,113510.9,,134082,,134800.8,,141376,,114050.2,,126628.3,,146051.8,,147768.13,,141642.35,,129940.23,,95222.74,,104060.77,,108265,,103249.88,,100238.97,,97278.99,,101556,,105516,,120355.12, -American Samoa,All,All,All,Tonnes - live weight,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,100,,82,,136,,113,,202,,193,,183,,148,,153,,112,,112,,152,,118,,181,,78,,123,,102,,42,,50,,60,,40,,132,,176,,225,,448,,610,,554,,851,,3677,,6949,,5099,,4051,,4029,,5367,,6537,,4442,,4962,,5328,,4921,,6129,,3293,,2599,,3158,,3201,,4062,,3039, -Andorra,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -Angola,All,All,All,Tonnes - live weight,139200,,183000,,157300,,224400,,265200,,294400,,425500,,400500,,283200,,272700,,257300,,246800,,274800,,245300,,362000,,263300,,335300,,299300,,303100,,425200,,374501,,324322,,606656,,478659,,399784,,160581,,81542,,120407,,125630,,113072,,85085,,130957,,111488,,110376,,95188,,100093,,84660,,107490,,126031,,137465,,133088,,117104,,113625,,126200,,132413,,122781,,137815,,146304,,163149,,175799,,239356,,254564,,255494,,212105,,240094,,202742,F,225897,,306626,,306050,,272302,,310310,F,342310,F,374310,F,407057,F,442389,,496104,,487145,,532914,,444007, -Anguilla,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,100,F,100,F,100,F,100,F,196,F,196,F,196,F,246,F,246,F,246,F,323,F,323,F,323,F,353,F,373,F,373,F,537,,500,,470,,434,,397,,372,,463,,364,,371,,379,F,433,F,458,F,483,F,508,F,538,F,563,F,588,F,613,F,638,F,663,F,688,F,713,F,739,,617,,620,,884,,981,,788,,876,,780,F,684,,758,,758,F -Antigua and Barbuda,All,All,All,Tonnes - live weight,100,,100,,200,,200,,200,,300,,400,,600,,800,,700,,600,,800,,800,,800,,700,,800,,800,,800,,800,,800,,900,F,900,F,1200,,1500,,1590,,1601,,1613,,1821,,1620,,1219,,1171,,1157,,950,,1063,,1465,,2407,,2400,F,1985,F,1488,F,990,F,875,,1481,,1692,,1046,,1095,,1576,,1434,,1639,,1675,,1626,,1723,,1797,,2343,,2541,,2473,,2948,,3044,,3042,,3389,,2416,,2219,,3059,,5961,,4616,,3127,,3175,,3175,F,3175,F,3175,F -Argentina,All,All,All,Number,946,,796,,798,,678,,1083,,947,,812,,1108,,1860,,923,,932,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,21,,16,,31,,29,,0,.,0,.,0,.,41,,96,,364,,117,,162,,0,.,0,.,15,,12,,489,,201,,350,,1083,,1132,,4072,,4636,,7209,,4866,,10658,,6413,,22680,,24500,,19600,,23501,,21550,,15926,,14207,,2822, -Argentina,All,All,All,Tonnes - live weight,58600,,78600,,79700,,78200,,79100,,79900,,77200,,83300,,84200,,91400,,103800,,101900,,101307,,130573,,168608,,205005,,250805,,240868,,223132,,202815,,214519,,228631,,237975,,302020,,296001,,232251,,281776,,402780,,541959,,580735,,400101,,374882,,488235,,428207,,322692,,419724,,422663,,461892,,496394,,494983,,561157,,642671,,745984,,985251,,1000460,,1176947,,1291387,,1390864,,1166429,,1088144,,923584,,932421,,947642,,911847,,946694,,931994,,1174508,,988366,,997783,,864556.95,,814403.35,,796501.25,,741018.42,,874530.12,,833937.71,,817962.65,,758899.48,,838628.76,,838592.31, -Armenia,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,7017,,7342,,7755,,4570,,4450,,4100,,2603,,1941,,1230,,1250,,1135,,2033,,2026,,2197,,1485,,1633,,1031,,989,F,1406,F,4715,,5701,,5859,F,5617,F,7095,F,9711,,15799,,18720,,23270,,20139,,17054,,17369, -Aruba,All,All,All,Tonnes - live weight,0,0,0,0,0,0,0,0,300,,300,,200,,100,,200,,200,,200,,200,,300,,300,,300,,300,,300,,400,,400,,400,,400,F,400,F,400,F,500,F,550,F,600,F,650,F,700,F,770,F,770,F,770,F,770,F,770,F,770,F,770,F,770,,700,F,620,F,550,F,480,F,420,F,350,F,300,,260,,260,,140,,160,,205,,182,,175,,163,,163,,163,F,160,F,162,,162,F,145,,159,,151,,163,,153,,168,,138,,145,,152,F,150,F,150,F,149,,149,F -Australia,All,All,All,Number,388,,1224,,1787,,2001,,2039,,1854,,2051,,2100,,2095,,1811,,1809,,1937,,1320,,720,,710,,668,,606,,587,,658,,679,,799,,860,,953,,971,,1080,,1172,,995,,624,,679,,0,.,0,.,0,.,0,.,11,,0,.,0,.,0,.,0,.,2524,,2929,,2964,,3588,,6293,,11210,,7767,,10417,,10717,,9002,,10264,,5134,,13333,,11881,,10457,,15539,,12274,,22847,,16231,,21381,,28673,,20964,,30556,,32201,,43774,,31774,,30793,,34942,,32846,,48887,,38861, -Australia,All,All,All,Tonnes - live weight,33100,,36800,,45664,,52209,,53524,,52422,,50364,,55564,,55222,,58786,,61345,,61778,,67136,,72999,,73332,,83876,,95708,,99757,,110337,,98579,,104218,,115969,,126697,,128690,,143486,,113404,,115016,,147697,,140839,,146614,,145867,,162333,,180767,,178157,,185799,,172379,,192817,F,222489,F,232526,F,210964,,246942,,258397,,266556,,266979,,239526,,250116,,245323,,247004,,252579.44,,268522,,238126,,240630,,246082,,263343.23,,287701.05,,293773.29,,262276,,246298.32,,242554.4,,249222.35,,261930.78,,258645.62,,251896.15,,246480.39,,241207.81,,252053.93,,280563.27,,270583.36,,283374.2, -Austria,All,All,All,Tonnes - live weight,1300,,1200,,1700,,1800,,2000,,2300,,2800,,3200,,3500,,4000,,4200,,4200,,4500,,4600,,4900,,5100,,4900,,4200,,4000,,3500,F,3200,,2900,,2600,,2200,,2070,,2040,,2360,,2470,,3700,,4100,,4300,,4400,,4500,,4700,,4700,,4800,,4700,,4600,,5100,,5000,,3659,,3635,,3619,,3560,,3491,,3322,,3399,,3483,,3360,,3499,,3286,,2755,,2683,,2605,,2667,,2790,,2863,,2889,,2437,,2491,,2517,,3254.3,,3425.5,,3587,,3740.3,,3853.4,,3833.4,,4211.6,,4340.6, -Azerbaijan,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,56996,,54406,,41665,,39663,,32029,,22934,,19939,,11061,,7121,,5525,,4971,,21082,,18937,,10988,,11268,,6565,,9608,,9501,,4661,,3849,,2524,,2206,,2131,,1578,,1277.1,,1121,,1211,,1155,,1362,,1645,,2079, -Bahamas,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,629,,602,,72,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Bahamas,All,All,All,Tonnes - live weight,600,,1000,,700,,1100,,1500,,1700,,1600,,1500,,1500,,1700,,1600,,1500,,1600,,1700,,1900,,2950,,3800,,2950,,2750,,2350,,2750,,2950,,3300,,4400,,4663,,4693,,4750,,5327,,5114,,5765,,7119,,7631.79,,7737.04,,8548.15,,8440.07,,11206.82,,8695.9,,10557.03,,10515.67,,9857.61,,9758.18,,11895.54,,12215.53,,13703.94,,14931.02,,13269.48,,14100.14,,14720.59,,14578.68,,13668,,15529,,13684,,15700,,16776,,15194,,15640,,16294,,10942,,14833,,13826.83,,16343.44,,15314,,16757.83,,12766,,11887.57,,11316.62,,11634,,11485.94,,11005.53, -Bahrain,All,All,All,Tonnes - live weight,800,,800,,900,,900,,1000,,1000,,1000,,1200,,1200,,1200,,1500,,1500,,1500,,1800,,1800,,2000,,2500,,2800,,3000,,3300,,3500,F,3600,F,3700,F,3800,F,3900,F,4000,F,4084,,4837,,4000,,3801,,5115,,5747,,5594,,4812,,5599,,7763,,8057,,7842,,6736,,9207,,8105,,7553,,7983,,8958,,7628,,9393,,12943,,10050,,9850,,10623,,11730,,11230,,11207,,13642,,14497,,11858,,15595,,15014,,14178,,16358,,13493.19,,9918.42,,27092.72,,14976,,15859.71,,15000,F,15014.21,F,15000,F,15000,F -Bangladesh,All,All,All,Number,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,430,,400,,600,,0,.,0,. -Bangladesh,All,All,All,Tonnes - live weight,200180,,200200,,200220,,250250,,300280,,300310,,300350,,350400,,350450,,400510,,400570,,450640,,500720,,500810,,550910,,601030,,601150,,601300,,651460,,651650,,690100,F,740100,F,818100,,820200,,822082,,640070,,641605,,643769,,646895,,647128,,646970,,651256,,689498,,726587,,750196,,766081,,793191,,805285,,830593,,839944,,846144,,900720,,936195,,1002567,,1034980,,1109462,,1193874,,1315290,,1413953,,1552417,,1661384,,1781057,,1890459,,1998197,,2102026,,2215957,,2328545,,2440011,,2563296,,2885864,,3035101,,3124677,,3261781,,3410254,,3548115,,3684245,,3878324,,4134436,,4276641, -Barbados,All,All,All,Tonnes - live weight,3013,,3013,,4513,,3713,,2813,,2813,,3213,,4113,,4513,,4413,,5213,,4613,,4713,,2813,,2113,,2613,,4813,,4613,,3213,F,3013,F,2313,F,2313,F,2313,,2613,,2668,,4223,,4952,,3188,,3597,,4267,,3747,,3423,,3492,,6534,,5784,,3832,,4112,,3610,,8939,,2585,,3029,,2292,,3585,,3225,,2829,,3592,,3523,,2820,,3655,,3281,,3185,,2731,,2530,,2849,,2159,,2193,,1988,,2237,,3564,,3509,,3282,,1828.5,,1371.5,,2990.5,,2227.5,,1520.5,,1761.5,,1720.5,F,1757.5, -Belarus,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,19000,,21457,,19626,,15543,,9922,,10011,,7721,,6178,,6859,,4821,,5184,,5804,,7269,,5610,,7543,,6196,,4733,,6242,,9051,,14780,,15445,,16485,,17162,,17415,,16464,,14245,,11424,,9999.9,,11885,,11129.5,,12382.9, -Belgium,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,19,,19,,29,,0,-,0,-,15,,0,-,0,-,15,,17,,0,-,0,-,0,-,0,-,0,- -Belgium,All,All,All,Tonnes - live weight,58400,,56800,,70600,,74300,,72700,,80100,,69100,,62900,,64000,,57500,,63700,,61600,,59900,,62000,,59500,,59700,,62800,,63800,,68200,,58700,,53000,,60200,,59000,,53300,,46820,,49031,,44503,,45372,,50597,,47157,,45705,,49426,,48044,,48835,,48181,,45555,,39996,,40831,,42241,,40368,,42135,,40737,,37966,,36935,,35100,,36445,,31769,,31346,,31681,,31473,,31671,,32039,,30862,,28092,,27474,,24981,,23147,,24669,,22735,,22298,,22957,,22578,,24936,,25874,,27007,,24803,,27014,,24724,,23116, -Belize,All,All,All,Tonnes - live weight,400,,400,,400,,500,,600,,500,,600,,600,,900,,1000,,900,,600,,800,,900,,900,,800,,900,,1300,,1300,,1600,,1500,,1800,,1900,,1600,,1618,,1603,,1613,,3082,,2808,,2554,,2066,,2252,,2288,,2641,,2672,,2427,,1861,,2059,,2151,,2312,,2132,,2679,,2715,,4573,,8326,,10669,,13172,,26185,,28242,,54990,,33952,,35206,,88760,,53518,,81472,,111585,,193397,,308817,,368802,,309942,,407338.7,,282517,,177825.6,,41823,,83402.76,,101727,,92579,,121862,,216670, -Benin,All,All,All,Tonnes - live weight,11000,,11000,,11000,,11000,,17000,,17000,,23000,,23000,,23000,,25000,,26000,,28000,,30000,,25000,,26000,,20000,,18800,,35600,,35900,,36600,,43110,,43404,,43202,,40673,,39402,,37533,,37104,,36528,,35879,,35700,,37832,,37768,,37521,,34628,F,35290,F,36371,F,38753,F,41903,,37346,,41860,,38234,F,35083,F,32488,F,39221,F,39923,,44379,,42175,F,43785,F,42139,F,40437,,32324,F,38415,F,40670,F,41655,F,39995,F,31847,F,41922,F,36564,F,37708,F,39236,F,40155,F,39248,,40864,,43083,,49008,,40936.66,,54013,,52732.66,,60073.14, -Bermuda,All,All,All,Tonnes - live weight,500,,500,,400,,500,,500,,400,,400,,450,,450,,500,,500,,500,,400,,400,,450,,450,,500,,500,,550,,550,,900,,2380,,2820,,3220,,3599,,4572,,7208,,7635,,8723,,6347,,4105,,2024,,2348,,927,,538,,670,,820,,821,,773,,774,,463,,428,,432,,404,,394,,449,,465,,461,,466,,453,,290,,309,,397,,363,,386,,401,,381,,425,,401,,416,,375,,478,,516,,461,,417,,407,,410,,384,,354, -Bhutan,All,All,All,Tonnes - live weight,50,,50,,60,,60,,80,,80,,100,,100,,100,,100,,100,,100,,100,,150,,150,,150,,150,,150,,150,,150,,150,F,150,F,150,F,150,F,150,F,150,F,150,F,150,F,150,F,150,F,150,F,145,F,140,F,135,F,130,F,125,F,120,F,115,F,125,F,117,F,131,F,130,F,125,F,115,F,110,F,105,F,100,F,95,F,90,F,85,F,80,F,75,F,70,F,65,F,70,F,65,F,60,,55,,56,F,54,F,52,F,79,F,80.34,,72.8,,144,,156,,197,F,238,,238.62, -Bolivia (Plurinat.State),All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,50000,,30000,F,0,.,300,,0,.,2724,,0,.,0,.,0,.,0,.,15961,,1757,,17500,,0,.,28170,,31018,,43528,,36299,,51330,,44443,,49115,,54588,,27885,,24192,,48612,,59087,,61500,,84970,,67606,,43492,,53940,,29734, -Bolivia (Plurinat.State),All,All,All,Tonnes - live weight,500,,500,,500,,600,,600,,600,,800,,800,,800,,500,,700,,500,,1300,,1400,,1900,,1800,,1700,,1500,,1300,,1200,,1100,,1300,,1500,,1500,,1050,,1050,,1250,,1550,,1550,,3650,,4379,,5617,,4105,F,4105,F,4105,F,4170,,3871,,4271,,4427,,6024,,7424,,5367,,5171,,6167,,5970,,6308,,6368,,6425,,6440,,6450,,6511,,6260,,6718,F,6974,,6886,,6790,,6535,F,6355,,7381,,8343,,7802,,7643,,7880,,8247,,8390,,9987.5,F,10250,F,10500,F,10500,F -Bonaire/S.Eustatius/Saba,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,161.7,F,156,,166,,222,,253,F,313,F,373,,43, -Bosnia and Herzegovina,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,201,F,201,F,201,F,201,F,252,F,253,F,254,F,255,F,255,F,255,F,4940,,6890,,6699,,7375,,7926,,7747,F,7894,F,7925,F,7925,F,5275.2,,3943.3,,3228.5,,3702,,4755.6,,4868.6,,4065.4,,3943.8, -Botswana,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,2000,,600,,718,,1324,,7414,,587,,699,,347,,338,,2,,9,,11,,152,,0,-,9,,0,.,0,.,0,.,320,,374,,1626,,1500,,2000,,2800,,4000,,4500,,4440,,900,,3200,,1600, -Botswana,All,All,All,Tonnes - live weight,300,,300,,300,,350,,350,,350,,350,,400,,400,,400,,400,,400,,500,,500,,500,,500,,600,,600,,800,,800,,800,,1100,,1200,,1200,,1200,,1200,,1600,,1500,,1000,,1000,,1250,,1450,,1400,,1250,,1500,,1500,,1700,,1900,,1900,,1600,F,1300,F,1000,F,800,F,600,F,400,F,200,F,81,,160,,191,,157,,166,,118,,139,,122,,161,,132,,81,,122,,86,,73,F,60,,234,,378,,431,,1168,,101,,53,,53,F,53,F -Brazil,All,All,All,Number,128,,179,,168,,175,,202,,213,,217,,122,,128,,315,,813,,1083,,756,,406,,304,,229,,448,,563,,559,,617,,803,,975,,774,,732,,797,,1096,,788,,1030,,714,,766,,932,,749,,854,,894,,600,,598,,0,.,0,.,0,.,0,.,0,.,55,,428,,7296,,43969,,593,,1154,,7471,,2232,,6582,,11351,,1326,,6181,,13210,,7357,,638,,979,,12053,,7051,,9141,,1176,,9082,,6645,,22345,,5258,,12720,,177,,11690,,0,. -Brazil,All,All,All,Tonnes - live weight,153100,,158300,,174600,,160700,,172000,,190200,,207900,,212100,,211900,,239100,,251000,,275100,,379400,,335588,,368976,,388912,,393162,,419796,,495176,,492303,,573040,,613469,,620167,,760012,,715188,,752790,,660241,,731716,,754655,,783573,,808606,,811565,,831884,,881274,,960569,,967557,,957550,,947992,,830102,F,798638,F,640295,F,671510,F,670333,F,676441,F,701251,F,652910,,693172,,732261,,710704,,744598,,839296,,935946,,1003260,,985412,,1015916,,1008045,,1050810,,1072225,,1123445,,1190539,,1197146,,1196780,,1299472,,1247767,,1337522,,1285603,F,1309519,F,1313910,F,1320022,F -British Indian Ocean Ter,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28,,28,,28,,28,,21,,24,,34,,44,,40,,33,,9,,11,,9,,12,,7,,14,,10, -British Virgin Islands,All,All,All,Tonnes - live weight,100,,100,,100,,200,,200,,200,,200,,300,,300,,300,,300,,400,,400,,400,,400,,400,,400,,300,,300,,300,,300,,300,,300,,300,,306,,318,,330,F,340,F,350,F,360,F,370,F,380,F,390,F,407,,470,,520,,543,,565,,582,,615,,624,,634,,453,F,343,,470,,532,,506,,105,,116,,115,,43,,837,,1062,,2771,,1262,,1295,F,1308,F,1251,F,1202,F,1201,F,1201,F,1200,F,1196,F,1203,F,1205,F,1200.5,F,1192.5,F,1193,F,1193,F -Brunei Darussalam,All,All,All,Number,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,5,,5,,0,.,0,-,0,-,0,.,0,. -Brunei Darussalam,All,All,All,Tonnes - live weight,500,,500,,700,,600,,500,,700,,700,,700,,800,,800,,800,,900,,900,,1000,,1000,,1100,,1200,,1200,,1300,,1300,,1500,,1500,,1500,,1500,,1560,,1570,,1644,,2193,,2704,,2792,,2225,,2367,,2357,,3055,,3425,,3967,,4106,,3916,,2042,,2318,,2354,,1652,,1709,,1764,,4517,,4822,,5848,,4678,,5221,,3380,,2600,,1696,,2215,,1949,,2632,,3163,,2754,,3172,,2830,,2391,,2850.98,,2684,F,5073,F,3386.85,,3897.07,,4353.14,,14239.63,,13496,,14682.49, -Bulgaria,All,All,All,Tonnes - live weight,4000,,4000,,4500,,5600,,9100,,6800,,6500,,6000,,6100,,6100,,8700,,8100,,9600,,7500,,13700,,21183,,27375,,44881,,61484,,84473,,94201,,100704,,111017,,102010,,118559,,158121,,167144,,137997,,99028,,86107,,125187,,106673,,115618,,121126,,115567,,100223,,109452,,111652,,118018,,102966,,57103,,57854,,32148,,21555,,12685,F,12627,,13581,,16674,,23198,,18336,,12317,,11612,,17316,,16500,,10741,,8579,,10801,,12935,,14021,,15700.54,,18689.62,,15150.17,,15556.06,,21821.65,,19859.79,,22365.55,,24376.16,,24311.15,,24940.83, -Burkina Faso,All,All,All,Tonnes - live weight,800,,800,,900,,900,,1000,,1000,,1300,,1300,,1500,,1500,,2000,,2500,,2500,,3000,,3500,,3500,,4000,,4500,,4500,,4500,,5000,,5000,,4000,,5000,,5000,F,5000,F,6000,F,6000,F,7000,F,7000,,6500,,7500,,7000,,7214,F,7374,F,7433,F,7630,F,7826,F,7907,F,8006,,7006,,7005,,7505,,7000,,8000,,8000,,8030,,8045,,8375,,7625,,8505,,8505,,8505,,9005,,9005,F,9065,,9750,,10548,,11568,,12075,F,14800,F,16360,,20602,,20820,,21001,,21030,,22210,,25720,,27847, -Burundi,All,All,All,Tonnes - live weight,1700,,2200,,4100,,4200,,5600,,5600,,5400,,9700,,11500,,11000,,9200,,5300,,7200,,10600,,9700,,20200,,16600,,12300,,15000,,15600,,13300,,16900,,7500,,8500,,10982,,14547,,20333,,18900,,15682,,11250,,14767,,11880,,12131,,11366,,11363,,11371,,11861,,12034,,11701,,10787,,17425,,21044,,24123,,17050,F,22050,F,21151,,3091,,20346,,13476,,9249,,17365,,9014,,11050,F,14747,,13905,,13450,F,13028,,11359,,18315,,12665,,17322.3,,10715.7,,12469.4,,13617,,16859,,21446,,23205,,20469,,21860,F -Cabo Verde,All,All,All,Tonnes - live weight,900,,800,,1000,,1400,,1700,,1600,,1300,,1300,,1700,,1500,,1600,,1600,,1500,,2000,,2500,,3500,,4000,,5900,,4900,,4000,,5181,,4153,,4078,,8333,,3428,,3900,,3800,,6000,,7000,F,7476,F,8837,,14730,,12453,,11863,,10730,,10190,,7335,,7312,,6387,,8614,,6579.04,,7378.1,,6573,,7000,,8256,,8495,,9255,,9705,,9424,,10360,,10586,,8676,,8851,,8136,,11031,,21931,,19826,,20127,,21119,,19213,,21412,,23077,,20943,,35984,,35677,,37744,,27397,,18697,,26586, -Cambodia,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,4372,,5654,,6100,,3664,,4816,,6200,,14691,,20200,,17000,,40700,,25380,,26300,,36000,,50850,,78008,,74820,,120000,,137642,,128945,,156500,,185000,,0,.,0,.,0,.,10000,,7602,,18050,,5755,,4652,,3119, -Cambodia,All,All,All,Tonnes - live weight,20000,,20000,,23000,,24000,,25000,,28000,,30000,,30000,,32000,,32000,,35000,,45000,,45000,,40000,,40000,,45000,,45000,,50000,,50000,,52000,,52200,,77100,,87100,,84700,,84700,F,84700,F,84704,F,84711,F,40925,F,30053,F,19706,,51594,,68715,,68161,,64424,,70578,,73651,,82224,,86896,,82184,,111427,,117854,,111228,,108969,,103254,,112511,,104310,,114600,,122000,,284156,,298798,,445700,,424432,,390657,,343332,,428000,,523510,,493760,,471000,,515000,,550094,,632839,,640695,,729468,,745310,,753193,,802450,,873493,F,943205,F -Cameroon,All,All,All,Tonnes - live weight,22000,,22000,,22000,,22300,,39100,,43800,,45400,,56500,,60600,,50200,,51700,,56500,,58100,,59400,,59800,,61100,,62600,,63400,,65100,,68100,,68800,F,64700,F,60400,F,57600,F,55736,F,66280,F,70397,F,70172,F,68878,F,81236,F,81085,F,79818,,83101,,77397,,75581,,74099,,72104,,70773,,70966,F,69967,,70848,F,69392,F,72032,,65307,,79070,F,94188,,98587,,102067,F,106867,F,110067,F,112159,,121181,,130465,,118186,,129395,F,142747,,137667,,139158,,155846,F,172546,F,181570,F,190571,F,199840,F,208195,F,216575,F,225955,F,235505,,269052,,284285, -Canada,All,All,All,Number,183839,,327787,,178474,,161662,,138593,,178411,,211917,,131589,,169079,,106716,,150590,,73391,,167316,,188325,,183087,,176052,,181382,,153893,,109958,,173221,,146616,,133920,,77869,,66733,,93721,,123963,,128522,,129633,,153519,,151202,,167608,,193850,,154736,,65339,,34285,,22967,,29804,,50141,,96973,,69477,,62502,,60950,,69618,,26590,,63227,,68057,,269386,,273115,,260423,,229697,,88924,,196801,,287936,,262926,,326065,,290242,,296749,,223641,,215440,,53531,,67007,,37921,,67571,,95224,,59488,,35844,,66506,,85310,,59557, -Canada,All,All,All,Tonnes - live weight,956772,,933799,,856968,,851271,,1052375,,979533,,1131570,,1018447,,1007238,,1054393,,931042,,1036870,,1123387,,1213157,,1213316,,1242713,,1358682,,1296355,,1495890,,1404864,,1396089,,1317649,,1174655,,1169095,,1045856,,1031900,,1134435,,1271867,,1408150,,1450442,,1376925,,1448401,,1430442,,1375319,,1312249,,1482788,,1544397,,1631973,,1728032,,1682396,,1726748,,1548466,,1375876,,1214701,,1112524,,945560,,998319,,1082952,,1122419,,1160246,,1160351,,1229970,,1277887,,1321586,,1358662,,1300181,,1294235,,1197468,,1123423,,1149101,,1138105,,1109394,,1015857,,1056834,,1020461,,1050750,,1079869,,1039318.78,,1030547, -Cayman Islands,All,All,All,Tonnes - live weight,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,500,F,1391,,3992,,933,,655,,423,,396,,478,,1097,,386,,615,,837,,586,,825,,445,,125,,125,,110,,125,,125,,125,,125,,125,,125,,125,,125,,125,,125,,125,,125,,125,,125,,125,,125,,125,,125,,125,,125,,125,,125, -Central African Republic,All,All,All,Tonnes - live weight,1000,,1000,,1000,,1000,,1500,,1500,,1500,,1500,,2000,,2000,,2000,,2000,,2000,,2500,,2500,,3000,,4000,,5000,,5000,,6000,,7000,,8000,,8000,,10000,,11883,,8049,,10480,,13030,,13047,,13064,,13080,,13094,,13111,,13128,,13309,,13288,,13193,,13088,,13079,,13082,,13105,,13605,,13338,,13551,F,13755,F,13960,F,14150,F,14330,F,14580,F,15117,,15120,F,17125,F,19125,F,21125,F,23125,F,25125,F,27140,F,29140,F,31140,F,33140,F,35170,F,35170,F,32182,,30180,F,29180,F,28180,F,29190,F,29190,F,29190,F -Chad,All,All,All,Tonnes - live weight,30000,,30000,,30000,,35000,,35000,,35000,,38000,,38000,,40000,,40000,,40000,,45000,,45000,,45000,,50000,,55000,,60000,,60000,,65000,,65000,,70000,F,70000,F,70000,F,70000,F,75000,F,75000,F,75000,F,80000,F,75000,F,70000,F,60000,F,55000,F,30000,F,40000,F,50000,F,55000,F,60000,F,70000,F,58000,,64400,,70000,,60000,,80000,,87300,,80000,,90000,,100000,,85000,,51300,,59600,,83200,,75000,F,70000,F,71000,,74000,,77000,,79000,,82000,,85030,,88030,,91030,,95030,,100030,,120060,,120080,,100154,,110094,,107495,,107450,F -Channel Islands,All,All,All,Tonnes - live weight,300,,300,,300,,300,,500,,500,,500,,500,,800,,800,,800,,1000,,1000,,1000,,1000,,1200,,1200,,1200,,1200,,1200,,1300,,1300,,1300,,1700,,1625,,1449,,1368,,2004,,2688,,2704,,2906,,2800,,2241,,3477,,3669,,3024,,2333,,2390,,2756,F,2923,,3072,F,2872,F,2885,,2935,F,2886,,3063,F,4537,,4368,,4313,,3850,,3979,,4414,,4029,,4210,,3976,,4155,,4128,,4357,,4200,,3673.6,,4503.2,,4067.1,,3837.24,,3821.13,,4300.75,,4343.2,,4484.65,F,4108.2,,4669.6, -Chile,All,All,All,Number,1093,,1094,,1374,,1198,,1328,,1292,,1633,,2512,,2317,,2232,,2084,,2334,,2285,,1540,,1508,,1348,,1098,,513,,428,,253,,291,,253,,352,,246,,164,,106,,6411,,61,,13738,,9226,,7055,,1477,,0,.,4,,0,.,0,.,0,.,0,.,452,,838,,938,,270,,0,.,0,.,0,.,389,,96,,16,,1,,2,,0,.,0,.,0,.,0,-,1,,4,,14,,2,,0,-,1,,0,-,2,,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Chile,All,All,All,Tonnes - live weight,92700,,99100,,124000,,112200,,149500,,220300,,195300,,220220,,232895,,279594,,346822,,440905,,643697,,773891,,1176119,,729277,,1404860,,1085695,,1392284,,1110093,,1230468,,1516362,,820980,,694470,,1171969,,970553,,1436857,,1378970,,1989159,,2697021,,2892854,,3503373,,3846789,,4167505,,4674028,,4986865,,5695537,,4931660,,5375983,,6632640,,5424055,,6166043,,6627364,,6191764,,8021446,,7890723,,7232746,,6365709,,3824607,,5587316,,4972652,,4663433,,5132740,,4528441,,6013542,,5477533,,5294185,,4939020,,4810224,,4702902,,3761557,,4436498,,4084462,,3334603,,3820149,,3189695,,2877054,,3554168.1,,3656688.58, -China,All,All,All,Tonnes - live weight,956100,,1396400,,1754543,,1976330,,2400067,,2636082,,2776737,,2746350,,2779528,,3032559,,3173600,,3173600,,3280400,,3348000,,3483200,,3690000,,3825200,,3825200,,3931900,,4067100,,3970690,,4494570,,4996282,,4922582,,5360583,,5654493,,5703574,,6411153,,6438622,,5973332,,6252312,,6174007,,6766618,,7327544,,8211953,,9236137,,10416481,,11733919,,13189029,,14326208,,15107495,,17147309,,20607711,,24697356,,29231422,,33865204,,37799524,,38467260,F,41144461,F,43209033,F,44573574,F,45506377,F,47586869,F,49490650,F,51958654,F,53793997,F,55906797.49,,57517796.3,,59230149.74,,61125855.71,F,62843813.78,F,64411221.25,F,67528903.66,F,70662625.06,F,73683870.09,F,76016876.37,F,78337639.41,,79935165.41,,80966368.94, -"China, Hong Kong SAR",All,All,All,Tonnes - live weight,35050,,35050,,40055,,39660,,46065,,44670,,44780,,51210,,48820,,54635,,50450,,54865,,59480,,61000,,63820,,66860,,64550,,68129,,112182,,117460,,136337,,128074,,135397,,128506,,142471,,151186,,157256,,158739,,162943,,189932,,194684,,182511,,181167,,189282,,200392,,198196,,213557,,228102,,238168,,242514,,234495,,230912,,229516,,226844,,219976,,203570,,192274,,194310,,186439,,133832,F,162000,F,179599,F,174092,F,162301,F,172159,F,166094,F,158661,F,158661,F,162880,F,163784,F,172348,F,174158,F,159323,F,173893,F,164686,F,149087,F,147033,F,131726,F,128432,F -"China, Macao SAR",All,All,All,Tonnes - live weight,4900,,4500,,4300,,4100,,4900,,5500,,5500,,5500,,5500,,5800,,5800,,5800,,5800,,5500,,5800,,7000,,7500,,7500,,7000,,7000,,5900,F,6100,F,5900,F,6000,F,6000,F,6000,F,7400,F,7160,F,9236,F,7881,F,6624,F,7545,F,6656,F,6250,F,8800,F,9100,F,8042,,3517,,2485,,3464,,2583,,2322,,2668,,1898,,1890,,1604,,1418,,1500,F,1500,F,1500,F,1500,F,1500,F,1500,F,1500,F,1500,F,1500,F,1500,F,1500,F,1500,F,1500,F,1500,F,1500,F,1500,F,1500,F,1500,F,1500,F,1500,F,1500,F,1500,F -Colombia,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,.,0,.,0,.,0,.,0,.,18002,,111880,,119612,,110987,,207696,,457749,,536501,,828533,,656522,,452707,,670389,,771456,,832203,,704413,,540579,,555849,,612071,,602086,,974730,,673312,,532761,,405396,,647765,,640095,,629746,,857696,,740043,,517275,,370648,,313164,,78813, -Colombia,All,All,All,Tonnes - live weight,16000,,16000,,16000,,17000,,17500,,18900,,21200,,30100,,25100,,22500,,29800,,43300,,52000,,47700,,50300,,53500,,59200,,86200,,83600,,57600,,54500,,37700,,110700,,105300,,62418,,66575,,75109,,63972,,79590,,63411,,76224,,94731,,71442,,57639,,79289,,71696.32,,83645.48,,86068.34,,89930.34,,97732.3,,130129,,115802,,168235,,148940.5,,125420.5,,167636,,160089,,191763,,180549,,180373,,198847,,203824,,181959,,191042,,172564,,182002,,174129,F,188220,F,181092,F,187073,,161535,,171172,,161629,,160029,F,183946,F,177828,,176023,F,206605,F,202138.22, -Comoros,All,All,All,Tonnes - live weight,835,,935,,918,,818,,818,,835,,835,,717,,717,,817,,817,,817,,917,,917,,917,,917,,875,,875,,1235,,1235,,1661,,2469,,2878,,3286,,4046,,4755,,4863,,5620,,6026,,6485,,6951,,7459,,7974,,8493,,8999,,9515,,9970,,10378,,10913,,10751,,11251,,11551,,12590,,12757,,13537,,13109,,12696,,12576,,12317,,11818,,12003,,11425,,11178,,11053,,10987,,10738,,10464,,14401,,22714,,44745,,63447,,38180,,36296,,43732,,9256,,12674,,16407,,16820,,13089, -Congo,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,534,,1343,,709,,544,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,. -Congo,All,All,All,Tonnes - live weight,7000,,7000,,7500,,8000,,8000,,9000,,9000,,10000,,10000,,11000,,11000,,11000,,12600,,12500,,13600,,14000,,14200,,13600,,14100,,13900,,14399,,16010,,24526,,22200,,23718,,23103,,26869,,24364,,26297,,29630,,31965,,29665,,30835,,35255,,32854,,29939,,32083,,37852,,41977,,45840,,48210,,45577,,40183,,46982,,42785,,45915,,45579,,38181,F,43095,,43699,,46024,,48894,,51995,,53934,,57096,,54696,,59103,,59241,,54169,,61274,,65244,,73301,,81152,,73236,,73572,,69395,,86925,,95162,,98745, -"Congo, Dem. Rep. of the",All,All,All,Tonnes - live weight,43500,,38300,,49200,,66600,,65700,,86100,,96200,,122400,,136700,,153400,,77000,,75000,,77000,,69500,,75000,,81000,,84000,,93400,,127700,,149300,,136600,,123900,,124200,,156900,,128810,,113380,,117858,,107000,,108706,,115182,,102415,,102640,F,100700,F,102000,F,148300,,148456,,156514,,162300,F,162600,F,167000,F,163400,F,166550,,188570,,197489,,156547,,159227,,163610,,163211,,179874,,210441,,246695,F,234122,F,240655,F,237340,F,238717,F,237779,F,237532,F,237222,,234417,F,231333,F,227461,F,224220,F,220869,,230283,,232483,,233682.5,,240533,,241200,F,241200,F -Cook Islands,All,All,All,Tonnes - live weight,700,,700,,800,,900,,800,,800,,700,,700,,700,,800,,800,,850,,850,,800,,800,,850,,850,,900,,900,,900,,850,F,850,F,850,F,900,F,920,F,920,F,1020,F,1020,F,1113,,854,,864,F,904,F,934,F,964,F,995,F,1043,F,1080,F,1083,F,1090,F,1136,F,1155,F,1138,F,1023,F,1050,F,1065,F,1147,F,1070,F,947,F,855,F,845,F,1025,F,827,F,1411,,3305,,4069,,3994.15,,3912.28,,4304.98,,3756,,2710,,10041,,6690,,8318.2,,3836,,3821,,5415.96,,2221.5,,4140.5,,4453.5, -Costa Rica,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,40,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,3,,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Costa Rica,All,All,All,Tonnes - live weight,500,,500,,600,,600,,600,,700,,800,,900,,1300,,1600,,2200,,2400,,2500,,2400,,3600,,3400,,3300,,3500,,5000,,4100,,8100,,10560,,12550,,12360,,15554,,16174,,14201,,15401,,22655,,20785,,16453,,16051,,12625,,11187,,16575,,20489,,23057,,17240,F,17069,F,17440,F,18943,F,18018,,18254,,19507,,21044,,24197,,31321,,33696,,32557,,37353,,44871,,44536,,50937,,50102,,44708,,45527,,39157,,45516,,48464,,44568.3,,44755,,41641.5,,42332,,48910,,41511.5,F,37252.5,,34493,F,34493,F,34493,F -Croatia,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,4,,1,,0,-,2,,0,-,3,,6,,2,,0,-,0,-,0,-,0,-,0,-,3,,4,,6,,0,- -Croatia,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,33534.08,,31233.27,,22422.01,,20275.05,,21126,,20551.49,,27935.08,,25222.19,,27944.24,,29019.12,,30330.54,,29462.04,,41089.12,,46820.09,,53373.13,,54270.63,,62940.08,,70017.05,,66857.81,,84242.06,,74829.54,,87850.24,,93518.24,,89515.5,,89106.26,,85973.46,,88624.5, -Cuba,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,99,,342,,506,,8,,3,,2,,3,,1,,0,-,1,,1,,0,-,0,-,1,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Cuba,All,All,All,Tonnes - live weight,9800,,10000,,10200,,10500,,11500,,12800,,15600,,22000,,21900,,28200,,31200,,30600,,35000,,35612,,37165,,41080,,43826,,65975,,66250,,78919,,104500,,127110,,139711,,150387,,164629,,144053,,194490,,184759,,213176,,153701,,186450,,164834,,195296,,198494,,199858.3,,219862.9,,244558.3,,214773.6,,231091.1,,191882.9,,188067.8,,171061.6,,111943.8,,97708.3,,93974.4,,102342.2,,120560.8,,113168.5,,98341.1,,100811.1,,101266,,80277,,59630,,60985,,59756,,50869,,54797,,67011,,65399,,64894,,55418,,48703.8,,48499.2,,51680.7,,56338.6,,57690,,52759,,52758,,51249, -Curaçao,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,22074,,24995,,26139,,28494,,30159,,35534,,32626,,37910, -Cyprus,All,All,All,Tonnes - live weight,500,,400,,400,,400,,500,,600,,500,,500,,600,,500,,500,,400,,600,,600,,600,,1006,,1009,,1014,,1412,,1409,,1363,,1255,,1340,,1459,,1221,,950,,1083,,1220,,1276,,1314,,1339,,1462,,1591,,1988.77,,2266,,2438,,2598.39,,2593,,2564,,2626,,2709,,3048,,9491,,10275,,9718,,9772,,13313,,25788,,20482,,41060,,70223,,56606,,142941,,49561,,81169,,63749,,32727,,4896,,4883,,4824.5,,5510.8,,5854.98,,5656.71,,6522.1,,6103.1,,6946.56,,8118.96,,9031.11,,8837.04, -Czechia,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,23427,,22610,,22608,,21724,,20881,,21183,,22965,,24129,,24744,,24193,,24797,,23912,,24697,,25077,,24723,,24559,,24183,,24410,,24869,,24796,,23118.4,,23947,,24041,,24459,,25242,,25401, -Czechoslovakia,All,All,All,Tonnes - live weight,3840,,4370,,7079,,7486,,6439,,6741,,6920,,7856,,8435,,9031,,9158,,10426,,10415,,10089,,11074,,11698,,12218,,12474,,14342,,14630,,13995,,14825,,16380,,16388,,16800,,18053,,18425,,19011,,18042,,17668,,16810,,17671,,19084,,20486,,23188,,23813,,25534,,25253,,25637,,25232,,26630,,26346,,28485,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,.,0,. -Côte d'Ivoire,All,All,All,Tonnes - live weight,15100,,15100,,19500,,20000,,20000,,20000,,24000,,30500,,30500,,34500,,40500,,42100,,43600,,45600,,54600,,59500,,60800,,66100,,68400,,67400,,64302,,77530,,75199,,60763,,66690,,58590,,75917,,67054,,65012,,73621,,70068,,75586,,73753,,79858,,73701,,101927,,103648,,101682,,89506,,99171,,95100,,83108,,87345,,70174,,74095,,70575,,73839,,67617,,73390,,79273,,81520,,77030,,70676,,69769,,55267,,43531,,55696,,48590,,45089,,44233,,49738,,55862,,56285,,65054,,78549,,102546,,108626,,95749,,110029, -Denmark,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1,,1,,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,103,,0,-,92,,119,,4449,,4449,,7000,,0,-,0,-,5206,,4227,,4149,,3887,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Denmark,All,All,All,Tonnes - live weight,244446,,281100,,312800,,331500,,353800,,418241,,455950,,526100,,598000,,668000,,574647,,637492,,785102,,847904,,871061,,840830,,850802,,1070415,,1466808,,1275401,,1226500,,1400901,,1442920,,1443661,,1835370,,1767039,,1919197,,1806612,,1750769,,1741256,,2032073,,1859445,,1931373,,1870909,,1851484,,1796989,,1849758,,1707816,,1974414,,1929294,,1517646,,1793244,,1997091,,1654028,,1916208,,2043763,,1723442,,1866549,,1599703,,1447670,,1577698,,1552267,,1474374,,1068993,,1133410,,949610,,904909,,684221,,726650,,812882.4,,864264,,752305,,537375.54,,706187.2,,779528.2,,904879.7,,706665,,942070,,825737, -Djibouti,All,All,All,Tonnes - live weight,500,,800,,500,,500,,900,,800,,500,,600,,800,,600,,900,,800,,900,,1000,,300,,300,,300,,300,,300,,300,,300,,300,,100,,200,,380,,300,,230,,230,,230,,231,,251,,385,,426,,409,,409,,380,,409,,426,,454,,399,,360,,252,,275,F,300,F,320,F,350,F,400,F,500,F,600,F,700,F,800,F,900,F,1000,F,1100,F,1200,F,1571,,1299,,1229,,1206,,1058,,1590,,1667,,2167,,1702,,2298,,2012,,2220,,2022,,2102, -Dominica,All,All,All,Tonnes - live weight,600,,600,,600,,800,,800,,600,,400,,400,,400,,500,,500,,500,,600,,600,,600,,500,,500,,500,,600,,600,,500,F,600,F,700,F,800,F,900,F,1001,,1024,,1047,,1070,,642,,1445,,1514,,1545,,800,F,700,F,640,,644,,500,F,500,F,500,F,458,,552,,717,,797,,885,,954,,1034,,1084,,1217,,1205,F,1207,F,1212,F,1211,,963,F,718,F,598,,718,,701,,723,,796,,711,F,668,,631,,533,,1035,,996,,815,,784,,762, -Dominican Republic,All,All,All,Tonnes - live weight,600,,600,,700,,700,,1000,,1200,,1400,,1700,,2000,,1600,,1300,,1600,,1700,,3200,,4200,,4100,,3500,,3200,,4600,,4800,,5200,,4600,,5600,,9200,,6810,,5906,,7053,,4594,,5086,,7885,,10658,,12003,,13169,,12852,,13925,,17202.9,,17289.1,,20452,,12940,,21884,,19775,F,17454,,13661,,14204,,26814,,20080,,13683,,15212,,10981,,9166,,13140,,15864,,20815,,20041,,16223,,12173,,13936,,14789,,16462,,15463,,15770,,16152.9,,14694.8,,14693,,15962.5,,14128,,16625,,18165,,16042, -Ecuador,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,3761,,227,,6,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Ecuador,All,All,All,Tonnes - live weight,7000,,8000,,8600,,9100,,12500,,15000,,21800,,26400,,31100,,35900,,44300,,38600,,42500,,49700,,46300,,53500,,48200,,57500,,68900,,64900,,91020,,102580,,107040,,152078,,176500,,215789,,282793,,425506,,614512,,605275,,639542,,539294,,607927,,372429,,883405,,1087676,,1004271,,680134,,876024,,682793,,366643,,437670,,343511,,374396,,419344,,614795,,811854,,671384,,468138,,629607,,657800,,645357,,396805,,494804,,465447,,602949,,638648,,579946,,678793,,718142,,672594.8,,816132,,834963,,845729,,1030813,,1069940.4,,1166835.86,,1093055,,1138562, -Egypt,All,All,All,Tonnes - live weight,43900,,49600,,53800,,52100,,56700,,63400,,70600,,75200,,80000,,85600,,88500,,92000,,118000,,115000,,135000,,102400,,99000,,80300,,80500,,80000,,76758,,84268,,79209,,87514,,98344,,106574,,102764,,104541,,99915,,137481,,140397,,141715.58,,155732.64,,157306.39,,163839.54,,215955.77,,229153.98,,231112.89,,284328.79,,293605.8,,312952,,346048,,346296.8,,354040.7,,368204.6,,407116.5,,431572.4,,457037.2,,545594,,648942,,724408,,771498,,801459,,876305,,865030,,889302,,970925,,1008008,,1067631,,1092889,,1304795,F,1362175,F,1371912,F,1454402,,1481883,F,1518944,F,1706274,F,1822801,F,1934743,F -El Salvador,All,All,All,Tonnes - live weight,2500,,2500,,2600,,2600,,2800,,2800,,2900,,2900,,5500,,5800,,7300,,8000,,8200,,8400,,9200,,9600,,10600,,9900,,10200,,10300,,11100,,10700,,10400,,12400,,9654,,8730,,7201,,6368,,9540,,12738,,13958,,20770,,12916,,7603,,12189,,16098,,20446,,21534,,11733,,10637,,9159,,11327,,12615,,13079,,14883,,15590,,14791,,12281,,11377,,10194,,9851,,20004,,36724,,36615,,44634,,43317,,46296,,58398,,57104,,52779.71,,44082.93,,59164.7,,54736,F,55001,,64874,,57183.71,,62039.68,,59581,F,62297,F -Equatorial Guinea,All,All,All,Tonnes - live weight,100,,100,,100,,200,,200,,300,,300,,500,,500,,800,,900,,500,,800,,1000,,1000,,600,,1200,,1100,,3700,,3500,,4000,F,4000,F,4000,F,4000,F,4000,F,4000,F,4000,F,4000,F,4000,F,4000,F,2500,,2500,,2019,F,2341,F,4000,,3600,,4400,,4000,F,4000,F,4000,F,3700,F,3500,F,3600,F,3507,F,5069,F,2306,F,5040,F,6090,F,6005,,7001,F,3634,,3600,F,3550,F,3550,F,3500,F,3750,F,4002,F,4535,F,5402,,7672,,7391,,7132,,7849,F,6955,F,6083,F,6435,F,6466,F,6395,F,6425,F -Eritrea,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,475,,2706,,3559,,3252,,1038,,1852.9,,6997.05,,12724,,8912,,7894,,6731,,7414,,4027,,8813,,2232,,1965,,3330,,3586,,2939,,4452,,4300,F,4300,F,4300,F,4300,F,4300,F,4300,F -Estonia,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,397782,,375454,,346083,,329213,,129689,,147515,,124508,,132891,,108992,,126317,,121854,,113312,,113572,,105959,,102622,,80580,,88877,,100136,,87587,,100216,,103393,,99157.6,,96304.04,,81968.34,,68325.4,,70514.21,,70030.48,,74414.7,,76921.73,,83691.46,,88405.66, -Eswatini,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,50,F,50,F,50,F,50,F,50,F,50,F,50,F,50,F,50,F,53,,44,,60,F,70,F,70,F,80,F,80,F,90,F,90,F,100,F,100,F,110,,110,F,110,F,105,F,100,F,110,F,120,,138,,143,,116,,131,,111,,119,F,122,F,60,F,60,F,60,F,60,F,60,F,60,F,60,F,133,F,269,,160,F,160,F,160,F,165,F,165,F,165,F,165,F,165,F -Ethiopia,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,.,0,.,0,.,0,.,0,.,0,.,9320,,2109,,5,,594,,2,,2005,,0,.,0,.,0,.,991,,44,,42,,220,,900,,446,,347,,727,,540,,492,,3,,4,,77,,400,,0,.,0,.,6,,7,,4,,0,- -Ethiopia,All,All,All,Tonnes - live weight,3000,,3000,,4000,,4000,,5000,,6200,,6200,,7400,,7200,,7600,,8300,,8600,,9100,,9600,,12000,,15900,,16200,,16200,,14600,,14400,,17200,,19100,,14800,F,4700,F,5900,F,3930,F,2970,F,2472,F,2515,F,2800,F,3507,F,3828,F,3750,F,3900,F,4300,F,4000,F,4100,F,4000,F,4067,,4269,,4981,,4262,,4607,,4203,,5318,,6380,,8808,,10394,,14014,,15873,,15696,,15405,,12315,,9228,,10030,,9475,,9915,,13279,,16795,,17072,,18083,,24066,,28990,,38416.3,,50204.5,,45609.5,,50095,F,56127,,57331, -Falkland Is.(Malvinas),All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2526,,2333,,2317,,1194,,0,.,2054,,1150,,239,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Falkland Is.(Malvinas),All,All,All,Tonnes - live weight,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,,8,,23,,2628,,4624,,5994,,1476,,1855,,1974,,5914,,27190,,31540,,34445,,52770,,49692,,75479,,68332,,53261,,74898,,55390,,84548,,75290,,72149,,81708,,62747,,99560,,66978,,95965,,69540,,79434,,60475,,61006,,67509,,96748, -Faroe Islands,All,All,All,Number,432,,361,,23,,169,,31,,161,,146,,199,,57,,1422,,1702,,1919,,1775,,2208,,1408,,1793,,1510,,2001,,1802,,1395,,448,,1116,,511,,1051,,677,,1080,,575,,898,,1271,,1725,,2773,,2973,,2652,,1709,,1921,,2580,,1921,,1500,,2319,,1266,,976,,782,,1618,,0,.,1467,,379,,1700,,1512,,1253,,608,,843,,1472,,1424,,692,,1343,,613,,0,-,0,-,8,,99,,1142,,726,,716,,1540,,66,,0,-,304,,1699,,880, -Faroe Islands,All,All,All,Tonnes - live weight,97500,,92700,,87200,,88800,,89400,,105600,,116300,,105700,,106700,,87200,,109375,,120220,,144252,,137830,,138990,,144686,,166294,,173324,,166187,,176296,,207772,,207279,,208162,,246286,,246675,,285698,,342013,,310592,,316980,,266845,,275286,,243788,,250878,,330345,,347352,,373958,,352195,,391042,,365943,,310970,,286096,,246599,,267528,,265046,,253040,,296407,,322171,,351683,,384933,,400906,,489222,,565076,,582093,,683737,,645463,,588715,,641696,,651533,,564940,,410313,,441450,,415429,,437035,,563049,,629290,,666164,,651735,,788692,,738956, -Fiji,All,All,All,Tonnes - live weight,2000,,2000,,2000,,2200,,2200,,2500,,2500,,2500,,2800,,2800,,3000,,3000,,3000,,3200,,3400,,3500,,3500,,3500,,3800,,3800,,3900,,3900,,4700,,4700,,4800,,5036,,5589,,7935,,9250,,20243,,20404,,23608,,24931,,24227,,25639,,26870,,35100.4,,38670.5,,34209,,35520.3,,39528.77,,33011.38,,27687.1,,30646.03,,30300,F,30507,,26877,,30086,,33075.61,,56066.02,,49357,F,50033,,41526,F,37631.8,,50194.16,,47951.5,F,50351,,48077,,47483,F,47057.03,,42710.5,F,46805,,45090,F,41500,F,44034,F,41978,F,45406,F,47906,F,44467,F -Finland,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,672,,461,,600,,0,.,0,.,300,,600,,300,,200,,200,,100,,100,,120,,175,,360,,64,,35,,52,,42,,39,,0,-,0,-,0,-,0,-,4,,30,,30,,90,,130,,233,,291,,312,,157,,218,,360,,475,,350,,298,,177,,128,,191,,172,,272,,434,,423, -Finland,All,All,All,Tonnes - live weight,34200,,31300,,29900,,55500,,57800,,55900,,53000,,56500,,62500,,69200,,65500,,67000,,64900,,80500,,62900,,73250,,70990,,74090,,92800,,87300,,81699,,91401,,89003,,100000,,111778,,111336,,119459,,117831,,139166,,133672,,164900,,155094,,163952,,175193,,178894,,179188,,167430,,166189,,175631,,165108,,151535,,138591,,169662,,173823,,180951,,184830,,196738,,196526,,187713,,176017,,171828,,165716,,157434,,134432,,148243,,146088,,162337,,177702,,162105,,168253,,167869,,164977,,174969,,181389,,196409,,197730,,207313.17,,205319.9,,198039.7, -France,All,All,All,Number,1404,,1726,,436,,0,.,0,.,0,.,0,.,0,.,0,.,178,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,35,,3026,,9,,23,,18,,8,,0,.,51,,25,,0,.,475,,1766,,59,,19,,32,,325,,50,,192,,271,,173,,272,,121,,148,,139,,76,,48,,41,,160,,10,,152,,0,-,3,,0,-,1,,0,-,0,-,0,- -France,All,All,All,Tonnes - live weight,582756,,621712,,635528,,705495,,621717,,776543,,744612,,706709,,767906,,781356,,762676,,791620,,761211,,799299,,807550,,856899,,902398,,925307,,938596,,855694,,858902,,846574,,854245,,892060,,884204,,879330,,861687,,837277,,860788.3,,838058.3,,859149.9,,873521.88,,860781.64,,884173.47,,870403.54,,922169.4,,960355.58,,926785.48,,968697.08,,944719.4,,955874.8,,902400.5,,918553.5,,957508.5,,984874.6,,969245.3,,928743.9,,929721.5,,878266.5,,921896.8,,961254.6,,923560.7,,953683,,948639.7,,907065.2,,843407.8,,832528.9,,791136.7,,738411.4,,674201.3,,655652.5,,695977.2,,678116.5,,740955.2,,737516.8,,670777.7,,738503.2,,724317.8,,797492.36, -French Guiana,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,1,,3,,1,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -French Guiana,All,All,All,Tonnes - live weight,400,,400,,400,,500,,500,,500,,400,,400,,400,,500,,400,,600,,600,,600,,700,,600,,700,,800,,800,,900,,1200,,1100,,1500,,1200,,1142,,1241,,1207,,1266,,1240,,1186,,1150,,1436,,1992,,2118,,2186,,2491,,3326,,5249,,5540,,6499,,6548,,7119,,7617,,6931,,7819,,8089,,7377,,6609,,6727,,6202,,4868,,4636,,4820,,5602,,5551,,5322,,4479,,4892,,3966,,4148,,3804,F,2693,F,2458,F,2232,F,1965,F,2260,F,1923,F,2145,F,1842,F -French Polynesia,All,All,All,Tonnes - live weight,1500,,1500,,1500,,1800,,2500,,1800,,2200,,1900,,1800,,2200,,2600,,2700,,3000,,2500,,2500,,1900,,2100,,2000,,2100,,2300,,2300,,2200,,2400,,2600,,2386,,2169,,2828,,2704,,2994,,2482,,2747,,2691,,2671,,2291,,2889,,2448.21,,2361.1,,2310,,2860,,3233,,4304,,4072,,3846,,8386.17,,9134.96,,9440.7,,10227.38,,12328.32,,13029,,13272.82,,14704.99,,16121.62,,16631.73,,15617.06,,14206.8,,14550.83,,15261.1,,15432.19,,14229.68,,15279.36,,15228.44,,15770.92,,17041.56,,17230.67,,17393.56,,16094.7,,15096.99,,14392.6,,14380, -French Southern Terr,All,All,All,Tonnes - live weight,300,,300,,300,,300,,400,,400,,400,,500,,500,,500,,600,,600,,600,,800,,900,,800,,900,,800,,900,,800,,800,,700,,700,,700,,632,,600,F,533,,689,,850,,823,,843,,994,,962,,921,,912,,911,,707,,673,,620,,429,,367,,388,,464,,460,,524,,519,,437,,375,,388,,425,,272,,263,,414,,463,,263,,263,,472,,472,,480,F,480,F,480,F,480,F,480,F,450,F,430,F,400,F,420,F,420,F,420,F -Gabon,All,All,All,Tonnes - live weight,3000,,3000,,3000,,4000,,4000,,5000,,5000,,5500,,6000,,6000,,6000,,6500,,7000,,7000,,7000,,8000,,8000,,8000,,9000,,9000,,10000,F,10000,F,10000,F,12000,F,12000,F,14900,F,14820,F,14010,F,17800,F,21000,F,19800,F,20146,F,20605,F,19449,F,21002,,20955,,20226,F,22188,F,22093,F,20502,F,20002,F,22003,F,24005,F,31797,F,31043,,40476,,46175,,44250,,53912,,51813,,48384,,42973,,41653,,45559,,46152,,43941,,41647,,37938,,31450,,29715,F,32822,F,36652,F,36548,F,36646,F,36900,,33045,F,31045,F,29045,F,29045,F -Gambia,All,All,All,Tonnes - live weight,2800,,2800,,2800,,2900,,3500,,3500,,3800,,3800,,4300,,4500,,4500,,4800,,5300,,5300,,5300,,6200,,6200,,6200,,6700,,6700,,7447,,7739,,7884,,12294,,12695,,12695,F,12695,F,18092,,14972,,11143,,13265,,14739,,9212,,11653,,11882,,10712,,13262,,14642,,13852,,19842,,21662,,24985,,18045,,21308,,22781,,23699,,31605,,32747,,29037,,32643,,29021,,34532,,37410,,37371,,32822,,34960,,37552,,43599,,42906,,45909,,46649,,41533,F,36095,,43759,,51535,,56003,,58296,,60496,,49561, -Georgia,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,202628,,148318,,105181,,60813,,39145,F,18820,F,7641,F,3717,,2554,,2644,,3097,,1496,,1877,,1716,,1863,,3362,,12060,,10081,,14896,,44248,,48865,,50731,,46899,,27547,,13225,,16825,F,20515,F,24175,F,46513,,139465.21,,215458.5, -Germany,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,13,,0,.,0,.,21,,30,,9,,12,,18,,8,,14,,4,,5,,3,,5,,8,,8,,10,,5,,4,,9,,27,,8,,8,,4,,5,,0,-,9,,0,-,0,-,15,,0,-,0,- -Germany,All,All,All,Tonnes - live weight,597750,,729610,,719760,,792920,,741080,,883730,,875890,,888640,,816900,,813850,,787910,,748670,,782420,,837580,,850030,,864690,,881650,,951500,,986660,,970714,,939498,,846312,,761507,,850953,,897600,,828055,,744507,,655548,,625086,,590774,,562566,,585026,,551637,,544751,,553756,,426457,,415388,,399721,,393112,,412656,,390739,,310593,,313544,,322510,,279013,,303985,,319648,,324785,,339107,,317421,,269975,,262551,,271624,,331927,,315576,,326053,,328416,,335505,,318258,,282954,,283840,,273024,,240768,,260396,,268775,,291653,,312986,,284216,,313409, -Ghana,All,All,All,Tonnes - live weight,16000,,16000,,21000,,21000,,25200,,26300,,28300,,30400,,32900,,38000,,33800,,36500,,45700,,59800,,76600,,76500,,85700,,99000,,74100,,131000,,171611,,232784,,281331,,221832,,219327,,261590,,246820,,272171,,274887,,237252,,231855,,240816,,244492,,251856,,270013,,276573,,320177,,382200,,362477,,362005,,396232,,362948,,423855,,373084,,335937,,353394,,477294,,447527,,447341,,499670,,459346,,456353,,381182,,394485,,421861,,375139,,377320,,320384,,356825,,331739,,358289,,363667,,393494,,333231,,328744,,392231,,385115,,438506,,453397, -Gibraltar,All,All,All,Tonnes - live weight,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F -Greece,All,All,All,Tonnes - live weight,52050,,43050,,42980,,46202,,52715,,56530,,60975,,75210,,80210,,82370,,87430,,85390,,80490,,80550,,84130,,90320,,91630,,94000,,100760,,95150,,86139,,95139,,93129,,104576,,93678,,96456,,107574,,108571,,102697,,105154,,107614,,103525.18,,106747.8,,102107.4,,108540.8,,114961.9,,124848,,134743.4,,123686.3,,136534.5,,141904,,151259.9,,172917.8,,191678.5,,214306.9,,184360.5,,190903.2,,205816.1,,170072.4,,204865.75,,194761.7,,191708.4,,184006.9,,195215.4,,191034.4,,198638.5,,211623.7,,209392.8,,200953.3,,205192.8,,191423.8,,174055.6,,172017.67,,178171.05,,164835.7,,172588.4,,200277.4,,203707.7,,209629, -Greenland,All,All,All,Number,46,,26,,21,,21,,20748,,17019,,11969,,14250,,18343,,10552,,18232,,14009,,10544,,12252,,14227,,13502,,11626,,8727,,12754,,47463,,82359,,75802,,80902,,93449,,87902,,85873,,87832,,100521,,111107,,119613,,97286,,100977,,99064,,95107,,102034,,67336,F,22137,,62260,,55862,,46231,,55766,,64658,,69345,,144323,,144348,,154570,,181218,,163853,,174299,,192099,,192077,,178043,,160972,,161141,,162140,,196299,,192886,,164500,,159890,,145756,,159736,,144132,,122843,,138086,,135954,,126877,,111604,,96009,,70690, -Greenland,All,All,All,Tonnes - live weight,25700,,22500,,24700,,25000,,24900,,25800,,27400,,31500,,32000,,33200,,34786,,41895,,43308,,33317,,38248,,40643,,44529,,44916,,34265,,39245,,39461,,38354,,41792,,44297,,51260,,47593,,44644,,59865,,68233,,83135,,103738,,107487,,106206,,99311,,86274,,94949,,102906,,100413,,120337,,168786,,143331,,114164,,113267,,116775,,117417,,128890,,116018,,113851,,123291,,160253,,159711,,158485,,195624,,175321,,212988,,234864,,252959,,233754,,241899,,214667,,209446,,214493,,222894,,275432,,292441,,268371,,273175,,310741,,301052, -Grenada,All,All,All,Tonnes - live weight,300,,300,,300,,400,,400,,400,,500,,300,,700,,400,,300,,500,,900,,700,,1100,,1300,,1200,,1300,,1300,,1400,,1300,,1500,,1700,,1700,,1634,,1700,,1700,,2958,,2238,,1491,,1419,,788,,982,,1479,,1555,,1727,,2296,,2214,,2092,,1782,,1911,,2071,,2115,,2212,,1599,,1499,,1577,,1530,,1837,,1829,,1705,,2250,,2171,,2544,,2039,,2048,,2163,,2207,,2386,,2648,,2182,,2322,,2263,,2695,,2850,,2708.4,F,2572.1,F,2568,F,2570,F -Guadeloupe,All,All,All,Tonnes - live weight,1500,,1500,,1500,,1700,,1800,,1900,,1900,,2700,,3000,,3100,,3200,,3300,,3000,,3200,,3300,,3100,,2600,,3500,,3600,,4500,,4700,,4900,,5000,,4620,,6000,,4740,,5060,,9525,,9000,F,8500,F,8000,F,8300,F,8730,F,8836,F,8980,F,8421,F,8546,F,8606,F,8233,F,8544,F,8642,F,8574,F,8575,F,8640,F,8826,,9530,F,9600,,10500,F,9488,F,9134,F,8814,F,8414,F,8023,F,7631,F,7231,F,6810.36,F,6412.26,F,6015.22,F,5617.5,F,5207.4,F,4811,F,6012,F,3212,F,3512,F,3025,F,2725,F,3123,F,3121,F,3124,F -Guam,All,All,All,Tonnes - live weight,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,100,,100,,100,,100,,100,,100,,100,,100,,100,,100,,100,,100,,100,,100,,91,,131,,95,,184,,200,,164,,257,,326,,444,,327,,395,,518,,567,,500,,593,,550,,688,,690,,575,,599,,613,,482,,480,,491,,575,,554,,856,,905,,668,,616,,692,,440,,580,,488,,491,,602.5,,523,,497,,465,,547,,520,,1422,,1501,,506,,480, -Guatemala,All,All,All,Tonnes - live weight,200,,200,,200,,500,,500,,700,,700,,700,,1000,,1000,,1000,,1300,,2100,,2500,,2700,,2100,,3000,,2500,,1900,,3000,,3400,,3300,,3100,,3700,,3881,,4530,,3653,,3074,,5504,,4898,,3507,,4273,,4293,,2388,,3024,,3166,,2647,,3077,,3725,,4327,,7798,,7344,,7813,,10868,,11604,,11928,,11074,,11303,,13971,,25868,,43166,,34804,,31405,,30042,,14920,,27987,,33862,,34365,F,42018,,36772,,45124.23,,41312,,37734,,41848,,43573,,42744.6,,42487.9,,46879,,45326, -Guinea,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,100,,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,.,0,- -Guinea,All,All,All,Tonnes - live weight,1000,,1000,,1000,,1000,,2000,,2000,,2000,,3000,,3000,,3000,,4000,,4000,,4000,,4000,,5000,,5000,,5000,,6000,,6000,,6000,,6000,F,8300,F,8300,F,9500,F,12100,F,13370,F,9920,F,9120,F,10000,F,18453,,20000,F,22000,F,24000,F,26000,F,28003,F,30003,F,33002,F,35002,F,38001,F,41001,F,44001,F,49501,F,55005,F,60605,F,63805,F,67864,,63364,,62441,,69764,,87314,,91513,,105404,,93756,,121927,,97732,,109238,,104828,,82183,,94683,,127923,,113689,,185149.2,,198574.5,,230371.8,,207699,,193551,,173369.8,,333769.9,,287361.6, -Guinea-Bissau,All,All,All,Tonnes - live weight,300,,300,,300,,400,,400,,600,,600,,600,,500,,600,,700,,700,,600,,600,,800,,900,,700,,700,,1300,,1400,,1500,,1400,,1700,,1700,,1700,,1669,,3575,,4275,,4283,,2936,,4166,,3643,,3977,,2671,,2751,,3743,F,3700,F,4100,F,4690,F,5400,F,5400,F,5000,F,5200,F,5350,F,6000,F,6328,F,7000,F,7200,F,6700,F,6200,F,6315,F,6848,F,7324,F,6153,F,6650,F,6983,F,7067,F,6500,F,6804,F,6721,F,6584,F,6549,F,6829,F,6707,F,6700,F,6700,F,6710,F,6735,F,6740,F -Guyana,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,.,0,.,9111,,80360,,158190,,57085,,45700,,47013,,7000,,12633,,9088,,3459,,1558,,685,,1556,,2650,,910,,0,.,10559,,10559,,6816,,9208,,3806,,3948,,3079,,4525,,17661,,24500,,28731,,20682,,28789,,19707,,29978,,34972,,21363,,1590,,16983,,11320, -Guyana,All,All,All,Tonnes - live weight,1000,,1000,,1100,,2900,,2700,,3600,,3400,,3100,,3500,,3000,,5700,,7400,,7900,,9200,,10400,,12000,,12300,,13900,,16000,,16600,,17400,,15500,,17600,,19000,,23617,,20123,,20956,,31907,,35978,,35288,,31530,,32798,,32794,,35380,,37256,,37606,,37395,,36785,,36523,,35339,,36922,,40806,,41337,,44263,,46577,,48130,F,48833,,54268,,53140,,54450,,49492,,54013,,48625,,60303,,57327,,53978,,54423,F,48108,,42460,,44115.96,,45675.6,,43448.1,,54105.18,,49488.65,,38113.5,,36975.64,,42478.7,,43673.27,,41694.76, -Haiti,All,All,All,Tonnes - live weight,2000,,2100,,2200,,2200,,2500,,2500,,2500,,2800,,2800,,2800,,3000,,3000,,3000,,3100,,3300,,3400,,3400,,3500,,3500,,3500,,4000,F,4000,F,4000,F,4000,F,4000,F,4000,F,4000,F,4150,F,4300,F,4500,F,5000,F,5500,F,6000,F,6502.1,F,6901,F,6400,F,6000,F,5767.9,F,5517.9,F,5517.9,F,5167,F,5216,F,5016,F,5615,F,5215,F,5514,F,6014,F,6313,F,6610,F,7010,F,7422,F,7822,F,8122,F,8322,,9692,F,11042,F,12292,F,13672,F,14972,F,16235,,14070,F,17130,F,17230,F,17310,F,17520,F,17730,F,17800,F,17910,F,17750,F -Honduras,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,.,0,.,0,.,0,.,0,.,0,.,0,.,6000,,6000,,5000,F,5000,F,4000,F,4000,F,3000,F,2000,,6000,,0,.,0,.,0,-,0,-,0,-,0,-,0,-,197,,76,,111,,54,,1944,,1090,,3,,200,,350,,0,.,0,.,0,.,0,.,0,-,0,- -Honduras,All,All,All,Tonnes - live weight,500,,500,,600,,600,,1200,,1200,,1200,,1200,,1700,,1700,,1700,,2550,,2550,,2550,,3400,,4050,,2600,,4000,,5700,,5450,,5000,,5700,,5750,,4450,,3863,,5014,,4643,,5947,,6804,,8858,,6636,,6422,,8130,,11599,,10052,,11650,,23173,,28023,,23424,,20781,,19541,,31552,,29670,,33161,,23327,,32982,,31555,,33252,,23548,,31358,,27968,,35678,,32746,,40890,,44605,,70388,,77902,,71325,,61240,,41266,,39584,,61476,,64634,,64949,F,70389,F,65859,F,64600,F,75100,F,75600,F -Hungary,All,All,All,Tonnes - live weight,4000,,4000,,5000,,6000,,8000,F,9500,F,10900,F,12300,F,13000,F,14400,F,14900,F,19300,F,21000,F,22000,F,22700,F,24800,F,26600,F,28200,F,29900,F,27800,F,26000,F,25800,F,28900,F,29100,F,30159,F,30788,F,31855,F,34661,F,32589,F,32838,F,33733,F,39319,F,42152,F,44037,F,39177,,36927,,36062,,36759,,38294,,35517,,33834,,22879,,22908,,17378,,18206,,16674,,15686,,16740,,17487,,19461,,19987,,19694,,18324,,18406,,19986,,21270,,22229,,22888,,23081,,21191,,20461,,22632,,21850,,21390,,22789,,23263,,21295.97,,23864.5,,23086.3, -Iceland,All,All,All,Number,265,,339,,265,,332,,334,,400,,440,,517,,508,,371,,379,,350,,483,,439,,444,,432,,437,,406,,280,,423,,377,,554,,481,,575,,455,,601,,586,,580,,589,,639,,638,,601,,633,,448,,440,,344,,116,,100,,78,,70,,27,,0,.,0,.,2,,2,,0,.,2,,2,,3,,0,.,1,,0,.,138,,39,,391,,261,,69,,45,,126,,206,,337,,245,,196,,333,,161,,184,,46,,17,,152, -Iceland,All,All,All,Tonnes - live weight,396400,,450100,,449200,,470100,,494700,,532500,,562900,,531900,,619800,,672800,,629985,,734064,,847335,,797444,,989173,,1217833,,1260336,,915201,,617356,,707209,,748800,,699881,,741506,,922000,,954849,,1001793,,1015145,,1393447,,1579042,,1661125,,1524817,,1449906,,801799,,855132,,1551774,,1695553,,1668788,,1644922,,1772660,,1518543,,1524183,,1058740,,1585735,,1729801,,1574166,,1627874,,2078266,,2229112,,1703589,,1758305,,2003669,,2008312,,2153093,,2014872,,1764040,,1694009,,1356284,,1425880,,1311691,,1169597,,1086705,,1170222.5,,1474182.3,,1387282,,1103941.5,,1344807.6,,1100631,,1218847.7,,1297478, -India,All,All,All,Tonnes - live weight,739817,,754904,,780028,,819595,,828500,,840025,,1013944,,1233100,,1064600,,823200,,1161400,,961000,,973900,,1045800,,1321600,,1331300,,1367200,,1400400,,1525600,,1605000,,1758500,,1853000,,1639100,,1960700,,2256991,,2267192,,2177096,,2314703,,2309709,,2343292,,2445337,,2448342,,2369320,,2508598.5,,2874519.1,,2839347.6,,3007532.9,,2994385,,3200962,,3718189,,3879722,,4132494,,4324546,,4566914,,4867135,,5016547,,5301693,,5485270,,5381977,,5686963,,5668958,,5937726,,5973298,,6074846,,6230871,,6698488,,7060154,,7005055,,7984490,,7893598,,8505837,,8013716,,9109121,,9220649,,9892495,F,10125040,F,10898947,,11738817,,12414190,F -Indonesia,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,.,0,.,0,.,0,.,0,.,0,.,0,.,20000,,15102,,17630,,25000,,9772,,1889,,3327,,12362,,0,.,0,.,250,,11647,,7661,,10387,,13402,,15228,,11558,,14467,,18302,,20400,,17910,,16306,,12251,,11752,,16780,,17860,,14146,,20308,,21216,,10688,,6578,,9598, -Indonesia,All,All,All,Tonnes - live weight,245000,,509000,,574500,,618900,,633500,,671800,,715900,,728400,,689000,,757100,,761700,,914800,,947200,,939200,,1004000,,1074800,,1241600,,1198400,,1180000,,1234800,,1257200,,1273302,,1296804,,1294441,,1361942,,1376566,,1486511,,1587817,,1668158,,1770087,,1877956,,1946989,,2026589,,2248468,,2313091,,2452695,,2607764,,2742804,,2947009,,3087137,,3243345,,3413930,,3627251,,3919996,,4144434,,4388520,,4648537,,4695769,,4760318,,4893860.5,,5152261.6,,5412689,,5541952,,5927292,,6208250,,6827210,,7277661,,8179552,,8857709,,9828602.53,,11668385.49,,13696745.54,,15465425.82,,19445006.94,,20905693.83,,22388969.1,,22586708.09,,22902137.46,,22032744.18, -Iran (Islamic Rep. of),All,All,All,Tonnes - live weight,20017,,20017,,25017,,25017,,25017,,25017,,25015,,23015,,23015,,20015,,20015,,20015,,20015,,18015,,18013,,20014,,21114,,22515,,24019,,24016,,26066,F,24369,F,19643,F,20260,F,69949,,68317,,72713,,73545,,72105,,72453,,52922,,55805,,106174,,112033,,117923,,120626,,154311,,215620,,235192,,260383,,269076,,275730,,327513,,343887,,333999,,368297,,381702,,400267,,400534,,411997,,424541,,413692,,401655,,441837,,474320,,522328,,575347,,561316,,562568,,599924,,663684,,734895,,833785,,879101,,940601,,977240,,1087851,,1195367,,1268590, -Iraq,All,All,All,Tonnes - live weight,8700,,7000,,7000,,5500,,5400,,5400,,8500,,8000,,8500,,8500,,9000,,9000,,9000,,11300,,19200,,16800,,14100,,10200,,19100,,22002,,32100,,27200,,32000,,27700,,24246,,21832,,28283,,26101,,26100,F,55228,,53245,,26219,F,24000,F,22500,F,21000,F,21441,F,20564,F,21250,F,26461,F,24555,,24229,,15235,,22823,,23941,,29247,,30808,,33237,,34702,,30074,,24606,,22512,,35300,,28000,,19200,,26883,,43949,,73942,,68842,,53826,,46977,,39668,,47831,,76117,,70913,,85625,,52099,,60135,,67034,,62673, -Ireland,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,11,,1818,,0,.,1,,499,,5,,31,,158,,5,,2,,22,,8,,8,,0,-,11,,2,,0,-,0,-,0,-,12,,1,,0,-,0,-,0,-,0,-,0,-,0,- -Ireland,All,All,All,Tonnes - live weight,17200,,17000,,18900,,19000,,21500,,23600,,30500,,36600,,36500,,38600,,42800,,32100,,29000,,27600,,31900,,35700,,39700,,50200,,53100,,66500,,78901,,74106,,92112,,90814,,89591,,88423,,95038,,93876,,104306,,93244,,150030,,191240,,212936,,204166,,240149,,260791,,261619,,282460,,292151,,240639,,275047,,293259,,307033,,338662,,355986,,450925,,400368,,364899,,402990,,363241,,363639,,453468,,381288,,363734,,368929,,357231,,294399,,301472,,279686.5,,346095.67,,394931.3,,287720.32,,341528.01,,310148.2,,337624.2,,304020,,300100.43,,321763.1,,288136.9, -Isle of Man,All,All,All,Tonnes - live weight,3500,,3700,,3100,,3200,,3400,,6100,,7000,,5100,,4800,,5800,,4900,,4900,,4500,,4000,,3600,,5100,,4600,,6000,,7200,,9800,,14700,,16900,,18000,,15500,,14791,,14505,,11242,,11844,,10504,,12407,,10106,,8642,,6298,,8322,,7668,,6974,,5778,,5561,,5624,,5696,,4085,,4562,,4481,,4850,,3571,,3734,,3537,,4289,,2214,,2609,,3552,,3112,,3127,,2984,,2627,,2764,,2579,,3812,,2770,,3555,,4814,,6844,,6172,,5745,,3755,,7781,,7040,,6759,,4431, -Israel,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,300,,430,,500,F,800,,900,F,1055,,1815,,348,,944,,210,,401,,977,,1894,,2295,,699,,0,-,0,-,0,-,0,-,0,-,0,-,2,,0,-,0,.,18,,0,.,0,.,27,,0,.,0,.,0,. -Israel,All,All,All,Tonnes - live weight,5815,,7174,,8041,,8305,,9859,,11416,,11058,,12390,,13422,,14121,,14671,,15954,,17385,,18282,,19512,,20573,,25426,,24537,,28927,,23280,,28216,,32339,,34831,,32578,,28394,,30192,,31155,,31865,,32807,,30259,,27982,,27677,,26307,,23206,,24444,,27103,,25146,,28558,,28248,,26468,,23567,,20675,,17671,,18729,,19045,,21121,,22782,,23468,,24856,,24661,,25916,,26342,,27299,,24831,,25643,,26560,,25937,,24869,,22725,,21889,,22507,,23392,,23566.4,,24234.6,,22208,,21737,,20502,,19039,,19054,F -Italy,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,12,,43,,87,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,19,,24,,0,.,11,,10,,6,,22,,1,,7,,9,,0,-,3,,12,,14,,0,-,0,-,0,-,0,-,0,-,0,- -Italy,All,All,All,Tonnes - live weight,187700,,187113,,216816,,210169,,219467,,220214,,221644,,212576,,213179,,216557,,216754,,245206,,223613,,234953,,327089,,356354,,372164,,375547,,366613,,372627,,417749,,426440,,439063,,424389,,468486,,450191,,471500,,423787,,451257.4,,482593.4,,504905.5,,513736.09,,536817.66,,543955.48,,580930.73,,586603.23,,564362.43,,554302.68,,570639.38,,548778.3,,530676.17,,586520.15,,566844.5,,563862.4,,575759.8,,612607.1,,557127.5,,541368.6,,517109.7,,495190.35,,520681.85,,530738.7,,456187.1,,489584.8,,407156.8,,479685.1,,490645.7,,468888.45,,386622.26,,416951.5,,388454.21,,381980.4,,339471.4,,318793.7,,332576.35,,347531.5,,354945,,354389.4,,350274.18, -Jamaica,All,All,All,Tonnes - live weight,5000,,5000,,5500,,5500,,5900,,6000,,6500,,7100,,7800,,8300,,8500,,11500,,11900,,10000,,10000,,9000,,9000,,9000,,8500,,8500,,8500,,9300,,9500,,9600,,10100,,10100,,10130,,10109,,9797,,9851,,9250,,8050,,8200,,8961,,9965,,10731,,11275,,11170,,14652,,16160,,17054,,18390,F,22340,,26340,,28717,,28300,,27755,,23485,,20956,,21414,,10052,,18831,,21042,,15451,,17966,,18766,,25722,,22014,,19005,,21338,,19342,,19150,,16034.05,,19186.9,,16080,,17627,,17747,F,20606,F,16961.63, -Japan,All,All,All,Number,4121,,5043,,6259,,5394,,8789,,11868,,14124,,15481,,19851,,20527,,19648,,20848,,22701,,25072,,25452,,26362,,24442,,21990,,22039,,18301,,16887,,16417,,17590,,14012,,14277,,13426,,9632,,9524,,6038,,5037,,5191,,4892,,30837,,29500,,26766,,19856,,26226,,34509,,49774,,55073,,47870,,38871,,14731,,16878,,18200,,15218,,18255,,20710,,13909,,17835,,19396,,19072,,19268,,17955,,16736,,17083,,15374,,14173,,10161,,12357,,7543,,3890,,2653,,3301,,3572,,3033,,2959,,3151,,2621, -Japan,All,All,All,Tonnes - live weight,3066842,,3655138,,4651509,,4515060,,4530322,,4903513,,4796747,,5433971,,5519131,,5921648,,6233547,,6752627,,6909629,,6744572,,6400607,,6952402,,7177695,,7979807,,8713293,,8649447,,9344607,,9949029,,10320775,,10769824,,10846007,,10553605,,10698190,,10782475,,10852291,,10612564,,11147813,,11407458.43,,11472056.12,,11982781.46,,12797866.62,,12135056.78,,12771375.47,,12532750.05,,12777784.68,,11976764.24,,11141468.93,,10037199.07,,9359007.29,,8790829.25,,8188003.92,,7536569.42,,7446856.12,,7422090.62,,6720585.5,,6634957,,6483310.14,,6147400.19,,5886827.35,,6101859.3,,5712155,,5695057.16,,5675394.79,,5714496.96,,5628024.53,,5474058.78,,5339477.65,,4787017.8,,4836515.09,,4763606.91,,4752477.09,,4603790.48,,4348849.11,,4298915.31,,4239799.66, -Jordan,All,All,All,Tonnes - live weight,100,,100,,100,,200,,200,,100,,0,0,100,,100,,100,,100,,100,,200,,200,,200,,200,,200,,100,,100,,100,,100,,200,,100,,100,,92,,65,,49,,31,,31,,36,,56,,35,,34,,32,,132,,197,,267,,322,,372,,407,,412,,390,,402,,455,,496,,595,,621,,650,,763,,1025,,1119,,1060,,1041,,1131,,981,,1071,,1057,,1015,,1040,,1009,,1027,,1075,,1248,F,1480,F,1758,,1758,F,1758,F,1758,F,1773,F -Kazakhstan,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,15288,,12072,,972,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Kazakhstan,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,85006,,89508,,86978,,82690,,68503,,59628,,47594,,50350,,45955,F,33747,F,26106,F,37363,F,37433,F,23269,,25446,,26015,,33494.2,,37744,,30301,,34371,,51473,,38473,,47051,,35754,,37677,,35503,,37547,,41191.4,,43213,,32720,,33600,F -Kenya,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,.,0,.,0,.,0,.,0,.,0,.,5000,,1400,,2650,,2540,,2918,,2883,,3721,,4258,,2250,,300,,1445,,714,,3350,,3460,,4250,,3967,,3811,,3862,,10950,,8710,,6354,,4504,,6906,,5259,,4180,,6403,,6332,,6229,,6504,,5959,,8130,,7946, -Kenya,All,All,All,Tonnes - live weight,18180,,18200,,18200,,18950,,17870,,30400,,32920,,25850,,22370,,22950,,12980,,13910,,18760,,20480,,21130,,23799,,28270,,27660,,28517,,32293,,33940,,28520,,30170,,29180,,28741,,27523,,41324,,43169,,46605,,51895,,48035,,58060,,81913,,98558,,91334,,106464,,120468,,131902,,138952,,147165,,202778,,199657,,164103,,184054,,204747,,194584.74,,182042.35,,161787.66,,173307.58,,206227.39,,216310.91,,165769.32,,145962.33,,121544,,128332.07,,148967,,160278,,136522,,140561,,139052,,152801,,203745,,180693,,187108,,193659,,184425,,157490,F,134596,,138524,F -Kiribati,All,All,All,Tonnes - live weight,1000,,1000,,1000,,2000,,2000,,3000,,3000,,4000,,4000,,4000,,5000,,5000,,6000,,6000,,6000,,6000,,7500,,7500,,7500,,7500,,8800,,8900,,9100,,9200,,9475,,9650,,9824,,10053,,10606,,10838,,12929,,13502,,13009,,22485,,19393,,22859,,30801,,38226,,25445,,32211,,31968,,37379,,33536,,30568,,32769,,37386,,43176,,39522,,43226,,66050,,46634,,42562,,40956,,37625,,34974,F,35574,F,34510,F,35287,,28393.14,,41224.16,,47872,,67220,,86070,,83792,,120551,,149638,,176474,,164788,,200547, -"Korea, Dem. People's Rep",All,All,All,Tonnes - live weight,100621,,100756,,120920,,123119,,236362,,313658,,302018,,293956,,302989,,303637,,304426,,347387,F,354556,F,374129,F,372410,F,399143,F,429841,F,461690,F,489218,F,501844,F,456743,F,516133,F,573263,F,597457,F,693138,F,737608,F,822889,F,838564,F,1204006,F,1211961,F,1200299,F,1450354,F,1527917,F,1288383,F,1446800,F,1484900,F,1628100,F,2034000,F,1822000,F,1967000,F,1378000,F,1327000,F,1463000,,1527398,F,1189000,,1065400,,1035600,,725783,,709500,F,701500,F,680550,F,714595,,713095,,713095,F,713105,F,713175,F,713180,F,713250,F,713550,F,715660,F,727285,F,728800,F,724110,F,720675,F,782545,F,776750,F,828750,F,833760,F,837760,F -"Korea, Republic of",All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,252,,346,,472,,266,,323,,356,,344,,421,,740,,755,,769,,886,,618,,574,,537,,1059,,1056,,924,,928,,763,,901,,488,,393,,124,,69,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,221,,156,,111,,151,,174,,477,,339,,480,,254,,551,,544,,610,,728,,556,,577,,644,,2637,,1925,,1835,,1351,,1304,,1351,,1119, -"Korea, Republic of",All,All,All,Tonnes - live weight,225300,,282900,,274700,,258000,,249500,,262200,,346000,,410099,,403299,,392099,,357201,,424517,,469200,,532001,,599811,,639833,,703234,,759881,,840723,,879124,,876609,,1067559,,1342357,,1686127,,2024288,,2133667,,2405159,,2422883,,2352830,,2420659,,2408361,,2810258,,2641382,,2791459,,2908151,,3093816,,3660725,,3333418,,3216014,,3327347,,3285474,,2983447,,3304093,,3335883,,3478501,,3359594,,3334151,,3268001,,2837464,,2909329,,2506078,,2677017,,2498788,,2501632,,2542602,,2719486,,3051622,,3287437,,3365700,,3203623,,3112233,,3256454,,3186497,,3136797,,3321654,,3334116,,3229715,,3696510,,3623637,F -Kuwait,All,All,All,Tonnes - live weight,1000,,1000,,1000,,1000,,2000,,2000,,2000,,3000,,3000,,3000,,3500,,3500,,3500,,3500,,4000,,4000,,4000,,4500,,4500,,4500,,4700,F,5700,,5000,,6100,,5502,,5934,,4648,,5913,,6489,,3065,,3689,,3714,,6628,,8722,,9639,,10118,,7633,,7704,,10796,,7653,,4454,,2034,,7871,,8466,,7752,,8706,,8345,,8031,,8018,,7662,,7354,,6041,,5555,,4426,,5436,,5222,,6203,,4721,,4270.22,,4980,,6683,,4357,,4339,,4936,,4494,,4549,,5689.16,,4173,,3068.65, -Kyrgyzstan,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1320,,1447,,1309,,1199,,843,,350,,294,,392,,330,,253,,247,,119,,110,,324,F,245,F,75,F,27,,46,F,57,F,121,,100,,143,,379,F,454.5,,387,,420,F,804.93,,1099.4,,2019.7,,2190,F,2577.7, -Lao People's Dem. Rep.,All,All,All,Tonnes - live weight,8000,,8000,,8000,,8000,,10000,,10000,,10000,,10000,,12000,,12000,,12000,,12000,,15000,,15000,,15000,,15000,,18000,,18000,,18000,,18000,,20000,F,20000,F,20000,F,20000,F,20000,F,22000,F,22000,F,22000,F,22000,F,24000,F,24000,F,24000,F,24000,F,26000,F,26000,F,26000,F,26000,F,28000,F,28000,F,28000,F,28000,F,29000,F,30000,F,30500,F,35000,F,40250,F,39000,F,40000,F,40858,F,60403,F,71316,F,81000,F,93156,F,94700,F,94700,F,86560,,86925,F,91660,F,93500,F,105800.5,F,113000,F,129600,F,136001,F,146946,F,168597,F,158600,F,170915,F,174900,F,179100,F -Latvia,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,544138,,541723,,459833,,412642,,157388,,142313,,138665,,149719,,143024,,106027,,102756,,125857,,136728,,128639,,117983,,115180,,125936,,151160,,140954,,156005,,158518,,163730.2,,165367.8,,154855.5,,94978.54,,116672.21,,120222.28,,82202.85,,115066.08,,118947.94,,136296.66, -Lebanon,All,All,All,Tonnes - live weight,1600,,1500,,1500,,1400,,1400,,1500,,1600,,1700,,1800,,1900,,2100,,1900,,2000,,2200,,2000,,2300,,2500,,1800,,2500,,3000,,2300,,2000,,1800,F,2400,,3100,F,2405,F,1710,F,1620,F,1740,F,1675,F,1740,F,1570,F,1520,F,1500,F,1500,F,1700,F,1875,,1913,,1813,,1813,,1510,F,1812,,1861,F,2212,,2437,,4385,,4485,,3955,,3920,,3860,,4066,,3970,,4760,,4693,,4661,,4611,,4624,,4518,F,4575,F,4580,F,4580,F,4580,F,4480,F,4280,F,4123,,4663,,5316,,4642,,3827, -Lesotho,All,All,All,Tonnes - live weight,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,,23,,27,,22,,24,,27,,23,,15,,19,,20,,13,,23,,25,,26,,30,,30,F,30,F,25,F,33,F,40,F,40,F,40,F,42,F,44,F,38,F,34,F,40,,32,,48,,46,,47,,46,,47,,179,,141,,152.6,,345.4,,345.37,,450.37,,550.43,,952.53,,1053,F,1102,F,1853,F,2552.46, -Liberia,All,All,All,Tonnes - live weight,2000,,2000,,2500,,2500,,2700,,3000,,3000,,3200,,3300,,3700,,4200,,3700,,4000,,4000,,4000,,4500,,5000,,6000,,6000,,7000,,8000,F,8200,F,8900,F,9300,F,10064,F,10046,F,10132,F,10190,F,10812,,13484,,11791,,13004,,13588,,15286,,14657,,11486,,16059,,18734,,16076,,14805,,6463,,9620,,8891,,7782,,7721,,8829,,8308,,8491,,10717,,14702,,11540,,10510,,11072,,10494,,13762,,12192,,8944,,14548,,7950,,10770,F,11780,F,13661,F,14259,F,15331,F,14687,F,14259,F,13918,F,12194,,14355, -Libya,All,All,All,Tonnes - live weight,2313,,2213,,2513,,2613,,2313,,2113,,2513,,3213,,2413,,2413,,2013,,1700,,1500,,1400,,1600,,2924,,3324,,4236,,5560,,6500,,5500,,5600,,2549,,2975,,4769,,4949,,4059,,2046,,4355,,4500,,12752,,13130,,14706,,10122,F,11979.77,F,15900.6,F,17947,F,18622,F,21690,F,22058,F,24804,F,26106,F,28908,F,31103,F,33518,F,34448,F,36024,F,36925,F,37959,F,43643,F,50009,F,47336,F,44053,F,41994,F,40190,F,37783,F,35040,F,32166,,47760,,52227.3,,50116,F,30014,F,35014,F,36014,F,25013,F,26012,F,30012,F,32010,F,32276,F -Liechtenstein,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -Lithuania,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,415287,,416392,,339080,,472814,,192366,,120078,,51036,,59082,,90051,,45518,,68094,,74612,,80984,,152832,,151896,,159561,,164685,,141798,,156772,,190890,,185768,,176113.6,,153041.99,,141836.61,,70551.96,,95081.88,,153902.8,,89594.17,,111337.74,,93519.07,,78133.09, -Luxembourg,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -Madagascar,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1000,,3800,,4000,,2000,F,1204,,1344,,1909,,2800,,2411,,4589,,5814,,6520,,4302,,6606,,9408,,6936,,7300,,4760,,4850,,6660,,5550,,2640,,2450,,0,-,0,-,0,-,0,-,3,,154,,500,,0,-,0,- -Madagascar,All,All,All,Tonnes - live weight,17900,,18000,,18000,,21100,,21600,,23600,,23600,,26000,,26500,,28000,,29500,,30500,,32000,,37400,,46000,,45300,,56000,,59100,,64700,,50063,,53901,,56745,,57733,,65916,,71399,,61983,,60943,,62471,,61089,,60525,,57461,,58061,,59605,F,63644,,67618,,69309,,84428,,92594,,101340,,98039,,103880,,99381,,107471,,117049,,120615,,121339,,120506,,125929,,119708,,124249,,128947,,132864,,140538,,140937,,145519,,143944,,151227,,163521,,135750,,141819,,140524,,137614,F,128062.5,F,117146,,111261,F,137445.21,,168331,,189941,,142326.06, -Malawi,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,500,,400,,1400,,2300,,1122,,1588,,266,,2036,,1732,,950,,636,,400,,200,,199,,200,,1256,,60,,301,,20,,637,,698,,1287,,3370,,3105,,3250,,3600,,6063,,5373,,2784,,6246,,12097,,11840,,13100, -Malawi,All,All,All,Tonnes - live weight,10000,,10000,,10000,,15000,,15000,,15000,,15000,,20300,,24800,,19400,,13600,,14300,,19500,,21700,,21200,,33900,,32500,,29300,,32900,,38200,,47000,F,60000,F,70000,,69300,,70277,,70992,,74900,,68200,,67800,,60002,,65764,,51401,,58429,,67006,,65068,,62072,,72858,,88596,,78817,,70771,,74100,,63729,,69484,,68206,,58804,,53890,,63809,,56571,,41340,,45982,,50530,F,41187,,41971,,54209,,57196,,60407,,74287,,68000,,71719,,70945,,100929,,85248,,128624,,115953,,120870.56,,146617,,160498.39,,211671.8,,230863, -Malaysia,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,.,0,.,0,.,0,.,0,.,0,.,1000,,1000,,1000,,1717,,2090,,2522,,398,,120,,120,,320,,120,,559,,375,,122,,307,,1450,,1058,,1790,,540,,1043,,587,,861,,436,,1807,,1173,,2483,,3555,,5215,,4413,,5320, -Malaysia,All,All,All,Tonnes - live weight,146079,,138174,,138565,,123554,,113939,,113520,,115297,,118472,,129101,,134967,,155641,,168011,,189845,,220141,,213602,,230008,,273509,,344311,,383208,,349602,,341748,,368506,,359450,,444767,,525366,,474267,,516339,,622301,,688729,,699496,,740265,,808984,,732408,,795251,,802801.4,,797307.4,,822770.3,,911067.57,,876258.18,,943787.09,,1008997.2,,979921.79,,1105678.76,,1154285.14,,1182006.49,,1249207,,1243317,,1284326,,1290583,,1422903,,1461295,,1415881,,1463643,,1483281,,1541989,,1434098,,1518694,,1658042,,1756909,,1874005.3,,2018698.92,,1909571.5,,2116236.9,,2023321.8,,1989740.47,,2003020.2,,1992258.09,,1901885.53,,1853678.48, -Maldives,All,All,All,Tonnes - live weight,12000,,12000,,12000,,14000,,14000,,14000,,14000,,15000,,15000,,15000,,13000,,12000,,12000,,12000,,12000,,19600,,22400,,25100,,23700,,32300,,37273,,35176,,32268,,35706,,37258,,28325,,34634,,29636,,31769,,31175,,38624,,40916,,37838,,44110,,56081,,62076,,59964,,58502,,72589,,72065,,80225,,81523,,82392,,90156,,104242,,104754,,105848,,108312,,118285,,124217,,119373,,127636,,163770,,156132,,158528,,186274,,185811,,144511,,133839,,117061,,122804,,120835,,120000,,130217,,128695,,127381,,129331,,142378,,151013, -Mali,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,15,,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Mali,All,All,All,Tonnes - live weight,60000,,60000,,60000,,60000,,70000,,70000,,70000,,70000,,80000,,80000,,80000,,80000,,90000,,90000,,90000,,90000,,110000,,100000,,106000,,108000,,110000,,90000,,80000,,85000,,75000,,75000,F,75000,F,75000,F,75000,F,83586,,88228,,75564,,73451,,61289,,54711,,54184,,61012,,55713,,55875,,71838,,70548,,68780,,68507,,64352,,62950,,133000,,111970,,99610,,98060,,98616,,109900,,100500,F,101008,F,101008,F,101008,F,101008,F,101000,F,100640,F,100821,,101355,F,102083,F,110217,F,72216,F,101558.25,F,81953,,94880,,106680,F,110135,,94312, -Malta,All,All,All,Tonnes - live weight,1000,,800,,1100,,1000,,823,,814,,821,,1019,,1128,,1124,,1219,,1325,,1322,,1634,,1420,,1228,,1312,,1414,,1228,,1135,,1212,,1247,,1247,,1644,,1546,,1529,,1571,,1483,,1079,,1322,,1078,,942,,1216,,999,,1226,,2585,,1168,,1052,,896,,941,,799,,973,,1079,,1495,,3269,,5540,,10751,,2840,,3130,,3249,,2820,,2137,,2201,,2025,,2006,,2142,,3268,,3951,,4093,,4509,,4752,,4046.72,,6537,,7622,,7322,,8476,,9633.92,,9269.7,,12755.38, -Marshall Islands,All,All,All,Tonnes - live weight,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,100,,100,,100,,100,,100,F,100,F,100,F,100,F,100,F,100,F,100,F,100,F,100,F,100,F,150,F,150,F,160,F,160,F,200,F,200,F,220,F,320,F,400,F,395,F,380,F,300,F,329,F,488,F,388,F,375,F,2772,,400,F,500,F,700,F,8261,,36581,,39859,,38885,,47887,,57583,,43974,,61230,,35103,,46316,,59751,,93272,,75388,,80624,,78743.61,,89742.2,,64800,,70577,,77702.27, -Martinique,All,All,All,Tonnes - live weight,2100,,2100,,2100,,2500,,2600,,3400,,3800,,3700,,3600,,3900,,3300,,3100,,2900,,2500,,3300,,3400,,4000,,4800,,4600,,4000,,4000,F,3700,,3200,,3100,,4056,,3665,,4233,,2432,,4375,,4664,,4891,F,4700,F,5506,F,5120,F,5204,F,4639,F,4088,F,3184,F,3247,,3314,F,3585,F,6391,F,4641,F,5967,F,5917,F,5377,F,4558,F,5566,F,5555,F,6060,,6361,,6251,,6280,,6300,,6310,F,5592,F,6403,F,7280,F,8223,F,7085,F,6082,F,5050,F,4050,F,955,,808.5,,799.1,,858,,831,,832,F -Mauritania,All,All,All,Tonnes - live weight,8000,,8000,,10000,,10000,,11000,,11000,,11000,,13000,,18000,,18000,,20000,,22000,,23000,,23000,,23000,,25000,,27000,,30700,,37000,,43000,,49570,,58925,,36291,,33190,,43697,,33921,,33834,,37897,,41467,,24541,,21598,,58779,,56288,,81600,,57676,,66277,,76614,,88397,,77666,,76000,,66000,F,67637,F,66054,F,59452,F,53746,F,56147,F,63324,F,74127,F,98043,F,104527,F,114456,F,140142,F,156131,F,199650,F,270733,,304877,,165312,,223207,,195328,,216900,F,276238,,372011,,437709,,387833,,378339,,403776,,609754,,794580,,967706.75, -Mauritius,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,118,,400,,150,,0,.,180,,0,.,100,,0,.,0,.,150,,0,-,100,,0,-,0,-,0,-,0,- -Mauritius,All,All,All,Tonnes - live weight,2000,,2000,,2000,,2000,,2000,,2000,,2200,,2200,,2200,,2500,,2500,,2500,,2800,,2800,,2800,,3000,,3000,,3000,,3300,,4000,,5400,,5200,,6600,,6400,,7679,,7038,,6660,,7668,,7113,,6540,,6364,,7157,,9810,,9458,,10388,,12229,,12899,,17351,,17190,,16980,,14176,,18662,,18961,,20689,,18287,,16571,,12034,,14143,,12176,,12290,,9702,,11045,,10762,,11001,,10321,,10255,,9124,,8500,,7116,,8293.4,,7933.5,,7843.3,,6864,,8310.3,,15432.5,,16571.99,,19232.32,,26240.52,,30383.6, -Mayotte,All,All,All,Tonnes - live weight,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,100,,100,,100,,100,,100,,100,,200,,200,,200,,200,,200,,300,F,300,F,300,F,300,F,300,F,400,F,500,F,500,F,600,F,600,F,742,,516,,420,,480,,550,F,780,F,800,,1200,,1000,F,1100,F,1600,F,1400,F,1100,F,500,F,600,F,1033,F,1554,,2869,,3020,,3473,,3051,,9615,,4756,,3677,,2476,,2360,,5101,,11210,,12202,,14665,,20406,,28646,,31139,,28038.2,,2082,,1206,,1563,,724,,1137, -Mexico,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,14,,0,.,2,,20,,147,,197,,6,,1264,,3661,,1591,,1037,,609,,855,,159,,12,,734,,485,,3,,184,,1314,,1300,,2031,,1296,,1640,,3002,,3921, -Mexico,All,All,All,Tonnes - live weight,97300,,98000,,71100,,82300,,101100,,116200,,144700,,133000,,173100,,189900,,196513,,224915,,220300,,245600,,250100,,259700,,291600,,356700,,366800,,354400,,385068,,423585,,458856,,479714,,443705,,500013,,541156,,581249,,831426,,1025373,,1285113,,1640147,,1435149,,1076677.8,,1138003.9,,1256312.3,,1357456.7,,1465561.74,,1394979.48,,1520514.69,,1447146.91,,1453288.89,,1246527.55,,1192965.58,,1262041.77,,1406094.39,,1531420.46,,1572454.35,,1234357.23,,1287370.53,,1403681.41,,1521929.5,,1554806.88,,1470939.05,,1393504.43,,1458214.51,,1522989.31,,1615256.73,,1746284.71,,1776349.47,,1654520,,1708788.63,,1724946.84,,1737917.9,,1723597.33,,1691184.6,,1745794.74,,1880687.82,,1946512.63, -"Micronesia, Fed.States of",All,All,All,Tonnes - live weight,0,0,0,0,0,0,0,0,100,,100,,100,,300,,300,,400,,400,,500,,500,,600,,600,,700,,800,,1000,,1000,,1000,,1500,F,1500,F,1500,F,1500,F,1500,F,1500,F,1500,F,1503,F,1505,F,1505,F,1720,,1720,,1777,,1777,,1777,,3649,,3865,,3767,,2974,F,2267,F,2367,F,12552,,16740,,18168,,22310,,8082,,8808,,9781,,15807,,12589,,23460,,19471,,22926,,33753,,31972,,32361,,15442,,20873,,25445,,28606,,31526,,37345,,47853,,36320,,52706,,71707,,88397,,98931,,129916, -"Moldova, Republic of",All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,8606,,8621,,9472,,5319,,3059,,2365,,1330,,1529,,1093,,1228,,1148,,824,,1735,,1737,,2373,,3466,,5735,F,6356,F,6851,F,7526,F,7836,F,8226,F,8844,F,9050,F,9130,F,9130,F,11050,,11216.05,,12061.4,,12133,,12580, -Monaco,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,0,0,0,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,2,F,2,F,2,F,2,F,2,F,2,F,2,F,2,F,2,F,2,F,2,F,3,F,3,F,3,F,3,F,3,F,3,F,3,F,3,F,3,F,3,F,3,F,3,F,3,F,2,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,F,1,,1, -Mongolia,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,250,,250,,280,,280,,290,,290,,290,,300,,300,,300,,300,F,300,F,300,F,300,F,300,F,300,F,265,,494,,537,,346,,262,,184,,347,,370,,374,,412,,383,,281,,254,,124,,100,,120,F,165,,184,,158,,221,,180,,311,,524,,425,,290,,263,,382,,305,,366,,326,,185,,88,,90,,100,,80,,61,,55,F,49,,63,,15,,22,,25, -Montenegro,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1878.8,,1492,,2294,,2226.2,,2085.4,,2357.6,,2201.6,,2391.5,,2415.4,,2299.8,,2524,,2101.7,,2389.8, -Montserrat,All,All,All,Tonnes - live weight,50,,50,,50,,50,,50,,50,,50,,50,,50,,50,,100,,100,,100,,100,,100,,100,,100,,100,,100,,100,,100,,100,,100,,100,,77,,89,,95,,98,,100,,102,,109,,104,,111,,120,F,125,F,132,F,140,F,145,,69,,69,,75,,126,,23,,58,,62,,49,,44,,46,,38,,34,,33,,32,,32,,34,,31,,50,,49,,35,,31,,37,,24,,33,,41,,44,,41,,34,,31,,26,,30,F -Morocco,All,All,All,Tonnes - live weight,142400,,110300,,143000,,151300,,117000,,108800,,122700,,161100,,177700,,160400,,167700,,176681,,171949,,180535,,208582,,225486,,318584,,273366,,231491,,233719,,258205,,234819,,275077,,399728,,288734,,230525,,288995,,265437,,298157.1,,290446.7,,335425.6,,395731,,369537,,459373,,472826.1,,478315.4,,600561,,499328.2,,556448.4,,527107.6,,575362.3,,604051,,559470.1,,632816.4,,761150.2,,858883.8,,649713,,793718.6,,716198.1,,749002.25,,916713.2,,1107504.9,,971209.6,,931316.6,,932998.7,,1041467,,892911.6,,893481.5,,1006746.7,,1176093.5,,1144394.23,,979354.39,,1180862.88,,1276496.7,,1376286.05,,1385067.61,,1473464.52,,1403400.81,,1387814.91, -Mozambique,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1000,,1000,,1000,,4000,,484,,2727,,3164,,1042,,3021,,523,,1430,,810,,585,,718,,477,,7322,,5130,,0,.,1323,,2021,,179,,566,,0,.,3449,,17058,,7234,,21977,,10781,,11161,,28854,,83868,,38868, -Mozambique,All,All,All,Tonnes - live weight,8300,,8700,,8500,,8300,,8200,,10300,,10300,,12500,,13100,,13700,,13400,,12800,,12800,,13500,,13900,,16200,,17300,,17000,,17700,,19000,,20100,,22900,,22900,,26300,,28660,,25490,,27900,,26950,,25940,,28630,,33850,,40630,,38180,,40940,,34947,,36326,,42683,,39805,,35453,,30187,,31197,,29351,,24402,,23325,,23778,,27411,,38230,,41709,,35453,,38026,,41530,,36556,,40432,,93543,,91954,,85778,,95080,,94151,,123047,,151500,,163419,,196076,,211300,,222822,,254202,,287850,,300933,,331396,,328403,F -Myanmar,All,All,All,Tonnes - live weight,43200,,45801,,100002,,100002,,200005,,200010,,360020,,360030,,360040,,360060,,360080,,360110,,360140,,360180,,360230,,360290,,360350,,381130,,396620,,414520,,432400,,442700,,453300,,463400,,433840,,485140,,501560,,518700,,539690,,564070,,580010,,594540,,584410,,587550,,613691,,648804,,686523,,685864,,704547,,733763,,743818,,735494,,757498,,791378,,810825,,823410,,673788,,863540,,912618,,1011124,,1192112,,1309146,,1474460,,1595870,,1986960,,2217470,,2398440,F,2512410,F,2582074,F,2671896.4,F,2813941,F,2795076.4,F,2850600,F,2849781,F,2934805.83,F,2970100.02,F,3090034.3,F,3204303.26,F,3164816.05,F -Namibia,All,All,All,Number,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,11735,,17325,,23400,,35730,,38130,,20965,,21024,,25903,,29528,,25276,,41858,,44223,,37670,,34000,,31971,,64567,,83350,,34728,,47115,,48003,,47776,,46115,,58684,,56862,,29059,,37057,,21245,,10695,,20419, -Namibia,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,5000,,5000,,5000,,5000,,6500,F,6500,F,6500,F,6500,F,6550,F,7050,F,7850,F,8750,F,9050,F,9750,F,10250,F,10850,F,11650,F,12100,F,12900,F,13700,F,14923,F,32437,,33395,,21225,,268292,,209025,,656404,,790615,,649440,,570699,,518831,,514129,,608790,,580810,,590744,,548847,,626156,,638349,,571391,,554187,,509858.5,,413693.5,,373427.6,,379512.6,,382624.64,,411880.13,,470074.35,,486251.56,,444614.34,,510488.73,,516263.2,,502007.93,,490621.48, -Nauru,All,All,All,Tonnes - live weight,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,100,,100,,100,,100,,100,,100,,100,,100,F,100,F,100,F,100,F,100,F,120,F,120,F,120,F,130,F,130,F,140,F,140,F,140,F,140,F,150,F,150,F,150,F,170,F,170,F,180,F,180,F,190,,377,,500,,500,,480,F,460,F,440,F,420,F,400,F,380,F,360.4,F,342,F,342,F,300,F,330,F,370.8,F,420,F,475,F,530,F,589,,540,F,493,,523.1,,530.3,,530.3,F,530.3,F,530.3,F,530.5,F -Nepal,All,All,All,Tonnes - live weight,500,,500,,500,,500,,1000,,1000,,1000,,1000,,1300,,1300,,1300,,1500,,1500,,1500,,1600,,1600,,1603,,1809,,1818,,1926,,1900,,2100,,2200,,2200,F,2200,F,2500,F,2800,F,3000,F,3300,F,3500,F,3654,,3868,,4400,,4700,,4860,,9076,,9443,,10716,,12100,,12522,,14546,,15595,,16540,,16950,,17003,,21148,,21879,,23207,,24866,,25780,,31723,,33270,,35000,,36568,,39947,,42463,,45425,,46779,,48750,,48230,,49730,,52450,,56000,,57520,,64900,,69500,,70543,,77157.1,,81070,F -Netherlands,All,All,All,Number,1295,,1660,,1650,,1575,,1711,,848,,1665,,1434,,1867,,2226,,2081,,2212,,1628,,1301,,1182,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,7,,0,.,0,.,2,,0,.,95,,20,,118,,11,,48,,48,,33,,0,.,2,,0,.,0,.,0,.,100,,150,,252,,173,,0,-,0,-,38,,0,-,1,,0,-,2,,1,,0,-,11,,11, -Netherlands,All,All,All,Tonnes - live weight,233100,,263800,,279000,,311300,,301600,,276400,,263600,,276000,,318400,,295400,,303400,,346100,,321200,,361000,,388100,,377200,,353000,,314500,,323300,,323200,,300800,,321200,,348300,,343736,,325861,,350558,,284999,,313094,,324486,,323773,,340512,,479772,,544867,,561989,,496061,,554911,,502877,,511773,,476409,,530228,,507239,,458814,,487076,,532882,,529431,,522031,,510669,,550010,,656719,,623371,,571005,,575205,,518465,,592821,,600234,,626976,,558632,,544433,,487186.7,,442582.6,,501459.1,,413990.9,,393488.4,,374057,,434451,,447270,,432434,,552368,,463999, -Netherlands Antilles,All,All,All,Tonnes - live weight,400,,400,,500,,500,,600,,800,,600,,500,,500,,500,,600,,600,,600,,700,,700,,700,,700,,600,,600,,600,,657,F,757,F,857,F,857,F,857,F,887,F,937,F,987,F,1010,F,1010,F,1090,F,990,F,1030,F,1030,F,1030,F,1030,,1060,F,1240,F,1342,F,1342,F,1342,F,1242,F,1215,F,1265,F,1165,F,1084,F,13826,,19597,,19819,,19496,,19946,,22870,,12961,,20209,,17346,,855,F,6640,,4314,,17009,,20636,,19575,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,. -New Caledonia,All,All,All,Tonnes - live weight,400,,400,,400,,500,,500,,700,,500,,400,,600,,600,,400,,400,,800,,1200,,1000,,1000,,900,,500,,500,,500,,500,F,500,F,500,F,800,F,868,,973,,883,,3042,,2963,,2629,,3349,,2448,,3384,,2974,F,3721,,3445,,4518,,5331,,4180,,3619,,5558,,5063,,4284,,3621,,4239,,3833,,4256.4,,3714.7,,4852.3,,5100.1,,5247.4,,5392,,5375.9,,5336.5,,6143,,6000,,5698,,5627.7,,6025,,5785,,5250,,5461,,5456,,5465,,5722,,5260,,5410.25,,5131.5,,4694.07, -New Zealand,All,All,All,Number,79,,111,,122,,109,,180,,112,,159,,186,,183,,320,,361,,81,,35,,123,,139,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,4,,0,.,0,.,9,,13,,40,,3,,0,.,23,,38,,22,,28,,3,,9,,16,,9,,14,,19,,13,,33,,31,,12,,11,,255,,111,,271,,165,,283,,296,,341,,409,,462,,267,,221,,35, -New Zealand,All,All,All,Tonnes - live weight,34000,,34600,,35500,,36800,,37100,,39500,,38700,,39300,,39600,,41800,,52289,,51134,,49815,,50046,,44557,,49007,,56418,,60971,,59651,,50543,,60203,,66800,,59068,,66884,,70040,,65076,,71637,,79229,,99727,,120169,,157585,,170302,,175682,,205347,,218943,,214819,,225147,,260335,,304555,,343092,,380303,,435024,,521751,,485518,,503846,,627114,,500241,,725767,,741836.1,,691838,,639099,,645860,,675232,,635570,,638292,,650762.01,,584322.01,,606600.01,,564892.32,,544720.4,,547185.36,,547604.01,,542087.49,,541150.25,,552572.02,,524588.16,,533807.2,,548093.4,,512347.9, -Nicaragua,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,14987,,20472,,9963,,8919,,4238,,10795,,1590,,3927,,250,,6440,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,134,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,. -Nicaragua,All,All,All,Tonnes - live weight,1000,,1000,,1000,,2000,,2000,,2000,,3000,,3000,,3000,,3000,,4000,,4000,,4000,,5000,,5000,,5600,,7000,,8000,,8700,,9500,,9800,,9400,,11200,,13900,,8689,,10778,,10513,,13914,,15900,,9121,,6996,,5944,,5000,,4565,,4356,,4191,,2501,,5031,,4728,,4656,,3162,,5783,,6743,,8212,,11721,,11989,,16224,,17423,,19974,,24042,,27954,,25783,,25647,,22659,,27548,,38222,,43418,,38849,,47390,,56796,,57950,,49293,,58135,,67385.9,,84845,,66070,,66205,,75881,,84022.4, -Niger,All,All,All,Tonnes - live weight,2000,,2000,,2000,,2500,,2500,,2500,,3000,,3000,,3000,,3000,,3400,,4200,,6000,,9000,,6300,,5000,,5000,,3700,,4300,,3000,,2400,,5400,,16400,,16200,,15050,,9142,,4715,,7372,,8783,,8934,,8892,,8208,,6840,,3251,,3000,,2000,,2345,,2283,,2499,,4741,,3354,,3145,,2464,,2172,,2533,,3651,,4167,,6341,,7025,,11014,,16265,,20821,,23600,,55900,,51506,,50058,,29875,,29768,,30000,,29954,,40070,F,53258,,46600,,45200,,47329,,27300,,34892,,25545,,31392, -Nigeria,All,All,All,Tonnes - live weight,40000,,40000,,40000,,42000,,45000,,45000,,45000,,50000,,50000,,50000,,58510,,55010,,65230,,66250,,85470,,97280,,110100,,119420,,120130,,115850,,155899,,245296,F,246394,F,250593,F,257336,F,256500,F,260585,F,262178,F,264068,F,268753,F,261293,,260132,,269715,,272312,,263933,,245353,,271573,,260808,,279514,F,300449,,316328,,267705,,318415,,255499,,281232,,366101,,357484,,412220,,483482,,477365,,467095,,476544,,511719,,505839,,509201,,579537,,636901,,615507,,744575,,751006,,817516,,856614,,922652,,1000061,,1073059,,1027058,,1041498,,1212474.97,,1169478, -Niue,All,All,All,Tonnes - live weight,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,F,20,F,30,F,40,F,60,,30,,45,,45,,54,,60,,72,,90,,105,,115,,115,,115,,115,,120,,120,,120,F,150,F,150,F,200,F,200,F,200,F,200,F,200,F,200,F,200,F,200,F,200,F,212,,400,,407,,478,,203,,113,,6,,7,,38,,38,F,38,F,38,F,38,F,38,F -Norfolk Island,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -North Macedonia,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1094,,916,,940,,1037,,604,,668,,783,,1305,,1425,,915,,911,,1026,,1172,,1114,,735,,1218,,1453,,1762,,1727,,1636,,1555,,1674,,1496,,1341,,1108,,1595.4,,1499.3, -Northern Mariana Is.,All,All,All,Tonnes - live weight,0,0,0,0,0,0,0,0,0,0,0,0,0,0,100,,100,,100,,100,,150,,150,,150,,150,,200,,200,,200,,200,,200,,150,F,150,F,150,F,150,F,150,F,150,F,150,F,150,F,150,F,145,,143,F,143,,98,,245,,345,,241,,311,,218,,227,,258,,175,,133,,153,,147,,178,,193,,249,,264,,255,,192,,336,,406,,296,,172,,169,,214,,227,,230,,184,,213,,266.58,,210.53,,248,,973,,1211,,1124,,1064,,1083,,1196, -Norway,All,All,All,Number,246771,,331016,,251023,,201335,,252230,,300866,,300061,,203806,,263912,,305573,,223120,,232303,,251803,,202569,,268644,,151006,,256909,,282149,,145706,,179643,,192279,,166246,,116076,,117977,,115732,,113909,,88451,,79915,,59554,,77426,,62928,,70635,,71362,,15353,,12240,,20673,,22316,,38614,,32535,,14070,,15497,,14721,,14171,,12888,,18386,,16199,,17147,,10600,,9692,,6990,,21123,,12564,,11330,,13517,,15290,,22236,,17731,,14640,,1798,,8921,,5439,,10918,,6181,,16650,,12807,,2984,,2162,,2518,,5486, -Norway,All,All,All,Tonnes - live weight,1283854,,1678583,,1680376,,1417841,,1935141,,1677508,,2030462,,1618011,,1442605,,1418126,,1388142,,1527010,,1321310,,1387812,,1607991,,2308843,,2867099,,3250881,,2804413,,2490990,,2983097,,3103016,,3204960,,3016131,,2671073,,2555454,,3420397,,3546340,,2715019,,2768418,,2535773,,2700340,,2650209,,2972432,,2601762,,2252236,,2073607,,2122605,,2010322,,2087169,,1950525,,2363389,,2751218,,2749236,,2769670,,2986759,,3143134,,3422357,,3451742,,3282008,,3383120,,3372900,,3474416,,3286434,,3309501,,3208377,,3114154,,3356219,,3433557,,3654663.85,,3858235.94,,3578577.34,,3612414.2,,3481353.08,,3788424.02,,3822151.06,,3529441.18,,3851694.79,,4013504.8, -Oman,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,13,,22,,5,,1,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Oman,All,All,All,Tonnes - live weight,30000,,30000,,35000,,40000,,40000,,50000,,50000,,50000,,50000,,55000,,55000,,55000,,60000,,60000,,60000,,65000,,70000,,70000,,70000,,70000,,92000,F,114000,F,136000,F,158000,F,180000,F,198850,,197984,,175000,F,152000,F,129000,F,106000,F,83650,,89376,,108766,,105200,,101180,,96353,,136140,,165676,,119224,,119783,,116454,,109218,,105772,,118572,,139861,,121618,,118995,,106184,,108809,,120421,,129907,,142669,,138833,,165531.4,,157544,,147782,,151840,,152031,,158669.4,,164054,,158723,,191731,,206522,,211319.4,,257192,,279709,,347616.25,,553896.45, -Other nei,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,29104,,5618,,40747,,56602,,129445,,85031,,59215,,84419,,47448,,26288,,15108,,3486,,4096,,3274,,2547,,3005,,1877,,1348,,1636,,2131,,1515,,1502,,1469,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Other nei,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,4,,8,,7,,23,,36,,72,,93,,195435,,142588,,159889,,165019,,108292,,174401,,94833,,232359,,223510,F,145069,,297246,,123183,,14088,,18998,,24535,,24581,,22936,,28575,,46295,,47292,,84498,,103393,,115380,,156683,,149112,,280131,,158465,,141569,,174553,,205198,,204603,,164029,,183868,,173790,,145042,,45317,,22726,,17512,,29220,,34197,,18916,,24326,,16345,,27333,,11827,,59029,,22577,,13323,,14263, -Pakistan,All,All,All,Tonnes - live weight,29845,,29845,,37945,,73867,,75268,,80272,,82667,,78473,,73687,,73840,,83817,,80996,,81727,,93593,,110668,,122758,,164642,,166565,,171167,,186016,,193624,,191611,,231862,,249884,,197861,,203889,,210679,,273425,,283329,,291279,,261842,,290988,,300111,,340861,,372014,,408303,,417966,,416269,,434924,,437064,,463828,,504433,,533245,,601034,,533857,,523739,,532339,,590107,,597457,,663531,,611228,,598436,,601640,,572413,,571482,,530614.97,,628299,,583387,,605763,,597157,,595615,,596408,,619027,,632766,,628504,,660653,,672786,,694395,,663893, -Palau,All,All,All,Tonnes - live weight,300,F,300,F,300,F,300,F,400,F,400,F,400,F,400,F,500,F,500,F,500,F,500,F,600,F,600,F,1866,,3370,,3386,,4106,,5756,,5462,,8882,,2943,,2319,,3150,,7608,,7069,,6136,,4883,,10602,,6699,,7516,,10276,,5053,,1041,,1037,,1204,F,1132,F,1187,F,1268,F,1350,F,1077,,1094,,1501,,1238.64,,1088,,1417.09,,1070.45,,913,,952,,961,,1273,,1086,,1031,,1051,F,1084,F,1435.9,F,972,F,1159.82,F,1188.2,F,1075.88,F,992.11,F,1023.78,F,940.91,F,906.89,F,893.33,F,874.19,F,841.8,F,832.7,F,782.7,F -Palestine,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1229,,2493,,3791,,3625,,3650,F,2623,F,1950,F,2379,,1507,,2951,,1824,,2340,,2739,,2908,,1640,,1979,,1507,,2360,,2214,,3038,,3483,,3586,,3708,,3950, -Panama,All,All,All,Number,0,.,1497,,1408,,2365,,2348,,2512,,2854,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,7869,,2840,,2005,,46,,500,,3022,,10,,10250,,11700,,13298,,14694,,15850,,4696,,2210,,2752,,2205,,995,,3556,,300,,400,,812,,2622,,2000,,7371,,6638,,1891, -Panama,All,All,All,Tonnes - live weight,1100,,1000,,1100,,1100,,2100,,2900,,4600,,6600,,6900,,14900,,11000,,11500,,15300,,14100,,25900,,39700,,73100,,72300,,71900,,36800,,58890,,78750,,70156,,114959,,88762,,116934,,185339,,239751,,140097,,166331,,216811,,144798,,120016,,167593,,135014,,293351,,148191,,166386,,122861,,185778,,133738,,168163,,176720,,190190,,191900,,209383,,155686,,170050,,210689,,124372,,229375,,279896,,259940,,236342,,222422,,272281,,272370,,231544,,239002,,245980.38,,188676.53,,178609.2,,187431.6,,208911.2,,171845,,153351.32,,157375.4,,156736.36,,185992.4, -Papua New Guinea,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,62892,,70220,,24664,,29636,,33189,,38043,,43100,,33506,,19302,,29586,,18567,,26413,,24842,,28871,,33914,,22676,,27795,,35230,,32471,,18441,,27548,,30147,,32464,,24831,,41489,,27216,,25005,,24354,,31345,,28130,,26482,,51401,,30832,,32278,,28579,,31029,,30750,,33620,,17218,,23363,,23976,,22090,,38395,,22048,,11569,,8288, -Papua New Guinea,All,All,All,Tonnes - live weight,6000,,6000,,6000,,8800,,9000,,9000,,11000,,10800,,10500,,12300,,12200,,12200,,13100,,15200,,15200,,15800,,15300,,15400,,15800,,16500,,15628,F,30668,,27271,,45600,,50027,,34774,,50671,,27555,,52224,,34488,,44620,,46509,,17589,F,15387,F,17663,F,25921,F,25815,F,26072,F,26526,F,26098,F,26562,F,26101,F,26541,F,25951.69,F,26466.32,F,39444.66,,38047.45,F,46505.43,F,78702.55,,56031.37,,110318.38,,125473.88,,141886.42,,177624.57,,243430.37,,255064.48,,254239.7,,248731.15,,223949.51,,232031.73,,228049.94,,187312.74,,260651.36,,218142.47,,261869.47,,240712.47,,314969.47,,330724.1,,256324.1, -Paraguay,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,.,0,.,5466,,19793,,725,,503,,4445,,0,-,9750,,3793,,8373,,4409,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,2473,,12331,,4539,,4594,,1000,,0,-,0,- -Paraguay,All,All,All,Tonnes - live weight,400,,400,,400,,400,,400,,500,,500,,500,,500,,600,,600,,600,,600,,700,,700,,700,,700,,800,,1000,,1400,,1800,F,2200,F,2500,F,2700,,2700,F,2800,F,2900,F,3000,F,3100,F,3200,F,3300,F,3350,F,3400,F,3500,,5000,,7500,,13000,,10022,,10054,,11051,,12551,,13050,,18000,,19075,F,20100,F,21190,F,22350,F,28350,F,28095,F,28095,F,28103,F,25570,F,25200,F,24700,F,24700,F,23920,F,23250,F,22550,F,21850,F,21200,F,20757.42,F,21929.06,F,21800,F,22350,F,22800,F,23181,F,22600,F,24181,,24536, -Peru,All,All,All,Number,0,.,6414,,95,,1340,,1827,,1887,,2027,,2381,,2554,,3400,,3451,,3602,,3301,,3241,,2066,,1289,,1366,,645,,2462,,2305,,1934,,1773,,1900,,1838,,1812,,1343,,1918,,1192,,1070,,1042,,665,,387,,320,,149,,515,,756,,573,,470,,426,,13662,,2087,,2302,,3971,,4157,,1877,,252,,15,,8,,0,.,485,,255,,55,,605,,2,,2,,14,,14,,2,,0,-,0,-,0,-,0,-,0,-,0,-,3,,0,-,0,-,0,-,0,- -Peru,All,All,All,Tonnes - live weight,73700,,97300,,106800,,117900,,146300,,184500,,268500,,454500,,901500,,2123700,,3502700,,5216500,,6884300,,6822000,,9037700,,7385400,,8712500,,10057700,,10444405,,9147515,,12484240,,10507154,,4678399,,2295861,,4126080,,3415487,,4343785,,2503892,,3443705,,3653017,,2709923,,2718481,,3515081,,1570508,,3320188,,4138458,,5616763,,4587842,,6642111,,6854349,,6874400,,6905099,,7508289,,9009882,,12005290,,8943526,,9523079,,7877802,,4353248,,8437389,,10665173,,7999263,,8782897,,6107545,,9634059,,9419469,,7049318,,7260861,,7451437,,6977772,,4394996,,8347157.35,,4925052.85,,6002185.89,,3714324.42,,4934836.46,,3928938.15,,4285651.36,,7312007.55, -Philippines,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,9,,47,,40,,0,-,0,-,0,-,0,-,0,-,50,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,34,,20,,892,,0,.,200,,2758,,3275,,9369,,9532,,7141,,1581,,8760, -Philippines,All,All,All,Tonnes - live weight,238876,,313075,,332316,,327401,,380537,,402424,,434177,,426788,,467660,,479014,,499237,,500047,,530661,,592516,,647476,,716738,,762077,,805663,,987154,,965920,,1104916,,1125592,,1230528,,1316774,,1403090,,1473553,,1381112,,1509246,,1592216,,1609762,,1717614,,1779854,,1905636,,2138456.4,,2083154,,2067244.7,,2100136.5,,2230404.4,,2282337.4,,2396003.4,,2526370,,2623659,,2661584,,2658650,,2746111,,2833066,,2829456,,2820537,,2867791,,2943871,,3021394,,3182445,,3376773,,3605597,,3922323,,4157388,,4391317.49,,4675622,,4900507.1,,4992736.44,,5050190,,4830283,,4749536,,4576328,,4587385,,4503067,,4229004.75,,4127776.84,,4356867.46, -Pitcairn Islands,All,All,All,Tonnes - live weight,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,F,2,F,2,F,2,F,2,F,2,F,3,F,3,F,3,F,3,F,3,F,3,F,3,F,3,F,4,F,4,F,4,F,5,,8,,8,F,8,F,8,F,8,F,8,F,8,F,5,F,5,F,5,F,5,F,5,F,3,F,3,F,3,F,3,F,3,F,3,F,3,F,3,F,3,F,3,F,3,F,3,F,3,F,3,F,3,F -Poland,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,4,,1,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Poland,All,All,All,Tonnes - live weight,80500,,87500,,92000,,96400,,105700,,126900,,139300,,138800,,145100,,162200,,184011,,185522,,180516,,226737,,264941,,297457,,334722,,338142,,406746,,407847,,469461,,517614,,544218,,578938,,679148,,801041,,751063,,663589,,581025,,599281,,640419,,623088,,609281,,740833,,720369,,678297,,645085,,672120,,653977,,565391,,474692,,458127,,511975,,414015,,462532,,454483,,370493,,377593,,271801,,269435,,253477,,260524,,256149,,215835,,227239,,193167,,181346,,187448,,179309,,260396,,220473,,227345,,230646,,249378.13,,231707.69,,242399,,256415,,265195.77,,266065.33, -Portugal,All,All,All,Number,448,,928,,784,,637,,807,,839,,705,,842,,701,,572,,606,,507,,583,,658,,611,,530,,410,,395,,142,,228,,249,,353,,390,,388,,234,,238,,57,,184,,205,,179,,330,,251,,95,,21,,63,,0,.,0,.,3,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,4,,7,,4,,1,,0,-,35,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Portugal,All,All,All,Tonnes - live weight,317384,,317199,,343822,,431250,,448436,,434577,,482090,,485430,,481112,,442256,,489781,,517403,,533385,,558368,,601929,,566143,,522866,,583161,,527572,,480362,,477628,,453740,,458419,,492306,,442645,,387966,,352766,,313971,,259874,,251249,,276121,,269469,,259938,,255798,,311486,,327919,,413184,,397710,,353693,,340030,,337660,,337132,,306884,,301751,,276668,,272795,,270798,,234992,,236328,,218875,,199657,,202673,,211706,,220850,,229256,,227778,,237731,,264099,,231798,,205893.1,,231770.58,,225324.39,,209055.8,,206625.21,,195002.94,,199662.88,,196332.24,,195123.74,,190084.3, -Puerto Rico,All,All,All,Tonnes - live weight,2100,,2500,,2500,,2400,,2500,,2600,,2700,,2700,,2800,,2900,,3100,,3100,,3300,,3300,,2900,,2800,,2500,,2500,,2200,,2200,,2100,,2200,,2100,,2000,,2000,,2275,,2663,,2881,,3197,,3597,,2557,,1784,,2202,,2655,,2354,,1496,,1327,,1288,,1697,,1977,,2062,,2357,,2330,,2322,,2726,,3817,,3101,,3685,,3949,,3937,,5608,,5457,,3699,,4095,,3799,,3912,,3112,,2389.3,,2485,,2363,,2610,,2044,,1881,,2021.92,,2077,,2168,,1921,,1626,,1669, -Qatar,All,All,All,Tonnes - live weight,400,,400,,400,,500,,500,,500,,500,,600,,600,,600,,800,,800,,1000,,1000,,1000,,1300,,1300,,1500,,1500,,1500,,1500,F,1500,F,2000,F,2200,F,2047,F,1989,F,2400,F,2433,F,2200,F,2200,F,2178,,2604,,2331,,2114,,3173,,2484,,1980,,2678,,3086,,4374,,5702,,8136,,7845,,6994,,5086,,4271,,4740,,5034,,5279,,4397,,7140,,8865,,7155,,11295,,11134,,13946,,16412,,15226,,17724,,14100,,13796,,13021,,11311,,12062,,16269,,15213,,14526,,15368,,14679, -Romania,All,All,All,Tonnes - live weight,20000,,20600,,27150,,29300,,27000,,33000,,34000,,40200,,36000,,35200,,38000,,38800,,33400,,36100,,34000,,37581,,36933,,48566,,40892,,45997,,62809,,72252,,84289,,102068,,129453,,136624,,127197,,150701,,137676,,179087,,173606,,192013,,235653,,242644,,232449,,237637,,271126,,264371,,267618,,224810,,127734,,125003,,95381,,34919,F,42651,,69105,,32159,,19614,,18675,,16841,,17099,,18427,,16228,,18932,,13232,,13337,,14751,,16495,,17942,,17151,,11669.32,,11607.27,,13964,,15364,,16927.98,,20348.75,,25302.03,,27796.2,,23477.45, -Russian Federation,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,157518,,133355,,133202,,133709,,114653,,107358,,82337,,54266,,49985,,49729,,30788,,50929,,50241,,47664,,46972,,48607,,14994,,28510,,23315,,13021,,17770,,6588,,5296,,7168,,5049,,4769,,3336,,4804,,3664,,10753,,8754, -Russian Federation,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,8183884,,8246556,,7658693,,6852132,,5552573,,4467715,,3789591,,4390952,,4747731,,4748500,,4551494,,4238575,,4104502,,3729276,,3345547,,3399975,,3063387,,3322873,,3402263,,3590028,,3509646,,3949267,,4196794,,4391167,,4484507,,4521642,,4429766,,4617875,,4947395,,5060320,,5320932, -Rwanda,All,All,All,Tonnes - live weight,200,,200,,200,,300,,300,,300,,300,,400,,400,,400,,500,,500,,500,,500,,700,,700,,700,,800,,800,,800,,900,,900,,800,,1000,,1368,,1198,,1008,,1325,,733,,974,,1223,,982,,1240,,1248,,825,,945,,1546,,1694,,1328,,1516,,2514,,3561,,3697,,3553,F,3460,F,3379,F,3052,,4546,,6769,,6733,,6996,,7263,,7612,F,8427,,8212,,6850.15,,7741.2,,9246.2,,11647.25,,11612.3,,13100.3,,17259.33,,19485.98,,23555.2,,26663,,30954.2,,26593,,28357,,29105, -Réunion,All,All,All,Tonnes - live weight,1000,,1000,,1000,,1300,,1600,,1500,,1800,,1700,,1000,,1600,,1600,,1600,,1500,,1700,,1900,,1700,,2100,,2300,,2400,,2200,,1901,,2100,,2100,,2000,,2533,,1905,,1981,,621,,1807,,1594,,1374,,1379,,1475,,1285,,1449,,1102,,1758,,1151,,1253,,1009,,943,,920,,1126,,1810,,2661,,2650,,3772,,4663,,4876,,4201,,4244,,4036,,3008,,3024,,3480,,4490,,3675,,4181,,3272,,2899,,2815,,3152,,2627,,2857,,2577,,2869,,3192,,2257,,2308, -Saint Barthélemy,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,100,F,100,F,100,F,100,F,100,F,100,F,100,F,100,F,100,F,100,F,100,F,100,F -Saint Helena,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,10,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Saint Helena,All,All,All,Tonnes - live weight,400,,400,,400,,500,,500,,500,,500,,600,,600,,600,,600,,600,,600,,600,,600,,700,,700,,700,,700,,700,,800,,756,,1057,,762,,754,,912,,1000,,719,,700,,632,,637,,687,,928,,619,,652,,572,,632,,741,,786,,1013,,802,,627,,651,,726,,702,,914,,819,,898,,1060,,633,,719,,867,,598,,985,,1061,,1130,,1120,,837,,794,,856,,864,,1302,,678,,574,,650,,613,,889,,778,,789, -Saint Kitts and Nevis,All,All,All,Tonnes - live weight,300,,300,,300,,400,,400,,500,,400,,500,,500,,600,,600,,600,,600,,700,,700,,700,,800,,800,,800,,800,,1000,F,1000,F,1000,F,1000,F,1000,F,1218,F,1684,,1695,,1790,,1777,,1935,,1900,,1930,,1177,,1115,F,995,F,901,F,924,F,800,F,713,F,623,F,453,F,300,F,250,F,357,,339,,687,,418,,967,,934,,971,,1040,,950,,874,,1112,,1517,,1180,,1276,,1343,,2065,,21755,,31072,,21888,,17718,,66143.2,,100399.5,,65770,,85458.5,,1048, -Saint Lucia,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,161,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Saint Lucia,All,All,All,Tonnes - live weight,300,,300,,300,,400,,400,,400,,500,,500,,500,,500,,600,,600,,600,,600,,800,,800,,900,,1000,,1100,,1100,,1419,,1519,,1519,,1627,,1700,,2000,,2200,,2500,,2600,,1024,,969,,891,,921,,910,,946,,1152,,940,,751,,826,,847,,988,,1056,,1128,,1404,,1356,,1354,,1414,,1419,,1711,,2037,,2028,,2189,,1913,,1690,,1712,,1605,,1657.24,,1742.39,,1902.04,,2078.36,,1984.9,,1832.8,,1971.9,,2035.3,,2018.2,,2056.1,,2149.44,,2118.45,,2048, -Saint Vincent/Grenadines,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,64,,71,,135,,117,,65,,0,-,0,-,40,,0,-,0,-,1,,0,-,0,-,2,,2,,1,,0,-,0,-,0,-,1,,2,,0,-,0,-,1,,0,-,2,,2,,3,,2,,2,,1,,0,-,2,,1,,1,,0,-,0,-,3,,2,,2,,4,,2,,0,-,0,-,1,,0,- -Saint Vincent/Grenadines,All,All,All,Tonnes - live weight,300,,300,,300,,400,,400,,500,,600,,600,,600,,600,,400,,400,,400,,500,,500,,500,,500,,500,,400,,500,,600,,300,,300,,200,,283,,549,,379,,581,,698,,547,,500,F,500,F,500,F,480,F,480,F,483,,599,,703,,4647,,5820,,9022,,8167,,2171,,2240,,3922,,1033,,989,,6145,,34108,,17804,,22799,,31944,,46076,,37767,,78315,,44104,,51584,,63309,,55898,,59587,,66367,,76576,,10185,,39507.5,,81385.4,,26168.4,,22744.4,,32986.1,,2366, -Saint-Martin,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,90,F,90,F,90,F,90,F,90,F,90,F,90,F,90,F,90,F,90,F,90,F,90,F -Samoa,All,All,All,Tonnes - live weight,300,,300,,300,,400,,400,,400,,400,,500,,500,,500,,500,,600,,600,,600,,600,,600,,700,,700,,700,,700,,900,,900,,900,,900,,900,,1000,,1100,,1250,,1090,,1890,,1990,,3095,,4020,,3820,,3720,F,3641,,3186,F,3076,F,2500,F,2530,,1505,F,1595,F,2436,,1986,F,2591,F,4094,,4410,,7868,,9364,,8562,,8595,,8613,,10881,,9703,F,9934,F,9836,F,12435,,14094,,13902,,13284,,12845,F,11457,F,11905.6,F,11204.73,F,7506.56,F,8712.34,F,8811,F,10864.12,F,9778.93,F -San Marino,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -Sao Tome and Principe,All,All,All,Tonnes - live weight,600,,500,,500,,300,,300,,500,,400,,400,,600,,500,,500,,700,,600,,500,,800,,900,,800,,900,,800,,800,,800,,900,,800,,800,,600,F,600,,646,,889,,1175,,1618,,1847,,2158,,2688,,4012,,4444,,3988,,2833,,2798,F,3391,F,3575,F,3867,F,2576,F,2406,F,2845,F,4106,F,4300,F,4620,,3546,,3600,F,3650,F,3700,F,3692,,3820,,4005,,4141,,4197,,4323,,4449,,4575,,4701,,4837,,6100,F,7400,F,8700,F,10000,F,11441,,11719,,10808,F,9730,F -Saudi Arabia,All,All,All,Tonnes - live weight,3000,,3000,,5000,,4000,,5000,,5000,,5500,,8000,,8000,,10000,,10000,,16200,,18300,,19600,,17700,,18600,,20200,,22100,,21100,,22000,,21700,,22300,,23800,,26400,,23600,,23000,,23300,,22800,,24950,,24460,,24775,,28570,F,31820,F,35300,F,38490,F,43696,,45526,,47888,,46063,,48390,,42618,,42442,,48290,,50525,,56847,,48388,,51544,,54085,,56392,,52320,,55084,,63549,,63955,,67264,,66590,,74796,,81071,,84631.4,,91238,,94105.85,,91519,,80557,,77907,,81212,,92540,,98130,,108002,,123000,F,140776,F -Senegal,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,2,,0,-,1,,0,-,0,-,0,-,0,-,0,-,0,-,7,,7,,0,-,0,- -Senegal,All,All,All,Tonnes - live weight,25000,,25000,,34000,,34000,,39000,,42000,,44000,,54900,,57400,,66600,,76200,,82600,,85900,,87500,,97600,,106600,,111700,,118076,,114976,,118981,,121065,,134582,,153039,,193041,,218108,,226578,,217663,,216696,,221219,,225238,,232530,,227216,,231971,,262960,,250359,,244982,,257938,,251955,,265009,,290350,,315161,,328743,,373391,,386158,,359929,,366372,,439287,,483542,,429554,,441081,,436109,,431357,,398149,,469382,,434577,,399902,,368433,,412405,,428352,,446271,,409794.6,,427468.66,,462504.3,,472138.8,,459621,,426649.5,,476240.83,,535878.7,,485858, -Serbia,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,7645.05,,9143.8,,10686.72,,11286.44,,13002.33,,13012.95,,12459.19,,10975.54,,10761.37,,10537,,8944.66,,7278.65,,9420.7, -Serbia and Montenegro,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,5339,F,4079,,4175.59,,3849.52,,4476.99,,4928.38,,9581.89,,5630.74,,4986.2,,4629.28,,4914.07,,4995.06,,7005,,6985,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,. -Seychelles,All,All,All,Tonnes - live weight,1000,,1000,,1000,,1500,,1500,,1500,,1500,,1800,,1800,,1800,,1800,,2000,,2000,,2000,,2000,,2000,,2500,,2500,,2500,,2500,,3000,,3000,,3000,,3000,,3500,,3950,,4010,,4600,,5400,,4957,,4676,,5221,,4045,,3938,,3734,,4118,,4542,,3900,,4343,,4419,,5452,,8162,,6713,,5278,,4633,,4203,,4985,,14627,,24535,,34450,,33203,,53873,,63617,,87104,,101846,,109452,,93443,,65882,,69488,,81114,,87111,,75481,,68687,,74127,,75116.1,,104984.1,,127128,,142765.1,,145614, -Sierra Leone,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,76,,491,,451,,276,,23,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Sierra Leone,All,All,All,Tonnes - live weight,5000,,5000,,5000,,10000,,10000,,10000,,15000,,17000,,17700,,20000,,20000,,22800,,26000,,28100,,29900,,32600,,32200,,33600,,23500,,25500,,31050,,31150,,51052,,66976,,68503,,68597,,55546,,55547,,52457,,57593,,48990,,49862,F,51938,,50090,F,51674,F,52430,F,52227,F,52124,F,52193,F,52127,F,56556,F,65761,,68530,,66303,,62464,,64895,,67334,F,72658,,63095,,59437,,74760,,75240,,83020,,95956,,132470,,143023,,144176,,139585,,197632,,199075,F,200075,F,202075,F,203575,F,205075,F,206552,,202185,F,202185,F,202185,F,202185,F -Singapore,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,.,0,.,0,.,0,.,0,.,0,.,2864,,1500,F,635,,962,,286,,301,,1004,,411,,296,,416,,350,,481,,1041,,1058,,1074,,1136,,1330,,1712,,1653,,1877,,1086,,1217,,1659,,3793,,2859,,2979,,1971,,5739,,150,,244, -Singapore,All,All,All,Tonnes - live weight,4000,,4000,,5000,,5000,,5000,,6200,,9600,,13800,,12300,,11500,,9200,,9700,,11500,,12500,,10400,,11000,,18500,,18200,,17300,,17000,,18310,,15230,,15735,,18640,,19276,,17598,,16504,,15157,,16206,,16971,,16082,,16301,,20202,,20764.6,,26289.6,,23917.9,,21487,,16908,,15250,,12633,,13348,,13099,,11551,,11654,,13661,,13727,,13510,,13338,,11439,,10518,,10483,,7785,,7796,,7109,,7579,,7837,,11676,,8025,,5141,,5688,,5230.87,,5953.79,,6201.33,,7211.11,,6695.32,,8161.29,,7346.36,,7000.86,,7012.65, -Sint Maarten,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,253,F,253,F,253,F,253,F,253,F,253,F,253,F,253,F -Slovakia,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2773,,3488,,3567,,2368,,2618,,2009,,2268,,2255,,2530,,2575,,2527,,2783,,2648,,2981,,3193,,2726,,2595.9,,2310.4,,2769.5,,3255.56,,3092.2,,3174.66,,3279.63,,4034.65,,4516.26,,4161.41, -Slovenia,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,4773,,3002,,3133,,2956,,3236,,3284,,3137,,3233,,3037,,3089,,2975,,2635,,2594,,2573,,2502.17,,2464.45,,2196.71,,2341.86,,1719.4,,2287.21,,1639.49,,1625.92,,1806.8,,1950.59,,2154.22,,2010.17,,2182.24, -Solomon Islands,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,300,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,. -Solomon Islands,All,All,All,Tonnes - live weight,1000,,1000,,2000,,2000,,3000,,3000,,3000,,3000,,4000,,4000,,4000,,4000,,5000,,5000,,5300,,5300,,7300,,7400,,7400,,7400,,8500,,13511,,17005,,15924,,19676,,16940,,26284,,22863,,29192,,35431,,34866,,34728,,32518,,46014.6,,48829.74,,43363,,56743.39,,43562.21,,51607.07,,50148.79,,41409.35,,63011.78,,47115.71,,43633.04,,47522.3,,64194.4,,52458.45,,61302.5,,60739.59,,54570.6,,19579.33,,24553.36,,26258.55,,37099.22,,36756.87,,32779.94,,40879.26,,33016.78,,28617.9,,32682.4,,44649.66,,45938.76,,45884.47,,48522.88,,85898.71,,85198.99,,76576.36,,61672.53,,67156.21, -Somalia,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,1,,2,,2,,2,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,500,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,. -Somalia,All,All,All,Tonnes - live weight,6000,,6000,,7000,,7400,,5300,,9500,,10000,,9000,,8000,,5000,,4500,,4500,,4500,,4500,,4500,,4600,,4600,,4700,,5000,,5000,,5600,F,5700,F,5800,F,5900,F,5980,,10350,,8268,,9830,,8384,,10984,,14330,,9823,,9030,,11495,,19939,,16767,,18655,,19996,,20327,F,21546,F,22695,F,23800,F,25750,F,27750,F,29850,F,32400,F,30200,F,28000,F,25800,F,28650,F,24150,F,31900,F,29000,F,30000,F,30000,F,25000,F,30000,F,30000,F,30000,F,30000,F,30000,F,30000,F,30000,F,30000,F,30000,F,30000,F,30000,F,30000,F,30000,F -South Africa,All,All,All,Number,4484,,5314,,4946,,4137,,3249,,3006,,3413,,4132,,3026,,3431,,3523,,3352,,3947,,4055,,100409,,68624,,52002,,68165,,74474,,79613,,87827,,76706,,80663,,83101,,67985,,75735,,62467,,77493,,73390,,75470,,66521,,68605,,91425,,46839,,27179,,50703,,10517,,20234,,13356,,17841,,4500,F,4819,,10722,,18538,,25581,,14900,,2380,,13473,,8909,,27691,,29733,,33211,,45865,,31391,,35838,,25603,,24089,,34837,,40197,,24988,,47267,,30695,,70899,,74232,,121021,,59638,,37973,,64053,,40940, -South Africa,All,All,All,Tonnes - live weight,324700,,478200,,656800,,654600,,639600,,618800,,548800,,598700,,674700,,753500,,879000,,1036600,,1088300,,1195114,,1253206,,1351309,,1369292,,1701900,,2132600,,1887100,,1277950,,1214550,,1188004,,1423825,,1477296,,1413953,,1210309,,1020512,,1039483,,1011209,,866050,,879406,,838126,,948732,,758281,,799023,,843614,,1447221,,1324334,,889864,,549197,,512178,,705926,,574396,,532331,,585979,,453193,,530260,,579466,,609605,,667086,,785547,,797769,,847226,,917685,,831004,,635071,,696526,,661336,,528334,,645803.6,,550267.28,,725162.53,,437459,,616473,,579042.4,,630164.27,,536248.32,,578413.45, -South Sudan,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,37020,F,37020,F,37020,F,37020,F,35020,F,33020,F,32020,F -Spain,All,All,All,Number,146,,236,,274,,128,,282,,292,,273,,301,,181,,237,,197,,192,,198,,126,,219,,285,,283,,287,,359,,315,,413,,361,,271,,251,,224,,451,,410,,234,,593,,547,,234,,146,,150,,120,,102,,48,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,35,,4,,3,,7,,12,,17,,32,,15,,49,,297,,261,,8,,18,,4,,8,,1,,2,,8,,12,,65,,3,,6,,25,,22,,24, -Spain,All,All,All,Tonnes - live weight,615603,,592524,,620585,,643112,,680123,,799633,,789649,,811672,,823678,,870847,,913401,,978920,,1050006,,1057655.1,,1133309.1,,1255355.4,,1269533.8,,1360394,,1524502,,1481900,,1582266,,1521038,,1569839,,1598054,,1603741,,1604441,,1605359,,1531484,,1548919.4,,1360031,,1375641.5,,1396653.5,,1465325,,1408215.5,,1441851.5,,1489514.8,,1502394.6,,1539932.6,,1598886,,1530990.7,,1334995.2,,1303615.3,,1260681.4,,1226032.6,,1286760.3,,1419688.1,,1421196,,1459464.2,,1570775.3,,1509503.6,,1372404,,1399888.3,,1138640.5,,1157665.4,,1105577.8,,1074617.1,,1254865.3,,1102038.5,,1173161.4,,1199111.1,,1227763.85,,1283388.06,,1195763.88,,1213852.04,,1347144.51,,1267785.24,,1200692.2,,1264800.86,,1276616.03, -Sri Lanka,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,.,15100,,15000,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Sri Lanka,All,All,All,Tonnes - live weight,20676,,24182,,24831,,25070,,26473,,27293,,32722,,29665,,35770,,41378,,48103,,59628,,73471,,71614,,91125,,74632,,79973,,82237,,98302,,95060,,89241,,74985,,91877,,96548,,100489,,109235,,131663,,136165,,156107,,164790,,183447,,201951,,201239,,205500,,162502,,177659,,183998,,187154,,205574,,220486,,183923,,211801,,206463.08,,232200.89,,248716.67,,237885.92,,263818.31,,274186.84,,289689.86,,314072.7,,301169.6,,271160,,275253,,323221.2,,336857.4,,220967,,284255,,313136,,331362,,344490.68,,398245.14,,440116.22,,484640.76,,525317.3,,564702.3,,538099.75,,551905.22,,536735.16,,541458.46, -St. Pierre and Miquelon,All,All,All,Tonnes - live weight,2500,,2500,,4000,,5900,,6800,,8300,,9300,,10700,,9300,,10900,,10782,,13696,,8208,,8278,,8954,,10146,,10559,,8512,,6479,,7716,,6807,,5541,,4677,,5467,,4515,,6397,,8327,,7384,,8905,,10542,,8617,,11111,,10474,,9561,,12026,,12304,,23771,,23679,,13970,,18520,,23181,,23800,,16443,,282,,294,,317,,747,,3571,,6108,,5892,,6690,,4206,,3889,,3894,,4399,,4694,,2878,,5249,,4621,,3879,,4887,,3174,,3743,,3042,,2604,,3501,,3030,,3250,,3285, -Sudan,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,41508,F,40208,F,39508,F,40008,,41502,F,47390,,51041, -Sudan (former),All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,5000,,5000,,5000,,5040,,700,,7903,,0,-,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,20,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,. -Sudan (former),All,All,All,Tonnes - live weight,10500,,10500,,11600,,13200,,13800,,14500,,14400,,10400,,19800,,16800,,17100,,18000,,19500,,19900,,20100,,20000,,20500,,20500,,22000,,22300,,22200,,22200,,22200,,22600,,22600,F,22600,F,24700,,23570,,26610,,28520,,26060,,28530,,29710,,29500,,29790,,26320,,23983,,27243,,29245,,30300,,31734,,33316,,35223,,40214,,44213,,45003,,46009.6,,48010,,50513,,50513.5,,54010,F,59007.5,,58607.5,,60607.5,F,64607.5,F,60607.5,F,58607.5,F,67466.5,,70402.5,F,73698,,73358,F,73008,F,0,.,0,.,0,.,0,.,0,.,0,.,0,. -Suriname,All,All,All,Tonnes - live weight,800,,1000,,1000,,2380,,2680,,3410,,4210,,2720,,3950,,3950,,4510,,7300,,5350,,5550,,5910,,6610,,6580,,6510,,7600,,9900,,11750,,10400,,12120,,11610,,10501,,12262,,5993,,6897,,4474,,4535,,4046,,4620,,3796,,4520,,5095,,4909,,4540,,6626,,4602,,7786,,7670,F,8186,,11272,,9580,,14766,,14301,F,16178,F,16618,F,21683,,24447,,24583,,28979,,31127,,35854,,33160,,29862,,32101,,31019.3,,24206.4,,26184.16,,35156.1,,35586,F,39338.2,F,39447.7,,38510.82,,44827.17,,47115.1,,47090,F,47090,F -Svalbard and Jan Mayen,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -Sweden,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,.,0,.,0,.,0,.,0,.,0,.,29,,19,,17,,35,,61,,112,,55,,0,-,0,-,25,,53,,124,,8,,14,,2,,5,,3,,3,,5,,1,,0,-,2,,3,,1,,1,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Sweden,All,All,All,Tonnes - live weight,175500,,173200,,193600,,190700,,193300,,211400,,188400,,211900,,239700,,262704,,250052,,268924,,294728,,342638,,377947,,370618,,320568,,341765,,319079,,269902,,286042,,229693,,218349,,220471,,205540,,207282,,205299,,182864,,185577,,198202,,233516,,257325,,255840,,261717,,274618,,239718,,215309,,214412,,250963,,257697,,260106,,245001,,314664,,347801,,394224,,412127,,379142,,364066,,416363,,357260,,343368,,318590,,300582,,293209,,275919,,262239,,276800,,243618,,238931,,211955,,222679,,194762,,165366,,202317,,194473,,223983,,221212,,243519,,233334, -Switzerland,All,All,All,Tonnes - live weight,2000,,2000,,2000,,2000,,2000,,2000,,2000,,2000,,2000,,2500,,2500,,2800,,2800,,3300,,2800,,2700,,3000,,3000,,3500,,3500,,2400,,2800,,3600,,3600,,3800,,3900,,3915,,3982,,3820,,3890,,3500,,3676,,3369,,3540,,4113,,4564,,4657,,4523,,4227,,4483,,4228,,4829,,3945,,2957,,2716,,2749,,3002,,3009,,2959,,2975,,2759,,2850,,2679,,2915,,2807,,2952,,2904,,2869,,3300,,3433,,3362,,3564,,3474,,3600,,3576,,3616,,3584,,3665,,3296, -Syrian Arab Republic,All,All,All,Tonnes - live weight,900,,900,,1100,,1100,,1300,,1300,,1300,,1400,,1400,,1500,,1500,,1600,,1600,,1600,,1600,,1700,,1700,,1700,,1801,,1804,,1700,,2100,,1600,,1345,,1650,,2001,,3307,,3617,,3755,,3826,,3971,,3838,,4063,,4411.55,,5415.4,,5890.11,F,5305.93,F,5381.66,F,5533,F,5095,F,5775,,7721,F,9654,F,9189,F,10041,F,11639,,12128,,11727,,14330,,14017,,13369,,14171,,15166,,16128,,17220,,16980,,17166,,17882,,15590,,15304,,15247,,13400,F,11500,F,8800,F,7400,F,6600,F,7000,F,6715,F,6724,F -Taiwan Province of China,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2051,,2347,,1833,,1831,,1936,,1817,,2,,0,.,1,,5,,4,,0,.,0,.,3172,,1710,,1771,,710,,17,,0,.,0,.,5668,,0,.,0,.,21,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,36,,47,,82,,34,,38,,34,,27,,30,,31,,33,,37,,32,,36,,32,,27,,23, -Taiwan Province of China,All,All,All,Tonnes - live weight,102010,,108036,,123898,,133404,,154679.14,,182542,,195365,,209814,,231555,,248370,,261460,,315250,,333845,,353391,,379145,,387369,,426625,,459825,,532867,,562612,,612605,,650078,,694063,,757568,,695725,,778690,,808889,,845197,,883621,,928358,,935290,,910643,,921704.8,,930260.8,,1002242.36,,1037553.32,,1094573.81,,1236082.99,,1357265.18,,1371583.72,,1455335.8,,1316125.4,,1326665.8,,1419030,,1255230.55,,1296791,,1239937.6,,1308284.24,,1347171.5,,1362902.3,,1350403.5,,1318352.1,,1389879.37,,1499512.13,,1307799.17,,1324665.2,,1283939.51,,1498759.92,,1346666.72,,1061134.12,,1168335.53,,1225395.59,,1255313.25,,1273632.96,,1409918.82,,1303587.23,,1007303.26,,1034515.46,,1099163.11, -Tajikistan,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,3536,,3547,,3887,,3945,,2023,,2715,,1107,,384,,203,F,191,F,216,F,224,,253,,236,,324,,325,,210,,193,F,241,F,251,F,406,F,790.1,F,1207.2,,1380,,988.8,,1582.9,,1617.6,,1626,F,1539,F,1575,F,1580,F -"Tanzania, United Rep. of",All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2000,,1800,,2000,,2000,,982,,459,,144,,342,,915,,1185,,630,,777,,827,,1302,,1639,,1383,,1502,,1572,,1427,,1100,,1556,,1784,,790,,601,,1702,,1209,,1379,,1287,,1294,,1294,,1290,,1291, -"Tanzania, United Rep. of",All,All,All,Tonnes - live weight,32600,,32600,,35600,,60000,,59900,,62500,,65100,,65600,,65600,,70500,,70800,,73100,,73300,,79000,,103000,,104700,,104200,,131000,,155600,,154200,,191220,,183601,,158026,,164192,,174826,,212076,,233744,,272541,,213484,,182832,,231192,,233897,,230886,,242774,,280909,,304787,,314000,,346498,,397565.31,,382058.03,,418393.2,,330779.76,,335133.31,,336250.36,,293171.08,,364555,,328716.2,,361953.17,,353354.89,,316450.01,,328474,,343287.74,,331687,,356691.75,,367257.68,,378854,,339636,,430711,,331503,,340676,,358729.5,,356156.75,,389342.4,,390426.1,,352329.2,,386192.44,,383700.4,,408628,,392607.22, -Thailand,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,3000,,2548,,2900,,1800,,3140,,586,,19,,2068,,4791,,3346,,5892,,0,.,0,.,4945,,2399,,3580,,10982,,20405,,28641,,32447,,40190,,41669,,26119,,31513,,34878,,34101,,41255,,33962,,34264,,21283,,16192,,20043, -Thailand,All,All,All,Tonnes - live weight,183590,,192810,,197630,,211390,,236570,,220190,,225820,,243245,,205270,,214405,,231420,,316340,,351160,,430905,,590050,,629865,,722092,,861366,,1102121,,1285154,,1437837,,1587432,,1679152,,1679559,,1516024,,1548924,,1661315,,2191381,,2101681,,1954258,,1799988,,1990460,,2121455,,2261922,,2140261,,2233375,,2541990,,2785311,,2652089,,2704781,,2789953,,2972104,,3246490,,3385003,,3524996,,3590578,,3570116,,3442715,,3524933,,3646070,,3735279,,3648095,,3797036,,3914076,,4099653,,4118527,,4053100,,3675407,,3204293,,3287370,,3096742,,3036581.43,,2991728,,2822346,,2567899.86,,2421693,,2493217,,2394421,,2598000, -Timor-Leste,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,3401,F,3621,F,3561,F,3720,F,3850,F,4015,F,3672,F,3323,F,3313,F,4295,F,4747,F,4754,F,4761,F,4768,F,4774,F,4780,F,4787,F,4795,F,4800,F,4810,F -Togo,All,All,All,Tonnes - live weight,3000,,3000,,3000,,3300,,3200,,3500,,3600,,4000,,3400,,3400,,3500,,4000,,4300,,4500,,5000,,6000,,7300,,7500,,10000,,10000,,9111,,10921,,10942,,11333,,11150,,14420,,12820,,10847,,15363,,8096,,9134,,10325,,14530,,14878,,14715,,15574,,14838,,15189,,15469,,16458,,15800,,12524,,10899,,17114,,13202,,12221,,15119,,14310,,16702,,22944,,22297,,23183,,20976,,27515,,28048,,27756,,24880,,19955,,22872,F,27132,,27635,,24142,,19340,,20038,,19874,,21555,,31989,,27047,,24910, -Tokelau,All,All,All,Tonnes - live weight,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,100,F,100,F,100,F,100,F,100,F,100,F,100,F,100,F,100,F,100,F,100,F,100,F,100,F,100,F,150,F,150,F,150,F,200,F,200,F,200,F,220,F,231,,191,,200,F,200,F,200,F,200,F,200,F,190,F,180,F,160,F,140,F,128,F,113,F,103,F,96,F,86,F,78,F,68,F,62,F,54,F,54,F,248,,198,,105,,198,,198,,86,,74, -Tonga,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,3,,4,,0,-,4,,4,,11,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Tonga,All,All,All,Tonnes - live weight,200,,200,,200,,200,,300,,300,,300,,300,,300,,300,,400,,400,,400,,400,,400,,500,,500,,500,,500,,500,,400,,500,,500,,600,,726,,901,,1019,,1197,,1210,,2000,,1994,,2093,,2134,,2237,,2385,,2561,,2814,,2790,,2731,,2699,,1644,,1935,,2237,,2375,,2499,,2727,,2955,,2924,,4172,,4698,,4308,,5017,,5327,,4826,,3385,,2647,,2887,,2732.5,,2595.61,F,2255.2,F,2376.3,F,2380.3,F,2587,F,1977.04,F,2007.31,F,1846.41,,1685.03,F,1420,F,1262,F -Trinidad and Tobago,All,All,All,Tonnes - live weight,1000,,1000,,1000,,1100,,1500,,1600,,1600,,1700,,1700,,2000,,2400,,2400,,2400,,2400,,2400,,3000,,3000,,3200,,3400,,3400,,3600,,4100,,3700,,4000,,3725,,4417,,4417,,4303,,4823,,3840,,4461,,3804,,4574,,4541,,3764,,5560,,4347,F,7714,,9211,,11468,,12364,,11504,,13003,,9002,,14050,,11515,F,9450,,11296,,14859,,14707,,14259,,16605,,18775,,14599,,14738.5,,17021.5,,13148.5,,13482.3,,13845.69,,13890.36,,13943.5,,13119.75,,11753.55,,13224.55,,13295.45,,12164.8,,13127.1,,12949.45,,13183.45, -Tunisia,All,All,All,Tonnes - live weight,11965,,15460,,13290,,11720,,11455,,11001,,12350,,14300,,15450,,15044,,16650,,21800,,20804,,26060,,21676,,23201,,26193,,28207,,28484,,30127,,22660,,25870,,28289,,30674,,42347,,45273,,50033,,55738,,56424.8,,59749.1,,61123.5,,58067.5,,63806,,68111.3,,75855.16,,89825.51,,91402.84,F,97716.5,F,100176.16,F,93937.5,,85236.52,,86513.55,,87347,,83417.25,,86946.81,,84215.71,,84885.01,,88265.31,,89939.6,,93490.23,,96223.83,,99394.1,,98707.57,,92348.12,,112679.67,,111756.31,,114249.54,,107402.19,,99750.75,,99443.81,,98602.8,,111171,,117047,,123070.7,,123578.8,,131688,,128330.5,,130743,,127872.5, -Turkey,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,100,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Turkey,All,All,All,Tonnes - live weight,89700,,100100,,99300,,92700,,110610,,103210,,125820,,108930,,95240,,94550,,87860,,81280,,60200,,130420,,121150,,134580,,117056,,188163,,133578,,169580,,174241,,160880,,175487,,146974,,129418,,122494,,153520,,165435,,245221,,350902,,428225,,471720,,505206,,559267,,569168.08,,580782.11,,582930.93,,627998,,674004.5,,454841.3,,384986.1,,364784,,456459,,557973,,603574,,652585,,553630.5,,504605,,543901,,636827,,582383,,594980,,627847,,587715,,644932,,546063,,662381,,773193,,647014,,623556,,653646,,703653,,645249,,607991.6,,536516,,670873,,585657,,627797,,625776, -Turkmenistan,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,46825,,52974,,44895,,43040,,33727,,18243,,17265,,11409,,9321,,8784,,7573,,9131,,12296,,12792,,12850,,14567,,15008,,15026,F,15026,F,15026,F,15026,F,15026,F,15026,F,15026,F,15026,F,15030,F,15030,F,15030,F,15055,F,15060,F,15070,F -Turks and Caicos Is.,All,All,All,Tonnes - live weight,730,,730,,730,,730,,730,,730,,730,,730,,730,,730,,830,,830,,1360,,1460,,1460,,1460,,1460,,1560,,1560,,1560,,1230,,1230,,1430,,1430,,1759,,3299,,4064,,2464,,3609,,4662,,3598,,2628,,2734,,3967,,3547,,4608,,5266,,2942,,3735,,3878,,2773,,3300,,3081,,4632,,4346,,5582,,5497,,4170,,4219,,4580,,3703,,4295,,4763,,4121,,3825,,4278,,4109,,4458,,3544,,4257.2,,4826.5,,3964.5,,2818.5,,2353.5,,1469.5,,1505.5,,1783.5,,2105.5,,2231.5, -Tuvalu,All,All,All,Tonnes - live weight,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,80,,100,,150,,180,,429,,784,,840,,313,,660,,933,,1409,,519,,518,,526,,499,,1460,,561,,399,,400,F,500,F,500,F,500,F,500,F,500,F,600,F,1505,,2451,,2561,,3161,F,3771,F,4380,F,5230.5,,11468.5,,11853.5,,17621.5,,12925.5,,7381.5,,6149.5,,7686.5,,7490.5,,12727.5, -US Virgin Islands,All,All,All,Tonnes - live weight,200,,200,,200,,300,,300,,300,,300,,400,,400,,400,,500,,500,,500,,500,,500,,500,,600,,600,,600,,600,,600,F,600,F,500,F,500,F,500,F,503,,500,,602,,557,,547,,688,,618,,891,,615,,703,,618,,939,,901,,694,,805,,702,,904,,940,F,1000,F,1070,F,1170,F,1240,F,1310,F,1480,F,1549,F,1673,F,1738,F,1796,F,1867,,1970,,1634,,2223,,1468,,1324,,1403,,1050,,1013,,621,,547,,491.64,,386,,559,,472,,295, -Uganda,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2500,,4019,,4817,,0,.,0,.,0,.,0,.,0,.,508,,900,,0,.,2,,600,,300,,0,.,0,.,290,,0,.,0,.,0,.,405,,0,.,515,,500,,500,,0,.,0,. -Uganda,All,All,All,Tonnes - live weight,15000,,20000,,23000,,23400,,24600,,34900,,45700,,51300,,53400,,55800,,62600,,61200,,64500,,69600,,70500,,76300,,83300,,99600,,108400,,125300,,129000,,137000,,166000,,169500,,167500,,188000,,152400,,219220,,223830,,179930,,165840,,166590,,170035,,172000,,212331,F,160833,F,197635,F,200038,F,214325,,212247,,245275,,214633,,264977,,219901,,213308,,208983,,195298,,218386,,220948,,226572,,220176,,223086,,226813,,247310,,377328,,427575,,399491,,482610,F,455750,F,488654.1,F,508805,,523127.7,,503544,,517312,,572219,,513795,,507295.2,,501972.6,,543091, -Ukraine,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,13,,40,,25,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Ukraine,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1065154,,993193,,954497,,762061,,524124,,365945,,320991,,426256,,441151,,399167,,490859,,442697,,422836,,342015,,296615.76,,283243.1,,248622.21,,296861,,238409,,243365,,258416,,244800.5,,232812.9,,203549.2,,178111.9,,226797.6,,167517,,142644.32,,97199.6,,113635.2,,94776.43, -Un. Sov. Soc. Rep.,All,All,All,Number,379939,,355988,,308335,,225252,,327427,,327307,,289409,,429761,,411977,,343576,,330908,,355111,,452515,,346179,,347592,,307171,,265605,,239633,,226482,,184422,,204142,,161379,,171644,,178896,,160556,,152750,,119636,,114177,,113458,,111598,,98012,,122115,,152831,,156417,,158257,,152735,,163038,,170146,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Un. Sov. Soc. Rep.,All,All,All,Tonnes - live weight,1749294,,1998272,,1961350,,2005321,,2285809,,2530845,,2644245,,2569270,,2671107,,2815512,,3121163,,3319844,,3705119,,4056789,,4579951,,5209803,,5426992,,5863925,,6206419,,6725015,,7427675,,7407580,,8001789,,8884021,,9332375,,10133548,,10285286,,9411940,,9127015,,9165897,,9660146,,9697570,,10116203,,9968720,,10661493,,10706186,,11385782,,11382525,,422745,,324728,,423411,,325681,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,. -United Arab Emirates,All,All,All,Tonnes - live weight,12000,,12000,,12000,,12000,,15000,,15000,,15000,,20000,,20000,,30000,,30000,,30000,,35000,,35000,,35000,,38000,,38000,,40000,,40000,,40000,,40000,,43000,,43000,,43000,,67800,,67800,,64600,,64600,,64600,,64600,,64600,,67760,,69755,,72716,,72723,F,72267,,79328,,85252,,89506,F,91162,,95129,,92336,,95046,,99600,,108600,,105884,,107000,,114358,,114739,,117607,,105456,,112561,,97574,,97450,,90570,F,87325,,101013,,97097,,75418,,77835,,79808,,75562,,73148,,73780,F,73991,,73790,,75684.9,F,76255,F,76350,F -United Kingdom,All,All,All,Number,7821,,6197,,7030,,6964,,7620,,8038,,7444,,6266,,7080,,5431,,5165,,5813,,4324,,1552,,0,.,2956,,3088,,2673,,3030,,3017,,3084,,2294,,3105,,2201,,2502,,3717,,2209,,2257,,1725,,1915,,2042,,1820,,1414,,38,,41,,50,,65,,152,,102,,21,,30,,18,,15,,786,,856,,1101,,754,,801,,48,,26,,49,,87,,68,,142,,467,,2,,54,,55,,28,,0,-,0,-,118,,0,-,58,,14,,22,,11,,21,,15, -United Kingdom,All,All,All,Tonnes - live weight,931830,,1118430,,1235130,,1197030,,1130130,,1157235,,1127935,,1083735,,1066435,,1059435,,1015201,,953738,,1003998,,1007404,,1034341,,1115108,,1138042,,1101935,,1115840,,1162368,,1172967,,1204252,,1169926,,1213294,,1172709,,1065062,,1125990,,1079911,,1115671,,992599,,895513,,881047,,915138,,850643,,845283,,901415,,860455,,953345,,948323,,914948,,821713,,858108,,875533,,933772,,969402,,1011201,,985986,,1028751,,1060640,,995519,,906963,,939997,,908023,,825657,,861698,,843147,,796532,,793892,,771695,,787450.82,,813992.85,,800234.6,,836793.7,,835578,,970361.35,,917350.42,,897726.18,,948592.11,,897845, -United States of America,All,All,All,Number,60204,,60976,,63870,,66669,,63882,,65470,,125184,,93905,,79517,,58784,,41092,,127089,,97711,,86598,,65709,,52805,,53176,,66063,,59162,,39091,,42252,,31877,,37445,,28619,,33048,,28864,,23152,,28357,,24849,,26271,,24326,,24103,,25196,,33704,,41935,,24264,,29526,,51152,,84219,,88345,,160686,,128564,,199005,,209564,,194180,,205034,,199228,,229896,,213265,,242486,,253026,,268599,,328497,,358470,,370883,,358772,,423440,,389536,,232476,,297633,,370293,,310935,,327458,,481867,,486505,,428898,,554293,,463539,,596306, -United States of America,All,All,All,Tonnes - live weight,2691843.9,,2520757.7,,2551249,,2614367,,2683421,,2720712,,2894135,,2651461,,2622956,,2866118,,2819044,,2954551,,3007924,,2777749,,2643835,,2782558,,2556493,,2439025,,2542058,,2555898,,2962979,,3050820,,2946225,,2978682,,3053006,,3065741,,3272174,,3211927,,3636483,,3722375,,3871666,,3931248.08,,4214408.04,,4421466.11,,5048228.24,,5043440.52,,5253141,,6077501,,6028322.96,,5860605.33,,5936132.44,,5607652.16,,5688778.38,,6025070.74,,6043959.56,,5622646,,5462704,,5594191,,5290998,,5441932,,5245362,,5486918,,5497389,,5532897,,5599950,,5476042,,5407735,,5326417,,4875380,,4710695,,4814054,,5577687,,5431856,,5528920,,5410932,,5469464,,5354093,,5477928.1,,5225182, -Uruguay,All,All,All,Number,2692,,4000,,4000,,4000,,6000,,6000,,6000,,6000,,6000,,11109,,7562,,8003,,8113,,10000,,10370,,9441,,12300,,12920,,13161,,15220,,15177,,16036,,13646,,13562,,14814,,15712,,17108,,14100,,13508,,10496,,8897,,6651,,1375,,800,,7070,,7146,,6386,,6985,,6215,,6709,,5439,,5375,,102,,112,,0,-,13,,52,,30,,41,,29,,32,,78,,64,,79,,93,,54,,35,,82,,63,,82,,80,,53,,55,,55,,106,,183,,138,,166,,168, -Uruguay,All,All,All,Tonnes - live weight,3500,,3500,,3500,,3400,,4000,,4900,,5400,,6900,,5400,,5900,,10900,,10500,,7700,,8100,,12200,,15800,,12500,,10900,,11000,,12700,,13200,,14400,,20600,,17500,,16000,,26333,,33804,,48374,,74299,,108170,,120399,,146919,,118988,,143416,,132969,,138413,,140753,,137805,,107347,,121716,,90832,,143714,,125758,,118820,,120742,,126466,,123351,,136973,,140726,,106615,,113411,,104999,,108119,,117293,,123011,,125867.05,,134024,,108753.2,,108833,,80926.41,,74237.97,,89346.82,,76253.23,,59413.86,,65297,,59743.83,,51569.65,,65368.98,,67170.77, -Uzbekistan,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,26221,,25526,,26763,,27439,,26479,,23401,,17654,,13808,,7206,,10565,,9765,,8964,,8958,,7388,,6424,,5218,,4993,,6700,F,7200,,7100,F,7900,F,9262,,10732,F,17235.3,,25884.9,,37505.2,,46391.8,,59852.4,,65321.7,,83900,F,90984,F -Vanuatu,All,All,All,Tonnes - live weight,500,,500,,500,,600,,600,,900,,1000,,1000,,1000,,1000,,1400,,1400,,1400,,1500,,1500,,1800,,1800,,1800,,1800,,1800,,2100,F,2200,F,2300,F,2400,F,2510,F,2610,F,2710,F,2816,,2825,,2819,,2937,,2715,,2715,,2542,,2956,,8196,,16888,,20078,,25886,,27200,,41975,,44367,,45019,,40755,,47357,,57904,,47696,,70357,,75019,,91433,,70589,,36176,,44968,,113867,,212787.63,,229554,,222675,,213728.2,,172635,,150581.05,,101349,,59372.5,,63483.88,,61938.18,,69936.9,,82201.54,,44018.8,,10478.05,,10207.2, -"Venezuela, Boliv Rep of",All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,104260,,153032,,138684,,91861,,100996,,104935,,78972,,54038,,55195,,29996,,33528,,35579,,24640,,23655,,19215,,20349,,33942,,63902,,58346,,60864,,23201,,15489,,16246,,13652,,27797,,26130,,10741,,16696,,15663,,21465,,25402,,43881, -"Venezuela, Boliv Rep of",All,All,All,Tonnes - live weight,78400,,75000,,62500,,63300,,51800,,69600,,61300,,83700,,78300,,83300,,84700,,84920,,94916,,97307,,110611,,119408,,116929,,107528,,126105,,134115,,126740,,139076,,152265,,162686,,145476,,154797,,150777,,154762,,182930,,168389,,188205,,181446,,211234,,230895,,260239,,265578,,285129,,299368,,285342,,328161,,336449,,340386,,333292,,398853,,442520,,506739,,503684,,479269,,514093,,411273,,373144,,484750,,527759,,519545,,597599,,422358,,339752.5,,367909.3,,314328.6,,331327,F,237078.33,F,226227.24,,239393.18,,240795,F,240433.66,,259647.41,,310278,,306598,F,304384,F -Viet Nam,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,831,,3376,,16125,,17190,,23062,,9483,,3285,,4242,,3615,,9319,,20580,,12595,,13041,,18503,,10051, -Viet Nam,All,All,All,Tonnes - live weight,70600,,70630,,70650,,70780,,140810,,214850,,250480,,265420,,300070,,360110,,473160,,474120,,544980,,629940,,648510,,676590,,682170,,712460,,711850,,765750,,618000,F,688300,F,778500,F,814300,F,572800,F,546800,F,610800,F,588800,F,584000,F,601000,F,559660,F,597866,F,662208,F,758138,F,777308,F,809510,F,830322,,869000,,885033,,954939,,941227,,999174,,1041006,,1120204,,1369993,,1474008,,1531932,,1608703,,1647874,,1897168,,2143129,,2327856,,2520639,,2808507,,3153651,,3440200,,3699327,F,4174900,F,4532150,F,4749480,,4951028,,5260518,,5628174,,5844683,,6098696,,6336087.61,,6659420.03,,7146984.05,,7500361.5, -Wallis and Futuna Is.,All,All,All,Tonnes - live weight,30,F,30,F,30,F,30,F,30,F,30,F,40,F,40,F,40,F,40,F,40,F,50,F,50,F,50,F,50,F,50,F,50,F,50,F,50,F,50,F,50,F,50,F,50,F,60,F,60,F,60,F,60,F,60,F,60,F,70,F,70,F,70,F,70,F,70,F,70,F,80,F,80,F,95,,115,,118,,97,,104,,160,,166,,227,,200,,210,,206,,325,,325,F,330,F,335,F,340,F,359.12,,415.2,F,529.75,F,629.2,,677,F,725,F,773,F,801,F,417,,210,,237,,181,,204,,182,,216,,260, -Western Sahara,All,All,All,Tonnes - live weight,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,. -Yemen,All,All,All,Tonnes - live weight,25000,,25000,,29300,,30000,,30000,,34800,,21800,,22600,,21500,,24400,,22300,,22000,,22000,,22000,,25000,,25000,,25000,,27000,,27000,,27000,,27400,F,26400,F,31600,F,43200,F,51244,,50829,,65022,,66096,,67047,,69600,,78444,,71164,,55449,,57906,,64273,,73855,,73018,,72916,,73943,,71881,,77310,,85998,,79893,,82356,,81885,,107970,,104955,,115654,F,127620,F,124384,F,114750,F,142198,F,179584,F,228116,F,256300,F,238780,F,229960,F,180216,,132212,,179753,,164011,,154211,,227215,,228783,F,213402,F,176778,F,152131,F,131290,F,131308,F -Yugoslavia SFR,All,All,All,Tonnes - live weight,27381,,23784,,22812,,26007,,23277,,22609,,28444,,30903,,31754,,29627,,31194,,37779,,30702,,34854,,38768,,42498,,46084,,48457,,45493,,44326,,46478,,49716,,49520,,50852,,54429,,56840,,59060,,61113,,63157,,56648,,58581,,71856,,66804,,79929,,73620,,75286,,77846,,81648.8,,72155.7,,72050.8,,65836.9,,36798,F,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,.,0,. -Zambia,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,3350,,5600,,6200,,3400,F,640,,3346,,8645,,6059,,11644,,2415,,12228,,9250,,19702,,19740,,20900,,22259,,28019,,26353,,28490,,39804,,37305,,28197,,43655,,23717,,43652,,15331,,45368,,44233,,43926,,112374,,31853,,27127, -Zambia,All,All,All,Tonnes - live weight,10000,,10000,,15000,,15000,,15000,,17500,,18400,,26200,,26900,,20900,,19500,,20400,,22200,,40100,,42300,,40100,,40100,,38500,,41300,,44000,,52100,,46800,,49900,,50400,,46929,,57464,,54267,,53737,,47577,,49526,,51015,,39598,,55881,,54377,,65895,,68363,,56924,,64984,,61977,,68106,,66328,,68945,,71564,,70423,,74587,,74627,,71102,,70641,,74097,,71507,,70911,,67520,,67630,,70833,,72850,,70873,,65447,,79418,,85044,,93221,,86686,,79894,,93626,,95458,,100107,,106473,,105518,F,105567.1,F,108300,F -Zanzibar,All,All,All,Tonnes - live weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,8080,,24970,,21230,,17680,,24590,,39170,,47740,,36670,,43050,,36670,,67832,,102401,,132171,,115499,,95348,,96830,,101178,,108430,,132439,,128075.95,,150847,,159157,,180695.79,,141156.77,,165902.59,,206596.94,,145044.73,,144870.46,,138674.22, -Zimbabwe,All,All,All,Number,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,10000,,10700,,11552,,17567,,35089,,36476,,54111,,39271,,39590,,38295,,52829,,40720,,63064,,81898,,76157,,79932,,73707,,60185,,69374,,80873,,64490,,81554,,62101,,80995,,90535,,88421,,115499,,130735,,115499,,113491,,84210,,98797, -Zimbabwe,All,All,All,Tonnes - live weight,300,,300,,300,,500,,500,,500,,1000,,1000,,1000,,1000,,1300,,1300,,1300,,1300,,1900,,2000,,2000,,2000,,2000,,2000,,2000,F,2500,F,3002,F,3012,F,3109,,3685,,4550,,4771,,7772,,9874,,13288,,16432,,17515,,13613,,16415,,17400,,18952,,19184,,22222,,24055,,25764,,22159,,21733,,21360,,20349,,16613,,16557,,18326,,16541,,13613,,15265,,14585,F,13763,F,13250,,13505,F,12922,,13000,F,13050,F,13152,F,13202,F,13982,F,19582,F,20740,F,23490,F,24750,F,25500,F,25796,F,26800,F,27085,F -Totals - Tonnes - live weight,,,,,19888588.9,,22466077.7,,24538144,,25096697,,27142592.14,,28809700,,30477291,,31081487,,31703869,,34346212,,36913544,,40671316,,44138470,,45350530.1,,50131773.1,,51402160.4,,55548586.8,,59127735,,62617937,,61364324,,67617410,,68097039,,64243332,,64978599,,68671201,,68447138,,72000948,,71760490,,74140567,,74709600.5,,76030775,,78486445.03,,80680082.74,,81438755.38,,88282624.86,,91175594.1,,97959115.02,,99868512.74,,104964281.19,,106555637.03,,103287929.45,,103841423.86,,108475737.6,,113253584.86,,122255592.19,,125953823.56,,130246920.21,,130141723.51,,124696508.92,,133939980.8,,137792422.77,,137539918.34,,141005436.67,,141099466.24,,149993439.03,,152871529.46,,154199468.64,,157897994.73,,160938863.21,,164014791.78,,166194716.16,,174388389.03,,177964824.22,,186032843.62,,191210735.8,,196625477.59,,198970149.48,,206482336.14,,211906372.32, -Totals - Number,,,,,896541,,1112912,,833142,,686141,,840710,,929342,,982503,,916892,,991236,,882232,,816588,,856453,,1032784,,887710,,1026607,,815909,,867250,,861966,,673800,,746627,,799357,,690626,,607324,,670772,,669954,,647965,,581668,,595097,,615118,,637481,,580516,,620597,,707870,,501655,,563252,,611793,,529507,,806775,,904381,,904707,,907254,,844023,,974729,,1296231,,1377267,,1603621,,1581843,,1456411,,1621088,,1789146,,1806624,,1801915,,1806434,,1868013,,1995210,,2074980,,2570143,,2050203,,1794637,,1508178,,1662139,,1642540,,1695355,,2229519,,2120961,,1730307,,1666472,,1521950,,1273018, -FAO. 2020. Fishery and Aquaculture Statistics. Global production by production source 1950-2018 (FishstatJ). In: FAO Fisheries Division [online]. Rome. Updated 2020. www.fao.org/fishery/statistics/software/fishstatj/en diff --git a/inst/extdata/sectoral/FAOSTAT_data_1-26-2021_FishesTradeTonns.csv b/inst/extdata/sectoral/FAOSTAT_data_1-26-2021_FishesTradeTonns.csv deleted file mode 100644 index 08227164..00000000 --- a/inst/extdata/sectoral/FAOSTAT_data_1-26-2021_FishesTradeTonns.csv +++ /dev/null @@ -1,676 +0,0 @@ -Country (Name),Commodity (Name),Trade flow (Name),Unit (Name),Unit,[1976],S,[1977],S,[1978],S,[1979],S,[1980],S,[1981],S,[1982],S,[1983],S,[1984],S,[1985],S,[1986],S,[1987],S,[1988],S,[1989],S,[1990],S,[1991],S,[1992],S,[1993],S,[1994],S,[1995],S,[1996],S,[1997],S,[1998],S,[1999],S,[2000],S,[2001],S,[2002],S,[2003],S,[2004],S,[2005],S,[2006],S,[2007],S,[2008],S,[2009],S,[2010],S,[2011],S,[2012],S,[2013],S,[2014],S,[2015],S,[2016],S,[2017],S,[2018],S -Afghanistan,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,9,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,4373, -Albania,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,291,,124,,200,,0,.,1184,,1443,,1992,,1981,,3970,,1864,,897,,1116,F,830,F,1029,F,2380,,1841,,2617,,2811,,1924,,2052,,2175,F,2644,,2900,,549,,597,,325,,468,,3645,,4344,,3907,,4719,,5861,,6691,,7727,,9229,,12163,,14517, -Albania,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,400,,425,,240,,160,,140,,157,,101,,358,F,420,F,1187,F,1965,,1948,,3516,,5882,,4985,,5470,,6919,,7440,,7997,,7946,,8449,,8651,,9516,,10071,,9868,,10841,,9368,,10294,,11629,,14778,,15766,,20033,,22793, -Albania,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1184,,1443,,1992,,1981,,3693,,1864,,897,,1040,F,860,F,652,,897,,600,,1401,F,1269,F,1593,F,1409,F,1730,F,1134,F,2263,F,2408,F,3163,F,2653,F,2928,F,3084,F,2881,F,2682,F,6235,F,6875,,8703,,11377,,11867,,16930,,14875, -Albania,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2338,,4021,,2759,,2883,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Algeria,All,Export,Tonnes – net product weight,Tonnes – net product weight,1392,,1889,,354,,260,,246,,248,,16,,61,,78,,92,,81,,100,,93,,75,,164,,408,,877,,541,,620,,1045,,717,,716,,379,,860,,1317,,1498,,2415,,1755,,1537,,1982,,2079,,2181,,2781,,1881,,1408,,1265,,1407,,1319,,1710,,1700,,2024,,2858,,3377,F -Algeria,All,Import,Tonnes – net product weight,Tonnes – net product weight,5868,,11261,,10168,,10979,,13192,,16858,,20219,,28510,,45917,,34212,,31039,,37197,,44216,,15680,,6862,F,3367,,9137,,6461,,9952,,20356,,4576,,7461,,5717,,7953,,8118,,7944,,11306,,21818,,22996,,20505,,23204,,20978,,18189,,28296,,29923,,29171,,29399,,32370,,42886,,43952,,40765,,40747,,33295,F -Algeria,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,4430,,4289,,2803,,2629,,2186,,2028,,3102,,2451,,2381,,2981,,4391,,6277,,6244,F,6323,F,6503,F,6899,,6545,F,6300,F,5896,F,5160,F,255,F,344,F,200,F,655,F,962,F,1005,F,1209,F,1257,F,1189,F,1713,F,1525,F,1515,,2132,,1442,,1154,,1074,F,1038,F,836,F,1223,F,1303,F,1376,F,1611,F,1807,F -Andorra,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,7,,0,.,0,.,0,.,0,0,0,.,0,.,0,0,0,0,9,F,0,.,0,.,0,0,2,,0,.,2,,1,,0,0,0,0,5,F,1,F,3,F,2,F -Andorra,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2585,,2342,,2485,,2615,,2661,,2663,,2760,,2755,,2716,,2942,,2812,F,2677,,2731,,2476,,2471,,2448,,2469,,2311,,2340,,2397,,2547,F,2538,F,2428,,2442, -Angola,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,3128,F,1896,F,1044,F,1224,F,1456,F,2279,F,2221,,2883,F,1358,F,3680,F,5790,F,5307,F,5427,F,14250,F,13741,F,7103,F,7859,F,7531,F,7837,F,7930,F,7017,F,8565,F,12035,F,5084,,11911,,11420,F,13617,F,24723,,70971,,45305,,50822, -Angola,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,97292,,96926,,126769,F,138284,F,157255,F,128556,,116174,F,88872,F,63263,F,5289,F,4980,F,4043,F,2708,F,3420,F,3224,F,3941,F,2640,F,5130,F,8014,F,7815,F,8818,F,11931,F,20090,F,14656,F,24776,F,28858,F,41843,F,49843,,65573,,97636,,162137,,133098,,229849,,171880,,92400,,114790,,89898, -Angola,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,32500,F,44300,F,47200,F,41554,F,46360,,46175,,45549,,36836,F,25607,,38569,,39919,,34696,,39192,,38869,F,38703,,28925,,23205,,35488,,31514,,28187,,20684,,38314,,79808,,54808,,76442,,64286,F,58815,,51386,,105043,,83103,,33832,,105954,,123554,,159646,,162090,F,162100,F,168800,F,142273,,184289,,421765,,305422,,414208,,414678, -Antigua and Barbuda,All,Export,Tonnes – net product weight,Tonnes – net product weight,86,F,92,,182,,50,F,510,,186,,216,,200,F,85,,549,,300,F,139,,171,,169,,107,F,98,F,36,F,99,F,100,F,103,F,208,,152,,100,,43,,27,,120,F,75,F,291,F,72,F,50,,161,F,36,,110,F,56,,59,,115,,114,,80,,57,,46,F,51,F,70,F,75,F -Antigua and Barbuda,All,Import,Tonnes – net product weight,Tonnes – net product weight,602,F,477,,463,,680,F,945,,661,,590,F,550,F,544,,685,F,637,F,626,,773,,1147,,597,,336,F,634,F,774,F,670,F,419,F,458,F,547,F,394,F,1338,,1643,,439,F,739,F,1679,F,1444,F,1554,,1807,,2199,,1591,F,1599,,1543,,1639,,1685,,1664,,1638,,1780,,1569,,1972,,2344, -Antigua and Barbuda,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,0,3,,0,.,21,,13,,0,.,0,.,0,0,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,12,,4,,0,.,0,.,0,.,0,.,0,0,0,.,5,,0,.,0,0,1,,0,0,0,0,1,,12,,3,,2,,2,,0,0 -Argentina,All,Export,Tonnes – net product weight,Tonnes – net product weight,105437,,159923,,210841,,239982,,168434,,149919,,235151,,185951,,130343,,147362,,199615,,235018,,207027,,224278,,255259,,309889,,273701,,476799,,536957,,582840,,671904,,688187,,604601,,599485,,539039,,452179,,450328,,486184,,494128,,494049,,627524,,540700,,560352,,481360,,457153,,456016,,429979,,489677,,495948,,459987,,444963,,475740,,479044, -Argentina,All,Import,Tonnes – net product weight,Tonnes – net product weight,831,,9664,,9295,,14855,,15466,,14525,,12467,,9116,,13594,,10247,F,12650,F,24853,,15837,,12913,,4698,,15936,,24408,,20711,,26940,,26777,,27383,,33388,,32691,,36678,,42882,,44494,,9267,,19212,,29430,,35491,,37345,,43724,,35734,,33326,,41760,,46848,,45030,,42036,,36798,,47605,,48900,,50012,,49688, -Argentina,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,130305,,150196,,253799,,313502,,214317,,209718,,297500,,264100,,187900,,205800,,249261,,293761,,237477,F,235360,F,275800,F,329465,F,390230,F,526985,F,564107,F,624723,F,656937,F,754931,F,558669,F,582449,F,518913,F,483761,F,473955,F,456813,F,481402,F,570537,,667958,,573836,,589034,,531411,,515445,,494601,,489836,,546586,,536844,,525527,,488297,,502707,,519084, -Armenia,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,59,,0,0,14,,52,F,320,,31,,102,,444,,789,,872,,847,,1048,,867,,987,,662,,1332,,2659,,3933,,6887,,6180,,4593,,4071,,3887,,4577, -Armenia,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,287,F,751,,237,,392,,1043,F,1590,,2640,,2466,,3366,,2957,,4454,,9547,,4976,,4038,,4979,,4756,,3321,,4862,,5280,,4844,,4513,,3421,,3344,,4369,,3495, -Armenia,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,6,F,10,F,14,F,2,F,4,F,19,F -Armenia,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,37,,30,,0,.,0,0,35,,16,,1,,12,,26,,0,.,0,.,18,,27,,0,-,0,-,0,-,0,-,0,- -Aruba,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,8,,112,F,257,,672,,18,,11,,18,,4,,5,F,4,F,5,F,0,.,0,.,73,F,0,.,166,,81,,110,,335,,242,,190,,106,,13,,10,,4,,2,,3,,0,0,0,0,23,,1,F,5,,20,,56,,32, -Aruba,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,919,,1462,F,2433,,1602,,1125,,1499,,1184,,1242,,1370,F,1976,F,2568,F,3782,,4072,,3967,,4111,,1674,,1705,,1575,,1630,,1959,,2013,,1782,,2188,,2021,,2145,,2387,,2316,,2118,,2683,,2602,,2770,,3141,,3235,,3255,,3324, -Australia,All,Export,Tonnes – net product weight,Tonnes – net product weight,24169,,29216,,26429,,30044,,42484,,31781,,43352,,45143,,41155,,32978,,33960,,37916,,41100,,36412,,46932,,52850,,50828,,55394,,64375,,72084,,69512,,77160,,81454,,83405,,81572,,91148,,84118,,75493,,73345,,67901,,67916,,50804,,51634,,50468,,49742,,46931,,41733,,42524,,47435,,61410,,66399,,57351,,51795, -Australia,All,Import,Tonnes – net product weight,Tonnes – net product weight,63318,,70072,,60796,,59869,,82251,,83343,,83972,,80725,,93435,,105008,,95365,,102460,,103203,,124411,,135171,,113447,,134309,,124981,,188737,F,156253,,189731,,199591,,184704,,235529,,277228,,262686,,265403,,252107,,280097,,269127,,295911,,292753,,302135,,282273,,277707,,277076,,304205,,309581,,324498,,302477,,308495,,298985,,301620, -Australia,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,33584,,35840,,35397,,31480,,39475,,36892,,46824,,50807,,46561,,38865,,40199,,42055,,47926,,39810,,43193,,45032,,41936,,39712,,41098,,46070,,36852,F,42763,F,49733,F,52929,F,52861,F,57915,F,53750,F,49832,F,50146,F,50085,F,43042,F,40260,F,38474,F,33743,F,34557,F,32322,F,30885,F,31263,F,34875,F,30653,F,38698,F,27923,F,26225,F -Australia,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,65,,0,.,1272,,296,,406,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Austria,All,Export,Tonnes – net product weight,Tonnes – net product weight,579,,433,,364,,492,,332,,600,,943,,1230,,945,,616,,847,,538,,540,,561,,648,,627,,1445,,1199,,1511,,4561,,4076,,4039,,2966,,5451,,4651,,4119,,7480,,10055,,12344,,5872,,4339,,6242,,7051,,9056,,9606,,11666,,14945,,15918,,12699,,12039,,12440,,13785,,15418, -Austria,All,Import,Tonnes – net product weight,Tonnes – net product weight,67540,,58881,,60441,,68484,,65504,,60320,,62992,,62527,,61816,,63223,,66330,,68740,,64152,,66017,,69016,,65199,,69927,,67589,,74347,,62280,,59681,,64395,,63330,,63438,,55445,,57682,,56229,,63882,,70434,,71734,,70193,,78410,,75352,,73167,,76400,,81826,,82880,,87179,,84142,,83456,,84207,,84535,,85332, -Austria,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,0,0,0,14,,17,,20,,27,,34,,40,,50,,50,,50,,50,,300,,600,,600,,500,,500,,500,,500,,500,,500,,500,,500,,500,,500,F,500,F,500,F,500,,500,,500,,500,,700,,700,,900,,900,,1100,,1200,,1000,,1001,,1001,,1001,,1001,,1001, -Azerbaijan,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1597,F,183,F,1016,,1954,,1840,,5808,,4593,,1792,,610,,568,,2590,,3113,,538,,856,,517,,493,,246,,270,,0,.,32,,0,.,0,0,110,F,188,,516, -Azerbaijan,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,608,F,176,F,726,,868,,1960,,1209,,2828,,2347,,3940,,4229,,5102,,6997,,7513,,7900,,11160,,11723,,12544,,13898,,15176,,18048,,12344,F,9019,F,15059,,22535,F,13839, -Azerbaijan,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,3,F,3,F,3,F,3,F,3,F,0, -Bahamas,All,Export,Tonnes – net product weight,Tonnes – net product weight,701,,849,,698,,752,,924,,1006,,1036,,1123,,1095,,1366,,1726,,1370,,1848,,1438,,2220,F,2502,,2560,,2786,,2904,,2551,,2878,,2600,,2737,,3092,,3930,,2734,,2810,,3604,,2785,,2310,,2649,,2330,,2475,,2800,,2493,,2653,,2696,,3244,,2409,,2639,,2677,F,3547,F,2794,F -Bahamas,All,Import,Tonnes – net product weight,Tonnes – net product weight,708,,503,,503,F,307,F,1549,,724,,1042,,950,,1011,,1323,,1177,,1400,F,1251,,1533,,1652,,1842,,1924,,2140,,2092,,2077,,2035,F,2405,,2985,,3084,,4381,,4096,,3621,,3712,,3356,,3539,,3937,,4017,,4492,,3660,,3153,,3903,,4277,,3916,,4173,,4140,,4231,F,3296,F,4003,F -Bahamas,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,691,,935,,849,,823,,989,,873,,1044,,1151,,1252,,1416,,1684,,1756,,1854,,2087,,2005,,2290,,2456,,2758,,2651,,2698,,2832,,2760,,2671,,2843,,3088,,2429,,2845,F,3276,F,3083,F,2125,F,2329,F,4348,F,3845,F,3531,,4392,,3865,,4198,F,3034,F,3325,F,2869,F,3235,F,3441,,6882, -Bahamas,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,6,,0,.,0,.,0,.,0,.,23,,3,,0,.,0,0,0,.,3,,27,,71,,0,.,0,.,47,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,0,0,0,8,,1,,2,,9,,14,,71,,0,0,2,,0,0,0,0,1,,0,.,3,,0,-,0,-,0,- -Bahrain,All,Export,Tonnes – net product weight,Tonnes – net product weight,1253,,769,,245,,0,.,11,,7,,1,,0,0,0,0,0,.,365,,788,,581,,962,F,1395,,2301,,1851,,2317,,2856,,3557,,5716,,6073,,5880,,4749,,4726,,5966,,7009,,7177,,8415,,7036,,7858,,9342,,8076,,8537,,6615,,9232,,10525,,10420,,8588,,10064,,9657,,13139,,24310, -Bahrain,All,Import,Tonnes – net product weight,Tonnes – net product weight,709,,1328,,664,,396,,750,,1417,,878,,1371,,1511,,1820,,1568,,1638,,1290,,1633,F,1448,,1916,,1921,,2415,,2468,,3301,,2043,,2164,,2052,,1832,,3144,,3573,,4235,,3364,,3367,,5357,,4798,,4637,,7910,,8136,,7220,,9325,,10453,,10094,,14138,,13582,,14889,,19089,,30354, -Bahrain,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,898,,405,,935,,156,,0,.,0,.,0,.,0,.,0,.,116,,283,,614,,434,,300,F,333,,89,,91,,315,,219,,177,F,1094,,972,F,1276,F,2017,,1024,F,841,F,1250,F,1430,F,1800,F,1180,F,1300,F,1600,F,1750,F,2380,F,2300,F,2660,F,2450,F,3130,F,2750,F,2790,F,2765,F,3050,F,3560,F -Bahrain,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,15,,0,.,54,,25,,12,,9,,1,,6,,176,,0,.,68,,777,,878,,517,,553,,139,,144,,0,.,2383, -Bangladesh,All,Export,Tonnes – net product weight,Tonnes – net product weight,3902,,3621,,2838,,4864,,9657,,7946,,9393,,14465,,20717,,27056,,24733,,26357,,27076,,25499,,31455,,29290,,32220,,31778,,40516,,40282,,39800,,45822,,30771,,28531,,38559,,40789,,37518,,41311,,52926,,58399,,61560,,91002,,87439,,67036,,80577,,88807,F,54383,,74529,,73906,,54482,,73323,F,61039,F,66442,F -Bangladesh,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,0,203,,20,F,110,,55,F,13,F,23,,0,0,0,.,0,.,2,,4,,10,F,11,,2,,17,,60,F,51,F,388,,1148,,2902,,768,,1962,,2265,F,2347,F,4021,,8907,,3909,,4807,,3402,,4464,,9146,,8577,,14850,,31663,,23697,,31564,,68251,,100517,F,154229,,124934,F,123657,F,142051,F -Bangladesh,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,3825,,3590,,5220,,4167,,8964,,7357,,8868,,22398,,24599,,34448,,38850,,41292,,36147,,32503,,38427,,44626,,38619,,42130,,46863,,59418,,63053,,64377,,65766,,73166,,77017,,80017,,78713,,86738,,94251,,106747,,94251,,119795,,132302,,137580,,130305,,134372,,120671,,123309,,123031,,122817,,122825,,126463,,128063, -Barbados,All,Export,Tonnes – net product weight,Tonnes – net product weight,89,,190,,11,,7,,2,,12,,1,,27,,18,,13,,27,,25,F,105,,34,,12,,19,,43,,57,,88,,209,,116,,260,,251,,208,,280,,329,,208,,241,,249,,391,,237,,236,,169,,52,,101,,125,,130,,153,,175,,188,,241,,160,,143, -Barbados,All,Import,Tonnes – net product weight,Tonnes – net product weight,1341,,1458,,1937,,1781,,1626,,2386,,2465,,1479,,1825,,2090,,1981,,2000,F,1832,,2600,,2275,,2388,,1844,,2101,,2302,,2686,,2688,,3263,,1888,,3170,,3810,,4353,,4826,,4903,,4469,,8281,,6651,,6374,,6066,,5603,,5426,,5978,,6173,,5172,,6020,,6364,,6304,,7605,,8254, -Barbados,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,15,,36,,28,,135,,21,,8,,6,,0,.,0,.,4,,0,0,0,.,0,.,20,,26,,17,,8,,3,,3,,11,,18,,19,,13,,16,,19,,19,,12,,0,.,0,.,4,,3,,0,0,0,.,5,,0,.,0,.,21,,20,,10,,10,,16,,8,,0,- -Belarus,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1868,F,1114,F,733,F,576,F,8826,,8844,,14106,,11604,,9723,,14157,,18007,,21402,,28670,,34558,,39075,,23079,,26552,,32833,,39644,,53493,,61883,,64718,,65870,,78952,,79580, -Belarus,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1197,F,8328,F,52656,,73540,,92676,,95239,,77746,,119410,,127490,,135193,,135748,,151968,,158349,,153946,,156002,,165981,,152938,,147626,,130579,,148960,,172368,,185763,,170067,,158544,,178929,,177238, -Belarus,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,0,0,0,3,F,2,F,1,F -Belgium,All,Export,Tonnes – net product weight,Tonnes – net product weight,29945,,31847,,40974,,36145,,46185,,53833,,54841,,77343,,84127,,53740,,38944,,44677,,44465,,48553,,55482,,49770,,44754,,55393,,66649,,75715,,78598,,92240,,92923,,94272,,117685,,119660,,121941,,147015,,165706,,235952,,271200,,236571,,225910,,187503,,175295,,170340,,156496,,147774,,153373,,156882,,161620,,170214,,190396, -Belgium,All,Import,Tonnes – net product weight,Tonnes – net product weight,181545,,175898,,169163,,188743,,194609,,191491,,213929,,202635,,221207,,228660,,223612,,228094,,222672,,257554,,265281,,241981,,239826,,232180,,298683,,303354,,265158,,283503,,259298,,267919,,286845,,257941,,249734,,290483,,309553,,326862,,334634,,331796,,342921,,327693,,325281,,322558,,303178,,296933,,306636,,296252,,311353,,316027,,310670, -Belgium,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,24400,,22200,,22790,,24515,,23405,,20358,,26620,,26312,,28625,,31135,,33138,,33596,,32666,,38256,,37282,,38406,,38242,,37060,,37258,,40074,,40003,,41887,,42424,,50747,,50168,,50986,,56222,,57512,,60361,,66060,,70901,,66061,,65186,,58648,,60155,,59449,,58110,,57185,,50912,,57140,,60695,,61069,,58755, -Belize,All,Export,Tonnes – net product weight,Tonnes – net product weight,879,,728,,1403,,670,,566,,670,,641,,769,,643,,633,,609,,725,,752,,759,,758,,830,,1020,,1086,,953,,1215,,1148,,1482,,2171,,3346,,2663,,2670,,1164,,7122,F,8555,,9595,,8045,,3160,,5313,,6519,,7097,,6006,,5720,,7661,,8515,F,11107,,3772,,3237,,3140, -Belize,All,Import,Tonnes – net product weight,Tonnes – net product weight,139,,155,,151,,235,,283,,178,,110,,59,,201,,121,,186,,319,,204,,434,,195,,236,,282,,224,,176,,241,,199,,198,,450,,1170,,1761,,5994,,4779,,7163,,1962,,2685,,1948,,1263,,1235,,739,,676,,184,,304,,286,,298,,664,,871,,537,,665, -Belize,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,674,,507,,513,,492,,405,,575,,527,,612,,639,,691,,587,,595,,617,,526,,654,,808,,1065,,1198,,1258,,1250,,1566,,1999,,2303,,3914,,4263,,5266,,4957,,10754,,12089,,11660,,8195,,3172,,5084,,6314,,7468,,5895,,5844,F,7933,F,7733,F,9666,F,2404,F,2113,F,2358,F -Belize,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,10,,29,,40,,0,.,0,.,0,.,0,.,18,,16,,18,,1,,0,.,0,.,0,.,0,.,20,,0,0,0,.,0,.,108,,0,.,1,,1,,1,,0,.,0,.,0,.,0,0,0,.,0,.,0,0,0,.,0,0,0,-,0,- -Benin,All,Export,Tonnes – net product weight,Tonnes – net product weight,328,,328,F,42,,77,,4,,17,,2,,0,.,66,,139,,165,,115,,176,,64,F,41,F,44,,32,,366,,488,,374,,412,,358,,631,,324,,487,,666,,772,,439,,244,,143,,333,,660,,65,,135,,1149,,1106,,531,,481,,1174,,281,,199,,211,,140, -Benin,All,Import,Tonnes – net product weight,Tonnes – net product weight,6247,,6216,,7186,,6955,,4126,,4896,,5858,,4359,,4523,,5658,,7481,,5403,,5160,,2214,,2600,F,10193,,13536,,18715,,11547,,13359,,11285,,11348,,8487,,15449,,21219,,11688,,21564,,29232,,28628,,45124,,56772,,66643,,80643,,75259,,78362,,81541,,79784,,86201,,150708,,148381,,174540,,105090,,107530, -Benin,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,2600,,2600,,2300,,2300,,2323,,2404,,2357,,2163,,2086,,2152,F,2212,F,2117,F,2176,F,2164,F,2141,F,2000,F,2032,F,2306,F,2345,F,2333,F,2312,F,2250,F,2453,F,2306,F,2456,F,2733,F,2680,F,2734,,2734,,2650,,30350,F,30300,F,30000,,30200,,30365,,30310,,31026,,26926,,29709,,20703,,34538,,30007,F,30802, -Benin,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,47,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,.,4,,0,-,0,- -Bermuda,All,Export,Tonnes – net product weight,Tonnes – net product weight,18700,,33400,,25190,,17000,F,15000,F,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,4,F,2,,0,0,2,F,15,F,0,0,3,F,94,F,20,F,9,F,21,F,2,F,20,F,25,F,19,F,0,0,0,0,3,,2,,1,F,1,,1,F,2, -Bermuda,All,Import,Tonnes – net product weight,Tonnes – net product weight,1061,F,824,F,906,F,1413,F,1052,F,1000,F,1094,F,1178,F,1276,F,1270,F,1240,F,1118,F,1322,F,1224,F,1686,F,1458,F,1568,F,1368,F,1350,F,947,F,950,F,1252,F,1010,F,1111,F,996,F,1300,F,1161,F,1089,F,1120,F,2075,F,2321,F,2280,F,2145,F,1933,F,1510,F,1660,F,1979,F,2231,F,2155,,2257,,2097,,1752,,2099, -Bermuda,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,18700,,33400,,25190,,17000,F,15000,F,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,428,,432,,399,,394,,449,,485,,455,,468,,460,,288,,315,,398,,365,,377,,401,,380,,420,,398,,417,,384,,479,,512,F,461,,417,,406,,403,,385,,355, -Bhutan,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,116,,12,,0,.,0,0,0,.,1,,3,,0,.,1,F,0,.,0,.,0,.,0,.,0,- -Bhutan,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2044,,2272,,2236,,1774,,1834,,2138,,2389,,2710,,2893,F,2920,F,2878,F,2793,F,2766,F,2983,F -Bolivia (Plurinat.State),All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,0,0,.,0,.,0,.,0,.,20,,50,,20,,122,,185,,134,,164,,192,,476,,405,,427,,117,,41,,10,,43,,42,,8,,29,,85,,7,,19,F,0,0,1,,0,0,12,,32,,33,,3,,0,.,0,.,0,0,0,-,0,-,0,-,0,-,0,- -Bolivia (Plurinat.State),All,Import,Tonnes – net product weight,Tonnes – net product weight,3375,,4683,,6037,,6595,,8351,,8721,,1009,,1425,,2246,,2288,,1390,,2022,,262,,1013,,229,,162,,505,,2719,,4437,,3417,,3505,,5486,,4795,,4884,,10876,,6710,,6474,,4720,,6206,,8084,,7971,,4869,,9574,,9026,,7681,,11023,,10840,,11041,,12251,,13814,,13945,,14578,,13722, -Bolivia (Plurinat.State),All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,19,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Bosnia and Herzegovina,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,76,F,60,F,300,F,519,,2756,,2681,,3096,,2562,,3060,,3256,,3340,,3090,,2504,,2098,,1753,,2344,,2395,,1980,,1800, -Bosnia and Herzegovina,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,218,F,1842,F,370,F,1370,F,305,F,1754,F,1116,F,6739,F,8286,F,8484,F,11227,F,12012,,16100,,17808,,13971,,13286,,13693,,13113,,12141,,11095,,10590,,10647,,11788,,11688,,12566,,11689,,12347, -Botswana,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,10,F,5,F,17,F,7,F,32,F,23,,0,.,15,F,69,F,230,F,288,,344,,573,,158,,79,,53,,45,,51,,51,,43,,55,,51,,30,F,19,,78,,36,,109,,87,,112,,281,,342,,38,,125,,36,,59,,190,,254,,698,,884,,1281,,254,,957,F -Botswana,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,362,F,940,F,734,F,1172,F,1580,F,911,,1020,F,1195,F,995,F,1360,F,1997,,3038,,5273,,6045,,7559,,4571,,3993,,3441,,3074,,3445,,4160,,3046,,3409,F,4688,,4699,,3237,,3026,,5870,,4913,,4842,,11375,,13836,,4419,,4208,,3382,,5010,,4150,,4589,,4645,,5915,,3543,,3895, -Botswana,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,61,,43,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Brazil,All,Export,Tonnes – net product weight,Tonnes – net product weight,14664,,26263,,31370,,29753,,36773,,53052,,51290,,58224,,48485,,61501,,49349,,45158,,55951,,49847,,36455,,48602,,49521,,55082,,44269,,29134,,26992,,31515,,34986,,38085,,58493,,75690,,100593,,112867,,107185,,93759,,81663,,65242,,53437,,40973,,36309,,40531,,44897,,40980,,33271,,38664,,49744,,50952,,56627, -Brazil,All,Import,Tonnes – net product weight,Tonnes – net product weight,78899,,64722,,65878,,94824,,70669,,48983,,60243,,42958,,34221,,38446,,101335,,104327,,64081,,136782,,173393,,143642,,87392,,160757,,163891,,214603,,298189,,213429,,199339,,173978,,203601,,181344,,163381,,171882,,174060,,161129,,190382,,218360,,226936,,247584,,287865,,352869,,371388,,427849,,412903,,341502,,364019,,411866,,368109, -Brazil,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,155800,,178242,,157704,,214524,,229515,,203162,,245057,,292746,,317449,,290584,,291111,,290512,F,287488,F,303710,F,276422,F,281819,F,280143,F,268849,F,263632,F,251722,F,257087,F,256702,F,237171,F,216648,F,231991,F,260619,F,282588,F,292550,F,278837,F,265530,F,251616,F,238115,F,232729,F,234428,F,223829,F,235962,F,246185,F,237372,F,232638,F,221803,F,233604,F,239273,F,232715,F -Brunei Darussalam,All,Export,Tonnes – net product weight,Tonnes – net product weight,58,,47,,15,,22,,10,,4,,3,,3,,1,,7,,0,0,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,22,F,0,0,109,,9,,33,F,285,F,149,,92,,144,,280,,452,F,736,,568,F,298,F,379,F,535,F,730,F,1271,,1497,,1724,,1540,,892,,1299,,1505, -Brunei Darussalam,All,Import,Tonnes – net product weight,Tonnes – net product weight,2250,,1645,,1846,,1677,,2143,,2945,,2992,,3126,,3213,,3195,,2825,,3133,F,3259,F,4634,F,3337,F,2840,F,2973,F,4908,,7518,,4523,F,4399,F,9197,,8791,,5153,F,6462,F,8281,,6483,,7156,,9094,,7215,F,7694,,6617,F,6505,F,5848,F,7181,F,7661,F,9926,,13993,,12196,,9380,,10461,,10684,,11937, -Brunei Darussalam,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,57,,65,,36,,29,,30,,46,,28,,28,,83,,114,,91,,100,F,100,F,120,F,130,F,150,F,150,F,170,F,170,F,0,.,0,.,0,.,0,.,0,.,0,.,37,,0,.,0,.,383,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,274, -Bulgaria,All,Export,Tonnes – net product weight,Tonnes – net product weight,49391,,49381,,56618,,55873,,62408,,46379,,50454,,32528,,25993,,28767,,31062,,42850,,51271,,39884,,20846,,28655,,15340,F,6765,,8799,,21222,,24544,,10839,,2706,,2802,,3109,,3322,,4001,,4370,,4899,,3245,,4024,,5919,,11668,,12472,,11235,,11129,,10391,,11497,,11106,,11242,,13433,,19775,,18680, -Bulgaria,All,Import,Tonnes – net product weight,Tonnes – net product weight,13411,,20810,,29561,,33778,,22057,,30535,,52007,,34868,,19322,,25482,,19221,,25578,,20894,,17509,,3005,,2725,,6633,,4940,,14695,,14160,,8711,,21467,,25594,,21323,,20567,,21824,,22605,,28520,,28321,,31639,,35101,,27989,,41179,,41239,,35837,,36114,,34185,,35661,,35856,,41831,,44574,,48573,,46854, -Bulgaria,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,110600,,95800,,72900,F,60800,F,89100,F,74600,F,84400,F,103856,,101856,,93665,,98414,,102681,,97550,,94414,,49034,,33850,F,20270,F,9128,F,7541,F,7150,F,8365,F,7200,F,7138,F,7300,F,7100,F,4100,F,5302,,5418,,14501,,13149,,14271,,16053,,15885,,15063,,16569,,15703,,15976,,17602,,16555,,21100,,20145,,20800,,19646, -Burkina Faso,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,0,10,,10,,4,,0,.,11,,31,,2,,0,.,0,.,5,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1,,0,.,63,,54,,9,,15,,0,.,0,.,0,0,688,,1500,F,2507,,3387,,4384,,3269,,2674,,2833,,2160,,2100,,3301,,1188,,450,,77, -Burkina Faso,All,Import,Tonnes – net product weight,Tonnes – net product weight,938,F,1216,,1158,,1139,,1544,,1676,,2334,,2290,,1579,,2268,,3571,,5438,,7625,,6850,F,5100,F,4533,F,4100,F,3895,,7528,,7598,,8563,,10963,,5358,,13357,,12401,,7657,,8328,,8547,,11455,,22542,,25151,F,27350,,26991,,45313,,53871,,62198,,61414,,64379,,66722,,76115,,83235,,109882,,120272, -Burkina Faso,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1000,,800,,800,,800,,1000,,1000,,900,F,900,F,930,F,860,F,900,F,850,F,850,F,900,F,900,F,1350,F,1425,,1630,,4020,,4720,,4800,,6000,,8120,,6210,,6300,,6390,,6621,,6700,F,8312, -Burundi,All,Export,Tonnes – net product weight,Tonnes – net product weight,13,,5,,7,,2,,0,.,3,,0,.,0,.,10,,0,.,1,,2,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,4,,1,,1,,2,,5,,2,,4,,6,,13,,6,F,8,,13,,13,,12,,11,,9,,11,,31,,158,,118,,47,,21,,1,,8, -Burundi,All,Import,Tonnes – net product weight,Tonnes – net product weight,43,,146,,38,,168,,306,,370,,422,,461,,428,,194,,4,,14,,37,,207,F,232,F,183,F,152,F,279,,435,,28,,226,,104,,135,,139,,64,,99,,79,,112,,89,,34,,93,,36,,39,,89,,627,,1686,,2942,,3038,,3000,,2534,,2899,,2516,,2574, -Cabo Verde,All,Export,Tonnes – net product weight,Tonnes – net product weight,1467,,725,,480,,1070,,1142,,428,,3624,,2205,,1921,,1801,,2071,,3541,,1124,,2890,,1633,,163,,2268,,1762,,1964,,1268,,2787,,3001,,2162,,1238,,334,,246,,261,,62,,350,,9123,,18368,,9815,,10105,F,13576,,16193,,18014,,14398,,16834,,28696,,30638,,24775,,14701,,21142, -Cabo Verde,All,Import,Tonnes – net product weight,Tonnes – net product weight,248,,12,,7,,5,,4,,3,,1,,30,,29,,18,,819,,208,,85,,249,,211,,218,,241,,385,,563,,752,,595,,1895,,1036,,1037,,733,,777,,621,,621,,540,,528,,762,,686,,859,,841,,672,,797,,889,,902,,822,,771,,965,,1199,,1143, -Cabo Verde,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,461,,708,,468,,1484,,2466,,2213,,2218,,1995,,1597,,2280,,2374,F,2746,,1976,F,2050,F,1702,F,2511,F,1307,F,1158,F,1140,F,1178,F,1182,F,1177,F,1180,F,1205,F,0,.,0,.,0,.,0,.,0,.,0,.,9600,F,9650,F,10000,F,8500,F,9640,F,13630,F,10500,F,11600,F,14256,F,15368,F,9494,F,9890,,14146, -Cabo Verde,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,126,,826,,463,,0,.,0,.,0,.,0,.,6013,,2733,,4660,,738,,0,.,0,.,0,.,0,.,0,.,6630,,8453,,0,-,0,-,0,- -Cambodia,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,8,,48,,118,,45,,179,,460,,486,,432,,34371,,32332,,27673,,23816,,24700,,36870,,40240,,44600,,43656,,38424,,52711,,56876,,47523,,53266,,30120,,24100,,25000,,30000,,35043,F,30000,F,31025,F,32000,F,31684,F,32664,F,32201,F,37007,F,41969,F -Cambodia,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2752,F,5248,F,4376,,2427,F,2465,F,2724,F,3174,,1074,,1267,,2218,,3071,,6664,,3731,,3543,,2942,,5767,,4970,,6755,,10776,,12564,,26766,,19890,,24482,,24118,F,18222,F -Cambodia,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,9442,,10090,,9980,,10838,,12864,,13083,,14509,,9001,,13177,,13196,,8774,,10254,,10697,,11617,,10147,,12293,,18477,,14631,,19458,,59838,,57592,,36445,,34428,,40775,F,49450,F,56027,F,65611,,69734,,76250,,89785,,91992,F,95480,F,95740,F,97025,F,109729,,117195,F,126915,F -Cameroon,All,Export,Tonnes – net product weight,Tonnes – net product weight,2887,,2875,,4258,,1581,,1783,,1434,,1800,,1987,F,3856,,7653,,6731,,10087,,3264,,1317,,2301,,393,,597,F,348,,323,,580,,1260,,669,,1836,,1189,F,437,,327,,106,,151,,72,,94,,1420,,2015,,1660,,3026,,4241,,1775,,3313,,3126,,3887,,3430,,4319,,4000,,3620,F -Cameroon,All,Import,Tonnes – net product weight,Tonnes – net product weight,10417,,14273,,13463,,16320,,20134,,28144,,32302,,34244,,44966,,62117,,84339,,65046,,67434,,64743,,60252,,24682,,59238,F,46861,,44098,,49886,,42823,,65480,,79301,,76225,,82300,,99360,,80159,,126288,,121132,,107756,,102924,,141271,,155327,,208816,,172809,,225140,,189618,,213816,,214511,,221409,,243403,,188030,,193335,F -Cameroon,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,4348,,687,,79,,0,.,0,.,344,,482,,528,,613,,853,,639,,879,,9747,F,10035,F,10270,F,9500,F,9150,F,9269,F,10736,F,12793,F,13932,F,15164,F,17410,F,17920,F,18645,F,17635,F,37754,F,32874,F,38000,F,39300,F,38900,F,38700,F,38900,F,38900,F,41000,F,45000,F,49500,F,54000,F,59000,F,63500,F,66000,F,66000,F,78000,F -Canada,All,Export,Tonnes – net product weight,Tonnes – net product weight,352984,,435226,,479800,,471543,,476546,,513942,,528666,,474107,,501180,,529591,,568781,,558704,,592772,,581695,,627785,,564083,,530764,,522435,,480668,,472012,,513291,,467301,,536724,,540156,,527284,,575963,,642634,,653509,,704533,,718643,,719040,,686803,,645918,,616123,,671734,,624243,,609314,,610820,,586789,,624618,,645603,,625843,,605403, -Canada,All,Import,Tonnes – net product weight,Tonnes – net product weight,71945,,88715,,83618,,84343,,103164,,99092,,87996,,100595,,117313,,117645,,133019,,153738,,154748,,178135,,173779,,179553,,203228,,275648,,356282,,425581,,453127,,439163,,448578,,488582,,522492,,570650,,539842,,495205,,482823,,468137,,477285,,467442,,465795,,483387,,499578,,518426,,527022,,499315,,519702,,508332,,545424,,533883,,523298, -Canada,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,406583,,454537,,522899,,550245,,534915,,570790,,527909,,482041,,461365,,540673,,561349,,562450,,640417,,540559,,546812,,518003,,466027,,509051,,433084,,393686,F,342420,F,324387,F,351060,F,352965,F,345158,F,383129,F,404183,F,424411,F,505389,F,516700,F,520309,F,493966,F,456696,F,436022,F,463791,F,442885,F,413677,,422575,,413971,,428390,,438591,,431793,,410694, -Canada,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,2193,,1192,,2394,,1039,,2004,,1934,,1803,,3758,,4861,,4036,,6746,,5700,,4070,,5190,,5855,,7149,,10013,,0,.,0,.,0,.,0,.,47575,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,33,,0,.,0,.,9171,,11915,,10699,,9768, -Cayman Islands,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,500,F,1391,F,3992,F,933,F,655,F,423,F,396,F,478,F,1097,F,386,F,565,F,762,F,473,F,233,F,422,F,180,F,303,F,350,F,188,F,78,F,134,F,100,F,62,F,60,F,8,F,36,F,102,F,75,F,34,F,50,F,99,F,50,F,35,F,0,.,0,.,0,.,25,F,4,F,0,.,0,. -Cayman Islands,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,138,,191,,200,F,220,F,230,F,240,F,240,F,450,F,500,F,515,F,460,F,660,F,680,F,560,F,620,F,506,F,121,F,75,F,127,F,71,F,139,F,223,F,260,F,250,F,261,F,171,F,500,F,857,F,423,F,554,F,610,F,556,F,626,F,503,F,496,F,398,F,503,F,736,F,692,F -Cayman Islands,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,444,F,951,F,230,F,377,F,727,F,461,F,233,F,239,F,250,F,171,F,162,F,133,F,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -Central African Republic,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,.,7,,0,.,0,.,22,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,81,,80,F -Central African Republic,All,Import,Tonnes – net product weight,Tonnes – net product weight,654,,556,,316,,211,,361,,23,,0,.,294,,91,,621,,880,,900,F,930,F,1012,,863,,349,,329,,408,,333,,229,,234,,566,,357,,747,,591,,570,,501,,1460,,1581,,1741,,1625,F,3010,,2568,,3679,,5354,,5198,,5387,,4954,,5546,,6079,,5523,,6780,,5084,F -Chad,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,4,F,2,F,0,0,0,.,0,.,13,,0,.,290,,45,,40,F,51,F,52,F,56,F,66,F,66,F,61,F,60,F,58,F,60,F -Chad,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,602,,359,,1520,,612,F,0,.,5,F,1,F,32,F,468,F,562,F,83,,113,,1201,,380,,446,,558,F,434,F,434,F,716,F,730,F,1118,F,389,F,416,F,459,F,474,F -Chad,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,19000,F,20000,F,20000,F,18000,F,15000,F,14000,F,6300,,7000,,9200,,8200,,11300,,16700,,11800,,12600,,13000,,18000,,22800,,25000,F,25000,F,26000,F,29000,F,25500,F,22500,F,26000,F,34000,F,37000,F,36000,F,41000,F,50342,,52167,,54059,,56020,,59227,,61984,,64787,,67704,,70747,,87000,,88515,,68000,,79200,,74100,F,74100,F -Chile,All,Export,Tonnes – net product weight,Tonnes – net product weight,230529,,255799,,355424,,479025,,617635,,594260,,932785,,842859,,951297,,1312609,,1290813,,1300072,,1144212,,1597144,,1296806,,1324077,,1362729,,1283282,,1633274,,1787547,,1517749,,1393613,,1045922,,1116553,,1109041,,1370796,,1225853,,1251783,,1308995,,1584188,,1384829,,1406325,,1348735,,1430103,,983020,,1169828,,1253627,,1242281,,1333989,,1230251,,1174984,,1232901,,1385954, -Chile,All,Import,Tonnes – net product weight,Tonnes – net product weight,459,,929,,1782,,1150,F,3751,,3960,,2969,,1521,,979,,651,,103,,737,,436,,1928,,8374,,7681,,17274,,15052,,12540,,15936,,27572,,25519,,25667,,71228,,108181,,87088,,54740,,118084,,158231,,98164,,153822,,177808,,160253,,78247,,157942,,195330,,191181,,166855,,190522,,195953,,144516,,169083,,185299, -Chile,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,313195,,341524,,487319,,671631,,734697,,860677,,1025230,,978946,,1307371,,1390718,,1628609,,1394731,,1471909,,1822994,,1459203,,1660292,,1689121,,1647632,,2134759,,2206605,,2025110,,1876285,,1248537,,1659115,,1611351,,1618686,,1862578,,1534651,,1989914,,1828885,,1645629,,1678591,,1547481,,1368689,,1109099,,1410714,,1345722,,1154459,,1366808,,1199939,,1117675,,1276862,,1330425, -China,All,Export,Tonnes – net product weight,Tonnes – net product weight,72000,F,68500,F,68000,F,80000,F,89500,F,100000,F,95500,F,96000,F,115200,F,117300,F,177300,F,227550,,276524,,307226,,369965,,380293,,470699,,500958,,666236,,721155,,787430,,910467,,991149,,1331209,,1515493,,1928966,,2057424,,2082080,,2396521,,2544577,,2989656,,3035682,,2948613,,2936417,,3566327,,3992877,,3747614,,3896453,,4097809,,3999296,,4173900,,4274503,,4250354, -China,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,154726,F,258562,F,277447,F,305676,,575073,,655915,,365439,,783703,,1032381,,936371,,1265644,,1342461,,1386166,,1508914,,1137347,,1304828,,2514321,,2280412,,2483798,,2324492,,2976970,,3650577,,3313111,,3453182,,3873215,,3726985,,3805651,,4229668,,4110160,,4155143,,4279265,,4073239,,4019939,,4875918,,5208261, -China,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,576535,,568210,,603218,F,582944,F,675643,F,692967,F,918045,F,746596,F,794533,F,932622,F,1216173,F,1175842,F,1359185,F,1419440,F,1836158,F,1863779,F,1885107,,1987082,,3081593,,3741813,,3987165,,4287139,,4619580,,4792260,,5202348,,5640061,,5847975,,7267149,,8019767,,8324813,,9392973,,10222213,,10919612,,12099835,,13330795,,14582161,,15533673,,16267661,,16509136,F,16649158,F,17670149,F,17304180,F,17281322,F -"China, Hong Kong SAR",All,Export,Tonnes – net product weight,Tonnes – net product weight,31582,,38365,,43624,,53252,,68758,,59988,,60887,,67244,,70003,,73645,,74420,,79671,,89390,,100777,,97690,,93050,,94197,,90462,,94410,,103763,,113183,,148294,,101757,,65895,,63057,,56451,,56705,,50263,,56283,,55171,,54269,,51214,,52452,,53540,,58193,,61160,,61215,,63122,,59770,,58130,,57648,,150562,,136999, -"China, Hong Kong SAR",All,Import,Tonnes – net product weight,Tonnes – net product weight,86724,,84377,,89437,,100959,,107865,,107623,,146500,,142685,,140540,,151156,,186863,,206877,,255852,,264852,,310575,,346942,,356290,,321455,,343389,,342657,,340121,,332610,,278194,,286015,,332408,,352491,,363593,,360790,,369105,,350867,,373388,,363494,,323345,,343709,,396649,,404100,,423357,,455047,,449995,,447281,,407367,,379633,,379941, -"China, Hong Kong SAR",All,Processed production,Tonnes – net product weight,Tonnes – net product weight,15475,,15288,,20002,,18583,,15378,,20757,,18152,,16310,,17949,,16938,,19529,,16577,,14943,,12780,,13386,,10594,,10008,,14339,,15473,,24920,,42184,,32221,,17817,F,52613,F,6579,F,2750,F,2479,F,1084,F,681,F,449,F,584,F,429,F,340,F,323,F,441,,352,,352,,504,,575,,668,,578,,543,,501, -"China, Hong Kong SAR",All,Reexports,Tonnes – net product weight,Tonnes – net product weight,11456,,9939,,12103,,16049,,17385,,20408,,39718,,30891,,38217,,48365,,79970,,83909,,127876,,143008,,157318,,168316,,172448,,143647,,158845,,136768,,105764,,72351,,67951,,83371,,105872,,118711,,119747,,127149,,119879,,107940,,119525,,136586,,93655,,88733,,126943,,127870,,144633,,232269,,197243,,92691,,82904,,0,.,0,. -"China, Macao SAR",All,Export,Tonnes – net product weight,Tonnes – net product weight,7428,,6162,,8594,,5217,,3693,,2453,,4511,,5343,,7988,,7910,,8477,,9135,,6565,,4984,,4901,,4295,,3054,,3189,,1839,,933,,739,,663,,457,,341,,318,,280,,266,,195,,679,,818,,1185,,641,,663,,1833,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,13,,31, -"China, Macao SAR",All,Import,Tonnes – net product weight,Tonnes – net product weight,3980,,3506,,4051,,4391,,4945,,5545,,4641,,5913,,7869,,7135,,8884,,11005,,10092,,10211,,11552,,11617,,10375,,11178,,11017,,11599,,11426,,12139,,12265,,10527,,11057,,13727,,16257,,20728,,19813,,20151,,21636,,22289,,21766,,22002,,22037,,22345,,21406,,21683,,25329,,26134,,24379,,26183,,28443, -"China, Macao SAR",All,Processed production,Tonnes – net product weight,Tonnes – net product weight,1611,,2279,,3728,,2989,,1907,,1956,,3127,,3988,,6619,,6922,,7621,,7799,,5187,,3332,,2679,,2892,,2700,,2478,,1334,,736,,700,,524,F,414,F,341,F,312,F,290,F,271,F,203,F,202,F,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,. -"China, Macao SAR",All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,143,,42,,208,,373,,399,,478,,681,,509,,675,,817,,1087,,636,,657,,915,,212,,111,,86,,204,F,151,,152,,63,F,68,F,0,- -Colombia,All,Export,Tonnes – net product weight,Tonnes – net product weight,3520,,3351,,2948,,2402,,2999,,3951,,4188,,3575,,3671,,6227,F,6938,F,7271,F,19794,,34011,,47045,,78127,,76880,F,65046,,93183,,89318,,76911,,75478,,75130,,81622,,92823,,79567,,72624,,67441,,76431,,77121,,66179,,72559,,90573,,79852,,60314,,65428,,60061,,56165,,66815,,53631,,47728,,34509,,30206, -Colombia,All,Import,Tonnes – net product weight,Tonnes – net product weight,52129,,46471,,66032,,113436,,93065,,99207,,111871,,65907,,120704,,80866,,84318,,49716,,66267,,37410,,72667,,91490,,65644,,100815,,132002,,129584,,109182,,110552,,95008,,74774,,108330,,94443,,88937,,94874,,116060,,130184,,128581,,124106,,130676,,123140,,125201,,131805,,141779,,167384,,178535,,166928,,166691,,147361,,164846, -Colombia,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,15967,,14521,,15748,F,14027,F,16166,F,15221,F,14340,F,7501,,13553,,8947,,12764,,11242,,30293,,50206,,54584,,54064,,57260,F,67566,,100497,,89158,,84558,,99812,,87653,,85065,F,92534,F,78647,F,71239,F,65955,F,74050,F,74772,F,64047,F,69559,F,80734,,56637,,85880,,111760,F,100077,F,94188,F,115665,F,95090,F,91265,F,85455,F,83275,F -Comoros,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,14,,16,,11,,10,F,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,54,,304,,73,,64,F,0,0,1,,0,.,0,0,1,,2,,0,0,0,.,0,.,0,0,0,.,0,0,0,.,1,,0,.,0,.,0,.,4,,2,,0,- -Comoros,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,254,,256,,151,,362,,86,,199,,207,F,270,F,341,F,343,F,350,F,334,F,505,F,460,F,404,,290,,226,,270,,228,,454,,277,,283,,358,,300,,259,,294,,251,,2763,,519,,835,,452,,611,,456,,684,F,560,F,1655,,1123,,1722, -Congo,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,30,,635,,850,,60,,20,,9,,24,,69,,24,,26,,15,,1,,1,,0,.,8,,841,,1058,,1018,,972,,1033,,538,F,40,F,44,F,267,F,620,F,734,F,864,F,290,,574,F,173,,334,,269,,266,,23,F,153,,211,,584,,490,F,6595,,8210,,5845, -Congo,All,Import,Tonnes – net product weight,Tonnes – net product weight,5131,,7255,,11563,,14675,,12901,,11260,,14510,,19341,,31749,,35764,,35175,,34432,,29700,,26118,,26417,F,25124,,29992,,28351,,23493,,13384,,16896,,15166,F,12472,F,11205,F,10783,F,17675,F,9951,F,7639,F,13045,F,11954,,9313,F,7138,F,25345,,23119,,9384,,16646,,16287,,28389,,39685,,21607,F,34266,,48601,,39034, -Congo,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,3000,F,3000,F,3000,F,3000,F,4000,,4000,,4000,,4000,,4000,,4000,,4000,,4000,,4000,,5973,,6504,,6741,,5274,,6963,,6938,,6703,,6468,,4747,,6363,,6364,,6350,F,6750,F,7160,F,7400,F,7800,F,7500,F,7450,F,7550,F,6900,F,7780,F,8200,F,9400,F,10400,F,9400,F,9400,F,8900,F,11200,F,12300,F,12700,F -Congo,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,,0,,0,,0,.,500,F,1300,F,1300,F,1300,F,3000,F,2400,F,1800,F,1100,F,910,F,1400,F,1300,F,1900,F,2400,F,2900,F,2200,F,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -"Congo, Dem. Rep. of the",All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,12,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,31,F,53,F,33,F,2,F,5,F,19,F,18,F,3,F,9,F,6,F,1,F,0,0,0,0,0,0,87,F,266,F,116,F,125,F,608,F,287,F,127,F,98,F,94,F,90,F -"Congo, Dem. Rep. of the",All,Import,Tonnes – net product weight,Tonnes – net product weight,43439,,26874,,22550,,25000,F,37000,F,57133,,40518,,60489,,57603,,112707,,119671,,89591,,89194,,86785,,76700,,83978,,78837,,132818,,69225,,85956,,104478,,84360,,110385,,83113,F,61584,,70350,F,66851,F,64241,F,66379,F,67526,F,71686,F,74507,F,71705,F,71839,F,77615,F,85832,F,91705,F,96016,F,109531,F,97416,F,104061,F,133408,F,125489,F -Cook Islands,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,13,,9,,102,,411,F,97,F,24,F,17,,8,,14,,6,,631,,772,,464,,1321,,740,,973,F,500,F,400,F,1300,F,1140,F,1777,F,1073,F,1203,F,1378,F,1377,F,1835,F,1585,F -Cook Islands,All,Import,Tonnes – net product weight,Tonnes – net product weight,250,F,350,F,210,F,220,F,190,F,240,F,146,F,273,F,202,F,199,F,96,F,167,F,122,F,180,F,240,F,217,,225,F,215,F,230,F,190,F,186,F,95,F,133,,133,,163,F,81,,41,,411,,170,,230,,169,,235,,216,F,131,F,162,F,115,F,186,F,198,F,347,F,353,F,206,F,228,F,247,F -Costa Rica,All,Export,Tonnes – net product weight,Tonnes – net product weight,2556,,2810,,2159,,5471,,2995,,4021,,4665,,4439,,6006,,7671,F,9206,,10348,,12385,,15262,,21620,,16173,,13857,,14508,,21912,,25647,,41138,,41044,,37166,,35100,,33341,,44564,,47317,,40378,,32821,,34469,,28380,,25456,,28773,,24835,,21459,,26386,,28104,,28429,,26230,,24419,,21774,,22517,,24976, -Costa Rica,All,Import,Tonnes – net product weight,Tonnes – net product weight,2451,,2614,,1939,,4012,,7372,,1755,,1620,,4451,,7468,,7747,,5042,,9533,,8878,,12794,,14786,,13867,,16476,,14809,,28831,,23132,,25183,,36201,,28085,,24870,,19792,,24319,,30477,,26854,,27392,,25626,,26479,,23808,,29614,,29650,,26278,,40599,,32538,,34181,,57968,,56024,,62675,,72274,,67630, -Costa Rica,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,2991,,2898,,3692,,3788,,2980,,1870,,2230,,860,,2387,,2365,,6307,F,5356,F,5498,F,6438,F,6036,F,7653,F,6332,F,10211,F,16717,F,21286,F,33882,F,29239,F,26921,F,26628,F,17284,F,20556,F,27842,F,27325,F,24870,F,26425,F,20894,F,17445,F,24280,F,21486,F,20670,F,23406,F,18641,F,21370,F,19088,F,18981,F,17290,F,17051,F,17263,F -Croatia,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,23390,,25963,,20448,,16177,,18046,,22100,,19073,,17521,,18130,,21471,,22009,,22585,,23059,,24084,,29051,,30582,,29444,,32825,,34021,,38493,,33206,,34907,,49264,,54825,,55304,,54792,,56938, -Croatia,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,21415,,24271,,23214,,28632,,23872,,31794,,25998,,25929,,36362,,64033,,75137,,70823,,51361,,64108,,59154,,52199,,56459,,48798,,43251,,39334,,38269,,34836,,43782,,46817,,48041,,49219,,53593, -Croatia,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,20633,,16176,F,12977,F,13766,F,12521,,12324,,12850,,11295,,11963,,13108,,13913,,15161,,11392,,14168,,16815,,19170,,19802,,24016,,19321,,20087,,16068,,14082,,15757,,14690,,13706,,12082,,11809, -Cuba,All,Export,Tonnes – net product weight,Tonnes – net product weight,16835,,21093,,38252,,39402,,45570,,28480,,25427,,33320,,25759,,34822,,38202,,23903,,24622,,21593,,17385,,15094,,9202,,7382,,9914,,12414,,11927,,9591,,8163,,8176,,7699,,6877,,8784,,6283,,10292,,6741,,7245,,7115,,6111,,5904,,5710,,4372,F,3649,F,3842,F,3553,F,5596,F,7272,F,8913,F,7610,F -Cuba,All,Import,Tonnes – net product weight,Tonnes – net product weight,75700,,76329,,102944,,91405,,129335,,127775,,57358,,67686,,42425,,70079,,72670,,75493,,74141,,43324,,42539,,14676,,32672,,52366,,45610,,53370,,35641,,28398,,35783,,23223,,35082,,33998,,22525,,37741,,54672,,43770,,22738,,35793,,41753,,28107,,13362,,14145,F,11212,F,10369,F,13045,F,16181,F,16228,F,14735,F,18189,F -Cuba,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,25300,,22000,,26100,,20100,,80615,,75154,,98119,,100388,,103384,,115036,,140805,,123808,,138984,,105537,,113269,,90017,,44994,,47637,,33147,,39608,,49135,,29150,,21221,,18781,,13800,,12958,,11126,,10255,,16188,,17541,,22130,,16710,,22233,,18683,,15841,,19381,,21134,,25702,,26596,,26663,F,26427,F,26024,F,27061,F -Cuba,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,106,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Curaçao,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,12873,F,15369,F,16368,F,18798,F,20084,F,23686,F,22075,F,23969,F -Curaçao,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2003,F,1666,F,1717,F,1555,F,1759,F,1896,F,1361,F,1959,F -Cyprus,All,Export,Tonnes – net product weight,Tonnes – net product weight,6,,0,0,133,,67,,61,,109,,0,.,1,,0,0,0,0,47,,78,,79,,27,,53,,336,,146,,911,,276,,293,,392,,178,,275,,335,,760,,792,,796,,611,,2618,,3108,,2755,,2576,,2685,,2278,,2721,,3373,,3219,,4422,,3605,,4653,,5417,,5323,,5460, -Cyprus,All,Import,Tonnes – net product weight,Tonnes – net product weight,1718,,2306,,3300,,2624,,3501,,4409,,7401,,6785,,6555,,7359,,8624,,10065,,9387,,10982,,12427,,12500,,14935,,13900,,14742,,16132,,14781,,14476,,15650,,15203,,16079,,18314,,17118,,17733,,22846,,21953,,18722,,20723,,27224,,21530,,22069,,23470,,26833,,21697,,23826,,25933,,28907,,30489,,29398, -Cyprus,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,0,0,0,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,7,,8,,46,,112,,184,,255,,195,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Czechia,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,12545,,14972,,12079,,12145,,9552,,10716,,9500,,12433,,13635,,13051,,13698,,15219,,17402,,19229,,20154,,19030,,22113,,23806,,24816,,25373,,25523,,27549,,29327,,35761,,37685,,31685, -Czechia,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,53347,,65764,,67230,,68562,,67227,,59750,,57844,,65199,,68554,,62529,,62056,,68073,,71514,,67166,,69433,,73209,,70818,,68839,,66212,,63911,,64184,,69032,,73052,,79779,,82535,,80597, -Czechia,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,14275,,13558,F,13985,F,15134,F,14772,F,9750,F,8709,F,9005,F,8869,F,8251,F,8618,F,8703,F,9067,F,9479,F,9748,F,9716,F,11781,F,15010,F,14855,,15663,,15289,F,16126,F,14525,,14100,,23587,,19819, -Czechoslovakia,All,Export,Tonnes – net product weight,Tonnes – net product weight,2559,,2210,F,2400,F,2800,F,2640,F,2802,F,2500,F,2700,F,900,F,870,F,1690,F,1310,F,5025,F,5080,F,5140,F,6678,,4562,F,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0, -Czechoslovakia,All,Import,Tonnes – net product weight,Tonnes – net product weight,133337,,95208,,97000,,91600,,106400,,111100,,109470,,86490,,104500,F,105769,F,119340,F,127070,F,136240,F,153844,F,111722,F,90508,F,80532,F,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0, -Czechoslovakia,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,26291,,25898,,26032,,26353,,26581,,26423,,26756,,25022,,26128,,27197,,27336,,27674,,28165,,28570,,27888,,13436,,16929,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0, -Côte d'Ivoire,All,Export,Tonnes – net product weight,Tonnes – net product weight,10911,,20419,,21473,,20681,,26970,,29393,,29652,,33040,,31432,,37529,,25756,,31323,,32801,,39576,,42634,,48070,,44104,,53502,,48698,,65177,,58299,,58848,,57330,,60269,,64870,,51237,,55794,,48260,,53388,,33341,,37400,,47682,,47640,,52509,,34385,,23623,,36653,,37701,,27951,,38136,,32394,,35358,,36246, -Côte d'Ivoire,All,Import,Tonnes – net product weight,Tonnes – net product weight,88121,,80128,,84676,,109439,,115314,,135394,,125252,,110348,,115115,,127932,,137046,,152924,,210348,,207528,,196827,,186439,,191922,,204079,,170141,,227907,,223885,,249251,,276438,,294275,,235771,,240459,,257024,,274822,,247360,,269882,,262096,,283285,,349611,,388084,,304292,,305006,,310307,,313251,,351997,,416005,,406260,,502446,,566779, -Côte d'Ivoire,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,29433,,35926,,29625,,31677,,32996,,36160,,38429,,42144,,38874,,40155,,41463,,48077,,47681,,56239,F,58382,F,63879,F,60100,F,63489,F,56446,F,76536,F,79254,F,66129,F,72158,F,60066,F,71722,F,71301,,134833,,60352,F,60932,F,36367,F,47494,F,48800,F,46000,F,38600,F,38285,F,34583,,49091,,46672,,41325,,47800,F,41235,,43820,F,44700,F -Côte d'Ivoire,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1607,,0,-,0,-,0,- -Denmark,All,Export,Tonnes – net product weight,Tonnes – net product weight,704461,,672393,,657405,,723133,,819764,,805254,,796539,,801653,,832482,,837542,,839072,,784997,,836564,,803235,,721659,,869280,,904900,,909972,,1026538,,1093582,,1196329,,1215137,,1181465,,1163265,,1265033,,1271923,,1273270,,1220005,,1261778,,1150724,,1152575,,1037774,,1102764,,1203081,,1167322,,1098211,,1071431,,1188741,,1144762,,1152403,,1138523,,1217553,,1157338, -Denmark,All,Import,Tonnes – net product weight,Tonnes – net product weight,284533,,273235,,303928,,311692,,373135,,375282,,372705,,359838,,454628,,453025,,494320,,506171,,572734,,635369,,492375,,490514,,499834,,531594,,889866,,882857,,923718,,1063944,,1157148,,1219680,,1301456,,1311749,,1400897,,1603283,,1424817,,1335635,,1376863,,1431119,,1242553,,1292724,,1464265,,1343288,,1244294,,1251230,,1382484,,1383377,,1500899,,1377571,,1430974, -Denmark,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,679755,,649447,,599846,,688016,,784419,,768355,,729386,,757078,,774402,,739186,,747399,,709656,,791979,,765182,,757725,,895394,,911623,,843026,,913782,,927567,,803824,,803408,,814246,,796771,,629662,,844633,,780438,,678120,,699851,,692582,,713344,,574779,,649553,,644813,,641349,,585233,,519233,,552514,,544674,,548267,,539080,,628302,,580603, -Djibouti,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,32,,19,,56,,66,,45,,18,,43,F,21,F,10,F,11,F,25,F,25,F,42,F,6,F,30,F,19,F,33,F,9,F,10,F,32,F,62,F,15,F,8,F,21,F,5,F,2,F,9,F,2,F,11,F,3,F,2,F,19,F -Djibouti,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,257,,170,,298,,477,,207,,98,,209,,164,,129,,139,,414,,426,F,436,F,310,F,301,F,552,F,486,F,614,F,510,F,290,F,402,F,275,F,550,F,330,F,669,F,621,F,484,F,429,,333,F,477,F,542,F,833,F,570,F,1215,F,1572,F,734,F,1203,F -Dominica,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,0,4,,3,,2,,4,,0,0,1,,1,,0,0,0,0,0,0,0,0,0,.,0,.,22,F,6,F,2,F,3,F -Dominica,All,Import,Tonnes – net product weight,Tonnes – net product weight,372,,388,,374,,308,,209,,180,F,300,F,260,F,325,F,332,F,302,F,268,,289,,385,,492,F,482,F,490,F,505,F,479,,880,,589,,411,,631,F,765,,678,,573,,576,,513,,587,,523,,432,,556,,523,,720,,334,,444,F,391,F,405,F,946,F,1028,F,562,F,417,F,457,F -Dominica,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,6,,1,,0,.,1,,0,.,0,.,0,.,0,0,0,0,0,.,0,.,0,.,81,,0,-,0,-,0,-,0,-,0,-,0,- -Dominican Republic,All,Export,Tonnes – net product weight,Tonnes – net product weight,646,,852,,771,,657,,856,,1070,,1089,,1310,,1069,,2193,,1542,F,1187,,916,,1361,,540,,124,F,218,F,401,F,169,F,752,F,748,,2054,,2215,,2085,,1907,,2178,,2869,,5205,,1280,,1780,,1079,,927,,1321,,1349,,2214,,5389,,3774,,3797,,4630,,3469,,2087,,2536,,2127,F -Dominican Republic,All,Import,Tonnes – net product weight,Tonnes – net product weight,9600,F,13554,,12008,,15753,,18845,,13415,,14119,,14415,,8046,,15375,,19068,F,16165,F,18593,F,18683,F,18589,F,14026,F,11682,F,15981,F,15939,F,16420,F,18174,F,22620,F,21679,F,25430,F,28630,F,28345,F,25515,,21356,,21971,,23440,,30783,,35206,,31370,,38210,,34505,,38466,,36119,,29010,,35169,,35380,,39776,,37691,,46186,F -Ecuador,All,Export,Tonnes – net product weight,Tonnes – net product weight,65271,,96975,,106665,,129142,,155165,,147911,,162443,,69781,,156584,,247081,,268864,,200058,,219624,,185084,,149564,,153257,,132398,,152129,,173202,,220888,,314729,,284778,,269650,,297710,,264238,,303994,,271707,,325628,,353563,,373843,,458575,,478074,,481468,,502555,,504667,,620888,,640055,,699770,,765775,,792059,,916611,,903015,,1099907, -Ecuador,All,Import,Tonnes – net product weight,Tonnes – net product weight,136,,285,,370,,508,,536,,153,,7,,0,0,6,,50,,0,0,0,.,0,.,0,.,6333,,11452,,1902,,10669,,18914,,20427,,22767,,14831,,39405,,12620,,8321,,24605,,45732,,61662,,54448,,48722,,58341,,97957,,169470,,205907,,201946,,212337,,129013,,100385,,179993,,134521,,111372,,109799,,147605, -Ecuador,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,89399,,128546,,166458,,155320,,198963,,226920,,210756,,86916,,264708,,382502,,335585,,246057,,290074,,244041,,193125,,190569,,161145,,194670,,239161,,296144,,335701,F,277000,F,383782,,314327,F,276176,F,321801,F,314832,,352007,,425852,,420569,F,466072,F,458169,F,470146,F,513188,,544850,F,653648,,591351,,738073,,792171,,792669,,923842,F,1000457,F,1093089,F -Egypt,All,Export,Tonnes – net product weight,Tonnes – net product weight,184,,309,,131,,364,,278,,275,,384,,215,,204,,216,,747,,717,,1034,,2200,,3460,,2363,,1790,,1262,,1700,,1860,,2225,,2017,,1296,,903,,1016,,1265,,2574,,3154,,4960,,5474,,4374,,4439,,6982,,5199,,10814,,11024,,15859,,21427,,28775,,28150,,48628,,37192,,23919, -Egypt,All,Import,Tonnes – net product weight,Tonnes – net product weight,66074,,42516,,79910,F,36941,F,57361,F,97002,F,104991,F,148098,F,166835,F,108314,F,141770,F,132165,F,121877,F,123851,F,144072,F,105482,F,138785,F,104306,F,159385,,129065,,207971,,166706,,217079,,245606,,261154,,260922,,176700,,177805,,241670,,243189,,259606,,276276,,212678,,249845,,342463,,271564,,342325,,288360,,585644,,526527,F,404291,,400566,,565634, -Egypt,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,93,,80,,57,,219,,168,,149,,294,,78,,90,,51,,73,,18,,13,,2107,F,3345,F,2128,F,1578,F,1175,F,6626,,8147,,6394,,7680,,9415,,10993,,10764,,10845,F,10678,F,10112,F,9320,F,9325,F,8794,F,8476,F,8210,F,7509,F,6910,F,6910,F,6248,F,5755,F,5420,F,5140,F,5697,F,6195,F,6362,F -El Salvador,All,Export,Tonnes – net product weight,Tonnes – net product weight,3511,,3943,,3759,,3840,,4148,,4348,,3513,,2274,,3642,,3484,,4843,,3938,,3594,,3326,,3679,,4191,,4022,,3710,,5230,,4966,,6040,,4894,,4962,,6793,,3083,,2955,,8013,,12192,,15796,,22611,,18329,,21251,,23451,,23826,,18576,,18504,,23520,,26301,,26936,,26154,,29806,,31663,,26853, -El Salvador,All,Import,Tonnes – net product weight,Tonnes – net product weight,1496,,2031,,2092,,2643,,4177,,1511,,685,,394,,1298,,816,,1117,,2051,,2420,F,2835,F,2724,,2441,,3745,,6206,,6976,,4676,,3748,,3707,,3101,,3508,,5404,,6573,,4719,,8364,,12303,,32763,,20294,,28248,,12701,,41709,,24773,,14469,,17555,,23461,,25058,,21963,,19907,,24102,,21811, -El Salvador,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,3008,,3054,,2905,,3087,,3216,,4953,,3685,,2796,,4289,,2647,,3590,,2841,,3112,,3386,,2467,,2477,,3812,,3292,,5970,,6718,,8032,,5794,,6128,,4339,,3702,,12538,,22470,,21045,,30195,,38187,,31302,,44516,,42663,F,34487,,29763,F,38539,F,44432,F,49945,F,64418,,55033,F,60860,F,58383,F,58220,F -Equatorial Guinea,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,2,,3,,207,,194,,149,F,0,.,0,.,0,.,257,F,40,F,0,.,0,.,0,.,0,.,15,F,246,F,281,F,683,F,746,F,395,F,144,F,29,F,20,F,28,F,6,F,26,F,30,F,20,F,30,F,10,F,21,F,30,F,24,F,23,F,24,F,24,F,21,F,81,F -Equatorial Guinea,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,1402,F,2423,,3023,,1561,,1655,,3585,,1696,,3288,,2649,,3039,F,4602,,3817,F,4259,,1635,,3162,,4106,F,3137,F,2710,F,1889,F,2055,F,2485,F,4320,F,3326,F,4460,F,6181,F,5899,F,7773,F,7551,F,6365,F,10461,F,7924,F,9645,F,12462,F,12092,F,9705,F,6383,F,6366,F,7658,F,6436,F -Eritrea,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,6,F,15,F,83,F,0,0,293,F,526,F,162,F,190,F,596,,277,F,273,F,73,F,306,F,101,F,47,F,0,0,1,F,91,F,4,F,0,.,5,F,6,F,37,F,42,F -Eritrea,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,13,F,90,F,7,F,0,0,31,F,46,F,104,F,104,F,1627,,747,F,72,F,207,F,80,F,89,F,13,F,49,F,2,F,81,F,28,F,67,F,12,F,9,F,5,F,10,F -Eritrea,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,54,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Estonia,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,14525,F,67170,,104622,,106974,,92997,,149383,,165443,,119787,,106289,,163381,,148549,,131914,,102424,,135995,,126031,,120969,,131055,,125951,,136529,,123461,,125090,,115843,,108036,,101690,,96520,,102436,,110257, -Estonia,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1799,,6314,,23510,,29188,,28548,,70503,,72199,,46213,,54705,,64815,,59932,,56843,,39073,,40624,,38434,,47997,,57253,,43440,,40464,,44235,,61837,,60933,,56361,,46867,,37737,,39895,,42609, -Estonia,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,96689,,98473,,103492,,98441,F,86500,F,117613,F,126554,F,94876,F,92123,,117085,,101669,,113048,,79165,,90154,,89089,,82964,,73263,,78436,,72584,,62511,,75792,,70186,F,69085,,68140,,57005,,63488,,64782, -Eswatini,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1005,,782,,1031,,1711,,1134,,1204,,1599,,2455,,1478,,910,,95,,97,F,24,F,20,F,20,F,3,F,1,F,1,F,1,F,30,,12,,0,0 -Eswatini,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,10179,,7382,,6643,,6343,,4572,,3969,,7366,,7543,,5033,,2925,,2121,,1619,F,1173,F,1115,F,1149,F,1456,F,1775,F,3226,F,2965,F,2620,,3146,,2986, -Eswatini,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,663,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Ethiopia,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,23,,1,F,8,,7,,6,,8,,11,,32,,32,,166,,102,,414,,755,,599,,730,,858,,1075,,1068,,807,,922,,964,,371,,357,,289, -Ethiopia,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,27,,15,,68,,169,,58,F,323,,160,,331,,241,,194,,420,,483,,558,,575,,1038,,832,,1150,,825,,751,,783,,857,,1446,,1187,,1310,,1215,,1128,,827, -Ethiopia,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,16,,18,,10,,7,,7,,8,,10,,200,,100,F,154,,154,,138,,137,,66,,300,F,500,F,1174,,1200,,1538,,1819,,2694,,3286,,3599,,4739,,6000,F,7500,F,10773, -Ethiopia,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,49,,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,-,0,-,0,- -Ethiopia PDR,All,Export,Tonnes – net product weight,Tonnes – net product weight,343,,211,,0,.,0,.,0,.,11,,12,,20,,17,,180,,80,,100,,20,,36,,97,,2,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,.,0,.,0,.,0,.,0, -Ethiopia PDR,All,Import,Tonnes – net product weight,Tonnes – net product weight,343,,528,,346,,81,,4,,678,,4,,33,,18,,77,,273,,3,,28,,500,,171,,4,,0,0,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,.,0,.,0,.,0,.,0, -Ethiopia PDR,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,127,,211,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,8,,9,,17,,15,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,.,0,.,0,.,0,.,0, -Falkland Is.(Malvinas),All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2598,,4597,,5967,,1447,,1824,,1980,,5915,,27192,,31538,,17112,,46069,,42216,,67688,,59250,,40386,,66330,,49672,,80728,,66050,,55382,,66656,,51659,,99559,,58074,,69981,,60462,,67651,,38583,,47190,,61136,,79938, -Falkland Is.(Malvinas),All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,17,F,21,F,14,F,23,F,23,F,44,F,18,F,12,F,195,F,102,F,212,F,342,F,113,F,42,F,140,F,127,F,181,F,39,F,32,F,33,F,34,F,42,F,28,F,37,F,28,F,28,F,153,F,18,F,10,F -Falkland Is.(Malvinas),All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2598,F,4597,F,5967,F,1447,F,1824,F,1629,F,5915,,27192,,31537,,17112,,44792,,39607,,62945,,59825,,25735,,60597,,43320,,84510,,66050,,71679,,66656,,51659,,99559,,63630,,69981,,61598,,60453,,49240,,51862,,55306,,79938, -Faroe Islands,All,Export,Tonnes – net product weight,Tonnes – net product weight,118550,,140144,,120042,,104996,,113873,,105745,,91691,,124340,,128852,,138615,,127464,,141636,,151608,,135328,,179200,,181131,,181953,,169500,,160926,,222594,,281085,,291284,,282345,,268372,,322133,,367841,,355728,,421594,,366179,,311000,,301785,,391177,,349815,,313716,,399215,,377040,,443485,,420479,,429734,,394591,,407853,,498846,,458309, -Faroe Islands,All,Import,Tonnes – net product weight,Tonnes – net product weight,3916,,106,,3819,,1500,,2031,,6247,,5788,,4343,,3067,,2646,,3421,,4204,,6747,,5386,,22702,,22032,,41369,,39873,,52269,,55132,,96148,,72197,,86520,,75404,,61994,,85600,,75680,F,92781,,76528,,52814,,51881,,36261,,46038,,83520,,47203,F,46185,F,59082,F,33722,F,23712,F,32275,F,40665,F,35152,F,36109,F -Faroe Islands,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,86895,,92841,,84937,,72603,,82760,,84889,,87011,,108191,,121715,,123513,,120756,,102889,,121865,,119921,,105439,,95361,,98124,,102425,,98647,,91743,,106486,F,94758,F,86607,,101699,,124908,F,150452,F,156920,F,151784,F,170648,F,182737,F,194298,F,210144,F,169153,F,173527,F,218039,F,204701,F,256403,F,330287,F,310171,F,330584,F,320757,F,428075,F,373356,F -Fiji,All,Export,Tonnes – net product weight,Tonnes – net product weight,477,,2384,,4135,,5586,,3406,,5504,,2685,,5144,,4827,,3211,,4995,,5987,,8794,,8253,,8815,,9796,,7045,,11942,,13874,,12828,,13365,,17584,,13949,,14411,,12933,,15177,,17505,,19394,,21487,,24294,,26833,,32262,,39636,,38427,,51282,,39208,,15017,F,35227,,41320,,32129,,36998,,32451,,23508, -Fiji,All,Import,Tonnes – net product weight,Tonnes – net product weight,8609,,11061,,14682,,10575,,12847,,11345,,8829,,8037,,8650,,11271,,15856,,13347,,26184,,23356,,17749,,24209,,13537,,22087,,14206,,17543,,15086,,17964,,16441,,12753,F,9161,,16406,,19842,,35694,,37929,,34572,,24248,,24639,,25155,,23495,,39018,,72777,,80588,,64377,,121927,,83469,,59819,F,44551,,31425, -Fiji,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,227,,561,,4520,,5681,,3936,,7349,,6595,,8572,,6764,,7610,,5698,,6738,,9286,,9578,,9351,,9319,,8199,,31192,,21406,F,20816,,15023,,17092,F,14723,,15614,,16219,,11143,F,12362,F,15716,,15324,,13537,F,17032,F,16942,F,23272,F,19216,F,28937,F,30684,F,41264,F,47520,F,62251,F,37232,F,49261,F,51238,F,41030,F -Fiji,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,3141,,3632,,4421,,1686,,3820,,1924,,734,,504,,428,,1153,,178,,1092,,1420,,2007,,2018,,763,,12,,31,,80,,1052,,2419,,10,,28,,28,,0,.,0,.,3,,0,.,0,.,506,,0,.,3424,,1709,,6390,,11843,,35417,,60800,,47836,,61334,,23098,,39192,,42198,,37534, -Finland,All,Export,Tonnes – net product weight,Tonnes – net product weight,441,,548,,1995,,2685,,3946,,5873,,4027,,7859,,8099,,6979,,2383,,5034,,5259,,3550,,3304,,1849,,2284,,2336,,8218,,11678,,12190,,18672,,15810,,22204,,16364,,16143,,13632,,10503,,18874,,24692,,26823,,43705,,51603,,59918,,58159,,59014,,59669,,60764,,66126,,69073,,71055,,76927,,85499, -Finland,All,Import,Tonnes – net product weight,Tonnes – net product weight,81814,,64376,,79588,,98329,,113378,,111959,,119846,,116786,,113142,,120115,,103958,,107744,,108023,,112211,,114978,,104508,,105250,,99992,,179340,,107019,,131917,,146441,,107462,,70698,,89896,,106272,,95833,,90758,,102816,,97002,,104021,,104635,,96660,,100882,,103586,,106059,,115309,,116567,,116199,,113122,,101479,,118093,,117573, -Finland,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,15200,,16300,,19300,,19700,,15850,,17600,,17500,,17850,,17850,,19290,,19420,,18680,,23830,,24060,,24530,,23260,,24030,,32998,,46199,,42080,,43072,,56999,,40100,,44582,,37530,,41960,,36380,,45600,,45650,,59480,,59740,,72991,,72869,,76699,,78999,,66526,,67526,,83875,,83778,,84275,,84379,,82082,,82079, -France,All,Export,Tonnes – net product weight,Tonnes – net product weight,130009,,126125,,134625,,132904,,141536,,144961,,162178,,180088,,166181,,203271,,229748,,238628,,261513,,313693,,348582,,350309,,378356,,405497,,412139,,387731,,411797,,409825,,385284,,447666,,480708,,442998,,406566,,482642,,516032,,424046,,437576,,421513,,441970,,358666,,353427,,326832,,369999,,342574,,322751,,348788,,353605,,353119,,360279, -France,All,Import,Tonnes – net product weight,Tonnes – net product weight,401753,,456439,,475390,,484524,,496461,,510859,,527680,,547058,,550401,,590079,,629348,,704672,,745553,,779601,,869361,,865724,,888215,,879601,,929084,,919988,,947635,,941400,,1008808,,1032035,,1013696,,1058048,,1025997,,1059721,,1104643,,1131102,,1133730,,1114096,,1112214,,1131076,,1150586,,1144640,,1121817,,1113042,,1121097,,1130650,,1151490,,1183611,,1204221, -France,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,157105,,174775,,175808,,159167,,156044,,158922,,159038,,171362,,194140,,204031,,230191,,229418,,237190,,240144,,255309,,262047,,259555,,273888,,301174,,461300,,563082,,563403,,525851,,565771,,597907,,629096,,638879,,720016,,684740,,652544,,658856,,621063,,603480,,542377,,531530,,537175,,556289,,533513,,498057,,479834,,486649,,470807,,472146, -French Guiana,All,Export,Tonnes – net product weight,Tonnes – net product weight,1003,,1852,,939,,2205,,3079,,3664,,3636,,2776,,2670,,2645,,2941,,3777,,3527,,4454,,4416,,4915,,4585,,4117,,5002,,4839,,0,-,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -French Guiana,All,Import,Tonnes – net product weight,Tonnes – net product weight,1468,F,1692,,1566,,2774,,3255,,3391,,3529,,3198,,2800,,2594,,2443,,1957,,1352,,1078,,1293,,1210,,1561,,1504,,1759,,1838,,0,-,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -French Guiana,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,1003,,1645,,813,,2027,,2882,,3472,,2986,,5141,,3608,,3458,,4201,,4869,,5153,,4750,,4811,,4349,,4315,,4587,,4859,F,4636,F,0,-,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -French Polynesia,All,Export,Tonnes – net product weight,Tonnes – net product weight,68,,164,,45,,200,,0,0,11,,4,,25,,1,,1,,16,,28,,53,,3,F,127,,130,F,116,F,223,,184,,217,,115,,1933,,1441,,1835,,2597,,3608,,3197,,3153,,2598,,3482,,3154,,3264,,2826,,2684,,3141,,3934,,4301,,4329,,3726,,3948,,3485,F,2300,F,3362,F -French Polynesia,All,Import,Tonnes – net product weight,Tonnes – net product weight,1161,,951,,1324,,1508,,1420,,1537,,1464,,1496,,1440,,1611,,1836,F,1739,F,1935,,2200,F,2048,,2100,F,2250,F,2275,,2346,,2169,,2342,,2189,,2794,,1941,,2266,,2296,,2311,,2564,,2509,,3079,,2640,,2958,,2899,,3002,,2858,,2617,,2967,,2587,,2863,,2492,,2215,F,1996,F,2859,F -Gabon,All,Export,Tonnes – net product weight,Tonnes – net product weight,27,,224,,43,,1025,,166,,2079,,1422,,1701,,1357,,1489,,1755,,2082,,1390,,2792,,918,,1063,F,945,F,728,,902,,1386,F,680,,1527,,2206,,2624,,3297,,2886,,3781,,2039,,4888,,4840,,4400,,2248,,2890,,1534,,1306,F,1179,F,1225,F,1188,F,1309,F,1163,F,1032,F,1044,F,937,F -Gabon,All,Import,Tonnes – net product weight,Tonnes – net product weight,2829,,4987,,3958,,6068,,7767,,8079,,7878,,7522,,9219,,8419,,10019,,11100,,8729,,8092,,6698,,7480,,7049,,9931,,8058,,3416,F,12094,,11553,,10575,,9522,,11073,,11595,,1864,,3914,,7828,,8218,,10699,,9179,,10133,,12171,,11095,F,15957,F,15972,F,18972,F,24223,F,24566,F,26315,F,20118,F,28110,F -Gabon,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,4400,F,4450,F,4450,F,4450,F,4500,F,4500,F,4500,F,4000,F,4000,F,4000,F,4000,F,4000,F,1602,,3000,F,3000,F,3100,,3100,,12665,,12982,,12304,,11625,,12304,,12304,,8000,F,9001,F,8001,F,8000,F,8000,F,8000,F,7000,F,6700,F,6300,F,6300,F -Gambia,All,Export,Tonnes – net product weight,Tonnes – net product weight,11697,F,17571,F,9777,F,1600,,1330,,1000,,1195,,644,,0,.,987,,828,,1070,,1068,,1085,,1450,,1546,,1442,F,1586,,2088,,1844,F,1803,,1747,,1313,,1656,,1474,,692,,916,,608,,293,F,837,F,86,,2050,,1976,,3971,,6146,,2406,,1971,,1519,,1625,,2087,F,2801,,5365,,5267, -Gambia,All,Import,Tonnes – net product weight,Tonnes – net product weight,9626,F,11069,F,7576,F,100,F,234,F,338,,146,F,615,,836,F,350,F,300,F,200,F,0,.,3,,7,,54,,88,F,320,,124,,187,F,334,,446,,817,,866,,834,,719,,1618,,2148,,1885,F,1170,F,1940,F,2416,,2774,,3529,,6014,,3665,,2146,,1714,,1075,,1423,F,1497,,2048,,1588, -Gambia,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,2000,F,2000,F,2000,F,1500,,1250,,940,,1086,,582,,500,F,528,,334,,473,,607,,725,,1491,,1538,,1272,,1501,,2087,,1589,F,1497,F,1206,F,1168,F,1246,F,894,,675,F,461,F,317,F,285,F,390,F,265,F,549,F,1353,,1484,,3563,,1651,F,3597,,3657,,2889,,2569,,5326,F,9920,F,15284, -Gambia,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,48,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,652,,542,,111,,69,,0,-,0,-,0,-,0,- -Georgia,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,4,,255,,147,,450,,1462,,206,,428,,880,,3049,,432,,7855,,3616,,17342,,6235,,7770,,14973,,21514,,26110,,11264,,2038,,13074,,20044,,22417,,23962,,14128,,19567, -Georgia,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1193,,464,,2302,,5198,,5186,,6504,,4528,,2808,,2703,,3774,,5126,,9911,,15718,,19396,,22117,,27398,,19857,,21755,,22553,,21645,,23311,,21440,,21419,,21194,,21977,,17168, -Georgia,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,7, -Georgia,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2,,0,.,1,,37,,0,.,0,.,0,.,0,.,0,.,261,,245,,97,,464,,250,,148,,147,,59, -Germany,All,Export,Tonnes – net product weight,Tonnes – net product weight,190181,,200907,,197092,,203497,,238198,,236181,,330017,,329102,,384546,,384096,,365339,,354849,,368526,,486890,,437775,,411388,,406107,,466940,,598363,,554417,,608988,,671889,,635526,,627945,,647025,,656591,,675758,,655752,,700281,,737835,,738374,,842271,,847667,,864241,,882127,,836773,,921721,,904839,,1014458,,964933,,925365,,903255,,962643, -Germany,All,Import,Tonnes – net product weight,Tonnes – net product weight,825267,,813987,,779706,,847886,,915996,,793269,,958476,,951871,,1007516,,1197527,,1210711,,1049438,,1009505,,1238554,,1155666,,1096767,,1081133,,1025595,,1198847,,1125699,,1123891,,1144731,,1166287,,1033934,,1154010,,1067841,,1076102,,1050496,,1029000,,1114430,,1159362,,1187364,,1147185,,1279389,,1231343,,1217334,,1257456,,1172779,,1303765,,1206889,,1241760,,1157561,,1173045, -Germany,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,523900,,465530,,475004,,473495,,490658,,466163,,450191,,458182,,462566,,448751,,450685,,427919,,439603,,552346,,596121,,600861,,608607,,571886,,539649,,620345,,629015,,609388,,611065,,628839,,599501,,635492,,587666,,640700,,698867,,696663,,674218,,726916,,727898,,659776,,650166,,647819,,629197,,626460,,629670,,615803,,614776,,594198,,646485, -Ghana,All,Export,Tonnes – net product weight,Tonnes – net product weight,2993,,7427,,38340,,39284,,29082,,33708,,32169,,29468,,25908,,26890,,33020,,21420,,25592,,13531,,25413,,31024,F,23304,F,24786,F,32734,F,29543,F,31568,,43694,,35730,,40941,,54742,,60236,,45338,F,45983,,47877,,35907,,24062,,25772,,23675,,19412,F,24536,F,24729,F,28479,F,26048,F,27707,F,34148,F,43342,F,69241,,80053, -Ghana,All,Import,Tonnes – net product weight,Tonnes – net product weight,102561,,61823,,64468,,52766,,32646,,29028,,19603,,22590,F,15160,F,26128,F,34874,F,21672,,14126,,23448,,22698,,26576,,41052,,40983,,39145,F,58708,F,104378,,219939,,186922,,198035,F,171361,,174317,,230054,,109875,,285518,F,269173,,302012,,372592,,294003,,256709,,216702,,333923,,241715,,356268,,279073,,299753,,370794,,361309,,357813, -Ghana,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,113600,F,85242,,104587,,107183,,96343,,103607,,104121,,103006,,91609,,102780,,115536,,112033,,112648,,116640,,130198,,127142,,112022,,113213,,128819,,137175,F,150830,F,149341,F,145160,F,166798,F,115568,F,114002,F,96935,F,99297,F,102720,F,77671,F,69689,F,72164,F,70159,F,68360,F,73964,F,73534,F,77681,F,71750,F,73118,F,85656,F,90235,F,114214,F,112683,F -Greece,All,Export,Tonnes – net product weight,Tonnes – net product weight,6961,,7453,,6385,,9240,,6502,,5011,,4900,,5928,,7980,,9226,,11311,,14395,,12223,,16937,,12454,,14695,,20669,,31047,,26019,,32394,,41644,,55712,,54435,,71780,,81328,,84530,,72030,,84638,,98542,,96938,,116751,,136319,,120081,,129457,,135934,,119828,,131144,,128088,,120037,,118271,,132347,,144266,,145547, -Greece,All,Import,Tonnes – net product weight,Tonnes – net product weight,33818,,27065,,33610,,35993,,33045,,55374,,60690,,55387,,62264,,68166,,76082,,78729,,71616,,84937,,82863,,79353,,77890,,81282,,91687,,95839,,116585,,129679,,144171,,138092,,160505,,195111,,194454,,205764,,173760,,211883,,208115,,242069,,240555,,245117,,211220,,189134,,198025,,194928,,210561,,201973,,215796,,216023,,229313, -Greece,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,31968,,33432,,23856,,26174,,29124,,22758,,17536,,15893,,18360,,23601,,23119,,26994,,24960,,33756,,30549,,26959,,29723,,24233,,20051,,28145,,18730,,15356,,13242,,14127,,12583,,19020,F,13583,F,13567,F,13946,F,15313,F,12154,F,12191,F,12967,,13167,,18688,,18142,,19872,,19680,,22117,,16669,,18219,,18482,,20232,F -Greenland,All,Export,Tonnes – net product weight,Tonnes – net product weight,22710,,21480,,23186,,32311,,42842,,49844,,39995,,41694,,35792,,47474,,52891,,55912,,70527,,89316,,72832,,58891,,60093,,57797,,58776,,62588,,75250,,73217,,74861,,76804,,104522,,112676,,128077,,144426,,121115,,138748,,136669,,111273,,120058,,105733,,114907,,112833,,132480,,197056,,226773,,184814,,190653,,228412,,241924, -Greenland,All,Import,Tonnes – net product weight,Tonnes – net product weight,210,,482,,416,,180,,229,,266,,255,,215,,1180,,318,,389,,1031,,361,,909,,635,,1935,,4068,,2929,,2248,,2048,,889,,326,,376,,372,,1012,,1347,,997,,1450,,1874,,783,,1305,,492,,805,,1996,,1724,,958,,748,,844,,906,,1270,,849,,603,,968, -Greenland,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,25293,,24899,,24671,,31302,,41763,,47273,,40378,,41193,,35409,,47474,,52435,,55834,,68104,,89464,,72846,,58955,,60673,,57744,,58911,,62470,,62549,,69112,F,73641,,73759,,79482,F,73595,,95957,,101866,,103225,,115231,,106719,,125867,,117818,,103751,,110512,,109790,,145918,,197056,,226769,,184814,,190652,,225597,,227945, -Grenada,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,10,,11,,16,,9,,0,.,0,.,0,.,0,.,0,.,19,,100,,71,,21,,70,,72,F,195,F,286,F,488,,415,,840,,989,,1419,,1143,,709,,624,,624,,455,,497,,519,,530,,436,,648,F,780,F,701,F,653,F,896,F,1111,F,757,F,1115,F,824,F,932,F -Grenada,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,479,,489,,508,,462,,470,,329,,504,,560,,457,,565,,578,,572,,537,,600,,420,F,376,F,402,F,459,,1012,,685,,790,,870,,673,,975,,923,,1144,,1193,,1310,,1555,,1406,,1122,,989,,617,F,588,F,611,F,488,F,634,F,504,F,746,F,624,F,760,F -Grenada,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,5,,6,,0,.,1,,1,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,3,,1,,21,,1,,0,0,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Guadeloupe,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,11,,8,,11,,8,,20,,19,,19,,14,,65,,34,,12,,11,,42,,25,,21,,15,,40,,15,,11,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -Guadeloupe,All,Import,Tonnes – net product weight,Tonnes – net product weight,2525,,2606,,2659,,2758,,2920,,3123,,2754,,3843,,3310,,3439,,3822,,3851,,4506,,4603,,4498,,5154,,5317,,5262,,5655,,6257,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -Guam,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,-,1035,F,2185,F,4080,F,2850,F,0,.,0,.,3400,F,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,,0,,0, -Guam,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,1035,F,2185,F,4080,F,2850,F,0,.,0,.,3400,F,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,,0, -Guatemala,All,Export,Tonnes – net product weight,Tonnes – net product weight,1461,,1556,,1868,,1252,,1449,,1296,,2083,,1448,,1763,,1723,,1618,,1907,,3685,,3463,,3609,,5004,,5459,,6908,,7443,,4637,,5658,,3799,,4913,,11757,,31510,,21899,,11165,,11537,,14655,,13306,,19683,,25607,,20910,,30583,,35382,,33164,,22916,,23031,,24787,,19089,,16370,,18999,,20553, -Guatemala,All,Import,Tonnes – net product weight,Tonnes – net product weight,1205,,1437,,1473,,2088,,1867,,1580,,1386,,1244,,1239,,1056,,2761,,4408,,4340,,7595,,5136,,4761,,3722,,8634,,12192,,12178,,4274,,5943,,5401,,5861,,6230,,8698,,13518,,16992,,25525,,30348,,28547,,29518,,19649,,26554,,31980,,25795,,36501,,34261,,40964,,33131,,35202,,38029,,43409, -Guatemala,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,900,F,1000,F,1216,,1452,,1695,,1973,,3007,,1669,,2332,,1817,,603,,817,,1868,,2227,,2805,,2739,,4111,,5166,F,6717,F,4412,F,5908,,3666,F,4980,F,4886,,4513,,2151,F,7056,F,10054,F,8960,F,5560,F,11576,F,15300,F,14695,F,12639,F,9400,F,8370,F,10870,F,10785,F,10953,F,9810,F,8325,F,11132,F,9755,F -Guinea,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1798,,1927,,475,,2086,,1112,,403,,1098,,2317,,3267,,4072,,5085,,8156,,17254,,14428,,7923,,7964,,6879,F,7692,F,7874,F,9276,F,9709,,10705,,11352,,10077,F,7725,,8572,F -Guinea,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,9585,,10588,,9926,,11671,,12453,,13578,,8489,,14372,,8542,,8193,F,7609,,11162,,15618,F,18084,,18562,,18252,,18732,,21211,,20875,F,20677,F,21974,,1013,,1341,,830,,1253,,636,,1029,,540,,773,,739,,1405,F,1513,F,1533,F,1917,F,1898,,3454,,2336,,2786,F,2871,,2544,F -Guinea,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,2850,F,2590,F,2880,F,5580,F,6050,F,6620,F,7230,F,7810,F,8380,F,8960,F,9440,F,9920,F,9920,F,10000,F,10000,F,10000,F,11000,F,11000,F,11000,,11000,,10000,F,10000,F,11000,F,11000,F,11000,F,12000,F,11000,F,12000,F,9000,F,10200,F,10100,F,8000,F,9000,F,12000,F,11000,F,11500,F,12500,F,14800,F,12300,F,12000,F,12000,F,19500,F,17000,F -Guinea,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,36,,0,.,79,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Guinea-Bissau,All,Export,Tonnes – net product weight,Tonnes – net product weight,1132,,2168,,1452,,1630,,689,,2284,,1162,,510,,1970,,2119,,518,,120,F,460,F,680,,1500,,2250,,230,,920,,1330,F,1149,F,1483,F,1254,F,1500,F,1352,F,2435,F,2829,F,4690,F,3092,F,5477,F,4883,F,5995,F,4976,F,3377,F,3771,F,5666,F,4401,F,3716,F,4579,F,5280,F,5500,F,4512,F,6359,F,5859,F -Guinea-Bissau,All,Import,Tonnes – net product weight,Tonnes – net product weight,517,,268,,85,,110,,208,,311,,70,,100,F,133,,313,,270,F,250,F,165,F,180,F,384,F,300,F,300,F,310,F,300,F,334,F,353,F,401,F,332,F,261,F,147,F,167,F,199,F,276,F,341,F,366,F,352,F,699,F,913,F,771,F,305,F,444,F,416,F,562,F,444,F,583,F,705,F,1018,F,566,F -Guyana,All,Export,Tonnes – net product weight,Tonnes – net product weight,3810,,3406,,4069,,3642,,3376,F,1905,F,3050,,2867,,2048,,2029,,2983,,2987,,3235,,3545,,3799,,5698,,5130,F,5423,,4850,,4024,F,5752,,7705,,19135,,12364,,21705,,26143,,22590,,25046,,27587,,29727,,28425,,34511,,36534,,23304,,19714,,20414,,27884,,26112,,17877,,20795,,23763,,27911,,23945, -Guyana,All,Import,Tonnes – net product weight,Tonnes – net product weight,248,,4,,0,0,0,0,8,,2,,1,,0,0,0,.,0,.,0,.,0,.,0,.,0,.,0,.,140,F,85,F,260,F,268,F,130,F,831,F,1025,,636,,2075,,2256,,2480,,2438,,852,,1161,,1037,,976,,1824,,544,,809,,876,,1214,,1428,,1342,,1289,F,1231,F,1784,,1839,,1887, -Guyana,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,3267,F,3855,,5830,,3651,,4394,,2757,,3180,,2706,,2574,,2589,,3084,,2895,,2668,,2657,,2413,,3384,,3820,,4023,,4684,,6800,F,8698,,10829,F,13746,F,13625,F,13155,,18110,,16666,,19630,,21638,,16616,,17492,,16055,F,19034,,19600,,15843,,17309,,21988,,21339,,19029,,21345,,22824,,26137,,24274, -Guyana,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,0,0,0,0,.,0,0,0,.,2,,13,,0,.,0,.,0,0,1,,2,,0,.,0,.,0,.,0,.,48, -Haiti,All,Export,Tonnes – net product weight,Tonnes – net product weight,139,,291,,385,,162,,227,,240,F,470,F,416,,380,,227,,128,F,197,F,183,F,174,F,175,F,154,F,120,F,138,F,148,F,458,,426,,529,,494,,408,F,361,F,364,F,523,F,456,F,414,F,327,F,309,F,343,F,297,F,286,F,367,F,529,F,414,F,435,F,362,F,486,F,344,F,346,F,269,F -Haiti,All,Import,Tonnes – net product weight,Tonnes – net product weight,2355,,2447,,3861,,5709,,5281,,4255,,4878,F,4740,F,7443,F,7222,F,7560,F,7155,F,7230,F,7459,F,7208,F,4188,F,4320,F,5939,F,5917,F,6859,F,6526,F,9646,F,12017,F,12364,F,9054,F,11006,F,6661,F,8856,F,10427,F,9432,F,13535,F,14484,F,15087,F,16392,F,12507,F,16926,F,10236,F,20163,F,19711,F,22128,F,17645,F,26926,F,25340,F -Honduras,All,Export,Tonnes – net product weight,Tonnes – net product weight,2658,,3304,,2753,,3056,,3420,,3754,,3707,,4349,,3716,,2794,,10782,,19725,,13423,,13194,,9601,,19129,,9795,,13257,,20219,,19665,,6036,,12569,,13927,,13613,,13179,,15345,,21905,,24679,,28665,,33393,,40016,,31286,,29559,,31096,,27401,,23059,,42228,,55431,,72680,,67951,,74197,,80402,,67706, -Honduras,All,Import,Tonnes – net product weight,Tonnes – net product weight,1045,,1506,,1009,,1191,,1845,,1631,,802,,482,,722,,733,,1311,,1603,,2475,F,2027,,1834,,2398,,1839,,3565,,6740,,3329,,4442,,7949,,7554,,7163,,5578,,9832,,8602,,11733,,13131,,13686,,17863,,15557,,22990,,15507,,16837,,17713,,15264,,16295,,12592,,15574,,16438,,12627,,13693, -Honduras,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,3155,,3989,,3441,,3841,,4126,,3715,,3125,,4975,,4874,,5186,,5721,,4313,,4363,,3811,,4864,,7604,,5192,,2304,,3062,F,5195,F,9412,F,6932,F,5894,,7049,,7065,,13502,,15551,F,19065,F,20603,,22662,F,28600,F,18864,F,17944,F,22909,F,20290,,14311,,29238,,35987,,33302,,50971,F,57976,F,67099,F,56254,F -Honduras,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,269,,2760,,0,.,0,.,0,.,0,.,0,.,5926,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,900,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Hungary,All,Export,Tonnes – net product weight,Tonnes – net product weight,3051,,3523,,3953,F,4637,F,6725,F,8319,F,7121,F,4815,F,5365,F,5211,F,7852,,6025,,3983,,6037,,6208,,4414,,2995,,2025,,4828,,3642,,3461,,4058,,7040,,4620,,4558,,4274,,3508,,3974,,2424,,5228,,1663,,943,,606,,653,,2877,,4406,,4079,,4297,,5240,,6957,,7597,,6361,,5419, -Hungary,All,Import,Tonnes – net product weight,Tonnes – net product weight,53828,,61631,,57402,,48742,,45920,,47784,,73936,,77669,,89965,,97915,,90585,,91267,,108652,,105903,,90921,,37305,,40781,,42833,,55479,,60583,,48811,,48882,,52713,,47116,,49293,,55172,,52359,,44715,,35610,,38425,,26416,,21793,,23003,,22291,,24853,,27329,,23311,,25475,,28021,,31194,,29703,,29273,,33210, -Hungary,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,7414,,6446,,5600,F,6620,F,8200,F,6904,,4849,,5400,,6109,,6840,,6589,,5690,,5440,,5600,,5400,,4000,,4000,,2800,,1800,,1600,,1600,,1800,,1565,,3070,,3300,,3210,,4100,,4340,,4320,F,4170,F,4310,F,4420,F,4470,F,4150,F,4154,F,4549,F,5297,F,4995,F,5614,,5307,,4922,F,5492,F,5372,F -Iceland,All,Export,Tonnes – net product weight,Tonnes – net product weight,325175,,395977,,482765,,558626,,542917,,489306,,361394,,329033,,483417,,688833,,706479,,632681,,645550,,591913,,610787,,464240,,571747,,637617,,644884,,610794,,783228,,794970,,718755,,689604,,730970,,792424,,814030,,818880,,846467,,768288,,675553,,629157,,721023,,689467,,639015,,676538,,756151,,797528,,664802,,643061,,597458,,631536,,698666, -Iceland,All,Import,Tonnes – net product weight,Tonnes – net product weight,217,,89,,675,,335,,202,,247,,1338,,1319,,3239,,4403,,826,,1526,,4237,,8775,,12647,,10606,,10267,,19644,,21707,,27567,,27415,,54884,,245410,,138545,,161740,,169938,,175533,,234425,,217799,,200943,,140909,,122608,,145988,,58815,,99717,,102168,,82448,,89319,,108330,,187397,,115689,,128606,,136215, -Iceland,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,309447,,420240,,487074,,531053,,506246,,476467,,305928,,318246,,480388,,518541,,528921,,517150,,574746,,483278,,499244,,393170,,509853,,586712,,597464,,575308,,740829,,691599,,674164,,633852,,633942,,674350,,698393,,697789,,709685,,670079,,566567,,521834,,536420,,527608,,560158,,588917,,708582,,722482,,614024,,601527,F,564638,F,696271,,680470, -India,All,Export,Tonnes – net product weight,Tonnes – net product weight,58864,,61996,,74180,,87912,,70900,,69714,,72741,,83420,,86378,,78506,,87318,,87619,,99247,,106906,,134474,,191219,,210198,,257819,,321348,,307245,,394427,,397637,,311094,,390679,,502593,,467248,,523395,,409091,,476224,,554205,,610310,,488796,,460743,,689302,,789456,,954982,,968888,,965658,,1068230,,1011920,,1075065,,1409386,,1436108, -India,All,Import,Tonnes – net product weight,Tonnes – net product weight,300,,547,,370,,484,,1292,,1080,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,110,,458,,1390,,1624,,4081,,7886,,12024,,11269,,15853,,32503,,20695,,12091,,24088,,40632,,55191,,46144,,52121,,34234,,26128,,21889,,27706,,28762,,34574,,28211,,32051,,27762,,40157,,61732,,36792,,54873, -India,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,202835,F,212968,F,223925,F,246236,F,254950,F,337384,,329947,,369787,,443944,,462822,,434323,,443039,,558701,F,624851,,645544,,624799,,707962,,888739,,936060,,849680,F,832835,,816954,,743814,,663456,F,1017122,,834678,,1086169,,1082255,,1092182,,1165795,,1627027,,1358776,,1361635,,1409583,,1514107,,1543114,,1792600,,1677764,,1971036,F,1949577,F,2130216,F,2097762,F,2194783,F -Indonesia,All,Export,Tonnes – net product weight,Tonnes – net product weight,44025,,48408,,53730,,57828,,69320,,65741,,80676,,75777,,64630,,70540,,90899,,120344,,159018,,201182,,304901,,387014,,397595,,497077,,522672,,526181,,551514,,554042,,628866,,608922,,489910,,457598,,539043,,830305,,881586,,824823,,885031,,814161,,868349,,839803,,1061945,,1100869,,1216681,,1225276,,1235452,,1049218,,1041066,,1060279,,1106086, -Indonesia,All,Import,Tonnes – net product weight,Tonnes – net product weight,23210,,19168,,19096,,21032,,28477,,62981,,82830,,57434,,50453,,53102,,56079,,64332,,36329,,55628,,72246,,70666,,77550,,172581,,268486,,159820,,153367,,143880,,56065,,108891,,171349,,151957,,110035,,92649,,126826,,128431,,165195,,126281,,198980,,252976,,301569,,355684,,270450,,266027,,238428,,212981,,206729,,295386,,273093, -Indonesia,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,287959,,424459,,434255,,499888,,514822,,499665,,539728,,616015,,582439,,603116,,627550,,660914,,689209,,786944,,812266,,877603,,985929,,1066244,,1713678,,1221783,,1334213,,943274,,1543651,,1286287,,1338703,,1398377,,1493973,,2033236,,2024599,,1916168,,2076594,,2234537,,1894202,F,1844426,F,2204836,F,1975048,F,2004454,F,2060437,F,2064746,F,2019370,F,2081127,F,2228652,F,2122729,F -Iran (Islamic Rep. of),All,Export,Tonnes – net product weight,Tonnes – net product weight,2163,,4896,,2635,,3062,,1512,,934,,1219,,1209,,1350,,1183,,764,,967,,975,,1201,,1056,F,1784,,2004,,5814,,10455,,5980,F,5396,F,6883,,7384,,7763,,6853,,5057,,14137,,17541,,22280,,22742,,31921,,16762,,18698,,36193,,46008,,56273,,62579,,74392,,68934,,72279,,104694,,114212,,119881, -Iran (Islamic Rep. of),All,Import,Tonnes – net product weight,Tonnes – net product weight,43747,,137380,,46425,,57880,,70048,,128361,,81999,,48982,,77376,,62980,,63151,,83301,,41762,,44138,,34200,F,84464,F,111400,F,117300,F,57222,F,91283,,139379,F,34622,,63325,,63724,,72235,,45429,,40520,,91052,,35478,,25796,,9620,,15214,F,22353,,33363,,80969,,61702,,43652,,39345,,64284,,58706,,77670,,55408,,34751, -Iran (Islamic Rep. of),All,Processed production,Tonnes – net product weight,Tonnes – net product weight,745,F,961,F,1409,F,5331,,5572,F,6493,,11061,,11621,,13582,,13543,,13223,,17661,,21001,,19567,,22459,,25001,,36414,,40725,,40761,,50271,,46578,,47021,,53590,,61956,,52889,,67418,,64928,,72644,,85927,,96715,,119659,,93653,,83866,,73748,,88721,,128532,,105950,,139817,,161386,,257987,,235902,F,368861,,373326, -Iraq,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,104,,155,,111,,110,F,90,F,10,,104,,100,,100,,100,,150,F,203,F,230,,235,F,170,F,120,,140,F,154,F,172,F -Iraq,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,539,F,1131,F,386,F,735,F,56,,18,,136,,1896,F,7881,F,8801,F,19226,F,24348,F,34202,F,38883,,38500,F,33752,F,31518,F,38505,F,41480,,24514,F,67929,F,50826,F,43604,F -Ireland,All,Export,Tonnes – net product weight,Tonnes – net product weight,39260,,39612,,49721,,55609,,94009,,145770,,176953,,180480,,176593,,192152,,182842,,213597,,221124,,182305,,195848,,240269,,255793,,290305,,262703,,292238,,290063,,250981,,252160,,207271,,207265,,315865,,312909,,259168,,254433,,198067,,163070,,167886,,179098,,203544,,249096,,210606,,331837,,300109,,318223,,320250,,269266,,325374,,395286, -Ireland,All,Import,Tonnes – net product weight,Tonnes – net product weight,23844,,23873,,22763,,22990,,23669,,30169,,44223,,39714,,36323,,40208,,39315,,59753,,55521,,44085,,67278,,61142,,60807,,48945,,67849,,66797,,70538,,55890,,49010,,51164,,63296,,64993,,89321,,62898,,56325,,52836,,53358,,70044,,59366,,96723,,110644,,107978,,159545,,126117,,132095,,142807,,125578,,164434,,270524, -Ireland,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,24657,,28158,,33980,,36569,,54435,,92185,,109271,,112438,,120087,,118381,,106304,,135533,F,143456,F,115999,F,137634,F,130830,F,131570,F,150231,F,230507,F,194144,F,211652,F,172053,F,169362,F,158753,F,177763,F,197788,F,165516,F,184325,F,181189,F,168227,F,145347,,158565,F,161404,F,168987,F,185881,F,183885,F,249609,F,218111,F,239317,F,247877,F,234487,F,267351,F,236318,F -Israel,All,Export,Tonnes – net product weight,Tonnes – net product weight,1362,,1397,,562,,672,,487,,479,F,527,F,3156,F,716,F,886,F,1020,F,918,F,810,F,975,F,700,F,538,,414,F,304,F,691,F,911,F,410,F,334,F,337,F,1331,F,1291,F,1399,F,1307,F,2452,F,2898,F,1357,,3025,F,2282,F,3184,F,3786,F,5659,F,5284,F,3316,F,7118,F,7810,F,6045,F,4912,F,3211,F,7402, -Israel,All,Import,Tonnes – net product weight,Tonnes – net product weight,17043,,28294,F,21484,,34803,,23348,,20790,,22936,F,32458,,30882,,28387,,36830,F,40572,F,52829,,58345,F,55435,F,48062,,49066,,81016,,105648,,80552,F,54577,,76008,F,65327,F,58634,F,56089,F,65186,F,60551,F,61928,,72121,,65694,,50031,,66763,,64654,,61319,,75216,,80280,,80088,,91771,,96567,,94917,,100174,,108265,,103581, -Israel,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,4900,,4100,,3820,,2995,,8750,,11274,,9718,,9704,,11335,,13220,,11970,F,6620,,6678,,5600,,3250,,0,.,0,.,0,.,90,F,90,F,30,F,20,F,20,F,30,F,20,F,40,F,20,F,80,F,100,F,120,F,305,F,340,F,545,F,600,F,125,F,130,F,505,F,706,F,529,F,305,F,335,F,491,F,760,F -Italy,All,Export,Tonnes – net product weight,Tonnes – net product weight,80053,,65728,,84969,,128095,,96316,,100384,,101886,,117698,,116694,,156821,,111438,,77401,,103387,,88289,,97779,,95375,,97783,,108675,,113759,,115103,,127472,,129044,,127606,,121730,,142571,,134722,,127932,,122190,,131471,,141223,,154772,,154940,,146189,,142222,,144501,,136618,,134033,,146664,,168388,,168641,,162273,,158588,,166427, -Italy,All,Import,Tonnes – net product weight,Tonnes – net product weight,358892,,326701,,366493,,422141,,387829,,345227,,406129,,408930,,424769,,591733,,549894,,624747,,623915,,686039,,727038,,762938,,735807,,699481,,719128,,688756,,732547,,754437,,765727,,826070,,827095,,883836,,868996,,910288,,932524,,962807,,988063,,999213,,974624,,994914,,1009882,,1026985,,963621,,992072,,1054524,,1078631,,1108828,,1115931,,1137854, -Italy,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,122561,,125299,,132333,,148019,,153354,,138466,,141231,,139769,,153766,,169012,,178859,,194642,,194954,,205688,,205364,,221061,,229104,,186337,,186144,,137628,,180352,,189139,,222383,,231802,,181751,,185998,,198308,,170858,,222127,,229497,,277582,,234870,,208776,,208824,,206475,,202775,,215598,,223101,,206534,,205594,,229736,,245216,,199558, -Jamaica,All,Export,Tonnes – net product weight,Tonnes – net product weight,4,,2,,1,,16,,11,,51,,24,,32,,56,,372,,349,,399,,341,,285,,1076,F,1582,,1750,,2671,,406,,2929,,3904,,3056,,2411,,1938,,811,,1352,,500,,904,,1241,,1467,,1601,,1124,,1249,,744,,929,,1137,,816,,823,,831,,697,,723,,838,,902,F -Jamaica,All,Import,Tonnes – net product weight,Tonnes – net product weight,17708,,11631,,15257,,19227,,15357,,16609,,17270,,13106,,15866,F,11879,,14641,,15686,,17781,,17601,,15007,F,14073,,13163,,15483,,15490,,17629,,17931,,21047,,22604,,23885,,23365,,27690,,28427,,28901,,28486,,30657,,32391,,35621,,30925,,27438,,26506,,28074,,27748,,27824,,28164,,27462,,30302,,32846,,35253,F -Jamaica,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,5,,0,0,2,,1,,0,.,0,.,0,.,0,.,0,.,0,.,52,,0,.,19,,0,.,0,.,0,.,10,,0,.,0,.,0,.,1,,0,.,0,.,0,.,0,.,29,,24,,40,,42,,27,,0,.,219,,297,,377,,352,,320,,259,,239,,236,,293,,409,,343,,0,- -Japan,All,Export,Tonnes – net product weight,Tonnes – net product weight,647114,,590598,,752595,,726705,,725835,,693250,,713491,,680421,,896237,,784483,,758844,,718047,,975064,,815912,,697619,,584903,,429691,,381733,,301262,,239837,,274972,,342967,,280553,,203909,,221868,,312769,,306353,,364655,,423577,,467829,,593388,,611433,,517849,,496670,,564903,,423439,,439484,,551386,,470384,,555842,,537816,,594752,,749481, -Japan,All,Import,Tonnes – net product weight,Tonnes – net product weight,784037,,1011479,,984448,,1125033,,1012972,,1110052,,1183107,,1296992,,1375872,,1559792,,1865089,,2072205,,2413024,,2286706,,2459863,,2773047,,2894397,,3045441,,3344413,,3653533,,3445608,,3407234,,3099607,,3411654,,3540479,,3726738,,3816227,,3210472,,3478778,,3335939,,3145844,,2883264,,2760307,,2589506,,2715416,,2684837,,2727086,,2478331,,2532091,,2477897,,2375153,,2467071,,2372128, -Japan,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,6690056,,6761255,,6800285,,6827364,,6825225,,7363023,,7837549,,8097287,,8619240,,8364235,,8478596,,8210090,,8362102,,8124039,,7653163,,7395805,,6953054,,6837912,,6383707,,5958073,,5765723,,5886309,,5484803,,5390905,,5347817,,4966172,,4620858,,4696207,,4673463,,4647315,,4634617,,4498200,,4545555,,4386337,,4299048,,3819525,,3831111,,4100567,,3998383,,3898451,,3759305,,3691657,,3456162,F -Jordan,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,32,,1180,,100,,115,,128,,34,,16,,2,,0,.,43,,0,.,0,.,0,.,2,,2,,1,,0,0,0,.,0,0,56, -Jordan,All,Import,Tonnes – net product weight,Tonnes – net product weight,3960,,4869,,4778,,4296,,4671,,5071,,5298,,6499,,6752,,9354,,5557,,10725,,5932,,7021,,6991,,9404,,10668,,12319,,11437,,14116,,17323,,12257,,14249,,14046,,14729,,23238,,23102,,21011,,26702,,22309,,25201,,23901,,30407,,32661,,29803,,27174,,34064,,31408,,30209,,33880,,33976,,31139,,31765, -Jordan,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,313,,136,,232,,81,,27,,99,,86,,56,,0,.,21,,0,.,46,,143,,1,,50,,940,,2514,,360,,487,,317,,1281,,329,,321,,985,,0,.,646,,247,,743,,1297,,1105,,1052,,668,,1945,,3810,,2354,,2602,,4669,,1999,,725,,1318,,482,,1201,,960, -Kazakhstan,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,7685,F,16973,,11546,,18133,,15172,,17264,,18833,,21772,,25973,,31408,,30558,,33200,,32543,,36581,,37035,,38387,,24444,,22018,,23978,,20985,,23641,,24462,,28253,,25175,,30135, -Kazakhstan,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2783,F,5721,,10422,,13331,,13468,,21848,,37574,,34911,,33221,,33596,,34300,,43032,,47695,,59613,,68021,,63067,,58040,,58335,,58985,,57805,,47221,,50586,,42671,,43641,,44380, -Kazakhstan,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,52867,,47936,,30911,,26381,F,24800,F,23551,F,23750,F,19084,,22845,,29094,,18337,,19730,,21546,,24768,,24907,,31300,,35421,,36847,,47378,,31955,,29675,,25467,,28459,,29693,,29358,,32746,,29067,F -Kenya,All,Export,Tonnes – net product weight,Tonnes – net product weight,550,,356,,580,,1183,,619,,1402,,917,,1003,,559,,1461,,1297,,3271,,5160,,8040,F,11031,,13612,,14161,,15022,,21564,,19343,,16186,,17465,,14241,,15951,,16896,,18585,,24622,,20240,,18545,,20192,,15857,,17771,,22638,,16551,,19111,,16397,,18257,,12670,,16223,,11768,,6368,,5734,,7624, -Kenya,All,Import,Tonnes – net product weight,Tonnes – net product weight,1754,,2146,,4169,,2116,,3605,,1542,,1809,,1652,,1969,,1984,,1429,,2211,,1844,,1773,F,796,,1772,,1842,,3919,,5793,,4304,,4060,,9519,,11252,,12563,,13497,,18162,,12796,,16427,,22479,,28475,,35846,,32909,,29085,,21532,,27651,,26185,,25614,,18714,,26134,,24967,,23326,,25997,,27828, -Kenya,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,5562,,4240,,4042,,4980,,4615,,5772,,11365,,15495,,12100,,14125,,14414,,20687,,25143,,26391,,33523,,36301,,42576,,51195,,52175,,55257,,54679,,50679,,51156,,54963,,61016,,63169,,62008,,59118,,62680,,48913,,38058,,38309,,37699,,39014,,37289,,38154,,40882,,43502,,44758,,44089,,32990,F,23428,F,24330,F -Kenya,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,2,,120,,5,,19,,10,,0,0,0,0,0,0,19,,0,0,47,,0,0,0,.,0,.,0,.,0,.,0,.,0,.,152,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,77,,0,.,0,.,0,.,0,.,0,0,0,.,0,.,0,.,29,,35, -Kiribati,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,0,0,0,108,,113,,744,,581,,1859,,2293,,1040,,1525,,724,,1489,,2683,,1660,,840,,773,,662,,2538,F,2579,F,4031,F,2114,F,2904,F,6203,F,7612,F,3545,F,4926,F,4131,F,4635,F,6010,F,5133,F,12299,F,12863,F,23855,F,26726,F,42251,F,52793,F,58807,F,82464,F,100931,F,102337,F,79414,F,73717,F -Kiribati,All,Import,Tonnes – net product weight,Tonnes – net product weight,119,,114,,210,,299,,257,,313,,269,,137,,207,,128,,124,,226,,226,,240,,251,,273,,371,,350,F,259,,382,,346,,237,,258,,343,,394,F,189,F,301,F,552,F,312,F,599,,352,,311,,289,,550,,462,F,687,,327,,142,,425,F,426,F,662,,910,F,1409,F -"Korea, Dem. People's Rep",All,Export,Tonnes – net product weight,Tonnes – net product weight,7799,F,5758,F,10460,F,21530,F,20620,F,19793,F,21643,F,24071,F,26508,F,20609,F,23293,F,79523,F,87944,F,61114,F,46242,F,38878,F,59809,F,58490,F,52675,F,62974,F,43861,F,47470,F,47404,F,60727,F,62930,F,70054,F,179544,F,126861,F,138321,F,93409,F,69891,F,33428,F,37168,F,58077,F,64436,F,65370,F,69479,F,69251,F,88518,F,63398,F,68604,F,102268,F,10053,F -"Korea, Dem. People's Rep",All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1467,F,1233,F,1846,F,1941,F,1127,F,2354,F,9983,F,15808,F,21396,F,18212,F,18962,F,18121,F,19584,F,20510,F,21227,F,22036,F,18078,F,25041,F,41122,F,53532,F,55997,F,53025,F,62679,F,91121,F,43358,F -"Korea, Dem. People's Rep",All,Processed production,Tonnes – net product weight,Tonnes – net product weight,510024,F,556819,F,582640,F,622282,F,655378,F,696953,F,731651,,745273,,752052,,763518,F,770001,F,773973,F,786266,F,787608,F,779589,F,565104,F,522320,F,528150,F,171669,F,132631,F,114347,F,78436,F,71892,F,78778,F,76485,F,75401,F,93006,F,90821,F,108009,F,110926,F,92216,F,74785,F,80839,F,100252,F,102650,F,106226,F,113448,F,115651,F,134482,F,107798,F,112545,F,151890,F,63720,F -"Korea, Republic of",All,Export,Tonnes – net product weight,Tonnes – net product weight,285822,,514935,,470358,,437288,,412490,,382143,,344801,,448632,,381459,,432737,,534418,,570314,,572354,,477266,,455652,,492137,,436356,,370658,,368871,,437670,,450787,,506500,,593176,,473665,,533289,,434600,,429800,,422996,,403089,,406657,,363972,,532644,,582698,,618609,,736179,,626104,,653052,,623100,,632758,,589792,,552963,,477594,,556807, -"Korea, Republic of",All,Import,Tonnes – net product weight,Tonnes – net product weight,26336,,23393,,53050,,85356,,46789,,58131,,91496,,86895,,98274,,119602,,143585,,310135,,390108,,328490,,294220,,376620,,335058,,362792,,390032,,427492,,542417,,533670,,379979,,754869,,762905,,1076686,,1201230,,1252015,,1296577,,1275764,,1404124,,1429092,,1155702,,1179023,,1240071,,1364443,,1290731,,1207027,,1352703,,1400238,,1436649,,1479637,,1555022, -"Korea, Republic of",All,Processed production,Tonnes – net product weight,Tonnes – net product weight,217119,,207573,,276041,,305323,,342462,,448806,,898762,,1039033,,1152332,,1161954,,1329040,,1410776,,1575744,,1685162,,1742495,,1232741,,1327870,,1218162,,1396543,,1365776,,1386912,,1177313,,1324524,,1359050,,968836,,1110978,,924411,,893179,,1021166,,1017016,,917424,,1008228,,1254436,,1527188,,1419885,,1639251,,1794888,,1757214,,2211937,,1852873,,1462900,,1205126,,1240823,F -Kuwait,All,Export,Tonnes – net product weight,Tonnes – net product weight,1635,,2248,,2239,,3905,,3855,,7071,,5542,,4669,,2161,,978,,809,,1255,,1713,,1264,,1089,,133,,413,,793,,260,,314,,520,,890,,957,,650,,610,,451,,487,,453,,225,,138,F,1543,,399,,619,,789,,534,,314,,347,F,290,,7,,267,,151,,297,,427, -Kuwait,All,Import,Tonnes – net product weight,Tonnes – net product weight,3650,,6926,,5303,,8796,,14647,,11896,,13580,,10673,,9748,,9267,,10121,,11023,,10898,,10106,,3513,,2626,,5080,,5512,,5279,,6806,,7406,,5627,,6828,,8466,,11035,,10487,,10228,F,14954,F,13827,F,16112,F,20026,,23227,,21778,,32741,F,25742,,26991,,36137,F,30404,,34818,,39180,,36123,,42453,,47140, -Kuwait,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,479,,397,,385,,908,,1365,,1331,,741,,1330,,968,,704,,372,,549,,1161,,826,,993,,90,,352,,711,,250,,263,,468,,839,,692,,622,F,510,F,405,F,460,F,315,F,175,F,100,F,235,F,140,F,270,F,190,F,220,F,180,F,150,F,160,F,100,F,80,F,20,F,130,F,75,F -Kuwait,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,120,,0,.,27,,138,,58,,0,.,701,,252,,321,,555,,0,.,0,.,0,.,0,.,251,,0,-,0,-,0,-,0,- -Kyrgyzstan,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,24,,4,,52,,9,,0,0,0,0,44,,17,,11,,235,,350,,177,,71,,231,F,0,.,0,.,7,F,29,,291,,1277,,676, -Kyrgyzstan,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,405,F,406,F,975,,1977,F,3798,,2995,,3315,,2737,,3525,,5745,,5932,,6234,,7773,,10185,,9708,,7585,,8119,,8858,,10356,,10390,,11494,F,7127,,3780,,5013,,4143, -Kyrgyzstan,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1079,,865,,369,,185,,190,F,168,F,140,F,120,F,58,F,52,F,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,5,F,0,.,0,.,1,F,1,F,1, -Kyrgyzstan,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2,,0,-,0,-,6,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Lao People's Dem. Rep.,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,0,16,F,1,F,2,F,4,F,30,F,7,F,24,F,10,F,0,0,1,F,0,0,1,F,2,F,1,,0,.,119,,43,,130,F,52,,16,,9,,6, -Lao People's Dem. Rep.,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,650,F,888,F,1057,F,1127,F,1024,F,521,F,655,F,1006,F,1320,F,1384,F,1482,F,1609,F,1454,F,1206,F,1175,F,1251,F,872,F,521,,608,,468,F,363,,520,,804,,570,,1129,,1410, -Latvia,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,12043,F,28985,,48319,,70403,,84582,,107285,,87094,,87254,,96452,,131822,,123962,,108420,,109032,,113210,,120242,,120548,,129050,,113870,,121160,,111999,,122435,,132014,,123663,,110737,,105251,,115054,,111708, -Latvia,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,6857,F,10251,,35308,,45422,,54170,,66741,,44502,,29321,,35141,,46612,,42505,,35011,,29999,,38117,,45149,,48832,,59887,,56548,,59923,,64300,,67274,,74903,,80627,,71525,,80995,,73447,,72638, -Latvia,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,142294,,117561,,112921,,123812,,80640,F,138494,F,112064,F,120246,F,135993,F,154180,,145138,,130224,,124848,,148196,,149382,,137437,,151074,,167662,,156597,,166829,,109115,,145424,,144595,,86247,,98942,,106299,,143111, -Lebanon,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,191,,135,,353,,38,,38,,107,,183,,138,,167,,230,,426,,720,,1150,,377,,462,,463,,541,,667,,577,F,323,,315,,336, -Lebanon,All,Import,Tonnes – net product weight,Tonnes – net product weight,5300,F,8313,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,5216,F,3750,F,4729,F,5978,F,6054,F,14677,,20860,,18500,,21743,,18203,,23713,,19072,,19930,,24739,,22455,,22867,,23477,,23360,,28263,,30036,,29634,,29976,,32243,,35218,,33810,F,35892,,36202,,38567, -Lesotho,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,11,F,5,,3,,0,.,0,.,0,.,26,,183,,427,,463,,136,,194,F,531,F,533,,567,F,812,,830,F -Lesotho,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,880,F,1430,F,1400,F,1790,F,2435,F,2000,F,1800,F,1700,F,1700,F,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2304,F,4574,F,5533,F,2538,,1599,,746,F,724,F,747,F,937,,1225,,1105,,2341,,1633,,1555,F,3249,F,1513,,1979,F,2438,,2811,F -Liberia,All,Export,Tonnes – net product weight,Tonnes – net product weight,1575,,879,,1524,,1201,,944,,280,F,304,,359,,480,,1016,,1206,,949,,640,,812,,341,,567,,161,,338,,322,,91,F,2,F,2,F,3,F,8,F,32,F,69,F,151,F,83,F,51,F,78,F,236,F,127,F,65,F,91,F,96,F,91,F,107,F,98,F,102,F,102,F,132,F,152,F,283,F -Liberia,All,Import,Tonnes – net product weight,Tonnes – net product weight,11891,F,12884,F,12881,F,14232,F,10301,F,7971,F,11100,F,14417,F,18643,F,14293,,13891,,11800,,12972,,12716,,9790,,5308,,3536,,2040,,2227,,2196,F,2540,F,2547,F,2560,F,2523,F,3770,F,1904,F,1150,F,2624,F,1168,F,1432,F,2395,F,1150,F,4069,F,3346,F,6090,F,6866,F,7087,F,11505,F,14435,F,12598,F,10861,F,6374,F,5423,F -Liberia,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,2048,,3075,,2470,,2545,F,2383,,1497,,3163,,1234,,2460,,2217,,3902,,2712,,2097,,2233,,2278,,2415,,2774,,6830,,11472,,7726,,7286,,6550,F,6300,F,6359,,5200,F,4100,F,5600,F,6437,,8200,F,8900,F,9700,F,10600,F,11450,F,11500,F,11600,F,11700,F,10742,F,17839,F -Libya,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,430,F,856,F,0,.,0,.,0,.,112,F,168,F,115,F,169,F,2152,,2150,F,3000,F,5500,,5500,F,1149,F,1288,F,1730,F,1279,F,1913,F,1405,F,1720,F,2222,F,3133,F,3845,F,2000,F,1137,F,1069,,630,F,625,F,463,F,980,F,1029,F,924,F,1063,F,2105,F,4200,F,2541,F -Libya,All,Import,Tonnes – net product weight,Tonnes – net product weight,3173,,262,,7099,,8209,,10167,,7676,,11897,,15280,F,29700,F,17500,F,10578,F,13108,F,19764,F,18644,F,4154,,9245,,9620,F,8873,F,4595,F,5032,F,7182,F,7572,F,4035,F,3829,F,4489,F,4969,F,4182,F,4438,,4782,,9360,F,13638,F,17067,,17489,,27843,,43472,,39252,F,55782,F,57135,F,51800,F,41572,F,43066,F,37117,F,48927,F -Libya,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,800,F,468,,1280,,1127,,2797,,3047,,3771,,1714,,1324,,1011,,1000,F,993,,1000,F,890,F,1050,F,1260,F,1650,F,1890,F,2330,F,2360,F,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -Lithuania,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,21769,,5365,F,20197,,44133,,61467,,63419,,73012,,30189,,31386,,65255,,71701,,94136,,79191,,100566,,93158,,107027,,95325,,108035,,98870,,99197,,100722,,114310,,123075,,123850,,126811,,128720,,144014, -Lithuania,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1639,,6894,F,42773,,40165,,87944,,101500,,99119,,59237,,66684,,103910,,85465,,93538,,87096,,103292,,94474,,102957,,108646,,112158,,114486,,109708,,120714,,138477,,150730,,142487,,147967,,152646,,161833, -Lithuania,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,18458,F,4618,F,15249,F,41033,F,51311,F,45570,F,69795,F,26905,F,29150,F,59864,,106297,,141204,,150566,,127798,,93204,,113818,,320942,,343916,,210920,,200643,,155841,,189445,,257728,,208023,,243597,,225247,,212910, -Luxembourg,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,6114,,4398,,3398,,2624,,2070,,1488,,911,,889,,1181,,1323,,1809,,1617,,1360,,1313,,1309,,1424,,1242,,1304,,1519, -Luxembourg,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,12919,,11032,,9856,,9870,,9105,,8354,,8513,,8353,,8641,,8764,,10652,,11028,,11798,,11419,,12122,,12368,,12466,,12083,,12949, -Madagascar,All,Export,Tonnes – net product weight,Tonnes – net product weight,3301,,4314,,3488,,3561,,3435,,3261,,3989,,3597,,4022,,4428,,4531,,6705,,5957,,6308,,7929,,9048,,16005,,19846,,24732,,27619,,26743,,16020,,17344,F,16042,F,14233,,31371,,32867,,31232,,28664,,24428,,33619,,30218,,22802,,20024,,22862,,25840,,17498,,19384,,24410,,20910,,22045,,26034,,24261, -Madagascar,All,Import,Tonnes – net product weight,Tonnes – net product weight,163,,314,,30,,62,,75,,137,,26,,73,,48,,148,,130,,100,,269,,262,,252,,248,,15308,,13616,,8100,,10632,,6962,,16594,F,12996,F,15647,F,13656,F,12686,F,17643,F,23356,F,19430,F,15842,,30357,,24024,,15483,,15346,,19684,,23203,,17769,,18737,,25012,,15548,,14463,,14338,,8255, -Madagascar,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,6866,,6037,,4927,,4308,,4662,,6595,,6576,,7586,,7624,,7515,,7780,,8749,,8938,F,9011,F,9908,F,11123,F,24991,,29373,F,29983,,33502,F,32963,F,26300,F,29360,F,28818,F,25705,F,29416,F,29370,F,37304,F,34853,F,24735,F,34220,F,33861,F,26298,F,28741,F,31086,F,34650,F,45788,,56586,,51162,,71161,,56750,,35240,,42272, -Madagascar,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,421,,531,,166,,36,,101,,32,,378,,214,,1322,,785,,996,,1166,,2061,,521, -Malawi,All,Export,Tonnes – net product weight,Tonnes – net product weight,823,,1065,,783,,1045,,2518,,1468,,1473,,911,,324,,254,,185,F,306,,207,,41,,97,,56,,43,,183,,58,,61,,71,,54,,33,,43,,42,,40,,26,,43,,42,,168,,585,,32,,34,,15,,21,,67,,49,,23,,14,,12,,20,,43,,65,F -Malawi,All,Import,Tonnes – net product weight,Tonnes – net product weight,466,,139,,292,,259,,237,,192,,358,,596,,140,,177,,87,,194,,517,,742,,1366,,1339,,1319,,798,,1050,,798,,463,,956,,474,,701,,1688,,513,,425,,598,,1047,,598,,2023,,1947,,3740,,2666,,2262,,1551,,1434,,1925,,2660,,2766,,3309,,2483,,3491,F -Malawi,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,5,,0,.,0,.,0,0,4,,0,.,5,,0,0,3,,14,,0,.,0,.,0,.,0,.,1,,9,,6,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Malaysia,All,Export,Tonnes – net product weight,Tonnes – net product weight,103027,,84160,,115973,,117158,,121762,,133696,,120008,,106444,,117829,,163500,,182754,,172894,,138275,,155079,,154060,,164739,,195139,,198846,,194989,,189238,,192184,,105888,,100324,,91844,,95435,,126229,,203327,,160262,,270695,,275006,,255890,,303461,,283494,,257413,,290662,,295022,,266469,,246024,,239451,,252718,,296626,,236614,,262745, -Malaysia,All,Import,Tonnes – net product weight,Tonnes – net product weight,115501,,121862,,134577,,176011,,151227,,149574,,184359,,161471,,232102,,217951,,238405,,256279,,240142,,265266,,225845,,243893,,254112,,259806,,273527,,257886,,288878,,297802,,250126,,300256,,322923,,353400,,464172,,386586,,325116,,400766,,440135,,440270,,386051,,411544,,424032,,365460,,417029,,463235,,465400,,419279,,408251,,419385,,422473, -Malaysia,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,47750,,46073,,50019,,74457,,77665,,79177,,60081,,76856,,80580,,66641,,60368,,99073,,112085,,88447,,93115,,91492,,84647,,82923,,85214,,80883,,90264,,87654,,92228,,85251,,108596,F,117927,F,128805,F,122792,F,169462,F,171799,F,164007,F,183448,F,180058,F,168587,F,182575,F,189043,F,141202,F,205918,F,217520,F,183850,F,172025,F,147860,F,150456,F -Malaysia,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,,0,,0,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,45284,,44915,,48966,,49797,,0,.,72150,,7879,,6499,,9428,,7424,,14575,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Maldives,All,Export,Tonnes – net product weight,Tonnes – net product weight,10102,,12618,,13037,,14742,,16945,,15848,,12166,,10186,,16413,,22396,,22613,,19928,,26581,,31385,,32005,,27777,,20842,,23954,,25017,,21442,,31683,,33158,,31961,,36794,,31108,,31609,,47637,,72199,,78021,,84287,,113273,,68158,,66697,,41252,,34933,,39809,,41769,,43549,,50000,,45958,,49114,,74474,,71774, -Maldives,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,59,F,0,.,303,,577,,464,,495,,348,,884,,1264,,1399,,856,,1471,,2748,,1970,,2253,,3965,,3712,,3510,,3836,,3782, -Maldives,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,10264,,12643,,12515,,14714,,16946,,15848,,12166,,10186,,16413,,22390,,22590,,19878,,26028,,30975,,31250,,27359,,20720,,23833,,24938,,21107,,32557,,32573,,30887,,36038,,30194,,30757,,44658,,67456,,71384,,127604,,147427,,97815,,84918,,63610,,56678,,49220,,45326,,57553,,55089,,65050,,58885,,98281,,110751, -Mali,All,Export,Tonnes – net product weight,Tonnes – net product weight,2602,,2029,,1154,,1147,,2163,,1237,,1360,,274,,399,,971,,1100,,519,,561,,714,,345,,363,,345,,301,,250,,256,F,1893,,670,,411,,400,,776,,990,,1136,,425,,461,,378,,337,,634,,496,,301,F,333,,909,,186,,389,F,1020,F,1090,F,1661,,1219,,1189,F -Mali,All,Import,Tonnes – net product weight,Tonnes – net product weight,234,,205,,222,,153,,315,,0,.,436,,49,,247,,1263,,1311,,2827,,1828,F,1610,F,2487,,2282,F,2222,F,2458,F,679,,1274,F,826,,1628,,1923,,2949,,2784,,3175,,2900,,5618,,7027,,11052,,13022,,14753,,16222,,16892,F,23664,,22648,,25013,,23849,F,34637,F,45148,F,54486,,62528,,58157,F -Mali,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,9076,,5800,,5859,,7157,,8865,,6319,,6020,,3613,,2258,,2103,,3658,,2706,,2733,,3410,,3200,,2778,,2134,,2140,,1614,F,5100,F,5749,F,8188,,7764,,7550,,9485,,9300,F,7900,F,7900,F,7900,F,7900,F,7900,F,8756,,15997,,15008,,20016,,12351,,8730,,13625,,11857,,29967,,19939,,16203,,19420, -Mali,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,20,,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Malta,All,Export,Tonnes – net product weight,Tonnes – net product weight,5,,0,.,0,.,0,.,45,,61,,73,,94,,595,,1548,,46,,388,,77,,83,,66,,123,,259,,1082,F,1013,,641,F,1300,,1585,,1602,,1853,,1992,,2269,,3150,,3314,,1470,,1380,,4489,,3175,,4698,,2455,,7293,,4445,,6025,,8533,,8541,,11133,,14472,,12242,,15041, -Malta,All,Import,Tonnes – net product weight,Tonnes – net product weight,1947,,2088,F,2369,F,2359,F,4278,F,3276,F,1955,F,2165,F,2438,F,3796,F,2953,F,3397,F,3301,,5385,,3932,,5374,,6165,,5480,F,6197,,6987,F,7172,,7788,,7555,,7894,,8081,,17360,,14614,,27246,,26481,,17005,,23413,,30563,,26879,,36241,,24636,,20181,,29497,,30028,,33699,,32184,,56705,,68196,,81198, -Malta,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,0,0,0,0,0,0,0,0,.,0,.,0,0,0,.,0,0,0,0,0,.,7,,0,.,0,.,579,,285,,86,,83,F,0,.,0,.,0,.,104,,14,,72,,93,,606,,276,,82,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Marshall Islands,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,0,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,0,0,0,110,F,100,F,81,F,90,F,6,F,570,F,99,F,33,F,174,F,3989,F,11131,F,16555,F,15006,F,21643,F,25589,F,15390,F,20406,F,25953,F,36418,F,40449,F,53581,F,55124,F,58123,F,61862,F,56185,F,50121,F,46755,F,45785,F -Marshall Islands,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,45,F,190,F,150,F,59,F,115,F,112,F,95,F,70,F,65,F,60,F,55,F,60,F,60,F,65,F,60,F,100,F,327,F,112,F,60,F,120,F,103,F,267,F,285,F,357,F,308,F,392,F,586,F,1077,F,762,F,1403,F,1151,F,3028,F,1700,F,2712,F,1411,F,1388,F,2610,F,3826,F -Martinique,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,23,,8,,56,,25,,41,,19,,26,,13,,43,,12,,31,,98,,41,,134,,50,,17,,59,,27,,16,,0,-,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -Martinique,All,Import,Tonnes – net product weight,Tonnes – net product weight,3410,,4538,,4866,,5217,,4477,,5485,,4278,,5419,,4540,,5391,,5810,,5758,,6505,,6936,,6609,,7181,,7616,,7693,,7933,,8026,,0,-,0,-,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -Mauritania,All,Export,Tonnes – net product weight,Tonnes – net product weight,17429,,20713,,23109,,10471,F,11418,,30193,F,33500,F,43774,F,37295,F,42965,F,53818,F,60030,F,52233,F,48351,F,42373,F,41511,F,41802,F,39599,F,35744,F,39402,,42936,F,34677,F,42117,,52755,,86425,,86781,,94868,,89515,,74836,,104165,,95645,,116566,,142449,,93193,,134263,,264473,,359722,,261949,,333615,,358788,F,496204,,643824,,788493,F -Mauritania,All,Import,Tonnes – net product weight,Tonnes – net product weight,79,,0,.,0,.,0,.,12,,0,.,111,,64,,176,,135,F,100,F,117,F,153,F,294,,258,F,555,,303,,374,,455,F,569,F,502,F,883,F,795,F,869,F,120,,173,,414,,457,,165,,1795,,321,,569,,684,,1433,,2332,,1987,,2915,,3142,,4430,,3860,F,4427,,3931,,6212,F -Mauritania,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,17785,,20742,,23109,,10471,F,11418,,30493,F,36230,F,45976,F,38191,F,61643,F,74577,F,78951,F,71606,F,67486,F,62441,F,61891,F,62932,F,57829,F,60346,F,58069,F,60188,F,53820,F,57785,F,52923,F,112998,F,109710,F,120364,F,113390,F,98036,F,131140,F,120940,F,137350,F,150680,F,189410,F,163881,F,291172,F,315574,F,281900,F,335020,F,349974,F,408760,F,611820,F,898841, -Mauritius,All,Export,Tonnes – net product weight,Tonnes – net product weight,1219,,1810,,1790,,1384,,1317,,1701,,2322,,2550,,3177,,4449,,3492,,4464,,5678,,5187,,3742,,6748,,7567,,8511,,9899,,13592,,14006,,15912,,13019,,15203,,18160,,27382,,28945,,32555,,33412,,36770,,52852,,52248,,49731,,52796,,66733,,61614,,69832,,70750,,84336,,76998,,79675,,146999,,141222, -Mauritius,All,Import,Tonnes – net product weight,Tonnes – net product weight,7077,,9365,,11080,,9397,,9088,,9028,,7433,,6762,,6891,,6560,,7180,,5471,,5820,,7978,,11104,,10273,,26115,,15857,,23748,,33866,,33256,,33538,,36328,,32164,,44496,,53227,,85406,,65365,,82521,,106135,,153602,,131917,,153131,,150043,,145859,,166676,,156347,,178024,,192985,,189893,,193736,,188408,,169687, -Mauritius,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,4557,,6068,,5827,,5171,,4453,,4904,,7201,,7040,,6410,,9311,,10402,,12025,,11679,,14431,,16007,,20910,,22673,,20206,,21522,,26307,,24506,,25947,,22914,,21011,,29174,,36062,,40199,,48117,,51663,,57236,,79210,,63516,,61393,,101120,,91508,,66760,,73869,,60985,,67413,,78761,,81174,,85688,F,91146, -Mauritius,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,116,,0,.,0,.,0,.,0,.,0,.,21097,,18075,,20720,,30507,,30215,,37208,,35940,,39780,,44781,,38975,,47439,,57598,,67240,,79251,,80225,,0,-,0,- -Mayotte,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1,,1,,1,,0,.,0,.,0,0,0,.,0,.,0,.,4,,29,,129,,202,,131,,124,,122,,122,,95,,83,,80,F,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -Mayotte,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,22,F,55,F,416,F,35,F,0,.,666,,918,,1190,,1195,,1063,,1290,,1345,,1160,,1740,,1384,,1250,F,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -Mexico,All,Export,Tonnes – net product weight,Tonnes – net product weight,46937,,51993,,56699,,53527,,60495,,53796,,50133,,45863,,40769,,67850,,112027,,128239,,130326,,139019,,123312,,120749,,107959,,105275,,87979,,203205,,249287,,240166,,175833,,207333,,189482,,223661,,214901,,203630,,141966,,128452,,196790,,195637,,258976,,290433,,232984,,362876,,336656,,257993,,234681,,172926,,176244,,290275,,327865, -Mexico,All,Import,Tonnes – net product weight,Tonnes – net product weight,34678,,15745,,34264,,56685,,40870,,34443,,34774,,8908,,21188,,8009,,7437,,5163,,37362,,63353,,87331,,77447,,71516,,131027,,144190,,108042,,95658,,98224,,64078,,122162,,172271,,117711,,79180,,95586,,158222,,151171,,180685,,206001,,195045,,144764,,190184,,199249,,200725,,220057,,254504,,278448,,312358,,300812,,294689, -Mexico,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,139447,,142288,,162868,,199900,,272074,,352826,,274367,,206978,,218620,,252595,,257273,,264350,,256828,,273260,,297397,,327652,,276600,,268718,,274394,,354477,,405817,,450576,,348591,,365456,,393227,,437218,,482205,,453481,F,414122,F,403923,F,491900,,496900,,547928,,564646,,499977,,576104,,563576,,510590,,525604,,556172,,572389,,607525,,526069, -"Micronesia, Fed.States of",All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,9310,,12546,,10964,F,17056,F,5495,F,6011,F,6694,F,11381,F,7456,F,17414,F,14311,F,17005,F,25434,F,23227,F,23299,F,8821,F,12978,F,16498,F,16698,F,19513,F,22531,F,30366,F,19779,F,31078,F,43718,F,38162,F,49288,F,72364,F -"Micronesia, Fed.States of",All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,324,F,645,F,541,F,378,F,956,F,2054,F,2207,F,3292,F,1871,F,1827,F,814,F,1098,F,1033,F,1414,F,1159,F,1575,F,1525,F,1177,F,943,F,1143,F,1065,F,1153,F,1614,F,2100,F,3162,F,2622,F,2322,F,2396,F,2076,F,2288,F,4759,F -"Moldova, Republic of",All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,356,,97,,122,,43,,0,.,2,,3,,22,,114,,0,.,0,0,12,,0,0,1,,0,0,1,,0,0,0,0,4,,0,0,0,0,0,0,1,,3,,0,0 -"Moldova, Republic of",All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,5460,,7984,,16236,,15583,,14885,,10140,,12827,,20693,,20107,,19738,,25041,,29763,,28183,,27310,,35134,,29690,,28852,,26381,,28347,,31085,,30651,,24694,,27282,,27748,,28528, -"Moldova, Republic of",All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1,F,5,F,2,F,2,F,3,F,4,F -"Moldova, Republic of",All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,89,,536,,2010,,206,,572,,4,,0,.,0,.,0,.,114,,5,,20,,36,,5,,30,,27,,0,.,0,.,0,0,14,,5,,0,0,20,,1, -Mongolia,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,104,,205,,181,,347,,525,,427,,150,,263,,382,,172,,392,,321,,135,,54,F,45,F,176,F,210,F,119,F,34,,61,,93,,31,,22,F,9, -Mongolia,All,Import,Tonnes – net product weight,Tonnes – net product weight,1500,,600,,1000,,1020,,959,,824,,675,,756,,1053,,826,F,1339,F,1155,F,1156,F,1051,F,1050,F,0,.,0,.,30,F,0,.,30,,54,,109,,35,,85,,342,,484,,446,,429,,222,,333,,321,,644,,833,F,729,F,998,F,1370,F,2008,F,1473,,1421,,980,,1023,,1132,,1370, -Mongolia,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,105,,182,,158,,205,,150,F,250,F,350,F,300,F,200,F,270,F,260,F,350,F,223,F,189,F,80,F,38,F,38,F,42,F,35,F,40,,32,,41,,63,,15,,22,,25, -Mongolia,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,9,,0,.,0,0,0,-,0,-,0,- -Montenegro,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,300,,253,,52,,255,,131,,42,,8,,3,,9,,1,,21,,14,,5, -Montenegro,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2581,,3370,,3218,,3215,,3168,,3126,,2998,,3299,,3163,,3333,,3760,,4215,,4231, -Montserrat,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,48,,71,F,72,F,73,F,62,,67,,67,,62,,77,,64,,64,,51,,50,,65,,72,,64,,74,,66,,72,,77,F -Morocco,All,Export,Tonnes – net product weight,Tonnes – net product weight,98305,,67905,,82691,,79989,,70591,,142701,,108297,,157644,,162487,,159875,,168010,,184539,,183579,,172979,,183031,,212654,,183337,,194507,,204987,,235799,,196662,,196382,,229574,,256601,,330744,,373164,,354842,,334953,,276397,,356035,,427263,,408700,,484714,,531203,,499531,,365997,,490664,,543544,,593414,,658405,,692486,,717141,,714912, -Morocco,All,Import,Tonnes – net product weight,Tonnes – net product weight,188,,81,,42,,36,,29,,24,,32,,32,,324,,45,,1303,,858,,1990,,3191,,2176,,1182,,4106,,11347,,9401,,5940,,6071,,18256,,22037,,16749,,14831,,13230,,18948,,22878,,33266,,32858,,44977,,49286,,69834,,47556,,52984,,59416,,50948,,54424,,74134,,78518,,66273,,70894,,99581, -Morocco,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,90566,,77312,,123742,,138988,,156234,,204526,,197871,,204530,,202407,,238462,,282891,,263034,,232432,,229813,,246378,,267986,,243033,,267178,,295370,,339815,,264425,F,277747,,225399,,287081,,245698,,243351,,281078,,296080,,233835,,315135,,407256,,368842,,500133,F,537088,F,512277,F,404340,F,463492,F,538308,F,574117,F,638679,F,672872,F,721610,F,729809,F -Mozambique,All,Export,Tonnes – net product weight,Tonnes – net product weight,3803,,3000,,3300,,3800,,5000,,7600,,5900,,4800,,4400,,5400,,4754,,4761,,4395,,4198,,6336,,7841,,8610,,8699,,9708,F,9671,,10435,F,13122,F,12354,F,15796,F,10925,F,8585,,12565,,11141,,13442,,14869,,16570,,12409,,12450,,9976,,9685,,8222,,7541,,10060,,14814,,11996,,10521,F,9938,F,14520,F -Mozambique,All,Import,Tonnes – net product weight,Tonnes – net product weight,9472,,7000,F,13343,F,18241,,15739,,9625,,15221,F,16968,F,11900,F,9032,F,11263,,11909,,13907,F,7515,F,6966,F,7544,F,4600,F,4790,F,2044,F,1995,F,1651,F,7588,F,2270,F,3400,F,6363,F,4567,,6693,,11422,,14973,,22012,F,17783,,14967,,19722,,18757,,16791,,27951,,27358,,45582,,50561,,52597,,37546,F,35677,,40604,F -Mozambique,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,9900,,8950,,8740,,9580,,16450,,16685,,14440,,14202,,10994,,11410,,9235,,8355,,7719,,7323,,9561,,10784,,11222,,11350,F,11462,F,11198,F,10077,F,12634,F,10880,F,11670,F,12843,F,14834,,12728,,15100,F,14580,F,15160,F,11270,F,10657,F,10550,F,10413,,11155,,12629,,15128,,18256,,16986,,29812,,20863,,20100,F,19965,F -Myanmar,All,Export,Tonnes – net product weight,Tonnes – net product weight,646,,1092,,1900,F,2800,F,4900,,6600,,8700,,10200,,6500,,5700,,4900,F,4446,,3578,,5432,,9274,,13928,,14259,,28488,,23216,,96740,,54361,,67401,,74180,,126873,,116609,,144623,,201667,,212999,,205463,,276699,,298071,,286054,,351652,,324710,,374187,,373898,,387371,,376848,,345247,,338284,,394397,,487799,,567521, -Myanmar,All,Import,Tonnes – net product weight,Tonnes – net product weight,2,F,2,F,25,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,406,F,1394,F,530,F,631,F,615,F,561,F,775,F,699,F,390,F,580,F,415,F,4071,,464,F,1026,F,1648,F,1826,F,1354,F,1668,F,2400,F,2827,F,4840,F,6102,F,6592,F,5414,F,7254,F,6009,F,6542,,5410,,7907, -Myanmar,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,129910,,134400,,140600,,146470,,151590,,161720,,142700,F,132780,,133250,F,139400,F,140300,F,168293,,168131,,159202,,168469,,180630,,178454,,184900,F,193105,,199391,,197215,F,202916,,213336,,248716,,274399,,313326,,337986,,362891,,422523,,491558,,530250,F,680994,,675349,,735733,,807857,,891219,,968621,,693425,,1006817,,1022741,,1120811,,1178583,,1152790, -Namibia,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,236940,F,263100,F,320804,,308583,,266783,,293877,,427647,,408677,,411198,,342132,F,363830,,341711,,328099,,284671,,431177,,351383,,373572,,362682,,327775,,372398,,359134,,404608,,409548,,433338,,404151,,468198,,402963, -Namibia,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,11658,,35788,,47874,,34393,,130948,,48138,,21307,,16723,,24193,,19232,,14717,,21152,,24560,,36808,,31948,,19330,,20255,,24691,,27505,,24441,,27150,,26793,,32147, -Namibia,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,104007,,77812,F,113913,F,142162,F,114118,F,117451,,120594,,164263,,284748,F,325768,,351129,,274662,F,285848,,238600,F,243700,F,177159,,215184,F,241255,,247858,,267364,,278845,,270952,,368875,,397765,,360959,,418894,,395031,,377185,F,374935,F -Namibia,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,4438,,3032,,4819,,0,- -Nepal,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,291,,413,,353,,246,,107,,76,,80,F,0,.,0,.,62,F,51,F,0,.,0,0,145,F,17,F,33,F,25,F,2,F,9,,41,F,18,F,0,.,0,0,0,.,9,,1,,6,,16,,12,,4,,6,,14,,19,,13,F -Nepal,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,767,,234,,203,,351,,291,,137,,0,.,0,.,0,.,548,F,450,F,338,F,429,F,115,F,178,F,204,F,177,F,142,F,575,,310,F,306,F,714,F,1024,F,2389,F,5972,,5151,,12455,,12731,,10127,,14052,,9779,,12153,,10545,,11813,F -Netherlands,All,Export,Tonnes – net product weight,Tonnes – net product weight,273405,,279298,,314169,,368427,,393744,,483498,,543107,,520570,,556837,,606431,,660933,,639238,,604152,,684336,,778236,,732211,,815922,,848448,,919522,,780037,,723049,,755142,,689188,,855736,,720774,,742912,,791346,,923872,,1048167,,1096505,,992260,,961649,,858915,,855772,,959875,,870561,,959488,,913187,,1134866,,1151588,,1199890,,1429915,,1518603, -Netherlands,All,Import,Tonnes – net product weight,Tonnes – net product weight,313980,,295534,,334390,,355529,,351404,,357860,,414574,,368225,,458605,,532019,,482097,,514205,,538434,,606767,,671551,,659022,,685767,,678934,,1001286,,842467,,625096,,601021,,525025,,752685,,687266,,722681,,693930,,780348,,728795,,817933,,829136,,865435,,779759,,869571,,977849,,894556,,967419,,890593,,975945,,859541,,891597,,1104170,,1099487, -Netherlands,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,116665,,120892,,162989,,174294,,209484,,262204,,271237,,277086,,258502,,281550,,307621,,258378,,209223,,232953,,237628,,252358,,296982,F,345587,F,268711,F,243687,F,228026,F,226074,F,228884,F,233697,F,231545,F,239089,F,211319,F,220809,F,238376,F,257100,F,225223,F,224878,F,199751,F,217127,F,218320,F,189967,F,242794,F,246458,F,279152,F,311551,F,319548,F,441308,F,383840,F -Netherlands Antilles,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,40,F,0,.,0,0,0,0,0,0,0,0,51,,21,,25,,13,,26,,226,,137,,36,,32,,26,,33,,206,F,390,F,3300,F,6190,F,7326,F,7925,F,6608,F,8320,F,9305,F,2223,F,3113,F,1990,F,10200,F,15519,F,12837,F,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0, -Netherlands Antilles,All,Import,Tonnes – net product weight,Tonnes – net product weight,2353,,2168,,2212,F,2492,,2396,F,2426,,2606,,2796,,2823,,2267,,2640,,2776,,2688,,2952,,2719,,1653,,1726,,1927,,1764,,2466,,1671,F,2114,F,1678,,2308,F,2681,F,2302,F,1459,F,1722,F,2071,F,2374,,3012,,2609,,3732,,3607,F,3195,F,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0, -New Caledonia,All,Export,Tonnes – net product weight,Tonnes – net product weight,12,,123,,33,,50,,59,,246,,715,,592,,361,,392,,648,,1187,,1381,F,1420,F,1685,F,1574,F,1448,F,1584,,1814,,1620,,1475,,1419,,1667,,2322,,2383,,3301,,2635,,3122,,2875,,3098,,2664,,2265,,2326,,2368,,2145,,1749,,1833,,1942,,1795,,1425,,1395,,1263,F,1120,F -New Caledonia,All,Import,Tonnes – net product weight,Tonnes – net product weight,1021,,1119,,984,,1052,,1261,,1089,,1040,,1010,,928,,979,,1198,,1227,,1110,F,1273,,1271,F,1274,,1378,,1388,,1523,,1411,,1694,,2852,,3382,,2147,,2769,,4255,,3308,,3797,,4844,,4887,,3765,,4956,,5072,,3552,,4347,,4758,,5114,,3714,,4353,,3299,,3866,,3812,F,5763,F -New Caledonia,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,53,,1,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -New Zealand,All,Export,Tonnes – net product weight,Tonnes – net product weight,20491,,25935,,34225,,64946,,110436,,128259,,123492,,130645,,145370,,145034,,158177,,156503,,210798,,250096,,194377,,262343,,294358,,306534,,290960,,324381,,313634,,314776,,325763,,325167,,287045,,282232,,323242,,303698,,340391,,336464,,323467,,318056,,283139,,290544,,323649,,304581,,330999,,309233,,293940,,291435,,289447,,296364,,267355, -New Zealand,All,Import,Tonnes – net product weight,Tonnes – net product weight,4111,,3403,,3612,,5053,,4142,,5366,,7107,,9067,,6360,,6916,,8137,,9331,,11554,,14670,,14574,,15738,,17403,,16916,,17387,,24648,,27697,,24216,,22124,,26478,,35252,,39350,,36962,,35936,,38047,,39324,,38870,,33042,,32763,,28257,,37681,,34296,,38583,,35149,,34337,,36638,,32179,,33118,,36787, -New Zealand,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,19870,,24533,,32666,,58560,,104571,,122364,,117928,,124551,,138365,,137150,,147901,,146956,,195211,,236793,,179395,,239290,,268239,,281110,,264637,,292203,,296621,F,304823,F,308205,F,305575,F,312185,,271426,F,311609,F,294529,F,328046,F,331610,F,319253,F,301400,F,301397,F,278313,F,305616,F,281586,F,314868,F,294871,F,280187,F,278082,F,277871,F,275246,F,254384,F -New Zealand,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1,,0,0,0,.,0,.,0,.,0,0,78,,73,,0,.,0,.,247,,0,.,573,,208,,594,,435,,457,,760,,602,,375,,0,.,940,,453,,389,,377,,429,,469,,339,,382, -Nicaragua,All,Export,Tonnes – net product weight,Tonnes – net product weight,6044,,6521,,5353,,4390,,3992,,2851,,2032,,1324,,1028,,1224,,846,,994,,1130,,1698,,1247,,2226,,2537,,4596,,8056,,10190,,9664,,12203,,9148,,10341,,12028,,7642,,9440,,13065,,16401,,19099,,17071,,19460,,20779,,22303,,23866,,22733,,34709,,39494,,43010,,39041,,43137,,31625,,41218, -Nicaragua,All,Import,Tonnes – net product weight,Tonnes – net product weight,835,,1031,,572,,400,,650,,108,,756,,500,F,46,,0,0,10,,0,.,0,.,0,.,0,.,1502,,2231,,1243,,1224,,1688,,1646,,1951,,2603,,3606,,3732,,3084,,3821,,3417,,2988,,4001,,4064,,5121,,3942,,4010,,4632,,6202,,7988,,6771,,8011,,9725,,10104,,6503,,6750, -Nicaragua,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,3857,,4625,,3853,,2698,,3120,,2167,,1856,,1356,,1361,,1354,,891,,1108,,1183,,1330,,857,,2060,,2371,,3495,,7622,,9050,F,8894,,11610,,12985,,12712,,14370,,10022,,10802,,11988,F,12254,,16444,,15499,,15486,,18097,,20558,,19529,,17985,,25791,,30455,,34326,,25360,F,26081,F,24562,,29901, -Niger,All,Export,Tonnes – net product weight,Tonnes – net product weight,10,,1487,,347,,2840,,12,,100,,40,F,35,,13,,37,,23,,9,F,0,0,5,F,0,.,0,.,0,.,142,F,185,F,98,F,0,.,119,,326,,1613,,1618,,6677,,8025,,5905,,3685,,2260,,681,,287,,470,,406,,556,,815,,831,,1282,,1367,,686,,573,,328,F,346,F -Niger,All,Import,Tonnes – net product weight,Tonnes – net product weight,41,F,77,F,101,,311,,242,,711,,435,F,172,,180,,230,F,512,,614,F,464,,1473,,1076,,1520,F,909,,259,F,528,F,565,F,547,,688,,528,,497,,898,,678,,589,,519,,568,,1245,,2237,,1544,,2666,,3424,,3670,,2860,,3899,,4817,,5820,,7486,,7402,,8260,F,7792,F -Niger,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,1162,,1956,,2600,,2700,,2650,,5530,,4120,,2276,,1692,,929,,946,,480,,556,,1781,,1162,,675,,89,,134,,100,F,100,F,100,F,100,F,100,F,100,F,100,F,100,F,100,F,100,F,100,F,162,F,228,F,320,F,500,F,470,F,600,F,770,F,700,,750,,850,F,400,F,550,F,550,F,675,F -Niger,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,75,,0,.,134,,3,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,11,,0,-,0,-,0,-,48,,0,-,0,-,0,-,0,-,0,-,0,- -Nigeria,All,Export,Tonnes – net product weight,Tonnes – net product weight,837,,179,,487,,779,,530,F,1307,,861,,1389,,732,,727,,730,F,2108,,3019,,8065,,6796,,5815,F,4289,,2802,,4081,,5883,,6228,,4263,,487,,4007,F,2421,,3310,F,3459,F,2806,F,4650,F,8054,F,16543,F,21296,,11562,,24944,,24059,,31968,,18445,F,82080,F,11864,F,11942,F,6770,F,5938,F,6234, -Nigeria,All,Import,Tonnes – net product weight,Tonnes – net product weight,226382,F,291378,F,432382,F,523850,F,504062,F,674026,F,795365,F,511866,F,364652,F,226216,F,190027,F,399699,F,292023,F,446586,F,453516,F,469083,F,231240,F,362417,F,234744,,271648,F,418461,F,350960,,435408,,416219,,318873,,640657,,554357,,637992,F,793941,F,857985,F,1345929,F,1024181,,1061225,,1211903,F,1412295,,1753165,F,1107761,F,886280,F,1008918,F,886361,F,610650,,485434,,524087, -Nigeria,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,12600,,20200,,21934,,31352,,34141,,36435,,35000,F,28600,F,28450,F,18300,F,17600,F,25400,F,30500,F,38000,F,38900,F,95495,,83593,,80892,,61927,,59400,,67648,,71416,,65818,,72749,,65897,,88075,,63182,,67621,,69033,,74498,,78010,,79111,,79857,,77856,,100793,,152689, -North Macedonia,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,240,,238,,453,,52,,20,,190,F,516,,486,,475,,192,,340,,274,,312,,202,,159,,356,,355,,410,,405,,384,,155,,354,,159,,186,,273,,727,,1207, -North Macedonia,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,5035,,7046,,7758,,7519,,9420,,6823,,8188,,7696,,6850,,6277,,7505,,6784,,7791,,6916,,7997,,8326,,7955,,7652,,7319,,7792,,6759,,7020,,8123,,8419,,8389,,8721,,9634, -Northern Mariana Is.,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,25,F,30,F,17,F,20,F,14,F,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,,0,,0,,0,,0,,0,,0, -Norway,All,Export,Tonnes – net product weight,Tonnes – net product weight,865927,,899203,,663946,,749397,,659348,,726752,,697536,,828701,,759978,,712715,,625753,,711729,,697915,,707942,,836180,,1088300,,1204551,,1358490,,1519677,,1594969,,1789596,,1949594,,1908514,,2040044,,2101499,,2008450,,2100516,,2140081,,1981239,,1996268,,1878115,,2166849,,2340718,,2581145,,2670319,,2438246,,2530809,,2467557,,2676762,,2632731,,2449962,,2632020,,2727215, -Norway,All,Import,Tonnes – net product weight,Tonnes – net product weight,38652,,39881,,44428,,60962,,83522,,74450,,59652,,39661,,57647,,117789,,128845,,91510,,178103,,217563,,198058,,283974,,307948,,370758,,408082,,538929,,573395,,708055,,637816,,734193,,902533,,819398,,750639,,671752,,621736,,591258,,582513,,665267,,653249,,842042,,629657,,648050,,674593,,595517,,681507,,626031,,631626,,646780,,631723, -Norway,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,997071,,988666,,819351,,875353,,817377,,809454,,804389,,902613,,867035,,742178,,691051,,712668,,690353,,769371,,735930,,870562,,1022157,,1184109,,1376089,,1464401,,1636930,,1600475,F,1478294,F,1715546,F,1788066,F,1761838,F,1842540,F,1632725,F,1627841,F,1696149,F,1588058,F,1871003,F,2066219,F,2613914,F,2113511,F,2062948,F,1897700,F,1772676,F,1940307,F,1800848,F,1813329,F,1846090,F,1994294,F -Oman,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,0,250,F,2384,,5186,,7521,,8871,,12508,,14216,,17773,,19462,,18472,,20670,,35314,,28279,,33860,,29406,,29597,,45811,,44601,,59579,,56573,,56329,,45900,,31261,,37568,,33919,,49950,,49883,,80369,,56582,,49793,,37151,,55401,,69549,,90934,,74888,,77902,,90865,,79456,,112576,,127958,,116380,F,283034, -Oman,All,Import,Tonnes – net product weight,Tonnes – net product weight,1000,F,1036,,902,,747,,718,,1353,,1051,,812,,1191,,1169,,1246,,1976,,798,,698,,982,,6777,,9128,,15519,,12422,,10726,,18194,,14435,,17854,,9877,,11415,,12809,,10264,,10336,,7912,,15153,,11386,,12848,,17168,,16893,,13724,,19360,,21530,,16856,,22878,,31883,,36993,,32494,,32284, -Oman,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,250,F,2381,,4911,,6909,,4878,,8302,,10284,,10195,,11365,,17150,,15294,,32639,,26137,,33356,F,28538,F,28841,F,45441,F,43903,F,57950,F,55042,F,53207,F,37976,F,25168,F,27186,F,23378,F,24306,F,28051,F,50161,F,30000,F,25000,F,18000,F,22000,F,19000,F,17800,F,17000,F,22000,F,28000,F,24000,F,45000,F,44590,F,49900,F,100945,F -Oman,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1341,,292,,81,,74,,1089,,1169,,250,,2351,,133,,681,,1936,,679,,0,.,0,.,0,.,4445,,3577, -Pakistan,All,Export,Tonnes – net product weight,Tonnes – net product weight,26308,,30051,,29186,,21715,,24623,,19112,,19350,,23112,,31615,,37300,,36314,,42306,,46917,,45874,,46254,,49846,,67416,,89618,,70348,,63926,,66543,,83166,,73882,,92688,,86294,,87862,,91759,,97625,,108463,,103148,,143083,,139770,,135737,,129908,,123089,,150720,,151405,,157541,,167077,,142848,,141790,,193898,,222756, -Pakistan,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,164,,166,,108,,271,,220,,134,,239,,70,,66,,159,,188,,389,,248,,149,,164,,231,,207,,150,,143,,78,,166,,262,,189,,164,,269,,759,,1201,,1800,,1984,,8826,,2686,,2400,,1577,,1950,,2628,,4661,,5054,,10482,,8672,,9029,,5446, -Pakistan,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,34320,,43273,,40077,,50704,,43508,,52683,,60478,,58490,,75282,,78318,,67168,,69260,,69345,,64867,,73944,,77723,,118920,,140463,,112164,,108620,,130383,,139818,,134519,,146147,,133124,,133965,F,124575,,131552,,144165,,141635,,136554,,140236,,141216,,143745,,149700,,153780,,156185,,160855,,166675,,169771,,173913,,177440,,179073, -Pakistan,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1,,0,.,0,.,0,.,20,,0,.,0,.,0,.,0,.,0,.,29,,0,.,26,,1,,0,0,0,0,0,0,0,0,0,0,0,.,0,.,0,.,0,.,0,.,46, -Palau,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,205,,100,F,0,.,0,.,0,.,89,,50,F,60,F,156,F,161,F,0,.,0,.,0,.,0,.,16,F,46,F,190,F,165,F,88,F,78,F,74,F,41,F,21,F,64,F,164,F,241,F,293,F,247,F,124,F,222,F,138,F,107,,117,F,169,F,139,,141,,400,,311,F -Palau,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,78,F,80,F,70,F,842,F,316,F,123,F,21,F,64,F,95,F,141,F,105,F,124,F,120,F,240,F,503,,600,,500,,411,,415,,443,,573,F,448,,649,,530,,588,,518, -Palestine,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,102,,50,,59,,19,,96,,42,,85,,0,.,119,F,112,F,115,F,195, -Palestine,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,3818,,4331,,3579,,3667,,4234,,2349,,4129,,4537,F,4723,F,6073,F,6034,F,6785, -Palestine,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,98,,48,,56,,19,,96,,42,,85,,122,F,0,-,0,-,0,-,0,- -Panama,All,Export,Tonnes – net product weight,Tonnes – net product weight,33385,,49098,,20720,,32763,,48861,,24217,,15063,,27380,,17890,,76553,,36625,,45706,,25453,,45086,,28559,,33040,,29992,,40121,,48403,,48848,,30088,,51046,,64396,,65864,,101229,,118873,,94055,,126730,,116555,,113793,,114536,,104500,,127843,,123164,,79698,,48521,,51678,,64538,,87714,,69753,,69476,,57650,,74457, -Panama,All,Import,Tonnes – net product weight,Tonnes – net product weight,1684,,1640,,2123,,2770,,3265,,3019,,3144,,3984,,4299,,4200,,3697,,4686,,3616,,5178,,4729,,4581,,5382,,5020,,5189,,5222,,6697,,6232,,8444,,7506,,8423,,8894,,9206,,11042,,9139,,11760,,11759,,12425,,13705,,13933,,14650,,16505,,19624,,19331,,20818,,23127,,22047,,20605,,26897, -Panama,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,42181,,49402,,27343,,36920,,58842,,33654,,21241,,36585,,33362,,83934,,33384,,46909,,37387,,64394,,42596,,42604,,45974,,51571,,57148,,59711,,48054,,78561,,86704,,59844,,83372,,112414,,91017,,101459,F,100370,,96051,,87603,,96323,,105535,,100509,,61709,,47762,,50824,,47591,,87018,,68390,,83780,,87150,,85150, -Panama,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,32,,0,.,0,.,0,.,11,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Papua New Guinea,All,Export,Tonnes – net product weight,Tonnes – net product weight,20092,,24976,,46698,,28485,,34444,,31207,,3886,,1719,,3729,,9952,,1870,,1789,,1211,,1526,,2165,,2418,,2633,,1748,,2501,,3608,F,2608,,2753,F,10910,F,20687,F,28677,F,38707,,45453,,61081,,41203,,43132,F,58914,F,66219,F,53140,F,50164,,61498,F,62037,,63721,,60082,F,58078,F,69065,F,81360,F,99678,F,81099,F -Papua New Guinea,All,Import,Tonnes – net product weight,Tonnes – net product weight,22548,,12000,F,17000,F,27900,F,29140,F,34449,,27151,,28699,,29161,,31119,,34655,,34719,,35320,,35021,,34266,,24060,F,22658,F,25267,F,24071,F,17964,,28387,,18301,F,18154,,10192,F,9533,,8073,,12642,,12665,,13773,,14265,F,14597,F,19127,F,18456,F,18594,F,15861,F,10533,,12976,,16653,F,19641,F,26292,F,26077,F,33885,F,29081,F -Papua New Guinea,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,.,2,,70,,0,.,1,F,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Paraguay,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,38,F,15,F,15,F,13,F,6,F,0,.,0,.,5,,26,,28,,16,,16,,105,,14,,25,,19,,10,,17,,64,,69,,24,,26,,29,,44,,117,,271,,320,,260,,143,,89,,33,,8,,5,,6,,5,,6,,6,,75,,105,,506, -Paraguay,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,212,,220,,130,,229,,230,,232,,88,,140,,218,,257,,335,,569,,500,,851,,1256,,977,,1641,,1374,,1212,,679,,807,,812,,520,,793,,1174,,897,,700,,1072,,1583,,1512,,1855,,1771,,1877,,2105,,2540,,2098,,2165,,2260,,2669, -Peru,All,Export,Tonnes – net product weight,Tonnes – net product weight,651093,,527715,,581287,,659620,,628102,,535790,,721841,,270362,,562222,,690907,,779759,,767664,,850194,,1254240,,1171762,,1220103,,1134637,,1749166,,2610033,,2030352,,1962284,,2234643,,804622,,1820966,,2711749,,2451988,,1855983,,1720783,,2273463,,2529903,,2008383,,1963254,,2262311,,2213911,,1668655,,1968993,,2107831,,1414591,,1481520,,1230631,,1049583,,1571649,,1600177, -Peru,All,Import,Tonnes – net product weight,Tonnes – net product weight,274,,267,,88,,397,,7677,,1023,,309,,50680,,20707,F,6250,F,713,F,30402,,20640,,5250,,5318,,909,,870,F,1040,,1704,,3844,,4751,,4618,,13876,,18866,,27852,,37463,,39830,,34800,,35649,,67580,,27757,,41274,,48156,,63145,,115437,,75217,,65104,,110739,,104454,,124859,,131104,,135657,,130012, -Peru,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,1093112,,734829,,972670,,1040311,,791788,,757603,,956959,,355421,,914998,,1002495,,1348008,,1068761,,1478669,,1686561,,1627876,,1617774,,1675985,,2230055,,3146958,,2376777,,2513049,,2161088,,1074615,,2418755,,2990363,,2132340,,2171123,,1644246,,2530384,,2436848,,1976707,,2082075,,2138222,,1984739,,1269191,,2486171,,1548128,,1798032,,1098151,,1368977,,1107117,,1214249,,2136860, -Philippines,All,Export,Tonnes – net product weight,Tonnes – net product weight,13995,,24080,,38267,,50803,,70822,,64193,,46750,,53797,,51914,,59476,,61162,,71037,,90829,,103622,,102004,,140553,,127182,,159217,,168055,,157955,,177349,,158583,,202995,,161518,,215531,,171361,,171279,,188789,,180648,,131789,,148297,,159406,,192982,,183801,,204375,,231711,,253838,,317973,,276455,,225190,,234418,,321989,,270879, -Philippines,All,Import,Tonnes – net product weight,Tonnes – net product weight,64188,,38591,,48013,,45934,,53475,,46913,,83483,,23048,,6107,,28674,,69280,,105421,,165173,,197944,,195527,,193646,,221520,,208873,,241137,,270163,,266057,,294753,,170543,,254181,,248407,,180992,,217069,,152389,,134375,,180945,,170834,,193578,,200331,,273623,,195037,,203682,,268477,,257910,,302917,,384843,,417022,,493535,,473963, -Philippines,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,182222,,237195,,244004,,320417,,337438,,339416,,337924,,418516,,423512,,398227,,394473,,378961,,396789,,421900,,436288,,494957,,497998,,546297,,529435,,521709,,492630,F,448014,F,454294,F,426042,F,435085,F,397447,F,410454,F,443381,F,439984,F,412100,F,442982,F,476570,F,525672,F,514904,F,520273,F,494581,F,502880,F,550561,F,526358,F,472708,F,467342,F,481260,F,498097,F -Poland,All,Export,Tonnes – net product weight,Tonnes – net product weight,99751,F,89983,F,59085,F,81532,F,77102,F,81953,F,100213,F,140426,F,124943,F,119133,F,114836,F,138768,F,146695,F,138795,F,129444,F,123867,F,213320,,157951,,201642,,179954,,206404,,198757,,157276,,172238,,164030,,179228,,185220,,167946,,187601,,200956,,220394,,243321,,239808,,270675,,295614,,344181,,320938,,339138,,347780,,385957,,423686,,397193,,435319, -Poland,All,Import,Tonnes – net product weight,Tonnes – net product weight,176705,,169594,,223156,,235950,,133332,,98799,,71340,,93830,,140665,,177811,,192294,,249466,,272484,,267474,,159353,,100154,,164739,,177888,,210110,,198938,,224764,,247552,,274329,,253361,,275850,,280895,,236529,,249830,,311918,,325478,,356755,,382033,,419992,,427689,,475074,,459917,,475147,,513221,,544506,,541736,,576329,,587142,,618950, -Poland,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,385400,,327272,,309880,,352541,,383249,,339341,,346836,,425383,,436155,,413343,,390601,,426618,,408143,,382798,,301665,,292747,,336908,,316184,,350450,,335200,,290340,,336206,,285934,,252732,,332590,,350701,,332784,,326641,,383480,,353521,,408700,,368966,,402695,,377814,,390634,,401029,,446790,,495785,,472625,,515065,,706116,,719342,,723817, -Portugal,All,Export,Tonnes – net product weight,Tonnes – net product weight,48813,,51110,,53549,,57753,,58361,,47781,,52175,,54979,,58987,,64908,,71872,,65974,,70632,,91427,,94299,,103635,,88657,,78725,,78303,,95674,,86882,,86474,,86490,,99011,,94374,,95820,,103614,,105641,,117843,,115967,,130434,,140549,,143343,,141406,,175049,,191418,,248228,,270501,,279597,,279565,,254012,,265520,,268861, -Portugal,All,Import,Tonnes – net product weight,Tonnes – net product weight,113509,,106501,,81817,,89889,,87005,,86843,,98332,,100283,,109933,,147636,,154708,,181241,,196778,,185660,,223602,,261561,,246763,,256626,,269685,,278806,,298182,,298003,,317969,,345860,,320125,,336122,,338555,,348192,,346957,,362673,,393680,,418642,,388025,,401109,,396571,,403876,,442894,,468299,,478963,,485818,,508797,,535571,,524897, -Portugal,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,88593,,94810,,94299,,81263,,82864,,79532,,76944,,78175,,81218,,92677,,138997,,115180,,112629,,114715,,127212,,148493,,77523,,79408,,88716,,128912,,118952,,100129,,101035,,88345,,78273,,93388,,100356,,124515,,132572,,135203,,163327,,170709,,186959,,192371,,209900,,205337,,208302,,235781,,231585,,222345,,247626,,251103,,243387, -Puerto Rico,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,,0, -Qatar,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,25,,0,.,0,.,0,0,0,0,14,,0,0,6,,1319,,1509,,2187,,2269,,2260,,2437,,4962,,3143,,2548,,2450,,3246,,5791,,3884,,1601,,2733,,1644,,1678,,1046,,763,,525,F -Qatar,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,222,,440,,497,,820,,517,,591,,593,,723,F,986,,837,F,843,F,930,,1294,,1713,,1788,,2263,,2294,,1549,,673,F,2874,,3097,,3158,,3820,,4023,,2901,,4384,,7970,,10506,,12428,,19524,,22834,F,23941,,24713,F,31400,,29835,,32605,,36488,,38141,,32512,,31822, -Qatar,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,19,,6,,32,,30,F,20,,14,,0,.,1,,52,,19,,0,.,4,,7,,18,,101,,14,,87,,69,,116,,93,,110,,27,,7,,9,,0,-,0,-,0,-,0,- -Romania,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,8926,,116,,386,,3137,,201,,668,,21,,189,,427,,914,,1615,,561,,509,,486,,224,,882,,873,,1944,,521,,762,,2968,,3507,,6770,,4816,,4899,,3931,,4496,,3838,,3911,,5134, -Romania,All,Import,Tonnes – net product weight,Tonnes – net product weight,73400,,70700,,60900,,96000,,110200,,83100,,58700,,49400,,41975,,46735,,37176,,39569,,42517,F,17258,F,80956,,14117,,6967,,25661,,34600,,22171,F,47180,,31578,,61527,,46238,,55834,,65145,,77238,,73684,,84779,,97418,,101214,,87329,,103827,,103644,,92530,,73361,,79997,,80443,,92529,,99746,,100962,,107912,,110895, -Romania,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,89400,,98000,,87400,,125726,,141361,,155316,,186486,,174963,,170321,,170858,,195855,,197711,,188447,,151212,,92855,,118152,,53050,,11764,,15198,,42198,,12963,,7180,,14464,,13621,,13735,,15428,,13574,,15960,,10903,,13039,,13521,,13500,F,14071,F,14882,F,13805,F,12400,F,14400,F,16300,F,18518,,22901,,24794,,29331,,20949, -Russian Federation,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,531653,,1137955,,1442116,,1237347,,1266473,,1180410,,952622,,941043,,1212440,,1243734,,1289534,,1229566,,1250706,,1404287,,1399286,,1362733,,1426074,,1425672,,1699064,,1814613,,1767981,,1960338,,1793548,,1879379,,1994626,,2221395,,2330007, -Russian Federation,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,53921,,50554,,365712,,481210,,529523,,767696,,561444,,575648,,553747,,749259,,712335,,832420,,922725,,1085771,,936436,,1166949,,1193837,,1030091,,1058080,,979359,,1024938,,1081610,,935047,,588555,,533521,,622707,,607307, -Russian Federation,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,3567104,,3043798,,2647882,,2816463,,2875294,,2850940,,2711845,,3120436,,3276214,,3125521,,2901094,,2988959,,2716768,,3267711,,3532569,,3860308,,3794528,,3981848,,4149985,,4353018,,4441148,,4574381,,4387099,,4486505,,4832422,,5170357,,5261036, -Rwanda,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,38,,0,0,92,,341,,591,,297,,120,,317,,205,,1271,,2098,F,1596,F,1448,F,1306,F -Rwanda,All,Import,Tonnes – net product weight,Tonnes – net product weight,40,,62,,51,,82,,338,,155,,88,F,102,,240,,512,,229,,730,,202,,165,,160,,417,,836,,258,,100,F,100,F,72,,141,,143,,52,,136,F,103,,198,,114,,7,,58,,1348,,2732,,3600,,5020,,7065,,11954,,13384,,12189,,15338,,23486,F,29001,F,30662,F,34052,F -Rwanda,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,10,,16,,26,,36,,223,,37,,74,,50,,125,,110,,264,,280,F,280,F,280,F,405,F,590,F,890,F,1340,F,1290,F,1740,F,1345,F,1000,F,1240,F,1510,F,1700,F,1902,,2330,,3616,,2539,,13000,,17159,,2353,,24701,,28693,,31994,,26593,,32778,,24876, -Rwanda,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,133,,311,,487,,504,F,355,F,300,F,300,F -Réunion,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,99,,169,,165,,114,,569,,894,,1308,,663,,814,,440,,492,,350,,775,,743,,1379,,889,,1351,,1992,,1926,,0,-,0,-,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -Réunion,All,Import,Tonnes – net product weight,Tonnes – net product weight,4141,,4494,,5052,,5378,,5929,,5109,,4974,,6504,,4957,,6029,,6254,,6716,,7059,,7691,,7304,,8291,,9299,,9942,,10961,,10927,,0,-,0,-,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -Réunion,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,1136,,1072,,1101,,892,,889,,2333,,2069,,1308,,1462,,1190,,1032,,1098,,1240,,1200,F,1230,F,1280,F,1000,F,900,F,1000,F,1005,F,697,F,750,F,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -Saint Helena,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,52,F,105,F,9,F,20,F,78,,163,F,284,F,252,F,220,F,228,F,366,F,333,F,337,F,309,,290,F,331,F,299,F,493,F,497,F,262,F,305,F,547,F,505,F,378,F,480,F,732,F,628,F,727,F,976,F,807,F,720,F,898,F,641,F,719,F,672,F,905,F,1120,F,937,F,958,F -Saint Helena,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,5,,4,,4,,3,,7,,6,,0,.,6,,0,.,0,.,0,.,0,.,0,.,0,.,75,,48,F,3,F,5,F,21,F,59,F,96,F,99,F,204,F,131,F,192,F,201,F,57,F,20,F,9,F,22,F,17,F,7,F,27,F,37,F,44,F,77,F,99,F,18,F -Saint Kitts and Nevis,All,Export,Tonnes – net product weight,Tonnes – net product weight,89,,102,,97,F,95,,90,,24,,51,,80,,127,,102,,105,,227,,127,F,100,F,120,F,110,F,60,F,48,F,29,F,28,,34,,22,,1,F,69,,73,,43,,35,,135,,156,,25,,62,,95,,107,,89,,141,,168,F,113,F,107,F,87,F,77,F,66,F,43,,47,F -Saint Kitts and Nevis,All,Import,Tonnes – net product weight,Tonnes – net product weight,310,,228,,218,,224,,245,,249,,279,,252,,319,,314,,298,,221,,305,,246,,270,F,169,F,130,F,110,F,74,F,895,,1440,,784,,157,F,749,,1157,,802,,857,,616,,663,,566,,805,,729,,790,,821,,899,,694,,444,F,621,F,598,F,720,F,488,F,911,,585,F -Saint Kitts and Nevis,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,0,1,,0,0,0,.,1,,6,,5,,4,,4,,0,.,0,.,0,.,0,.,0,.,21,,0,- -Saint Lucia,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,6,,1,,1,,0,0,6,,1,,0,0,0,.,5,,27,,43,,37,,4,,21,,14,,2,,4,,0,.,0,.,0,.,0,0,0,0,0,.,2,,1,,15,,2,,1,,0,0,0,0,0,0,25,,0,.,0,0,0,0,14,F,0,.,2,,1,,18,,0,0,0,- -Saint Lucia,All,Import,Tonnes – net product weight,Tonnes – net product weight,471,,333,,1193,,452,,921,,519,,987,,995,,1128,,557,,478,,634,,713,,895,,926,,1556,,900,,933,,1015,,963,,1180,,1080,,1214,,1416,,1244,,1154,,1321,,1516,,1956,,2649,,1831,,1846,,1870,,1354,F,1226,F,1194,F,1432,F,1119,F,1887,,1834,,1749,,1925,,1356,F -Saint Lucia,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,8,,0,.,7,,1,,3,,7,,0,.,0,.,0,.,3,,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,0,0,-,0,-,0,- -Saint Vincent/Grenadines,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,0,59,,295,,1,,0,0,4,,126,,39,,38,,119,,107,,151,,4060,,5377,,7784,,7022,,920,,682,,265,,251,,106,,236,,191,,201,,251,,140,,140,,95,,74,,53,,49,,46,,51,,49,,95,,39,,53,,67,,133,,112,,103,,241,,468, -Saint Vincent/Grenadines,All,Import,Tonnes – net product weight,Tonnes – net product weight,403,,264,,275,,268,,260,,281,,261,F,253,,281,,285,,189,,224,,226,,243,,257,,264,F,269,,278,F,190,,296,,484,F,573,,521,,638,,429,,433,,437,,475,,472,,490,,476,,503,,589,,477,,467,,394,,461,,439,,570,,473,,627,,438,,498, -Saint Vincent/Grenadines,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,26,,0,.,0,.,0,0,1,,0,.,0,.,0,.,0,0,0,.,0,.,5,,2,,0,-,0,-,0,0 -Samoa,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,.,0,.,0,.,5,F,8,F,3,F,76,,60,F,70,F,11,F,15,F,68,,755,,1798,,2267,,5073,,2496,F,1621,,1573,,874,,1966,,1286,,1769,,2711,,3714,,3364,,4097,,1488,,2397,,3257,,1364,,5462,,8065,,7121,,4618, -Samoa,All,Import,Tonnes – net product weight,Tonnes – net product weight,5342,,2305,,1536,,2555,,2166,,2752,,1787,,1169,,1538,F,1965,F,1436,F,2247,,2500,F,2800,F,3065,,3100,F,3200,F,3349,F,2738,F,2813,F,4939,F,4383,F,4338,F,3142,F,7677,F,4486,,4478,,5663,,3436,,2255,,2935,,3487,,4645,,3634,,4436,,3281,,4209,,2963,,3861,,4895,,7271,,4536,,5900, -Sao Tome and Principe,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,761,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,18,F,10,F,8,F,12,F,28,F,150,F,300,F,82,F,3,,0,0,0,0,2,,6,,5,,2,,2,,1,,2,,1,,6,,8,,9,,1,,1, -Sao Tome and Principe,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,48,,0,.,0,0,0,.,0,.,0,.,251,,141,,88,,186,F,200,F,267,F,168,F,345,F,117,F,73,F,211,F,96,F,101,F,11,F,19,,39,,5,,3,,19,,5,,4,,35,,21,,42,,32,,34,,73,,52,,57,,51,,38,,46,,29,,76, -Sao Tome and Principe,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,723,,1266,,2429,,2567,,1525,,380,,260,,50,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,146,F,212,F,723,F,669,F,959,F,195,F,280,F,300,F,310,F,310,F,310,F,310,F,330,F,340,F,350,F,400,F,500,F,600,F,700,F,800,F,820,F,750,F,680,F -Saudi Arabia,All,Export,Tonnes – net product weight,Tonnes – net product weight,1263,,1361,,1251,,1240,,4022,,1813,,2622,,1794,,372,,832,,477,,1860,,1773,,2317,,911,,1740,,1962,,1461,,240,F,224,F,1736,,2269,,2662,,2476,,2288,,2321,,2889,,9274,,9370,,14173,,15028,,17344,,20468,F,23806,F,33963,,32281,,26195,,24446,,34435,,34416,,43398,,62127,,65266, -Saudi Arabia,All,Import,Tonnes – net product weight,Tonnes – net product weight,9307,,16444,,18852,,28894,,38798,,42655,,49643,,51980,,46648,,46716,,38441,,39082,,40422,,47418,,37546,,46261,,37161,,46230,,43711,,64996,,58304,,61552,,64039,,72049,,78061,,88791,,89659,,99849,,127752,,141739,,158096,,160509,,123865,,125496,,219136,,220294,,241320,,245456,,244363,,243662,,247341,,250995,,234417, -Saudi Arabia,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,5805,,4253,,2808,,2532,,3536,,12214,,12950,,11346,,9508,,12661,,12613,,13977,,13364,,12906,,8761,,11201,,11106,,11153,,7084,,11550,F,11850,F,13340,F,14474,F,13330,F,10341,F,8553,F,7090,F,6475,F,7330,F,7461,F,7546,F,8825,F,10455,F -Saudi Arabia,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,618,,1455,,893,,348,,31,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,343,,691,,369,,84,,60,,884,,1276,,434,,567,,662,,777,,0,.,1,,1048,,1131,,1521,,1748,,2541,,4156,,0,.,0,.,3273, -Senegal,All,Export,Tonnes – net product weight,Tonnes – net product weight,45917,,57285,,62168,,64439,,78029,,74427,,91742,,93344,,94102,,95453,,93975,,79569,,72267,,108286,,120459,,142674,,79071,,52272,,93673,,103458,,107033,,112152,,111549,,124453,,88033,,83517,,91748,F,96885,,129001,,101793,,96184,,117650,,96968,,101787,,99688,,125361,,117967,,149979,,164089,,208866,,221295,,239055,,257085, -Senegal,All,Import,Tonnes – net product weight,Tonnes – net product weight,9211,F,17505,,22897,,13463,,16128,,20722,,15036,,23261,,21234,,24362,,21093,,19726,,35267,,49284,,52660,,31921,,24868,,17691,,19057,F,17730,F,15023,F,17841,F,10583,F,3562,F,963,F,526,,295,,336,,521,,562,,453,,1013,,1247,,422,,6614,,14315,,8820,,8914,,9644,,19384,,18631,,31109,,28393, -Senegal,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,73844,,73752,,77015,,92540,,102310,,102767,,105211,,103783,,110392,,109245,,108983,,132828,F,114473,F,115502,F,115959,F,143713,,125976,F,96934,F,102009,F,125686,,99969,,106426,F,96329,F,121173,F,94001,,94649,,93305,,98564,,107670,,105045,,101749,F,124414,,103836,,105168,,83698,,95710,,106701,,112303,,101399,F,93558,,131107,,239136,,139012,F -Senegal,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,5,,0,.,0,.,0,.,49116,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Serbia,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,575,,593,,747,,732,,764,,635,,610,,831,,1274,,1278,,2008,,1957,,2223, -Serbia,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,36165,,39716,,38718,,31036,,27082,,35897,,35937,,29557,,32899,,27919,,26307,,36248,,38602, -Serbia,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,200,F,230,F,183,F,215,F,268,F,191,F,240,F,285,F,300,F,307,F,465,F,495,F,460,F -Serbia,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,.,30,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Serbia and Montenegro,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,5624,F,159,F,30,F,372,F,938,,800,,647,,748,,387,,366,,381,,124,F,399,,625,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0, -Serbia and Montenegro,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,43887,F,18869,F,30967,F,38082,F,62227,,54599,,61877,,42808,,43417,,46194,,52861,,41674,F,52067,,43499,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0, -Serbia and Montenegro,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,43,,347,,449,,452,,400,,434,,650,,617,F,600,F,600,F,600,F,600,F,600,F,600,F,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0, -Seychelles,All,Export,Tonnes – net product weight,Tonnes – net product weight,437,,418,,375,,438,,677,,333,,619,,748,,664,,760,,380,,1922,,4684,,5183,,5727,,7627,,6644,,5189,,9066,,6846,,14622,,32722,F,26309,,41830,,48427,,74658,,75429,,137896,F,104878,F,124617,F,107703,F,102907,F,80312,F,80464,F,89503,,114234,,119361,F,106825,,90301,,117768,,166801,,159278,,166657, -Seychelles,All,Import,Tonnes – net product weight,Tonnes – net product weight,52,,67,,39,F,49,F,26,,29,,13,,21,,60,,40,,18,,2530,,7476,,6746,,7521,,9885,,7968,,9814,,8298,,12011,,19872,,20673,F,17636,F,4093,F,20302,,20265,,33195,,87054,,82552,,88003,,92457,,116360,,36332,,64259,F,71264,,71308,,64020,,61552,,67422,,68066,,75684,,77437,,78834, -Seychelles,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,636,,647,,643,,678,,912,,601,,918,,1041,,957,,1053,,1000,F,7206,,12087,,6295,,6146,,6657,,5280,,5791,,12703,,12897,,18569,,21127,,27450,,69911,,67129,,90634,,111552,,135278,,135002,,146529,,143278,,101243,,98736,,103282,F,115056,,98488,,92350,F,115606,,111030,,140978,,165664,,186239,,194113, -Seychelles,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,0,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,.,1,,1,,0,0,0,.,0,.,0,.,0,.,0,.,3,,0,.,0,.,0,.,0,.,4,,0,0,0,0,0,0,0,0,0,0,4,,4,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Sierra Leone,All,Export,Tonnes – net product weight,Tonnes – net product weight,1,,4,,0,.,69,F,440,F,850,F,940,F,1752,,741,,1605,,1500,,1404,,1513,,2525,F,4761,F,3840,F,3900,F,14452,,18165,,16050,,14036,,13448,F,7596,F,8192,F,6594,F,6185,F,7766,F,6318,F,5509,F,5940,F,5468,F,5574,F,5447,F,5554,F,5672,F,5670,F,6091,F,5420,F,5446,F,5993,F,5845,F,3823,F,3984,F -Sierra Leone,All,Import,Tonnes – net product weight,Tonnes – net product weight,4606,,7774,F,9450,F,11077,,23391,,28700,,6539,,11373,,7671,,8467,F,7140,,2514,,2458,F,2734,F,2600,F,2106,F,1638,F,1432,F,2120,F,2184,F,1922,F,2561,F,725,F,405,F,740,F,1046,F,720,,2142,F,1357,F,1163,F,931,F,1778,F,1529,F,2038,F,996,F,1366,F,1079,F,1371,F,2014,F,974,F,1654,F,1643,F,1762,F -Sierra Leone,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,17500,F,21050,,15400,,19605,,16700,F,17400,F,18400,F,18400,F,18500,F,30000,F,29800,F,29700,F,29800,F,30200,F,29100,F,38457,,37982,,37542,,37000,F,37000,F,36000,F,35000,F,33000,F,32000,F,30000,F,31000,F,33000,F,32000,F,35000,F,35000,F,35000,F,38000,F,38000,F,39000,F,38000,F,40000,F,40000,F,39000,F,41000,F,40000,F,40000,F,40000,F,40000,F -Sierra Leone,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,10,,0,- -Singapore,All,Export,Tonnes – net product weight,Tonnes – net product weight,29797,,48237,,57198,,64461,,72508,,71534,,79389,,78603,,81873,,78410,,102560,,121357,,124507,,127679,,136366,,151941,,139552,,130234,,161500,,123598,,94432,,87172,,85011,,108382,,112158,,102137,,88741,,87811,,102378,,109564,,96978,,86493,,71721,,66030,,68667,,57363,,52931,,48189,,35558,,44219,,43757,,39738,,36584, -Singapore,All,Import,Tonnes – net product weight,Tonnes – net product weight,131630,,146009,,150775,,156381,,161359,,168166,,174969,,159929,,181055,,161974,,195564,,189759,,203430,,190802,,199767,,209773,,217066,,209306,,228299,,140865,,116162,,116285,,90103,,175472,,182377,,173118,,177869,,215342,,227405,,253553,,244644,,239688,,225704,,221987,,223131,,223138,,215681,,209369,,207398,,207868,,209231,,199087,,198175, -Singapore,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,2490,,7191,,4958,,6003,,3632,,5507,,6562,,7532,,9104,,7585,,9026,,12414,,15237,,21724,,27648,,24230,,24105,,31127,,27193,F,27310,F,25322,F,35931,,29369,,44807,,33424,,33921,,38053,,49205,,35717,,43450,,48614,,48625,,43375,,46031,,46668,,46174,,45566,,46104,,46130,,46112,,46771,F,49419,F,47167,F -Slovakia,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1049,,643,,593,,686,,976,,1162,,666,,677,,531,,762,,918,,1523,F,1527,,2337,,1411,,1507,F,1419,,1991,F,2430,F,2183,,2428,,2266,,1938,,1669,,3381,,3806, -Slovakia,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,26726,,32947,,30197,,34048,,35109,,34474,,29836,,29895,,28182,,30017,,26728,,26360,,26278,,28496,,23748,,24479,,23891,,25985,,25269,,24536,,26626,,29270,,30834,,30117,,34141,,29832, -Slovakia,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1238,,1486,,1317,,704,,800,,459,,781,,887,,999,,829,,881,,1180,,953,,1263,,1199,,1071,,623,,5709,,5542,,5232,,5188,,6235,F,6754,F,5790,F,6735,,5936,F -Slovenia,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,4139,,2701,,2666,,1608,,2514,,2068,,2071,,2222,,2372,,2345,,2094,,2195,,3415,,7556,,5739,,4691,,5143,,5026,,5345,,4991,,4128,,4903,,6194,,7517,,11948,,9605,,9155, -Slovenia,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,12575,,12957,,14739,,14266,,13248,,12493,,11851,,12490,,12211,,13624,,12933,,13311,,15741,,19120,,19061,,17812,,18354,,17688,,18193,,18065,,16567,,17355,,18505,,19858,,22896,,22593,,22660, -Slovenia,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,3265,F,2084,F,2000,F,1148,F,1539,F,1664,F,2010,F,2061,F,2129,F,2238,F,1789,F,1583,F,2495,F,2565,F,3020,F,2975,F,1930,F,1750,F,1220,F,1595,F,1500,F,2170,F,1950,F,2330,F,2710,F,2440,F,2550,F -Solomon Islands,All,Export,Tonnes – net product weight,Tonnes – net product weight,12466,,10652,,11265,,24352,,22536,,24556,,16522,,19808,,34334,,28518,,40862,,28147,,37331,,29801,,20988,,54477,,34449,,22038,,31905,,47384,,38055,,37795,,42088,,31251,,13803,F,13124,F,12778,F,21214,F,18159,F,14921,F,21923,,14558,,12561,F,14554,F,14870,F,17156,F,23083,,22562,,35522,F,37032,F,36821,F,31620,F,35879, -Solomon Islands,All,Import,Tonnes – net product weight,Tonnes – net product weight,272,,326,,320,,340,,502,,385,,493,,279,,422,,701,,806,,222,,447,,734,,107,,147,,61,,11,,16,,50,,68,,60,F,8,F,26,F,117,F,335,F,591,,214,,517,,479,,1322,,1806,,1207,F,1024,F,1567,F,860,,734,,1480,,835,,540,F,649,F,643,,771, -Solomon Islands,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,13787,,10498,,15641,,22220,,21272,,24042,,18166,,32269,,33875,,29441,,41867,,28960,,38239,,30749,F,24333,F,45193,F,33722,F,21832,F,31725,F,46310,F,29740,F,34379,F,38887,F,29067,F,11150,F,12500,F,14000,F,20300,F,19100,F,15800,F,22400,F,16800,F,13700,F,14100,F,14159,F,20789,F,22521,F,20760,F,35374,F,36887,F,44563,,36800,F,53046, -Solomon Islands,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,0,0,-,0,- -Somalia,All,Export,Tonnes – net product weight,Tonnes – net product weight,3250,,1187,,1206,,1108,,682,,3540,,2931,,1163,,890,F,1023,F,873,F,1511,F,3016,F,4703,F,5552,F,3490,F,4070,F,3598,F,2923,F,4654,F,2971,F,5075,F,1580,F,1416,F,1119,,1971,F,3584,F,2581,F,4526,F,3548,F,2990,F,2441,F,3515,F,4157,F,1187,F,1473,F,979,F,1347,F,1494,F,2364,F,2937,F,3656,F,4225,F -Somalia,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,0,229,,0,0,0,0,99,,0,.,7,,62,,0,.,0,.,0,.,0,.,0,.,2,F,5,F,5,F,52,F,45,F,39,F,0,.,26,F,0,.,0,.,63,F,71,F,15,F,111,F,142,F,281,F,295,F,1145,F,684,F,907,F,1431,F,901,F,1371,F,1154,F,2004,F,2417,F,3761,F,3471,F,3447,F,2893,F -Somalia,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,1885,,744,,1734,,1333,,815,,662,,981,,971,F,942,F,1014,F,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -South Africa,All,Export,Tonnes – net product weight,Tonnes – net product weight,125050,F,66250,F,58650,F,105840,F,65840,F,65540,F,46530,F,64030,F,49520,F,65528,,74332,F,147318,F,78289,F,49233,F,60362,,73870,,150568,,122671,F,176798,,95913,,85420,,99156,,133410,,126001,,162359,,155941,,168678,,183462,,142601,,177045,,142551,,144072,,134649,,147099,,179803,,165779,,177369,,135597,,182529,,154993,,205840,,182775,,193696, -South Africa,All,Import,Tonnes – net product weight,Tonnes – net product weight,8820,,11204,,22817,,16602,,44334,,112065,,107372,F,104304,F,128881,,143016,,123134,,104355,,51884,,197968,F,212240,,225839,,177440,,140417,,275967,,324569,,148324,,213522,,52742,,38056,,44221,,51655,,35310,,42655,,53743,,56176,,64854,,91490,,109799,,122236,,99330,,97592,,136201,,139906,,196441,,164895,,193079,,214601,,276770, -South Africa,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,480048,,377257,,409938,,412663,,341008,,344919,,318095,,343572,,279968,,303235,,321480,,510649,,423598,,302838,,215462,,217256,F,265156,,234569,,240934,F,210329,F,208542,F,200615,F,264772,F,246507,F,274612,F,291943,F,299707,F,296681,F,280449,F,284516,F,220359,F,233868,F,222091,F,216005,F,244941,F,193800,F,236134,F,163561,F,229304,F,211159,F,234308,F,200434,F,200977,F -South Sudan,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,145,F,289,F,253,F,182,F,81,F,3,F,211,F -Spain,All,Export,Tonnes – net product weight,Tonnes – net product weight,232009,,190077,,200122,,210123,,157112,,258846,,229833,,203325,,203110,,225662,,224238,,243874,,339747,,347161,,341560,,377664,,310709,,383690,,441024,,471671,,558032,,639918,,678406,,762229,,802244,,924102,,818452,,860632,,866025,,922671,,922208,,950107,,948970,,1032109,,1043455,,1078478,,1038625,,992512,,1115313,,1116949,,1115898,,1186375,,1206754, -Spain,All,Import,Tonnes – net product weight,Tonnes – net product weight,136713,,128481,,200558,,265451,,288489,,272787,,329358,,269843,,294989,,320952,,323748,,579869,,770128,,734503,,841608,,904256,,803091,,971570,,999676,,1015591,,1061899,,1126822,,1262082,,1282166,,1373416,,1533284,,1458374,,1612166,,1559651,,1583190,,1657053,,1683145,,1587701,,1574281,,1641003,,1640452,,1523887,,1503854,,1617854,,1669245,,1715516,,1768442,,1766380, -Spain,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,491123,,473218,,618253,,567132,,562282,,545393,,541752,,535984,,563572,,601829,,574483,,641835,,765109,,793021,,775640,,828375,,944738,,914103,,1000906,,1050209,,992982,,998314,,1085594,,1144990,,1243091,,1218886,,1299720,,1302900,,1240902,,1165981,,1166195,,1210952,,1174560,,1173595,,1128699,,1198887,,1185543,,1176482,,1265978,,1264605,,1210414,,1195860,,1219671, -Sri Lanka,All,Export,Tonnes – net product weight,Tonnes – net product weight,1886,,1965,,2962,,3134,,2347,,2721,,3263,,2548,,3580,,3281,,3457,,2466,,3428,,3631,,2959,,2270,,3892,,4448,,4130,,5708,,7654,,8680,,11513,,9681,,19719,,15530,,14081,,16238,,14938,,17240,,21596,,22231,,21574,,19707,,19127,,19563,,19292,,24729,,27304,,18217,,18225,,25314,,28337, -Sri Lanka,All,Import,Tonnes – net product weight,Tonnes – net product weight,7648,,5220,,5190,,18982,,23200,,7512,,15419,,15573,,46435,,38034,,33991,,39677,,38778,,26577,,50766,,57661,,63151,,38190,,35566,,60365,,66846,,82091,,80635,,79645,,93634,,89165,,84622,,86241,,77560,,88946,,90496,,95530,,88762,,86293,,94222,,94874,,85947,,92545,,90997,,134867,,128327,,116188,,94320, -Sri Lanka,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,17102,,17839,,19923,,13656,,16901,,21146,,23228,,16186,,12393,,20055,,20825,,16745,,17901,,19049,,16377,,16536,,18017,,18750,,19270,,20094,F,23945,,26175,,31187,,31732,,40680,,31469,,37634,,35736,,38050,,23030,,51500,,58890,,62220,,61860,,73140,,82420,,92150,,107120,,98768,,75390,,84020,,87700,,89931, -Sri Lanka,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2,,3,,0,.,8,,17,,0,.,0,.,14,,0,.,0,.,66,,88,,111,,92,,419,F,0,0,257,,14,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,188,,0,.,4,,592,,290,,219,,0,- -St. Pierre and Miquelon,All,Export,Tonnes – net product weight,Tonnes – net product weight,2612,,2923,,2429,,2459,,4251,,4936,,3680,,3253,,4692,,6807,,7565,,8614,,4872,,6816,,7948,,8852,,6893,,34,,79,,83,,260,F,761,F,1301,F,1765,F,2103,F,1296,F,1386,F,995,F,1149,F,1446,F,1672,F,1878,F,1764,F,1069,F,981,F,615,F,802,F,996,F,569,F,1557,F,1728,F,783,F,758,F -St. Pierre and Miquelon,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,39,,47,,41,,39,,126,,85,,72,,147,,105,,115,F,105,F,110,F,101,,81,,100,,162,F,980,F,550,F,531,F,592,F,714,F,65,F,464,F,110,F,27,F,552,F,591,F,360,F,226,F,26,F,10,F,12,F,30,F,28,F,42,F,62,F,55,F,45,F,32,F,38,F -St. Pierre and Miquelon,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,3103,F,3600,F,4086,,4342,,4284,,4863,F,4203,F,3653,F,4848,F,7263,F,7541,F,8851,F,4941,F,6713,F,7958,F,7285,F,5510,F,0,.,100,,375,,378,F,712,F,1201,F,1235,F,1465,F,1014,F,930,F,990,F,730,F,1125,F,1277,F,1479,F,1350,F,908,F,333,F,153,F,307,F,112,F,112,F,108,F,76,F,201,F,59,F -Sudan,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,195,,705,F,717,F,1379,F,1118,F,1730,,2707, -Sudan,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2987,,2848,F,2267,F,3128,,2718,F,1713,,2466, -Sudan,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2003,F,2288,F,2360,F,2582,F,2746,,2562, -Sudan (former),All,Export,Tonnes – net product weight,Tonnes – net product weight,18,,0,.,87,,244,,530,,639,,600,F,407,,9,,0,.,100,,64,,464,,20,,20,F,25,F,30,F,56,F,60,F,102,,323,,606,,802,,588,,616,,540,,1542,,1993,,2942,,2714,,391,,302,F,937,,777,,184,,494,,0,,0,,0,,0,,0,,0,,0, -Sudan (former),All,Import,Tonnes – net product weight,Tonnes – net product weight,24,,76,,44,,33,,633,,526,,813,,406,,89,,470,F,60,,209,,224,,943,,900,F,780,F,200,F,200,F,100,F,228,,121,,611,,603,,872,,973,,471,,572,,300,F,283,F,386,,1998,,1693,,1375,,2412,,2613,,3478,,0,,0,,0,,0,,0,,0,,0, -Sudan (former),All,Processed production,Tonnes – net product weight,Tonnes – net product weight,1400,,1270,,2130,,1880,,1980,,2220,,1760,,1900,,3111,,2965,,2256,,2500,F,2800,F,2860,F,3320,F,3320,F,3300,F,3300,F,3300,F,3750,,4000,,4000,,5000,,5000,,5000,F,7500,,7500,,7500,F,7500,F,7500,F,7500,F,11266,,12954,,14898,,16000,F,16450,F,17632,F,0,.,0,.,0,,0,,0,,0, -Sudan (former),All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,6,,0,-,0,-,0,,0,,0,,0,,0,,0,,0, -Suriname,All,Export,Tonnes – net product weight,Tonnes – net product weight,471,,673,,184,,295,,372,,1157,,1196,,1536,,1476,,1758,,815,,1851,,715,,926,,1178,,705,,916,,1267,,6791,,6284,,7265,,11094,,5599,F,14488,,16226,,17202,,10660,F,11206,F,11617,F,10228,F,11005,F,11272,F,14562,F,17262,F,15863,F,15788,F,17031,F,18200,F,17618,F,18714,F,19715,F,24091,F,17351,F -Suriname,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,2469,,1650,F,2000,F,2463,,2120,,2641,,231,,165,F,10,F,25,F,50,,9,,11,,36,,57,,60,F,437,,351,F,1209,,1015,,1598,,1283,,1884,,1569,F,2089,,2420,,2252,,2469,,1688,,2226,,1623,,1773,,2111,,1337,,1840,,2138,,2026,,2310,F,2164,,2241,,2374, -Suriname,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,471,,923,,389,,484,,502,,650,,519,,605,,541,,535,,878,,1004,,738,,800,F,800,F,800,F,1734,,2397,,5610,F,5600,F,5600,F,5800,F,7100,F,8700,F,9700,F,10900,F,11000,F,13132,,13786,,12357,,13200,,16242,,12059,,12891,,12880,F,13430,F,13700,F,13820,F,13420,F,15540,F,16400,F,16450,F,16450,F -Suriname,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,46,,44,,322,,272,,491,,443,,499,,305,,0,-,22,,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Sweden,All,Export,Tonnes – net product weight,Tonnes – net product weight,92139,,85294,,66231,,109157,,127691,,137592,,135388,,134489,,138716,,107966,,91815,,77730,,98250,,120151,,157137,,154920,,162084,,173864,,195939,,266000,,279071,,281389,,312603,,281400,,306942,,296945,,276175,,309366,,356865,,396722,,471315,,472007,,510936,,559890,,624757,,613025,,666234,,707587,,729470,,831465,,814874,,779652,,850167, -Sweden,All,Import,Tonnes – net product weight,Tonnes – net product weight,161698,,157811,,169862,,201552,,195234,,175600,,207061,,207557,,221558,,214029,,207392,,174701,,140663,,148112,,159328,,145048,,140733,,122775,,148376,,154523,,169915,,182934,,181697,,204976,,212999,,224698,,235293,,288095,,334677,,377925,,404977,,469196,,502372,,533393,,576246,,609214,,689300,,709427,,755547,,825325,,806859,,736587,,820477, -Sweden,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,76707,,72874,,75937,,74630,,78863,,78152,,76365,,81168,,85872,,79998,,82468,,78813,,82362,,77791,,78132,,76031,,72104,,104920,,90946,,73602,,89964,,128843,,127566,,114026,,98460,,103158,,115469,,124972,,117702,,86923,,93746,F,103537,F,120446,,124583,F,122913,F,131145,F,134368,,124413,F,123681,,138036,,136082,F,119583,F,132062,F -Switzerland,All,Export,Tonnes – net product weight,Tonnes – net product weight,603,,573,,245,,355,,307,,246,,290,,294,,529,,653,,537,,551,,804,,860,,888,,796,,662,,528,,551,,589,,368,,306,,314,,282,,280,,575,,642,,738,,993,,1223,,1659,,1790,,1857,,1433,,1970,,1941,,1977,,2829,,2240,,1776,,1322,,1056,,1237, -Switzerland,All,Import,Tonnes – net product weight,Tonnes – net product weight,119431,,122795,,135405,,133564,,129618,,142100,,129934,,135582,,123094,,124520,,120746,,111658,,105428,,102615,,96293,,89791,,87315,,82016,,87926,,71954,,67456,,66267,,65486,,67113,,67884,,70300,,68172,,68337,,68323,,67187,,71492,,71590,,72689,,74638,,75779,,76877,,74999,,79784,,78474,,79768,,80200,,77765,,77561, -Switzerland,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,850,,864,,813,,1260,,1260,,1360,,1560,,1660,,1660,,1970,,1970,,1970,,1170,,1170,,1200,,1220,,1220,,1220,,980,,900,,1000,,1000,,1115,,1100,,1100,,1100,,1100,,1100,,1200,,1200,,1200,,1200,,1200,,2935,,2910,,3108,,3045,,3309,,3310,,3310,,3302,F,3752,F,3294, -Syrian Arab Republic,All,Export,Tonnes – net product weight,Tonnes – net product weight,59,,59,,117,,77,,166,,61,,26,,42,,100,,81,,0,.,35,,53,,65,,0,.,48,,38,,64,,56,,157,,21,F,76,F,55,F,11,F,54,F,52,F,109,F,40,,47,F,157,,153,,367,F,32,,149,,72,,89,F,47,F,35,F,15,F,48,F,53,F,57,F,69,F -Syrian Arab Republic,All,Import,Tonnes – net product weight,Tonnes – net product weight,3840,,8176,,7340,,10899,,10120,,12095,,9774,,13510,,13618,F,9721,,1051,F,1341,,318,F,1251,,490,F,271,F,370,F,2479,F,2126,F,10548,,5494,,9083,F,15550,F,17307,F,21208,F,10757,,22827,,21437,,15760,,14013,,22692,,23665,,22118,,31864,,29450,,31502,F,22309,F,22394,F,23805,F,13501,F,12237,F,12138,F,16385,F -Taiwan Province of China,All,Export,Tonnes – net product weight,Tonnes – net product weight,165882,,203478,,222469,,225153,,238730,,230636,,242512,,270529,,282724,,315985,,408293,,459750,,529430,,378251,,408234,,381318,,482955,,628754,,593772,,573907,,565182,,567396,,687746,,666185,,697851,,689235,,729511,,706105,,671466,,680962,,659906,,757694,,639278,,690454,,676627,,657571,,687385,,717176,,780376,,787224,,685019,,716259,,685568, -Taiwan Province of China,All,Import,Tonnes – net product weight,Tonnes – net product weight,95323,,105382,,144854,,169303,,163758,,158762,,241046,,219435,,279436,,326598,,359262,,468014,,450968,,493805,,516457,,487664,,471991,,575855,,578568,,550738,,504773,,463402,,312495,,436670,,454497,,423693,,388207,,377958,,385364,,380489,,342929,,318222,,392901,,465620,,472318,,482373,,537100,,464746,,486638,,457142,,430263,,470975,,495226, -Taiwan Province of China,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,108807,F,131390,F,137907,F,133324,F,120376,F,113600,F,149092,F,143596,F,125413,F,141876,F,189509,F,194371,F,220441,F,317421,,350241,F,341440,,475758,F,570195,F,563294,F,593333,F,608664,,613260,F,725730,F,743246,F,679236,F,735482,F,798101,F,811226,F,887687,F,741965,F,697648,F,927966,,859559,,688720,F,696527,F,679888,F,664291,F,674627,F,667861,F,630599,F,499506,F,615066,F,576589,F -Taiwan Province of China,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2653,,4393,,6836,,7514,,1959,,2428,,1084,,939,,2696,,2496,,1340,,2084,,627,,565,,565,,3592,,317,,884,,392,,1016,,290,,642,,1866,,0,-,0,-,0,-,0,- -Tajikistan,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -Tajikistan,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,131,F,127,F,101,F,71,F,154,F,129,F,93,F,189,F,344,F,576,F,686,F,504,F,1404,F,1172,F,1729,F,1144,F,1161,F,1790,F,2090,F,1839,F,2320,F,2000,F,1247,F,2696,F,2030,F -"Tanzania, United Rep. of",All,Export,Tonnes – net product weight,Tonnes – net product weight,269,,270,F,154,,275,,217,,222,,185,,968,,664,,350,F,650,F,889,,1320,F,868,F,1719,,1540,,1398,,7763,,12491,,17350,,22596,,27213,,48698,,32998,,49843,,55678,,54997,,59415,,61495,,58794,,49104,F,62836,,71883,,52885,,54881,,56928,,56760,,49270,,50139,,45968,,44469,,48779,,54308, -"Tanzania, United Rep. of",All,Import,Tonnes – net product weight,Tonnes – net product weight,2402,,2595,F,4105,,1855,,1545,,624,,1055,F,1215,F,1145,F,296,F,170,F,40,F,400,F,60,F,213,,162,,293,F,593,F,229,F,407,,930,,607,,281,,197,,340,,276,,185,,311,,421,,572,,2951,,6877,,6878,,6259,,5946,,3970,,6419,,9291,,19543,,19644,,25033,,17302,,12110, -"Tanzania, United Rep. of",All,Processed production,Tonnes – net product weight,Tonnes – net product weight,52500,F,47337,,46436,,30241,,44600,,38371,,35145,,37231,,42877,,46870,,48519,,63614,,65168,,67289,,72210,,56893,,56429,,65792,,71000,,74599,,81058,F,93996,,104257,F,86361,,92757,F,101885,,96568,,104494,,99968,,99230,F,91120,,57550,,31150,,41147,,39772,,39660,,39553,F,38574,,43354,,41164,,38187,,12622,,38097, -"Tanzania, United Rep. of",All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,19,,1,,0,.,0,.,0,0,0,.,21,,4,,0,-,0,-,0,0,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Thailand,All,Export,Tonnes – net product weight,Tonnes – net product weight,131774,,181107,,239429,,284423,,268965,,313351,,308378,,336076,,402586,,456752,,591349,,673757,,662347,,738995,,765400,,914515,,953671,,944448,,1052863,,1019665,,971948,,1013936,,1160453,,1204215,,1162099,,1250204,,1280563,,1440364,,1436475,,1570762,,1743974,,1823612,,1755255,,1732874,,1862012,,1762955,,1762131,,1604445,,1664372,,1545968,,1515437,,1354237,,1394091, -Thailand,All,Import,Tonnes – net product weight,Tonnes – net product weight,24868,,18575,,29183,,79938,,43530,,46947,,45948,,58562,,118741,F,152392,,266323,,224575,,315751,,454343,,514167,,723303,,711734,,758329,,966045,,918851,,788194,,701113,,716304,,913316,,813789,,977656,,1006347,,1078966,,1240567,,1445348,,1470636,,1407414,,1533690,,1585850,,1586764,,1668020,,1662765,,1667819,,1624878,,1620658,,1868170,,1924536,,2129605, -Thailand,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,280586,,365811,,512077,,450065,,460073,,482184,,593569,,657037,,729636,,756607,,874279,,1017980,,1202594,,1157810,,1253180,,1636977,,1669408,,2214916,,2388132,,2396085,F,2255829,F,2373476,F,2512237,F,2634236,,2602625,,2700518,F,2948355,F,3121441,F,3154837,F,2957357,F,2916029,F,2679622,F,2676513,F,2598516,F,2659717,F,2818060,F,2701894,F,2549228,F,2589951,F,2489458,F,2397765,F,2241921,F,2317296,F -Thailand,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,190,,335,,441,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,685,,1433,,0,-,0,-,0,-,0,- -Timor-Leste,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1,,1,,24,F,10,F,27,F,44,F,0,.,0,0,31,F,16,,77,F,47,F,16,F,132,F,0,- -Timor-Leste,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,101,,101,,120,F,144,F,213,F,602,F,1316,F,1981,F,2429,F,2378,F,2999,F,2552,F,2847,F,5004,,3856,F -Timor-Leste,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1,,1,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Togo,All,Export,Tonnes – net product weight,Tonnes – net product weight,6,,37,,27,,8,,72,,22,,39,,52,,32,,94,,8,,145,,294,,164,,211,,109,,168,,541,,1293,,3407,,216,F,578,,674,,3610,,6441,,9448,,9132,,7340,,6964,,6121,,5018,F,324,,2366,,3814,,485,,834,,74,F,176,F,225,,271,,66,F,47,F,144,F -Togo,All,Import,Tonnes – net product weight,Tonnes – net product weight,13127,,11030,,8576,,15105,,14048,,14764,,16728,,9875,,8317,,15180,,14992,,18832,,26448,,25408,,26201,,28822,,28992,,18913,,36999,,38231,,53416,,46603,,36442,,32346,,30437,,28089,,18037,,20293,,13181,,21820,,15865,F,14062,,10983,,15577,,39628,,53305,,54281,,51685,F,49464,,60512,,47887,,58939,,66579, -Togo,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,1100,,2100,,3900,,2000,,2300,,2500,,3600,,3610,,3430,,3674,,2725,,3600,,3720,,3810,,3580,,2813,,2826,,3851,,2870,,4479,,5145,F,5517,,8202,,12293,,12229,,10878,,9788,,14486,,14006,,15851,,21147,,19905,,19501,F,21216,,27535,,24010,F,17310,F,15715,,14862,,21497,,32201,,27000,,24910, -Togo,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,6194,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,450,,51,,177,,25,,28,,69,,0,- -Tonga,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,70,F,0,0,2,,3,,5,,7,,51,,321,,334,,1152,,463,,377,,305,,362,,433,,449,,507,,381,,313,,973,,604,,488,F,1272,,457,,2807,,2623,,2956,,2005,,2023,,1754,,561,,498,,627,,644,,1137,,785,,1570,,751,F,618,F,603,F,471,F -Tonga,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,205,F,851,,335,,313,,532,,218,,317,,248,,161,,260,,173,,237,,145,,304,,249,,354,,453,,562,,604,,618,,745,,713,F,695,,904,,880,,894,,1074,,943,,761,,1249,,915,,1202,,875,,905,,518,,825,,743,,594,F,697,F,906,F,921,F -Tonga,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,7,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Trinidad and Tobago,All,Export,Tonnes – net product weight,Tonnes – net product weight,784,,674,,501,,466,,1031,,1113,,552,,219,,134,,310,,1032,,715,,1049,,1105,,1149,,1090,,1922,,3850,,4409,,4791,,5576,,4931,,6985,,8157,,4366,,5098,,5076,,4008,,3091,,3065,,6506,,3939,,3877,,4383,,3524,,3902,F,3619,F,4043,F,3802,F,5439,F,4877,F,4363,F,4061,F -Trinidad and Tobago,All,Import,Tonnes – net product weight,Tonnes – net product weight,3544,,2658,,4687,,3614,,4265,,6418,,5194,,6141,,6305,,6015,,8712,,7540,,4319,,3973,,2231,,2924,,2165,,2196,,2568,,3073,,3172,,4337,,4504,,5674,,4137,,5903,,8679,,7111,,8405,,12939,,8953,,9058,,8917,,9248,,8886,,12099,F,14109,F,16389,F,13375,F,14311,F,10600,F,12813,F,11416,F -Trinidad and Tobago,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,9,,95,,4,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,0,0,.,310,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Tunisia,All,Export,Tonnes – net product weight,Tonnes – net product weight,4073,,4917,,6909,,7424,,6368,,6679,,3638,F,7364,,8562,,9358,,13004,,14072,,16985,,14729,,15532,,13403,,11844,,15135,,13440,,9863,,12738,,15297,,15889,,11834,,13452,,15051,,17438,,14827,,15382,,21613,,20349,,24459,,22022,,20597,,23930,,31079,,25776,,22044,,21803,,21436,,21461,,20831,,23388,F -Tunisia,All,Import,Tonnes – net product weight,Tonnes – net product weight,331,,530,,723,,266,,430,,355,,455,,1529,,813,,1004,,13,,26,,1176,,737,,1356,,356,,491,,789,,4555,,7646,,5364,,5924,,7904,,9562,,11647,,17336,,17811,,28264,,26287,,37903,,39651,,39794,,46794,,47045,,45619,,43548,,47213,,38202,,42653,,29802,,35636,,32598,,31487,F -Tunisia,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,5416,,6008,,8315,,8421,,8312,,9446,,9991,,8080,,11105,,12456,,14119,,17808,,22377,,18736,,18318,,14915,,14776,,14482,F,16088,,15588,F,14491,F,19446,F,21189,,21144,,21246,F,21580,F,28092,,31770,,23854,,31653,,32902,,33368,,26458,,25740,,21908,,30519,,38798,,34111,F,44289,,40457,,35613,,40312,,47857, -Turkey,All,Export,Tonnes – net product weight,Tonnes – net product weight,6929,,7185,,6756,,8112,,9785,,15427,,15790,,18834,,27224,,22848,,23699,,33977,,36805,,32710,,28937,,18887,,15570,,10786,,29631,,37664,,27781,,35767,,28076,,31005,,39433,,27476,,39996,,47920,,41282,,46243,,47548,,58581,,73150,,62018,,72663,,78598,,83093,,105909,,128199,,126965,,156429,,162796,,193453, -Turkey,All,Import,Tonnes – net product weight,Tonnes – net product weight,514,,755,,431,,31,,73,,581,,110,,140,,4565,,746,,2872,,3605,,2552,,22175,,51799,,47619,,51901,,33905,,53372,,57924,,66124,,87874,,75080,,98662,,75725,,49853,,37208,,63776,,110827,,89392,,116970,,128121,,133467,,144236,,159326,,133198,,159529,,177472,,203344,,236793,,235991,,265555,,288992, -Turkey,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,5104,,24370,,24960,,46898,,57004,,39479,,47285,,56049,,45665,F,30340,F,28380,F,42924,F,42047,F,23028,F,20557,,27605,F,23453,F,46503,F,37855,,35840,,20585,,33836,,27333,,39643,,52403,,22122,,26693,F,26416,,25049,,24914,,37115,,70299,,81704,,83970,,95905,F,106678,F,95170,F,97378,F,86825,F,109570,F,100550,F,110345,F,106520,F -Turkmenistan,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1071,F,622,F,311,F,152,,1754,F,1645,F,2408,F,805,F,592,F,114,F,520,F,142,F,94,F,71,F,78,F,0,0,0,.,0,.,0,.,0,.,0,.,0,.,2,F,0,.,0,. -Turkmenistan,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,713,F,158,F,305,F,244,F,158,F,18,F,65,F,173,F,139,F,370,F,396,F,697,F,541,F,633,F,926,F,1085,F,2149,F,1543,F,2383,F,2302,F,2493,F,2769,F,2049,F,953,F,698,F -Turks and Caicos Is.,All,Export,Tonnes – net product weight,Tonnes – net product weight,78,F,140,F,129,F,159,F,165,F,491,F,367,F,440,F,517,F,512,F,754,F,477,F,452,F,306,F,239,F,313,F,434,F,577,F,600,F,667,F,513,F,527,F,396,F,415,F,574,F,632,F,543,F,473,F,730,F,1268,F,925,F,1371,F,839,F,816,F,530,F,206,F,200,,224,F,465,F,459,F,247,F,132,F,181,F -Turks and Caicos Is.,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,22,F,30,F,36,F,26,F,3,F,11,F,15,F,43,F,94,F,10,F,32,F,286,F,327,F,377,F,345,F,362,F,415,F,769,,1209,,1412,,1410,,1519,,999,F,1401,,1685,,1486,F,1601,F,1628,F,1277,F,1350,F,1420,F -Tuvalu,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,264,,264,F,150,F,123,F,100,F,120,F,103,F,100,F,395,F,640,F,1501,F,1640,F,1980,F,2320,F,2674,F,2970,F,8813,F,4879,F,5308,F,9687,F,5325,F,4337,F,5364,F,3432,F,7481,F -Tuvalu,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,16,,22,,25,F,5,F,0,0,19,,12,,36,,36,,16,,29,F,45,,21,F,76,F,65,F,11,F,41,F,11,F,101,F,26,F,44,F,127,F -Tuvalu,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,153,,303,,0,.,2,,2,,2,,2,,2,,2,,2,,3,,3,,564,,263,F,150,F,100,F,100,F,100,F,100,F,70,F,70,,63,,39,,50,,2051,F,2408,F,2770,F,3057,F,8923,F,4925,F,4185,F,9046,F,5159,F,3432,F,5154,F,3220,F,5825,F -Uganda,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1664,,4687,,4851,,6943,,7216,,13471,,16396,,9839,,13320,,9596,,14911,,17318,,25534,,26555,,31958,,39324,,36920,,31953,,27249,,23106,,23033,,19720,,21690,,20486,,17597,,17876,,19039,,18530,,24594, -Uganda,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,38,F,0,0,24,,22,,88,,29,,290,,794,,310,,423,,326,,381,,975,,626,,832,,1352,,1310,,1256,,1942,,2176,,1998,,4934,,5805, -Uganda,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1664,F,4687,F,4851,F,5681,F,7094,F,13471,F,16396,F,9839,F,12866,F,9476,F,36191,,37389,F,43994,F,27671,F,49666,F,54886,F,48981,F,44881,F,39402,F,33549,F,30498,,30460,,30704,,32299,,30133,,29587,,31830,,27950,,51189, -Uganda,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,134,,0,0,363,,0,.,36,,108,,31,,107,,511,,550,,477,,628,,729,,641,,762,,0,- -Ukraine,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,80635,F,116459,,118386,,93114,,145710,,100260,,107435,,83901,,56819,,40085,,33053,,17274,,20620,,21940,,25948,,24111,,60465,,56277,,49062,,55778,,53220,,41791,,10759,,9124,,10336,,10595, -Ukraine,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,94338,F,79388,,127101,,178972,,186528,,246897,,177162,,234720,,322431,,306262,,297100,,306698,,420947,,402238,,469565,,583328,,451506,,362903,,396647,,430485,,470701,,349384,,230908,,299753,,328608,,380570, -Ukraine,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,112502,F,114728,F,89965,F,140848,F,104035,F,116519,F,99622,F,73750,F,68600,F,68700,F,67500,F,86300,F,86200,F,111900,F,175942,,173573,,152984,,142010,,141054,,147716,,71900,,59838,,62543,,64711,,65745, -Un. Sov. Soc. Rep.,All,Export,Tonnes – net product weight,Tonnes – net product weight,527000,,479954,,530322,,546241,,549853,,434236,,351614,,543914,,567538,,640177,,823951,,771176,,1015396,,879321,,1251930,,819854,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0, -Un. Sov. Soc. Rep.,All,Import,Tonnes – net product weight,Tonnes – net product weight,31600,,50469,,79585,,111561,,181938,,57298,,44196,,320086,,431229,,504288,,537503,,501224,,663080,,597908,,552225,,226970,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0, -Un. Sov. Soc. Rep.,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,5851606,,5447499,,5366098,,5313805,,5455861,,5682981,,5744682,,5576512,,5932387,,6148911,,6256093,,6109639,,6268760,,6252345,,5982489,,5542012,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0, -United Arab Emirates,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,782,,462,,771,,2063,,2861,,4500,,5841,,7361,,4900,F,3584,,3800,F,1752,,5002,F,4676,F,5369,F,7033,F,10534,F,13544,F,19154,F,7388,,7675,,10436,F,16569,,21696,F,14972,,15151,,18790,,22270,,30718,F,26446,,34140,,33059,,46576,,40661,,36884,,54243, -United Arab Emirates,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,7515,,7257,,14279,,12617,,8760,,8143,,6640,,7974,,6642,F,6003,,6000,,6262,,6677,,9979,,8034,F,8046,,15586,,9274,F,9631,F,8555,F,11183,F,34542,F,44944,F,53961,F,53102,,75347,,70022,F,53676,,59687,F,89998,,105716,,137200,,161869,,134013,F,200000,,217015,,229086,,235601,,243195,,243936,,257768, -United Arab Emirates,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,25000,F,25000,F,25000,F,25000,F,25000,F,26000,,22500,,18960,,19000,,18000,,18000,,16700,,16700,,20000,,21500,,20600,,22300,,22300,,23000,,22308,F,22978,F,20548,F,22429,,21727,,21083,,21346,,23526,,19981,,18600,,16704,,18935,F,18383,F,15180,F,15580,F,16050,F,15510,F,14300,F,14420,F,14620,F,14670,F,14470,F,14720,F,15050,F -United Arab Emirates,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,4153,,6402,,4764,,5501,,2579,F,3718,,1484,,958,,793,F,1813,F,5000,F,8731,,5082,,4150,F,4150,F,2991,,2118,,0,.,0,.,0,.,0,.,6539,F,9303,F,7062,F,8148,,12556,,69,,16371,,82,,8467,,9318,,5530,,7674,,14218,,19060,,30520,,24527,,20407,,22547,,25639,,46712, -United Kingdom,All,Export,Tonnes – net product weight,Tonnes – net product weight,165291,,188510,,377595,,468435,,411504,,311889,,354093,,421554,,359938,,403786,,450676,,457079,,447955,,489761,,473048,,556385,,603454,,598293,,601383,,590818,,599852,,613540,,762005,,695649,,674751,,699710,,626771,,687185,,679026,,687371,,630075,,652245,,622708,,707886,,766976,,697235,,759639,,734459,,878050,,820445,,788220,,855328,,811190, -United Kingdom,All,Import,Tonnes – net product weight,Tonnes – net product weight,691955,,618719,,679753,,799903,,814317,,743129,,797361,,718773,,776142,,903505,,841929,,886929,,910540,,977336,,981765,,920224,,871175,,879482,,917765,,860105,,857233,,911612,,855701,,850535,,867868,,949237,,878183,,896802,,882549,,901589,,956615,,894876,,889995,,897524,,854457,,836345,,870400,,840390,,801401,,768950,,822805,,801837,,798933, -United Kingdom,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,386047,,375156,,408142,,351302,,321776,,247073,,320345,,318373,,298093,,293371,,294603,,327422,,350677,,338562,,327571,,330019,F,333108,F,355573,F,357676,F,312142,,300634,,323012,,345033,,378841,,361469,,348699,F,363208,F,381883,F,401175,F,395670,F,362512,F,382424,F,455128,,406604,,470431,,432602,,466865,,440456,,464085,,433348,,439632,,463422,,423766, -United States of America,All,Export,Tonnes – net product weight,Tonnes – net product weight,220080,,226034,,350048,,365936,,528961,,593958,,657951,,961489,,1124451,,1363610,,1765993,,2022596,,2114148,,1548547,,1456138,,1238297,,1180809,,1120440,,1115916,,1156362,,1160364,,1142595,,978983,,1115614,,1181589,,1438516,,1356459,,1312560,,1497693,,1588497,,1517646,,1487408,,1374457,,1315943,,1446664,,1716548,,1643270,,1705167,,1765957,,1613815,,1539549,,1706546,,1537232, -United States of America,All,Import,Tonnes – net product weight,Tonnes – net product weight,1106578,,1027857,,1074193,,1104295,,963072,,1043522,,1049103,,1116128,,1140178,,1436486,,1447266,,1560646,,1394034,,1482959,,1446404,,1477138,,1422672,,1725078,,1650829,,1497459,,1530880,,1620436,,1726347,,1823468,,1825936,,1904600,,2068020,,2239897,,2299680,,2358181,,2460939,,2420518,,2385194,,2360393,,2479941,,2467458,,2497552,,2518854,,2590612,,2649406,,2733021,,2808762,,2892634, -United States of America,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,1505750,,1493925,,1797036,,1676033,,1712749,,1641023,,1703108,,1754049,,1759035,,1681561,,1864246,,1955641,,1859668,,2189812,,2264429,,2251302,,2244049,,2366293,,2357986,,2060786,,2120060,,2186798,,2148058,,2213794,,2174315,,2243178,,2287712,,2223036,,2062498,,2224652,,2190393,,2189798,,2236420,,2053351,,2189372,,2423324,,2278596,,2504520,,2457181,,2542775,,2346903,,2433742,,2346903, -United States of America,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,36558,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,55694,,56788,,55301,,50489,,49751,,0,.,41833, -Uruguay,All,Export,Tonnes – net product weight,Tonnes – net product weight,10904,,17625,,32671,,47406,,67388,,82953,,69486,,71872,,58999,,68308,,74602,,62491,,62315,,71207,,60585,,75600,,81860,,68617,,75622,,85021,,94818,,96985,,103185,,71806,,79714,,71666,,78011,,87042,,93776,,88065,,85213,,86262,,112327,,90070,,81764,,95282,,78680,,57897,,65311,,54347,,46815,,52841,,59754, -Uruguay,All,Import,Tonnes – net product weight,Tonnes – net product weight,688,,680,,1047,,2828,,4218,,741,,796,,401,,346,,663,,1131,,1976,,1433,,3064,,2579,,2520,,4028,,3452,,3689,,3930,,5718,,6988,,7712,,9698,,7587,,11892,,12030,,15460,,15421,,16169,,22730,,31601,,34702,,35673,,37207,,29808,,27341,,24606,,21943,,16824,,14969,,16499,,17268, -Uruguay,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,14500,,20513,,39242,,58660,,70664,,79579,,58649,,62316,,62760,,67671,,73332,,70378,,58960,,63635,,40583,,75040,,65145,,63675,,67582,,73143,F,68633,,67191,,69499,,53222,F,67655,,66865,,88274,,90732,,98224,,94837,,110966,,113611,,114136,,96788,,85477,,102384,,84874,,63492,,70179,,57642,,49296,,56103,,54028,F -Uzbekistan,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,401,F,1433,F,389,F,88,F,85,F,29,F,0,0,65,F,124,F,48,F,48,F,613,F,621,F,1081,F,819,F,663,F,1119,F,622,F,118,F,52,F,48,F,92,F,215,F,488,F,705, -Uzbekistan,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1300,,1371,,1039,F,1559,F,1011,F,653,F,994,F,1191,F,1311,F,1903,F,2185,F,2037,F,2378,F,4280,F,2523,F,4451,F,6584,F,8461,F,10638,F,9049,F,10624,F,5352,F,4358,F,6421, -Uzbekistan,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,8140,,6323,,3836,,2150,F,1206,,1140,F,1385,,1180,F,1100,F,300,F,200,F,300,F,400,F,500,F,600,F,1000,F,800,F,700,F,1000,F,1400,F,2100,F,3000,F,3700,F,4801,F,5200,F,6701,F,7255,F -Vanuatu,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,6,,3,,4417,F,11314,F,13925,F,18431,F,19523,F,29410,F,33052,F,34054,F,30482,F,36138,F,44440,F,36317,F,54584,F,58442,F,71576,F,54829,F,27342,F,34378,F,47206,F,67640,F,79250,F,69349,F,71895,F,48328,F,48053,F,39308,F,29989,F,33410,F,32908,F,39103,F,45757,F,28887,F,27334,F,33373,F -Vanuatu,All,Import,Tonnes – net product weight,Tonnes – net product weight,7500,F,11400,F,10600,F,9090,F,9183,F,5925,,4600,,5396,F,4940,,4959,F,3401,F,588,,841,,446,,879,,490,,862,,1064,,1091,,904,F,1337,,920,F,639,F,673,F,1005,,483,F,530,F,1263,F,901,F,988,F,1405,,1520,,1819,,1768,,1792,,2592,,2213,F,2603,F,2380,F,3137,F,2612,F,2133,F,2413,F -Vanuatu,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,6096,,10015,,9203,,7752,,8407,,4854,,3885,,4550,,3967,,4077,,2497,,0,.,0,0,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,20,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -"Venezuela, Boliv Rep of",All,Export,Tonnes – net product weight,Tonnes – net product weight,9755,,5624,,10774,F,6465,F,4059,F,9197,F,19577,F,21911,F,28531,F,45275,F,42747,F,30711,,19761,,42241,F,33625,F,35684,F,27723,,35381,,48607,,47621,,51393,,41675,,42919,,40967,,43213,,62643,,49774,,62852,,84212,,62132,,11365,,19450,,17990,,10730,,11003,,10664,,11185,,12419,,12293,,18025,,20150,,24029,F,17772,F -"Venezuela, Boliv Rep of",All,Import,Tonnes – net product weight,Tonnes – net product weight,8731,,5622,,13251,,12955,,17964,,26274,,24454,,5588,,2624,,1256,,119,,159,,1731,,1883,,3848,,2935,,5318,,28753,,30730,,32592,,19771,,25072,,29467,,36271,,66564,,66527,,35659,,17748,,31067,,28525,,30565,,57283,,63271,,71109,,53302,,56051,,76858,,66856,,70952,,20654,,10859,,9467,F,7556,F -"Venezuela, Boliv Rep of",All,Processed production,Tonnes – net product weight,Tonnes – net product weight,50489,,46462,,50152,,46769,,46124,,42931,,59259,,51782,,69042,,84702,F,80657,,72619,,75675,,87005,,100174,,116215,,142982,,127616,,73781,,86999,F,66434,F,59170,F,59746,F,59581,F,47500,F,63013,F,56737,F,64749,F,93023,F,63718,F,15909,F,21604,F,23990,F,17610,F,19780,F,20180,F,18620,F,19560,F,21700,,22050,,29547,,31400,F,31475,F -"Venezuela, Boliv Rep of",All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,3,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Viet Nam,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,4149,F,2955,,5327,F,11351,F,11878,F,16058,F,22427,F,25060,F,28496,F,38875,F,31264,F,43663,F,66780,F,80720,,94825,,109699,,83868,F,98726,F,211170,,209257,,237780,,302970,,514843,,607483,,525370,,625888,,671046,,889266,,891048,,1057399,,850596,,1191367,,1373363,,1409253,F,1524257,,1714701,F,1596160,F,1665798,,1823542,,1717841, -Viet Nam,All,Import,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,886,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1600,F,3300,F,6239,F,7013,F,9358,F,2917,F,17487,F,7960,F,42192,,45361,,85675,,104652,,164388,,200356,,228375,,253680,,229727,,308368,,332027,,330521,F,339272,,413326,F,431110,F,478819,,626474,,546114, -Viet Nam,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,92469,,120250,,131124,,136978,,162928,,150601,,167243,,174501,,153922,,182119,,277117,,303332,,313355,F,333245,F,294411,F,304214,F,347196,F,328790,F,329701,F,372475,F,436098,F,512026,,514081,F,702085,F,840557,F,1056003,F,1219000,F,1456700,F,1462800,F,1596700,F,1794487,F,1821324,F,1986576,F,2206404,F,2214952,F,2456253,F,2643803,F,2570958,F -Yemen,All,Export,Tonnes – net product weight,Tonnes – net product weight,7533,,8906,,5415,,4075,,7153,,2513,,4052,,2502,,6400,,5628,,5883,F,3054,F,3400,F,5100,,3980,,2708,,1504,,2706,,2354,,14248,,20388,,35324,,30410,,25456,,26673,,53009,,58432,,68125,,65237,,81679,,91743,,100780,,115515,,103500,,120062,,126915,,114705,,114424,,91305,F,59637,,54936,F,46425,F,53123,F -Yemen,All,Import,Tonnes – net product weight,Tonnes – net product weight,1263,,1263,F,2805,F,3307,F,4751,,2344,,3152,,3212,,2536,,2402,F,2204,F,1209,F,1246,F,1520,,863,,1377,,1337,,1852,,2498,,2293,F,2597,,9812,,3399,,3835,,3748,,3888,F,3101,,4381,,3606,,6207,,10147,,8947,,11334,,9628,,14800,,13098,,18150,,15282,,13732,,10484,,9487,F,10899,F,13935,F -Yemen,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,19382,,29579,,13353,,16305,,22333,,8824,,9267,,7236,,11194,,13240,,12829,,12125,,11546,,11260,F,15659,F,5914,F,6019,F,6761,F,6470,F,11730,F,10669,F,11298,F,11386,F,12682,F,11925,F,16080,F,13776,F,16946,F,15470,F,16802,F,14975,F,12385,F,15581,F,12406,F,11281,F,135574,,204753,,191530,F,182022,,183340,F,159888,F,152275,F,142445,F -Yemen,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1,,0,.,41,,0,.,140,,1,,89,,714,,1,,627,,673,,140,,125,,0,.,239,,0,.,0,.,0,.,0,. -Yugoslavia SFR,All,Export,Tonnes – net product weight,Tonnes – net product weight,19895,,16011,,17095,,21502,,16457,,18254,,21640,,20411,,16387,,16579,,18330,,20343,,20582,,20424,,17061,,963,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0, -Yugoslavia SFR,All,Import,Tonnes – net product weight,Tonnes – net product weight,90877,,109889,,116257,,111300,,115735,,116547,,111316,,155105,,109777,,125598,,149952,,132858,,130705,,121655,,143510,,46981,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0, -Yugoslavia SFR,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,30996,,33454,,36992,,38452,,39773,,42677,,42004,,44426,,44078,,43302,,38358,,38935,,40270,,40768,,37129,,0,.,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0, -Zambia,All,Export,Tonnes – net product weight,Tonnes – net product weight,18,,22,,0,.,0,.,0,.,2,,0,.,0,.,7,,0,.,400,,800,,960,,850,F,669,F,567,F,464,F,460,F,120,F,120,F,101,F,243,,80,F,966,,711,,1020,,773,,615,,1002,F,1295,,265,,277,,1817,,772,,749,,1001,,995,,1078,,468,,374,,315,,806,,727, -Zambia,All,Import,Tonnes – net product weight,Tonnes – net product weight,3199,,3917,,11917,,10979,,4012,,3659,,5281,,2229,,1060,,350,F,534,,362,,654,,900,F,500,F,900,F,1848,,2000,,2142,,1395,F,2314,F,3614,,500,F,1489,,2068,,3015,,3696,,5170,,7851,,8738,,6148,,6967,,7473,,5572,,7080,,9699,,20827,,35741,,58154,,80393,,126890,,122001,,107040, -Zambia,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,2443,,3812,,3661,,4182,,6488,,10570,,20130,,16560,,14680,,18060,,17000,,18670,,14000,,18800,,20200,,19600,,18000,F,18500,F,17900,F,17700,F,16900,F,16900,F,17700,F,18000,F,17800,F,16500,F,19800,F,20000,F,22000,F,21000,F,20000,F,22000,F,21000,F,22000,F,23000,F,25000,F,25000,F,25000,F -Zambia,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,348,,0,- -Zimbabwe,All,Export,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,32,,34,,79,,56,,0,0,57,,64,,22,,24,,40,,0,.,51,,54,,67,,467,,93,,399,,847,,1628,,995,,520,,824,,940,F,720,,531,F,768,,563,,805,,1441,,1487,,966,,1137,,2081,,2519,,3255,,5878,,4972,,3223,,2522,,2951, -Zimbabwe,All,Import,Tonnes – net product weight,Tonnes – net product weight,2200,F,2400,F,1600,F,2429,,4781,F,3806,,2746,,2375,,2774,,117,,1905,,1196,,1087,F,856,F,209,,599,,1277,,9648,,15106,,19143,,18214,,17064,,10405,,10481,,9055,,4141,F,1827,,1050,F,3740,,1844,,5412,,3152,,4124,,4337,,13178,,16850,,18656,,18875,,19921,,23002,,22536,,19887,,12594, -Zimbabwe,All,Processed production,Tonnes – net product weight,Tonnes – net product weight,2000,F,2000,F,2300,F,2900,F,3900,F,4800,F,5100,F,4000,F,6663,F,6900,F,7000,F,7100,F,7420,F,7620,F,7930,F,7530,F,7530,F,7500,F,7500,F,7100,F,7300,F,7500,F,7300,F,7000,F,6400,F,6250,F,5900,F,5800,F,5900,F,5750,F,2350,F,2400,F,2500,F,2600,F,2800,F,4000,F,4500,F,5300,F,5600,F,6000,F,25796,,26000,F,26450,F -Zimbabwe,All,Reexports,Tonnes – net product weight,Tonnes – net product weight,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,16,,6,,0,.,0,.,0,.,9,,3,,0,-,0,-,0,-,0,-,0,- -Totals - Tonnes – net product weight,,,,,41200940,,41398153,,44226904,,46919497,,47723835,,49093552,,52285880,,52286982,,56687171,,60339154,,64134350,,65581685,,69454926,,71610413,,71296885,,71701044,,70835155,,75567622,,84187833,,83729107,,84991671,,86646523,,82697819,,88714267,,94044406,,96800658,,97761375,,100379504,,106222963,,109780810,,112328385,,114692806,,116844554,,118768725,,122362498,,127773737,,129236333,,130618120,,135470989,,133299563,,135021844,,141223442,,144795880, -"FAO. 2020. Fishery and Aquaculture Statistics. Global Fisheries commodities production and trade 1976-2018 (FishstatJ). In: FAO Fisheries Division [online]. Rome. Updated 2020. www.fao.org/fishery/statistics/software/fishstatj/en"">" diff --git a/inst/extdata/sectoral/FAOSTAT_data_1-26-2021_FishesTradeUSD.csv b/inst/extdata/sectoral/FAOSTAT_data_1-26-2021_FishesTradeUSD.csv deleted file mode 100644 index 75a4e900..00000000 --- a/inst/extdata/sectoral/FAOSTAT_data_1-26-2021_FishesTradeUSD.csv +++ /dev/null @@ -1,524 +0,0 @@ -Country (Name),Commodity (Name),Trade flow (Name),Unit (Name),Unit,[1976],S,[1977],S,[1978],S,[1979],S,[1980],S,[1981],S,[1982],S,[1983],S,[1984],S,[1985],S,[1986],S,[1987],S,[1988],S,[1989],S,[1990],S,[1991],S,[1992],S,[1993],S,[1994],S,[1995],S,[1996],S,[1997],S,[1998],S,[1999],S,[2000],S,[2001],S,[2002],S,[2003],S,[2004],S,[2005],S,[2006],S,[2007],S,[2008],S,[2009],S,[2010],S,[2011],S,[2012],S,[2013],S,[2014],S,[2015],S,[2016],S,[2017],S,[2018],S -Afghanistan,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,9,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,6853, -Albania,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,615,,333,,475,,0,.,1822,,1577,,2951,,2905,,5904,,2925,,1556,,2280,F,1660,F,2453,F,5453,,5476,,5231,,8887,,5470,,6910,,8727,F,13516,,17173,,1732,,1793,,1919,,2858,,32604,,36386,,37604,,36682,,40766,,46117,,47963,,63189,,94047,,107682, -Albania,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,171,,182,,102,,80,,70,,120,F,80,F,420,F,460,F,2055,F,1881,,2697,,2914,,6108,,4615,,5246,,9798,,11190,,13570,,15292,,16352,,17754,,26091,,27190,,28893,,34361,,28309,,34088,,43675,,47422,,53962,,75792,,87241, -Albania,All,Reexports,Value (USD 1000),USD 1000,0,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,16516,,22202,,23206,,26371,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Algeria,All,Export,Value (USD 1000),USD 1000,4731,,1653,,366,,297,,389,,347,,37,,163,,182,,188,,247,,336,,351,,428,,1064,,1944,,5122,,2117,,2361,,4544,,3515,,2288,,1178,,2734,,4205,,4876,,5816,,6454,,8200,,10924,,11244,,12164,,13335,,8498,,5899,,5868,,5820,,5759,,7047,,6317,,6675,,9543,,13538,F -Algeria,All,Import,Value (USD 1000),USD 1000,3276,,7482,,6949,,8468,,9372,,13633,,13066,,19545,,22941,,30471,,35786,,23969,,31147,,13409,,6257,,1714,,8229,,5073,,9460,,33028,,7247,,9973,,7864,,13352,,12546,F,14015,,9260,,16616,,23320,,21077,,30607,,27581,,31417,,54058,,50643,,58593,,65886,,91477,,136431,,109897,,109461,,123779,,125738,F -Andorra,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2,,15,,0,.,0,.,0,.,0,0,0,.,0,.,1,,0,0,51,F,0,.,0,.,1,,24,,0,.,21,,3,,4,,1,,8,F,3,F,8,F,5, -Andorra,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,12309,,13076,,12741,,13261,,13409,,11910,,12550,,14229,,17743,,20140,,21421,F,20402,,22000,,22256,,19961,,19206,,20574,,18116,,19029,,20385,,21072,F,20846,F,19544,,19842, -Angola,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,6296,F,3560,F,3490,F,4830,F,5657,F,4932,F,7165,,5768,F,3922,F,9273,F,11618,F,10043,F,10839,F,21411,F,28181,F,8874,F,11945,F,16840,F,21772,F,28039,F,34372,F,41598,,44764,,34342,,59262,,66463,,71878,,74159,,98739,,69597,,81289, -Angola,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,57400,F,47200,F,65950,F,71100,F,62810,F,76670,F,70240,,62105,F,53400,F,19050,F,17700,F,13400,F,8489,F,9735,F,10668,F,14435,F,12052,F,14524,F,16336,F,17942,F,18125,F,20342,F,32225,F,33381,F,61299,F,70452,F,124430,F,117402,,124092,,206027,,346072,,257197,,321949,,236096,,139266,,217060,,197810, -Antigua and Barbuda,All,Export,Value (USD 1000),USD 1000,78,F,72,,146,,106,,847,F,202,,300,F,500,F,265,,1160,F,750,F,420,F,500,F,460,F,325,F,310,F,150,F,420,F,420,F,613,F,1215,,1014,,585,,237,,190,,180,F,310,F,367,F,170,F,328,,248,F,259,,249,F,345,,400,,890,,1007,,623,,319,,354,F,270,F,558,,680, -Antigua and Barbuda,All,Import,Value (USD 1000),USD 1000,828,F,823,,903,,1170,F,1375,,1493,,1230,F,1150,F,1422,,1739,F,1630,F,2320,,6356,,2657,,2100,,1122,F,2120,F,2016,F,1940,F,1828,F,1851,F,2378,F,1786,F,4002,,3954,,1618,F,2623,F,5391,F,4194,F,4884,,6429,,7899,,6695,F,5866,,6049,,6422,,6712,,6561,,6697,,6828,,5928,,8286,,10024, -Antigua and Barbuda,All,Reexports,Value (USD 1000),USD 1000,0,.,6,,6,,0,.,24,,39,,0,.,0,.,5,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,47,,26,,0,.,0,.,0,.,0,.,4,,0,.,68,,0,.,0,0,1,,9,,3,,3,,28,,13,,4,,10,,11, -Argentina,All,Export,Value (USD 1000),USD 1000,40425,,86441,,154079,,205524,,143285,,139352,,190588,,168152,,157729,,149922,,216632,,262794,,266775,,280900,,315938,,448355,,484405,,635997,,727902,,909703,,1014563,,887827,,889668,,783657,,806452,,890669,,714014,,885182,,818199,,804356,,1249411,,1104465,,1298552,,1124932,,1338067,,1471838,,1327026,,1495292,,1586903,,1466760,,1701084,,1978786,,2082262, -Argentina,All,Import,Value (USD 1000),USD 1000,1130,,6867,,10045,,21360,,23907,,25900,,13907,,7668,,9464,,8077,F,10780,F,21562,,13438,,11998,,6159,,18763,,48355,,45062,,67924,,71519,,73607,,91281,,88585,,90385,,86208,,78441,,15280,,29923,,47636,,63546,,78795,,102513,,103005,,99885,,127136,,163792,,179218,,195582,,159523,,176131,,188283,,221527,,229133, -Armenia,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,324,,4,F,18,,254,F,495,,95,,158,,1707,,3171,,2935,,3180,,4559,,4683,,5403,,3539,,7695,,16197,,22493,,33612,,31472,,14962,,12551,,21734,,28125, -Armenia,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,555,F,1667,,482,,693,,1099,F,2284,,4120,,3035,,3609,,2074,,2757,,4368,,3836,,4289,,9170,,8142,,6128,,9073,,9507,,9189,,9469,,6920,,6312,,9330,,8413, -Armenia,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,21,,20,,0,.,1,,5,,87,,7,,15,,46,,0,.,0,.,64,,81,,0,-,0,-,0,-,0,-,0,- -Aruba,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,17,,135,F,281,,853,,123,,65,,84,,33,,35,F,15,F,20,F,0,.,0,.,599,F,0,.,873,,246,,358,,777,,435,,1017,,251,,70,,82,,23,,16,,17,,1,,3,,63,,69,,86,,382,,455,,149, -Aruba,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,3548,,4090,F,4175,,4467,,4574,,5426,,5629,,6644,,6243,F,8512,F,10655,F,16233,,17042,,17450,,18499,,14678,,9926,,9640,,10262,,11546,,12828,,12190,,13074,,14164,,14888,,15066,,15882,,16948,,19130,,19163,,21550,,23487,,24932,,26819,,26762, -Australia,All,Export,Value (USD 1000),USD 1000,102284,,155166,,169291,,222431,,277143,,268866,,317237,,325382,,352239,,285899,,328212,,418415,,569784,,473457,,539993,,579940,,620373,,656234,,802653,,863952,,834245,,907489,,755309,,899632,,1006114,,904212,,901954,,897654,,921956,,945528,,939899,,947855,,956165,,829684,,952236,,1008875,,1008236,,1008367,,1113559,,1078156,,1049644,,1112029,,1120939, -Australia,All,Import,Value (USD 1000),USD 1000,93913,,131899,,139403,,145385,,185039,,226840,,242554,,210501,,244313,,245263,,238140,,286346,,304853,,372673,,349870,,365810,,387034,,369564,,462382,,426015,,501012,,524759,,515866,,530405,,563482,,557234,,595545,,666656,,730745,,862087,,933322,,1108788,,1138876,,1090054,,1275267,,1506418,,1600464,,1667802,,1772824,,1470383,,1511061,,1665603,,1632407, -Australia,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,353,,0,.,4940,,1408,,2602,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Austria,All,Export,Value (USD 1000),USD 1000,1206,,1066,,1262,,1860,,1482,,1365,,1909,,1852,,1389,,1198,,2169,,1945,,2141,,2580,,2903,,3011,,5096,,4451,,5465,,17330,,12133,,10293,,8017,,11157,,9368,,8651,,20479,,33472,,44245,,20233,,18624,,29571,,40337,,46056,,52584,,81963,,93579,,100354,,88663,,76443,,80600,,81597,,93310, -Austria,All,Import,Value (USD 1000),USD 1000,57458,,64636,,71068,,84995,,90752,,78381,,72840,,75398,,68617,,67300,,92894,,117705,,118424,,122139,,149861,,162902,,179612,,158913,,195343,,187810,,205253,,188547,,201246,,210272,,162213,,182542,,195774,,244759,,289218,,278919,,326590,,437731,,466496,,447649,,457480,,545492,,531582,,594639,,619461,,538695,,573626,,603151,,632973, -Azerbaijan,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,480,F,265,F,751,,1176,,856,,3850,,3711,,1366,,1422,,4169,,4755,,7276,,659,,5851,,5601,,5954,,1021,,226,,0,.,24,,0,.,23,,209,,454,,794, -Azerbaijan,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,900,F,352,F,558,,523,,932,,846,,1444,,1314,,1961,,2142,,2336,,4827,,2917,,5808,,11168,,16057,,11411,,12197,,12768,,15842,,13421,,11106,,22835,,28340,,29139, -Bahamas,All,Export,Value (USD 1000),USD 1000,5444,,8744,,6996,,8907,,11131,,12073,,13006,,14023,,15272,,19044,,19968,,22345,,29091,,22286,,45821,,54010,,53369,,47493,,60951,,59398,,57557,,61764,,61190,,102132,,109952,,73770,,91574,,108339,,87158,,78036,,95041,,84100,,82509,,65914,,74772,,76218,,82799,,92920,,70226,,63361,,66892,F,88161,F,74417,F -Bahamas,All,Import,Value (USD 1000),USD 1000,2091,,1625,,1625,F,665,F,4334,,2244,,3405,,3048,,3520,,4156,,4473,,4972,F,4221,,5922,,6287,,6541,,5889,,6093,,6433,,7662,,7510,F,8866,,9525,,10133,,14833,,13788,,14443,,13273,,14716,,16814,,18149,,17998,,21937,,20897,,20208,,22050,,26413,,22519,,25353,,24078,,20786,F,18557,F,19602,F -Bahamas,All,Reexports,Value (USD 1000),USD 1000,56,,0,.,0,.,0,.,0,.,174,,29,,0,.,1,,0,.,74,,438,,731,,0,.,0,.,443,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,40,,4,,0,0,177,,3,,22,,86,,170,,1069,,0,0,39,,0,0,27,,26,,0,.,26,,0,-,0,-,0,- -Bahrain,All,Export,Value (USD 1000),USD 1000,2253,,2280,,232,,0,.,11,,8,,3,,0,0,0,0,0,.,989,,2254,,1929,,2288,F,3170,,3264,,2655,,3768,,4445,,6192,,9652,,14283,,8753,,6925,,9559,,9913,,11832,,11528,,14024,,13026,,12568,,15600,,18820,,16238,,14293,,20874,,24786,,23074,,23570,,27366,,27189,,38922,,62705, -Bahrain,All,Import,Value (USD 1000),USD 1000,859,,1597,,1309,,1066,,1866,,2787,,2543,,3562,,3241,,3774,,3510,,3058,,3173,,3327,F,3704,,5034,,4600,,5506,,5470,,6368,,4996,,4825,,4216,,3203,,4733,,6629,,9653,,6909,,6956,,10944,,8279,,9052,,17107,,14431,,16530,,21213,,31348,,27203,,37825,,38473,,45922,,47367,,72564, -Bahrain,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,128,,0,.,242,,67,,22,,98,,37,,30,,364,,0,.,336,,1628,,2073,,1145,,1361,,411,,1370,,0,.,6352, -Bangladesh,All,Export,Value (USD 1000),USD 1000,11922,,18066,,12876,,29758,,42705,,39724,,50208,,70260,,81219,,88653,,118154,,146853,,162402,,162455,,167050,,160817,,159023,,201505,,293376,,322854,,317229,,291651,,271597,,251868,,342617,,355893,,301484,,325111,,372691,,635077,,450515,,631244,,562565,,398243,,541466,,642909,,498659,,539326,,603607,,447913,,559838,F,497141,F,447906,F -Bangladesh,All,Import,Value (USD 1000),USD 1000,126,,123,,51,,99,,76,,12,,14,,0,0,0,.,0,.,1,,6,,14,,29,,3,,52,,197,,168,F,381,,803,,3343,,2302,,5122,,1653,F,4051,,7512,,10959,,6397,,13868,,5539,,3424,,7163,,7643,,21194,,21581,,19389,,20227,,35472,,55373,F,88448,,85851,F,93258,F,122095,F -Barbados,All,Export,Value (USD 1000),USD 1000,385,,998,,58,,37,,13,,74,,7,,46,,41,,46,,105,,100,F,425,,168,,156,,114,,183,,237,,330,,844,,515,,1503,,1226,,902,,1252,,1347,,928,,887,,878,,1440,,780,,993,,771,,340,,594,,546,,532,,357,,387,,465,,656,,412,,490, -Barbados,All,Import,Value (USD 1000),USD 1000,1732,,1950,,3236,,3579,,3013,,4596,,3991,,2928,,3580,,3813,,4123,,4240,F,4931,,6185,,6309,,6810,,6077,,6466,,7026,,8388,,7959,,9639,,6048,,11075,,10932,,12395,,13442,,13614,,11815,,17435,,18883,,18925,,20374,,18197,,18042,,22254,,25912,,22329,,24926,,25840,,23577,,26197,,29689, -Barbados,All,Reexports,Value (USD 1000),USD 1000,11,,51,,43,,225,,68,,24,,34,,0,.,0,.,21,,1,,0,.,0,.,43,,151,,94,,37,,16,,19,,27,,122,,57,,35,,49,,62,,45,,31,,0,.,0,.,15,,12,,0,0,0,.,21,,0,.,0,.,78,,71,,46,,42,,56,,21,,0,- -Belarus,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2030,F,8750,F,5118,F,3459,F,16684,,14252,,19007,,15779,,20798,,38829,,51970,,60650,,83606,,107439,,138334,,98108,,113167,,150082,,188500,,268178,,319780,,258859,,255632,,324910,,339941, -Belarus,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,3615,F,9000,F,39588,,43639,,71045,F,76505,,58183,,88123,,93407,,102930,,113190,,150639,,204032,,249377,,286696,,342978,,299681,,279592,,298207,,363492,,486911,,513047,,385406,,361619,,409828,,430739, -Belgium,All,Export,Value (USD 1000),USD 1000,42761,,48215,,62849,,69763,,78986,,73355,,73128,,87412,,87813,,84658,,126639,,167618,,174887,,185666,,230504,,229827,,220036,,232483,,325790,,371479,,393222,,441835,,484964,,454867,,478577,,525532,,576372,,771307,,882910,,975101,,1158142,,1213593,,1284684,,1079841,,1140616,,1298704,,1075762,,1106353,,1205317,,1069004,,1148541,,1285343,,1181491, -Belgium,All,Import,Value (USD 1000),USD 1000,216298,,256515,,300395,,375507,,408381,,348974,,328059,,320191,,300250,,305346,,427918,,532330,,586806,,608738,,757462,,779070,,831210,,733640,,927730,,1041241,,972651,,985149,,1068456,,1075478,,1038537,,1005707,,1069334,,1396914,,1530953,,1666342,,1935975,,2129583,,2284883,,1953403,,2019433,,2314358,,2053310,,2165122,,2394019,,1993149,,2159397,,2330104,,2292171, -Belize,All,Export,Value (USD 1000),USD 1000,2899,,2883,,3496,,4800,,4468,,6434,,5983,,6977,,6663,,7383,,7178,,8436,,6239,,6704,,5209,,10243,,12034,,12318,,13248,,15764,,12427,,17935,,21848,,33288,,32284,,14057,,9511,,55128,,53968,,43023,,43069,,20856,,23838,,26284,,31334,,25408,,29053,,56499,,57297,,44637,,21634,,20432,,21539, -Belize,All,Import,Value (USD 1000),USD 1000,244,,295,,434,,604,,554,,371,,225,,141,,392,,434,,523,,748,,871,,1238,,519,,766,,939,,780,,707,,966,,740,,790,,1729,,2999,,3313,,1815,,1756,,2578,,1763,,2589,,2002,,1561,,2090,,1343,,1214,,454,,650,,602,,1262,,934,,952,,671,,905, -Belize,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,71,,269,,262,,0,.,0,.,0,.,0,.,125,,65,,134,,7,,0,.,0,.,0,.,0,.,18,,2,,0,.,0,.,141,,0,.,7,,10,,10,,0,.,0,.,0,.,4,,0,.,0,.,1,,0,.,0,0,0,-,0,- -Benin,All,Export,Value (USD 1000),USD 1000,173,,168,F,22,,46,,2,,16,,1,,0,.,55,,142,,391,,113,,260,F,180,F,120,F,139,F,56,,1030,F,1518,F,1263,F,1723,F,1414,F,1274,,940,,1467,,2408,,3210,,1934,,745,,997,,1185,,387,,33,,156,,1342,,1262,,374,,413,,919,,243,,138,,117,,58, -Benin,All,Import,Value (USD 1000),USD 1000,1549,,1771,,3372,,5173,,1896,,2127,,4708,,2065,,3187,,3830,F,6414,,1634,,2350,,936,,2030,F,6098,,8343,,11068,F,7123,F,9705,F,6512,F,6316,,5073,,7569,,8656,,4949,,8035,,10629,,11586,,16210,,19160,,24487,,32103,,27681,,27497,,32603,,35461,,39307,,112693,,93796,,117064,,91159,,105822, -Benin,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,291,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,3,,0,.,13,,0,-,0,- -Bermuda,All,Export,Value (USD 1000),USD 1000,5678,,15810,,11496,,6700,F,7600,F,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,259,F,44,F,2,F,3,F,272,F,46,F,46,F,78,F,4,F,94,F,44,F,15,F,283,F,356,F,61,F,0,0,11,,43,,31,,22,,17,,25,,31, -Bermuda,All,Import,Value (USD 1000),USD 1000,3011,,2959,,3322,,3757,,4417,,5200,,5163,,6227,,6365,,6198,,6880,F,7717,,9175,F,8920,F,9794,,8525,,9780,,9320,,9250,F,7061,F,5677,F,7213,F,6381,F,7578,F,7049,F,8390,F,8210,F,8302,F,8435,F,12625,,13866,,13449,,11782,,10175,,8913,,9677,,11246,,13350,,12983,,11804,,14194,,16253,,16897, -Bhutan,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,377,,0,0,0,.,0,0,0,.,0,0,2,,0,.,3,F,0,.,1,F,0,.,0,.,0,- -Bhutan,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1939,,2041,,3081,,2293,,2461,,3182,,3613,,4664,,5078,F,5251,F,5129,F,5036,F,4999,F,6073,F -Bolivia (Plurinat.State),All,Export,Value (USD 1000),USD 1000,0,.,0,.,2,,0,.,0,.,0,.,0,.,12,,18,,11,,52,,80,,58,,82,,97,,477,,282,,341,,138,,182,,4,,130,,122,,7,,24,,35,,3,,22,F,1,,1,,0,0,14,,49,,18,,0,0,0,.,0,.,0,0,0,-,0,-,0,-,0,-,0,- -Bolivia (Plurinat.State),All,Import,Value (USD 1000),USD 1000,2062,,2733,,3804,,4143,,4724,,6321,,1423,,1133,,1860,,2616,,1151,,2280,,472,,2137,,420,,366,,612,,1481,,2814,,1673,,2580,,2984,,4764,,2957,,9193,,5527,,5471,,3587,,2640,,3343,,4862,,5354,,10062,,10321,,10020,,17848,,16529,,18164,,19056,,21960,,19516,,22404,,22383, -Bolivia (Plurinat.State),All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,31,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Bosnia and Herzegovina,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,113,F,89,F,431,F,1227,,5403,,6100,,7802,,7919,,10736,,10975,,9946,,10403,,9063,,8338,,6873,,7753,,8062,,7120,,7262, -Bosnia and Herzegovina,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,436,F,4630,F,541,F,2942,F,693,F,5391,F,3496,F,9788,F,11562,F,15668,F,19899,F,20238,,22969,,32425,,30551,,34218,,39693,,36697,,33756,,34421,,35194,,35814,,39660,,32293,,36175,,35892,,41927, -Botswana,All,Export,Value (USD 1000),USD 1000,0,.,30,,7,,31,,25,,77,,58,,0,.,55,,217,,630,,839,,925,,719,,394,,166,,126,,31,,11,,32,,92,,104,,77,,54,F,35,,65,,65,,114,,64,,220,,363,,96,,31,,284,,134,,192,,458,,523,,381,,1413,,1187,,453,,1768, -Botswana,All,Import,Value (USD 1000),USD 1000,0,.,790,,1165,,1041,,1639,,2044,,1701,,1773,F,2059,,1719,,2336,,3033,,3558,,4753,,5268,,6709,,7098,,5599,,5222,,5960,,5082,,6420,,5214,,5121,F,11300,,4891,,4259,,6969,,8520,,8036,,7062,,10208,,12711,,9369,,13105,,11234,,13394,,11601,,13933,,11223,,10842,,10263,,11879, -Botswana,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,152,,52,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Brazil,All,Export,Value (USD 1000),USD 1000,53888,,74300,,96834,,145454,,132759,,155859,,161600,,137290,,179316,,174275,,153333,,180099,,187671,,130167,,141037,,158683,,172289,,193402,,180465,,161633,,135726,,128096,,124741,,140351,,242035,,287046,,346831,,423403,,432115,,409456,,374441,,320069,,282766,,209921,,232903,,242543,,221607,,225879,,208600,,223359,,244236,,253989,,277007, -Brazil,All,Import,Value (USD 1000),USD 1000,52141,,54167,,71465,,106450,,89647,,68083,,77305,,43162,,40699,,47790,,130528,,128636,,82171,,186228,,195154,,193452,,133873,,201136,,263092,,400461,,481824,,488074,,458491,,293108,,328132,,270522,,225135,,216779,,282931,,309902,,477847,,572901,,695211,,727815,,1064325,,1271980,,1253918,,1480923,,1571972,,1222328,,1193859,,1416792,,1374772, -Brunei Darussalam,All,Export,Value (USD 1000),USD 1000,110,,133,,49,,101,,33,,17,,11,,7,,5,,11,,1,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,99,F,29,F,80,,231,,184,F,296,F,334,,459,,706,,1119,,3053,F,5305,,5038,F,2477,F,1613,F,1797,F,1701,F,2435,,4311,,4146,,3342,,3057,,5819,,4169, -Brunei Darussalam,All,Import,Value (USD 1000),USD 1000,2110,,2252,,2704,,2790,,4113,,5115,,6379,,6799,,7014,,6154,,7230,,6420,F,7404,F,10779,F,7160,F,6780,F,6550,F,12342,,20387,,8120,F,7979,F,24101,,16877,,11596,F,15239,F,13379,,13136,,11847,,15527,,17316,F,25813,,20987,F,20054,F,20374,F,27517,F,32605,F,42728,,51302,,45897,,40660,,39783,,43438,,48922, -Brunei Darussalam,All,Reexports,Value (USD 1000),USD 1000,68,,62,,47,,39,,44,,65,,51,,69,,137,,215,,246,,275,F,300,F,350,F,380,F,440,F,450,F,510,F,520,F,0,.,0,.,0,.,0,.,0,.,0,.,348,,0,.,0,.,537,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,3966, -Bulgaria,All,Export,Value (USD 1000),USD 1000,18400,F,15700,F,20900,F,24000,F,27800,F,21400,F,23600,F,16125,F,13390,F,15043,F,21154,F,30758,F,27720,F,25143,F,15056,,15426,,10230,F,8009,,12175,,21731,,26426,,11434,,6550,,8018,,7224,,7488,,5852,,8637,,10737,,10722,,16786,,18021,,24746,,25347,,33537,,34661,,34399,,40731,,45885,,49019,,67199,,88446,,88647, -Bulgaria,All,Import,Value (USD 1000),USD 1000,7110,F,11510,F,12810,F,15330,F,14450,F,18560,F,19580,F,16535,F,9200,F,11300,F,8785,F,11675,F,11475,F,7260,F,2486,,1396,F,7023,,5333,,10049,,11402,,10398,,15882,,21835,,13085,,11543,,15295,,15455,,21318,,21917,,29133,,38483,,41670,,74765,,72060,,71054,,81633,,76546,,90734,,96546,,99885,,106782,,120684,,125705, -Burkina Faso,All,Export,Value (USD 1000),USD 1000,4,,7,,8,,1,,0,.,7,,10,,1,,0,.,0,.,5,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,.,24,,14,,3,,11,,0,.,0,.,0,0,224,,450,F,801,,1159,,1025,,655,,625,,756,,650,,626,,838,,300,,269,,102, -Burkina Faso,All,Import,Value (USD 1000),USD 1000,911,F,935,,1344,,1210,,2741,,1828,,2045,,1897,,1428,,1043,,2824,,4047,,5378,,5120,F,5522,F,4975,F,4450,F,4292,F,4986,,4566,,5218,,4727,,1078,,2029,,1381,,716,,774,,1151,,1877,,3872,,4890,F,5768,,6486,,9083,,9087,,10004,,10661,,10732,,11579,,11139,,12086,,14807,,17393, -Burundi,All,Export,Value (USD 1000),USD 1000,125,,85,,99,,119,,0,.,91,,0,.,0,.,159,,0,.,201,,301,,264,,328,,301,,279,,267,,243,,230,,297,,122,,172,,206,,153,,132,,183,,163,,137,,162,,173,,214,,228,,207,,226,,184,,217,,180,,320,,324,,215,,122,,2,,11, -Burundi,All,Import,Value (USD 1000),USD 1000,69,,253,,56,,212,,602,,600,,607,,571,,622,,274,,24,,19,,18,,237,F,323,F,257,F,218,F,723,,906,,21,,411,,169,,48,,79,,55,,98,,116,,70,,125,,37,,138,,41,,36,,161,,435,,1372,,2275,,2485,,2026,,1725,,1930,,1776,,1833, -Cabo Verde,All,Export,Value (USD 1000),USD 1000,659,,701,,552,,1173,,2048,,612,,2884,,2299,,1986,,1805,,2487,,4460,,1600,,4836,,2749,,1461,,3024,,2412,,2398,,2093,,2866,,3098,,1987,,1851,,838,,298,,501,,493,,1243,,7159,,11301,,7009,,8094,F,25729,,37519,,56050,,44545,,58281,,68137,,57001,,49546,,37559,,61773, -Cabo Verde,All,Import,Value (USD 1000),USD 1000,164,,11,,10,,9,,8,,9,,6,,39,,27,,29,,170,,147,,115,,264,,280,,299,,313,,407,,520,,923,,688,,2157,,1260,,939,,617,,905,,766,,958,,927,,1117,,1691,,1977,,3295,,3029,,2361,,3694,,3823,,4511,,4218,,3600,,5211,,6271,,7396, -Cabo Verde,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,575,,740,,478,,0,.,0,.,0,.,0,.,3963,,1857,,4281,,1223,,0,.,0,.,0,.,0,.,0,.,16522,,19966,,0,-,0,-,0,- -Cambodia,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,3,,19,F,45,F,18,F,75,F,190,F,200,F,200,F,15607,F,20358,F,18378,F,15106,F,15611,F,23126,F,39546,F,34935,F,34469,F,32114,F,36284,F,37816,F,42400,,48551,,26835,F,23285,F,24679,F,30362,F,40011,F,60000,F,61020,F,62500,F,63900,F,66046,F,65442,F,75361,F,85306,F -Cambodia,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,3168,F,7017,F,6328,,3365,F,2439,F,2798,F,2724,,467,,586,,3090,,3225,,9602,,4206,,3626,,2973,,5163,,4573,,6250,,12739,,15436,F,27491,,17363,,20571,,28748,,26417, -Cameroon,All,Export,Value (USD 1000),USD 1000,3655,,4202,,3662,,1237,,3972,,1151,,1576,,1901,,2670,,3333,,3190,,7005,,7107,,7746,,8724,,2265,,2300,F,1145,,986,,1907,,1420,,1393,,2604,,1543,,938,,365,,129,,315,,51,,102,,897,,1390,,1357,,2190,,2859,,1262,,2412,,2153,,2611,,1967,,2656,F,2817,,2970,F -Cameroon,All,Import,Value (USD 1000),USD 1000,4536,,5327,,6779,F,9276,F,11028,F,17164,F,17505,F,21130,F,21236,F,24675,,38359,,41210,,49266,,46562,,53833,,22291,,26460,F,37638,,19809,,27288,,19254,,31377,,35600,,29595,,27740,,29365,,23938,,37780,,45212,,63846,,77428,,126709,,178148,,243357,,188210,,323828,,249615,,301842,,306252,,295024,,289002,,206324,,241125,F -Canada,All,Export,Value (USD 1000),USD 1000,604232,,757325,,982620,,1111171,,1082397,,1252113,,1292928,,1261469,,1256507,,1343496,,1732873,,2066936,,2186706,,2032387,,2256088,,2153493,,2065445,,2068014,,2210098,,2327286,,2306452,,2168150,,2279240,,2631777,,2835295,,2812348,,3061186,,3317675,,3506676,,3614782,,3682836,,3732408,,3729834,,3262738,,3875003,,4224530,,4249656,,4390982,,4560096,,4659362,,4946738,,5295231,,5345148, -Canada,All,Import,Value (USD 1000),USD 1000,183631,,205843,,216644,,267078,,301589,,298680,,281406,,335268,,373051,,355939,,433087,,511901,,593327,,659238,,630829,,686403,,698952,,833516,,933574,,1052045,,1178457,,1145158,,1215022,,1358379,,1408800,,1392804,,1374759,,1448839,,1567148,,1690115,,1842119,,2024531,,2083226,,2045265,,2294186,,2688920,,2732632,,2867326,,3022065,,2735844,,2858892,,2979009,,3068623, -Canada,All,Reexports,Value (USD 1000),USD 1000,4980,,4326,,6447,,3579,,12092,,8696,,6723,,15818,,15337,,15752,,18936,,25234,,19733,,18786,,27743,,24467,,30059,,0,.,0,.,0,.,0,.,113640,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,.,0,.,82497,,98698,,97840,,111501, -Cayman Islands,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,4680,F,5770,F,11230,F,8920,F,6880,F,5690,F,4310,F,6120,F,17000,F,4590,F,9355,F,12820,F,11040,F,5185,F,7320,F,1680,F,1754,F,1198,F,879,F,218,F,193,F,180,F,94,F,108,F,50,F,213,F,196,F,397,F,77,F,130,F,446,F,202,F,155,F,0,.,3,F,5,F,185,F,71,F,0,.,1,F -Cayman Islands,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,765,,1102,,1150,F,1180,F,1200,F,1210,F,1203,F,2255,F,2662,F,2560,F,2294,F,3213,,3361,,2913,,3147,,2538,F,1135,F,463,F,665,F,641,F,954,F,1492,F,1905,F,1880,F,1627,F,855,F,2997,F,4006,F,2833,F,3394,F,3461,F,3468,F,4266,F,3814,F,3779,F,3156,F,3586,F,4957,F,5425,F -Central African Republic,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,.,16,,0,.,0,.,40,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,393,,400,F -Central African Republic,All,Import,Value (USD 1000),USD 1000,546,,597,,487,,343,,733,,99,,0,.,401,,173,,635,,1504,,1615,F,1231,F,1422,,1937,,1110,,972,,1074,,507,,302,,161,,628,,174,,503,,234,,271,,353,,1075,,1104,,1086,,1317,F,1750,,1999,,2958,,4033,,3104,,3020,,2609,,5704,,7280,,3686,,5970,,5351,F -Chad,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2,F,0,0,1,F,0,.,0,.,9,,0,.,263,,35,,30,F,49,F,43,F,67,F,53,F,54,F,45,F,48,F,46,F,50,F -Chad,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1548,,940,,4399,,1165,F,0,.,28,F,5,F,71,F,763,F,1125,F,215,,198,,683,,517,,280,,604,F,840,F,708,F,1120,F,1175,F,1831,F,843,F,1343,F,1345,F,1746,F -Chile,All,Export,Value (USD 1000),USD 1000,100761,,124229,,172510,,225196,,322982,,326551,,386335,,419027,,419364,,437132,,514677,,632437,,804744,,899947,,885074,,1084598,,1265820,,1138189,,1378560,,1770918,,1768410,,1853272,,1650369,,1763102,,1858390,,2006707,,1922057,,2194757,,2564538,,3042750,,3638936,,3774414,,4026806,,3702645,,3510784,,4630913,,4489928,,5173331,,6047557,,4963976,,5292426,,6161316,,6932195, -Chile,All,Import,Value (USD 1000),USD 1000,2006,,1500,,2368,,1583,,4914,,5884,,4529,,2300,,1600,,800,,200,,1400,,1070,,4400,,13280,,15649,,28329,,25563,,35810,,52452,,50588,,46923,,45851,,65415,,58527,,64507,,47028,,86141,,127805,,99826,,176379,,204690,,284201,,132490,,278692,,396203,,402489,,437915,,461645,,455039,,345527,,401990,,451439, -China,All,Export,Value (USD 1000),USD 1000,126500,F,118000,F,182500,F,240500,F,263000,F,288500,F,267000,F,267000,F,290519,F,267916,F,494794,F,721185,,969058,,1039516,,1302533,,1184881,,1562513,,1545840,,2388556,,2926479,,2955499,,3045439,,2744392,,3064160,,3706339,,4106214,,4600704,,5362366,,6779909,,7674305,,9150328,,9450995,,10356951,,10473061,,13475208,,17228927,,18444831,,19710552,,21191537,,19924138,,20323122,,20701805,,21856930, -China,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,71196,F,95390,F,112646,F,144281,,333251,,359498,,224460,,464783,,712150,,607355,,866219,,957379,,1200992,,1202099,,1011028,,1146031,,1820699,,1816022,,2226628,,2426254,,3167656,,4031009,,4188548,,4585381,,5256087,,5102345,,6342604,,7797598,,7695049,,8362626,,8890462,,8722361,,9055258,,11027653,,14692233, -"China, Hong Kong SAR",All,Export,Value (USD 1000),USD 1000,76225,,88883,,92546,,103262,,92538,,97082,,101544,,93958,,86050,,82855,,112324,,129178,,149460,,151371,,144968,,154588,,148781,,155718,,170856,,189323,,216722,,300117,,141088,,88887,,97743,,73018,,72015,,63493,,77235,,74608,,75398,,67508,,70858,,74027,,79671,,86463,,93887,,99463,,91698,,91302,,91134,,782786,,773349, -"China, Hong Kong SAR",All,Import,Value (USD 1000),USD 1000,182459,,215346,,254876,,309811,,361434,,361504,,469351,,439507,,439621,,471609,,624726,,794280,,1030588,,988063,,1112127,,1232267,,1398728,,1377174,,1652348,,1861022,,1954760,,2117506,,1631323,,1615169,,1972035,,1786497,,1788019,,1775146,,1930169,,1903977,,2059757,,2247293,,2429061,,2562040,,3069839,,3546356,,3669103,,3811009,,3663054,,3587244,,3782272,,3625373,,3879524, -"China, Hong Kong SAR",All,Reexports,Value (USD 1000),USD 1000,45785,,49013,,68196,,98439,,95643,,100085,,134607,,135801,,167956,,216095,,309281,,382339,,594834,,556938,,552481,,492676,,478085,,409844,,525128,,479353,,459512,,381059,,316007,,336208,,489077,,386949,,376772,,379878,,384751,,351592,,345346,,407263,,420881,,399906,,465068,,549408,,784144,,1095385,,992185,,779041,,708610,,0,.,0,. -"China, Macao SAR",All,Export,Value (USD 1000),USD 1000,6588,,7326,,10970,,11542,,8557,,3714,,5755,,5692,,7620,,7717,,7400,,7776,,6045,,5083,,6309,,6878,,5033,,5963,,4362,,2580,,2245,,2330,,1725,,1878,,2619,,2371,,2350,,1470,,1141,,1153,,1782,,2596,,3218,,7506,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,649,,1061, -"China, Macao SAR",All,Import,Value (USD 1000),USD 1000,3839,,4338,,5845,,9791,,15027,,6201,,7595,,9268,,9913,,8986,,10508,,13489,,12984,,13515,,18795,,15154,,13549,,15251,,16682,,19373,,18432,,17689,,17253,,13366,,13742,,16805,,22409,,28059,,29046,,34430,,36525,,46277,,55574,,63600,,73853,,88922,,91933,,109318,,134062,,143367,,138494,,149977,,176012, -"China, Macao SAR",All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,387,,97,,483,,1017,,998,,1189,,1502,,1372,,1138,,1150,,1772,,2585,,3202,,3748,,2323,,1849,,2745,,2481,F,3012,,2519,,730,F,454,F,0,- -Colombia,All,Export,Value (USD 1000),USD 1000,20177,,20479,,22676,,27231,,35036,,33170,,32712,,27367,,29956,F,31694,F,36080,F,50050,F,62849,F,86242,,117774,F,171061,,169521,,161209,,259259,,249658,,203859,,218262,,210078,,183907,,191247,,175317,,166458,,160229,,160232,,181180,,165444,,189232,,240585,,209084,,179903,,188791,,201383,,192129,,219141,,167515,,161744,,135535,,149465, -Colombia,All,Import,Value (USD 1000),USD 1000,22584,,27991,,41098,,63385,,62983,,84249,,84183,,46928,,76697,,43715,,48142,,36182,,48572,,30412,,43556,,52100,,53546,,71925,,95962,,119210,,114551,,111325,,112123,,71789,,75966,,73963,,77855,,81214,,102329,,131853,,144666,,175778,,242106,,230029,,261326,,315989,,372481,,465704,,518093,,438380,,424807,,402517,,477982, -Comoros,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,14,,13,,9,,10,F,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,95,,492,,94,,155,F,1,F,0,0,0,.,1,,4,,7,,0,0,0,.,0,.,3,,0,.,0,0,0,.,1,,0,.,0,.,0,.,137,,6,,0,- -Comoros,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,436,,502,,222,,378,,87,,517,,460,F,520,F,600,F,650,F,750,F,730,F,1160,F,915,F,465,,331,,331,,517,,461,,335,,439,,229,,623,,859,,709,,735,,463,,1772,,890,,1192,,539,,1118,,864,,1381,F,1120,F,1990,,1691,,2494, -Congo,All,Export,Value (USD 1000),USD 1000,0,.,0,.,28,,636,,854,,102,,50,,20,,54,,107,,36,,68,,85,,31,,1,,0,.,20,,8218,,9815,,10006,,10558,,9821,,3309,F,1690,F,2161,F,1362,F,2843,F,4307,F,5140,F,1279,F,3066,F,591,,1066,,919,,894,,831,F,415,F,641,,913,,1154,F,5804,,6898,,5946, -Congo,All,Import,Value (USD 1000),USD 1000,4286,,4231,,11134,,13525,,15012,,15651,,17102,,19126,,29572,,31953,,38750,,32171,,24300,,20423,,21993,F,25689,,31509,,34966,,21726,,27048,,34795,,28194,F,19744,F,18717,F,20692,F,24714,F,12306,F,9928,F,18088,F,10804,,16343,F,11886,F,30041,,21580,,17036,,34151,,39483,,65572,,84856,,33184,F,53178,,73627,,67557, -Congo,All,Reexports,Value (USD 1000),USD 1000,0,,0,,0,,0,.,1130,F,3000,F,3000,F,3000,F,5350,F,4800,F,4100,F,2900,F,2600,F,3000,F,3300,F,4900,F,6200,F,7500,F,5700,F,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -"Congo, Dem. Rep. of the",All,Export,Value (USD 1000),USD 1000,0,.,0,.,42,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1083,F,1351,F,742,F,618,F,431,F,586,F,334,F,327,F,433,F,446,F,419,F,393,F,402,F,391,F,595,F,720,F,700,F,767,F,787,F,1112,F,1018,F,2147,F,1059,F,1143,F -"Congo, Dem. Rep. of the",All,Import,Value (USD 1000),USD 1000,31363,,29119,,31993,,33000,F,31200,F,45634,,35680,,59395,,32150,,46789,,58510,,54770,,50699,,63803,,40000,F,25032,,38816,,39694,,38512,,47985,,51231,,45827,,51432,,40701,F,26217,,35663,F,38565,F,38708,F,45437,F,54778,F,62475,F,85040,F,93769,F,81007,F,112927,F,144517,F,142410,F,145253,F,160649,F,136770,F,106960,F,135660,F,153674,F -Cook Islands,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,111,,89,,385,,936,,288,,603,F,132,,75,,115,,93,,1183,,2611,F,2030,,2576,,816,,1293,F,874,F,632,F,1896,F,1783,F,7068,F,4878,F,7142,F,5187,F,6274,F,10538,F,7563,F -Cook Islands,All,Import,Value (USD 1000),USD 1000,163,,284,,282,,327,,275,,397,,230,F,354,F,230,F,225,F,140,F,250,F,197,F,280,F,446,,589,,620,F,585,F,620,F,438,F,385,F,337,F,543,,551,,323,F,592,,488,,1119,,745,,1015,,997,,1115,,861,F,760,F,879,F,675,F,1020,F,1127,F,1690,F,1281,F,1258,F,1620,F,1544,F -Costa Rica,All,Export,Value (USD 1000),USD 1000,5203,,5776,,7257,,11347,,9246,,7469,,6738,,12276,F,17648,,28877,,33759,,35121,,51493,,67439,,61270,,62583,,74855,,93732,,104876,,122547,,216290,,251943,,254612,,148795,,117891,,133749,,138503,,133571,,116433,,122224,,105977,,107913,,127860,,116874,,106679,,132369,,182345,,157152,,145878,,139069,,118207,,131442,,134888, -Costa Rica,All,Import,Value (USD 1000),USD 1000,946,,1546,,1462,,2636,,4693,,1243,,908,,2931,,5342,,5211,,4341,,8538,,9511,,11846,,15033,,12941,,14427,,14069,,23986,,22361,,27522,,48228,,37274,,25445,,19732,,23177,,32169,,28271,,31927,,31642,,39926,,45185,,61199,,54902,,49490,,73566,,87901,,95121,,134583,,143153,,159964,,199275,,185897, -Croatia,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,55527,,49358,,50484,,44749,,49346,,57280,,43534,,35232,,44166,,64138,,80068,,115153,,103999,,97331,,159938,,153249,,150538,,165154,,136519,,179304,,156299,,153877,,187643,,196005,,196954,,210698,,239540, -Croatia,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,21380,,23405,,25427,,33438,,37636,,43921,,40716,,35376,,39103,,70902,,90614,,94597,,83231,,106177,,115147,,129178,,144850,,109883,,106771,,126641,,121649,,114046,,145756,,143162,,151057,,172569,,196119, -Cuba,All,Export,Value (USD 1000),USD 1000,85493,,79684,,109428,,131667,,123839,,121332,,115918,,125261,,101686,,127309,,146317,,141670,,146348,,129330,,101101,,129792,,110562,,73332,,103359,,126717,,130334,,97643,,98012,,97648,,88116,,80110,,93121,,65443,,89221,,76868,,69471,,87739,,79837,,45695,,59658,,66716,F,48562,F,44845,F,42810,F,53729,F,69690,F,98488,F,73819,F -Cuba,All,Import,Value (USD 1000),USD 1000,44448,,44001,,59650,,52562,,81168,,86068,,47788,,57149,,30499,,61719,,55479,,51846,,60697,,43180,,36486,,15361,,11844,,18921,,18672,,27317,,21951,,16692,,23985,,32822,,43809,,38641,,29403,,36281,,51723,,54175,,39755,,53878,,67503,,43643,,26399,,31869,F,30808,F,28666,F,33373,F,41575,F,42841,F,37978,F,40842,F -Cuba,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,532,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Curaçao,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,19679,F,33631,F,34839,F,30087,F,24606,F,34035,F,40268,F,37210,F -Curaçao,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,11786,F,9325,F,8932,F,8465,F,9048,F,9428,F,7054,F,13253,F -Cyprus,All,Export,Value (USD 1000),USD 1000,10,,64,,194,,185,,254,,181,,0,.,46,,14,,20,,199,,280,,885,,1536,,4058,,1491,,568,,3534,,2505,,3457,,2568,,2948,,4715,,4967,,7143,,6836,,5504,,4489,,27821,,30912,,25202,,31600,,42808,,13657,,16895,,27715,,21092,,32731,,30503,,33002,,34698,,34789,,38785, -Cyprus,All,Import,Value (USD 1000),USD 1000,1730,,2689,,4903,,4532,,6255,,6532,,8895,,8067,,7944,,9253,,11575,,17013,,17214,,21467,,23666,,26504,,33631,,28555,,30911,,38821,,35220,,32525,,38381,,33593,,30782,,34720,,33704,,37786,,52531,,54769,,58020,,80038,,100674,,78305,,80082,,94348,,93428,,83811,,95394,,88404,,99085,,105750,,109496, -Cyprus,All,Reexports,Value (USD 1000),USD 1000,244,,176,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,95,,83,,156,,391,,499,,3044,,2931,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Czechia,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,22379,,24478,,31060,,33352,,29727,,30436,,26301,,26375,,31792,,36352,,45002,,55370,,64309,,71243,,89055,,108095,,86673,,101569,,131006,,127253,,150418,,171652,,159589,,187886,,208547,,191975, -Czechia,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,53736,,69636,,84761,,97977,,92243,,90772,,75864,,75663,,88766,,92342,,99430,,116125,,140396,,159939,,190912,,233363,,205384,,216452,,257726,,255077,,285922,,314558,,288589,,324344,,366112,,389519, -Czechoslovakia,All,Export,Value (USD 1000),USD 1000,2664,,2610,,2818,,3452,,3592,,3142,,2856,,3126,,1733,,1622,,2252,,2201,,7751,,7764,F,9195,F,17064,,12780,F,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0, -Czechoslovakia,All,Import,Value (USD 1000),USD 1000,81649,,73887,,85881,,89562,,100760,,95014,,89345,,77250,,79542,F,73388,,89253,,95146,,113710,,98907,,71662,,61402,,57500,F,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0, -Côte d'Ivoire,All,Export,Value (USD 1000),USD 1000,14526,,22101,,25241,,39489,,56819,,49132,,47807,,55703,,54743,,53899,,59616,,78601,,92876,,94073,,118022,,102573,,100366,,117977,,135666,,237290,,222871,,224072,,215939,,153142,,128876,,107679,,140481,,140902,,159478,,103059,,125080,,171924,,198535,,170271,,103283,,77711,,115058,,110847,,94999,,144158,,109484,,151179,,154856, -Côte d'Ivoire,All,Import,Value (USD 1000),USD 1000,29562,,31729,,42831,,58567,,77547,,85733,,81297,,70268,,56264,F,67699,,78673,,106750,,139236,,136234,,109262,F,114927,F,117001,F,110420,F,157267,F,164825,,172586,,177637,,233714,,172839,,132147,,150365,,180362,,201711,,189769,,204695,,230519,,279699,,399295,,364552,,261727,,316756,,332442,,373414,,405539,,397471,,381279,,462674,,535911, -Côte d'Ivoire,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,4648,,0,-,0,-,0,- -Denmark,All,Export,Value (USD 1000),USD 1000,520935,,629594,,732016,,860197,,1000738,,948382,,909600,,934349,,905735,,963531,,1391625,,1775125,,1862858,,1754000,,2165497,,2302299,,2317758,,2150665,,2369213,,2471239,,2715111,,2669663,,2915017,,2891381,,2765888,,2670738,,2883986,,3227679,,3576980,,3694745,,3999146,,4144577,,4619526,,4002236,,4208403,,4506888,,4159505,,4682363,,4777606,,4283325,,4710353,,4894491,,5066677, -Denmark,All,Import,Value (USD 1000),USD 1000,117133,,176415,,222513,,271084,,332449,,315544,,306513,,316906,,339532,,382055,,609031,,853329,,880063,,893952,,1116108,,1148255,,1197370,,1094253,,1459043,,1621522,,1667566,,1586437,,1774799,,1832234,,1860058,,1787230,,1879327,,2184847,,2368838,,2626929,,2938978,,3028084,,3218930,,2818976,,3121789,,3349940,,3216401,,3582220,,3731729,,3264881,,3693369,,3790005,,3952936, -Djibouti,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,101,,73,,163,,225,,186,,73,,120,F,55,F,34,F,87,F,80,F,47,F,130,F,18,F,73,F,83,F,146,F,58,F,29,F,145,F,160,F,91,F,38,F,90,F,158,F,68,F,86,F,43,F,243,F,592,F,996,F,1002,F -Djibouti,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,809,,430,,613,,767,,496,,371,,810,,570,,569,,697,,1087,,1150,F,1130,F,1029,F,868,F,1336,F,1687,F,2040,F,1518,F,915,F,641,F,679,F,1531,F,1382,F,2334,F,2012,F,2314,F,2872,,1398,F,2288,F,2913,F,4829,F,2988,F,5284,F,6995,F,4041,F,5783,F -Dominica,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1,,0,0,11,,21,,8,,37,,1,,6,,6,,2,,0,0,12,F,0,0,7,F,2,F,39,F,49,F,25,F,14,F -Dominica,All,Import,Value (USD 1000),USD 1000,670,,492,,591,,500,,357,,324,,587,F,537,F,659,,685,,664,F,648,,723,,1029,,1361,,1353,,1390,,1420,F,1357,,3062,,1676,,1397,,2102,F,1555,,1627,,1286,,1424,,1337,,1573,,1666,,1672,,1816,,1965,,2385,,1842,,2192,F,2134,,2173,F,3652,F,3232,F,2189,F,1564,F,1838,F -Dominica,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,10,,1,,0,.,1,,0,.,0,.,0,.,0,0,0,0,0,.,0,.,0,.,56,,0,-,0,-,0,-,0,-,0,-,0,- -Dominican Republic,All,Export,Value (USD 1000),USD 1000,952,,1101,,1103,,913,,1098,,1271,,1287,,1443,,1108,,2771,,2386,F,1274,,1106,,807,,565,,322,F,636,F,952,F,638,F,2389,F,509,,2645,,3234,,3003,,2965,,2905,,4247,,6710,,2807,,7278,,4165,,3540,,4425,,4451,,7278,,14791,,9801,,12710,,13749,,14254,,10561,,14787,,17176,F -Dominican Republic,All,Import,Value (USD 1000),USD 1000,5775,F,14412,,14451,,19958,,24205,,19027,,16848,,15984,,7901,,15256,,25010,F,18410,F,21050,F,23400,F,24000,F,21032,F,24494,F,29635,F,28835,F,38621,F,36623,F,47693,F,51994,F,53199,F,53000,F,57221,F,69853,,55849,,60045,,71231,,82013,,90469,,116780,,126330,,137669,,158302,,164353,,133899,,153438,,158010,,168620,,185652,,185204,F -Ecuador,All,Export,Value (USD 1000),USD 1000,50787,,74898,,80645,,104380,,199966,,188821,,219562,,219372,,216640,,247639,,383565,,481039,,449586,,435897,,468410,,588142,,611573,,573959,,726126,,902276,,925012,,1179568,,1204045,,955205,,588020,,668317,,700508,,781790,,822216,,1005394,,1337867,,1401973,,1756592,,1612094,,1791113,,2496615,,2861799,,3616331,,4279327,,3654874,,3940346,,4611189,,4896310, -Ecuador,All,Import,Value (USD 1000),USD 1000,17,,34,,58,,57,,69,,61,,28,,0,0,10,,123,,5,,0,.,0,.,0,.,2922,,7644,,1267,,4718,,11394,,14479,,18255,,7231,,33974,,7673,,6772,,9590,,27769,,33496,,25203,,13212,,17250,,61347,,238154,,232941,,238317,,303439,,208813,,141412,,164858,,123799,,112286,,124688,,184766, -Egypt,All,Export,Value (USD 1000),USD 1000,422,,979,,431,,751,,550,,763,,1193,,990,,845,F,681,,4685,,1871,,3506,,8425,,13026,,11050,,8462,,5227,,4196,,3654,,4895,,2982,,2166,,1555,,1242,,1348,,2328,,3089,,3919,,4371,,3495,,4522,,10775,,14184,,15428,,23849,,18513,,25855,,35160,,31734,,42415,,39437,,35353, -Egypt,All,Import,Value (USD 1000),USD 1000,25518,F,26807,F,54400,F,21768,F,33222,F,66704,F,66600,F,81027,F,94256,F,55604,F,79881,F,57841,F,64249,F,67537,F,80922,F,74610,F,78410,F,50158,F,92174,,79387,,119355,,102793,,148116,,153096,,171061,,162613,,107516,,110225,,134382,,149622,,167741,,224192,,378192,,476135,,500385,,531452,,751693,,600332,,752457,,768367,,720410,,633817,,915685, -El Salvador,All,Export,Value (USD 1000),USD 1000,9927,,10689,,10951,,13484,,17319,,23344,,22010,,14484,,17601,,14597,,18121,,21787,,17217,,13718,,11531,,14482,,23650,,21699,,30040,,29252,,42478,,34535,,35375,,33602,,26613,,24630,,21692,,33396,,49678,,75621,,69757,,109848,,119766,,94177,,77940,,79151,,107123,,120075,,108918,,87101,,98969,,114728,,103660, -El Salvador,All,Import,Value (USD 1000),USD 1000,1471,,2400,,2333,,2506,,3738,,2417,,948,,570,,1541,,1470,,3782,,2814,,2301,F,3521,F,2283,,2325,,4778,,3743,,5842,,6277,,5589,,5918,,5158,,6644,,8846,,8528,,6534,,10084,,17612,,46083,,36812,,54344,,29453,,77055,,42777,,31705,,44527,,62027,,50994,,37576,,40597,,55095,,44049, -Equatorial Guinea,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,1,,3,,351,,306,,420,F,0,.,0,.,0,.,915,F,245,F,0,.,0,.,0,.,0,.,364,F,1072,F,1133,F,1719,F,2269,F,1588,F,596,F,51,F,108,F,169,F,46,F,73,F,80,F,63,F,80,F,26,F,38,F,57,F,46,F,44,F,46,F,50,F,54,F,73,F -Equatorial Guinea,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,1827,F,3256,,3408,,1820,,1658,,4583,,1597,,2647,F,2299,,2109,,1713,,1580,F,1042,,852,,307,,2068,F,1419,F,2469,F,1265,F,2508,F,2674,F,5079,F,4832,F,9078,F,12529,F,11344,F,16115,F,17413,F,16081,F,25692,F,19362,F,23223,F,31866,F,29384,F,26789,F,18211,F,17268,F,17618,F,15423,F -Eritrea,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,88,F,178,F,291,F,14,F,973,F,2116,F,849,F,867,F,2222,,729,F,1039,F,745,F,1510,F,861,F,1601,F,12,F,7,F,741,F,115,F,4,F,176,F,49,F,133,F,108,F -Eritrea,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,36,F,5,F,34,F,2,F,54,F,117,F,258,F,305,F,998,,638,F,191,F,492,F,233,F,315,F,106,F,247,F,46,F,278,F,138,F,213,F,66,F,32,F,41,F,52,F -Eritrea,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,3,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Estonia,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,12359,,49821,,96388,,107777,,100026,,82183,,102722,,78326,,79130,,128278,,130701,,142976,,117296,,129056,,132941,,137744,,149189,,140770,,184690,,215555,,247264,,255582,,244658,,190874,,151406,,159412,,175883, -Estonia,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,541,,2613,,12893,,18062,,19640,,41447,,50959,,31144,,31052,,44720,,61991,,78375,,59690,,74260,,83728,,121282,,134579,,95662,,101694,,143579,,166012,,203075,,197891,,157280,,126762,,140569,,148065, -Eswatini,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1281,,1715,,2242,,2585,,1565,,2609,,2069,,5981,,2187,,399,,64,,203,F,178,F,152,F,112,F,16,F,0,.,14,F,0,.,8,,18,,0,0 -Eswatini,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,13634,,10486,,9881,,8859,,6293,,6400,,11091,,15325,,11472,,6668,,5358,,4654,F,3568,F,3369,F,3592,F,4654,F,4947,F,8484,F,7498,F,6005,,6942,,6992, -Eswatini,All,Reexports,Value (USD 1000),USD 1000,0,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,973,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Ethiopia,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,55,,5,F,19,,13,,8,,13,,19,,49,,51,,100,,121,,384,,863,,508,,408,,454,,599,,591,,414,,500,,559,,219,,180,,124, -Ethiopia,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,130,,39,,186,,139,,229,F,236,,153,,270,,249,,97,,513,,715,,398,,623,,1080,,898,,1857,,1213,,1351,,1725,,2324,,3989,,4259,,4024,,3447,,2688,,1877, -Ethiopia,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,26,,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,-,0,-,0,- -Ethiopia PDR,All,Export,Value (USD 1000),USD 1000,65,,28,,0,.,0,.,0,.,6,,6,,3,,13,,35,,20,,14,,2,,23,,27,,1,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,.,0,.,0,.,0,.,0, -Ethiopia PDR,All,Import,Value (USD 1000),USD 1000,301,,466,,447,,76,,25,,492,,31,,42,,65,,82,,110,,13,,114,,1114,,209,,80,,0,0,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,.,0,.,0,.,0,.,0, -Falkland Is.(Malvinas),All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2030,F,3510,F,4950,F,1200,F,1500,F,1664,F,10201,F,48115,F,60748,F,29412,F,73218,F,65703,F,117449,F,113194,F,79781,F,145666,F,109750,F,179715,F,143520,F,128610,F,164760,F,120580,F,261475,F,186857,,189534,,168040,F,167900,F,95401,,181936,,304331,,273035, -Falkland Is.(Malvinas),All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,18,,27,,19,,29,,49,,87,F,70,,114,F,288,F,137,F,317,F,301,F,79,F,114,F,334,F,260,F,310,F,133,F,186,F,217,F,211,F,283,F,178,F,165,F,218,F,191,F,743,F,145,F,67,F -Faroe Islands,All,Export,Value (USD 1000),USD 1000,94067,,133229,,129962,,125245,,164488,,144637,,142230,,165914,,148561,,166019,,222073,,270844,,297769,,312171,,374224,,412132,,398260,,318996,,318741,,349866,,391216,,365127,,408652,,432658,,417696,,493593,,521014,,581321,,580059,,567151,,603624,,713891,,725147,,713356,,717764,,830313,,836597,,1013424,,1091455,,1013797,,1118701,,1265986,,1176903, -Faroe Islands,All,Import,Value (USD 1000),USD 1000,1314,,158,,2037,,1278,,1438,,4010,,3817,,3235,,2721,,1874,,3909,,5724,,12228,,6533,,13225,,18280,,29033,,13647,,18196,,24555,,19307,,19557,,25301,,19836,,20813,,17988,,17970,,17784,,22739,,21064,,24425,,21977,,36513,,37956,,35400,F,54446,F,96742,F,55360,F,42200,F,49357,F,56086,F,55674,F,55842,F -Fiji,All,Export,Value (USD 1000),USD 1000,1230,,5345,,10855,F,14356,,11057,,19708,,10082,,14784,,13942,,10136,,16045,,20551,,33414,,30254,,35861,,39160,,26922,,28394,,39334,,39684,,43723,,36585,,26327,,23228,,36071,,42450,,40199,,48405,,54243,,54373,,62373,,68489,,87964,,83728,,118301,,116093,,55220,,79794,,108892,,76488,,86869,,83863,,98261, -Fiji,All,Import,Value (USD 1000),USD 1000,8079,,11838,,17208,,11827,,16815,,16248,,12734,,9373,,9702,,12210,,15571,,19943,,28950,,18627,,20019,,20867,,25554,,37080,,18155,,26822,,19921,,22608,,21327,,16309,F,8503,,21216,F,17149,,40049,,39299,,37458,,35072,,34351,,40840,,37287,,59570,,127820,,157043,,112139,,205562,,104222,,84049,,80992,,50858, -Fiji,All,Reexports,Value (USD 1000),USD 1000,3856,,4777,,6415,,1873,,7519,,3512,,974,,579,,523,,1581,,189,,2569,,1474,,3056,,3142,,670,,245,,89,,105,,1082,,5388,,61,,53,,54,,0,.,0,.,7,,0,.,0,.,1612,,0,.,5458,,3714,,8818,,21262,,94573,,145773,,92531,,135786,,58335,,72939,,92523,,96350, -Finland,All,Export,Value (USD 1000),USD 1000,1115,,1617,,2574,,3132,,7246,,7848,,5197,,7856,,10923,,16564,,6803,,8631,,22725,,16228,,15510,,10670,,15234,,13374,,20286,,22475,,21946,,20989,,18500,,21590,,17207,,14551,,14512,,13118,,13677,,17251,,25895,,48748,,49449,,50213,,46790,,52327,,59443,,59838,,55964,,42041,,63699,,167399,,214008, -Finland,All,Import,Value (USD 1000),USD 1000,53237,,54902,,67492,,79433,,104562,,101121,,94829,,94611,,88167,,85530,,106456,,130177,,139592,,148739,,138177,,134532,,129661,,105791,,148082,,114757,,148610,,140475,,142588,,120967,,121304,,136540,,146679,,174421,,207816,,218686,,270417,,330159,,350571,,349915,,399938,,467742,,451127,,534861,,523182,,419227,,444911,,577084,,622097, -France,All,Export,Value (USD 1000),USD 1000,180999,,210674,,214779,,273301,,409889,,319677,,308220,,329807,,306802,,364833,,509136,,661818,,741415,,782522,,939883,,934463,,965543,,865883,,919410,,1002072,,1015648,,1109207,,1111653,,1120250,,1108596,,1032036,,1103801,,1345729,,1543762,,1601107,,1691540,,1953230,,2037873,,1623536,,1639140,,1753083,,1776994,,1851890,,1784952,,1642327,,1723648,,1792703,,1912379, -France,All,Import,Value (USD 1000),USD 1000,575159,,727125,,906678,,1084782,,1238560,,1062211,,1055155,,1066360,,990967,,1057273,,1532886,,2051901,,2274966,,2222700,,2849268,,2957993,,2964685,,2578046,,2830331,,3256251,,3227901,,3090503,,3537734,,3317915,,3018121,,3087695,,3237053,,3803281,,4216736,,4604497,,5108709,,5414876,,5894002,,5639111,,6010140,,6627740,,6095919,,6570913,,6672044,,5802808,,6241455,,6766261,,7078772, -French Guiana,All,Export,Value (USD 1000),USD 1000,2433,,4569,,2094,,5538,,9358,,26078,,21069,,28711,,23206,,20862,,23792,,33507,,30972,,30137,,41743,,37274,,42215,,30504,,36261,,40495,,0,-,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -French Guiana,All,Import,Value (USD 1000),USD 1000,2319,F,2971,,3427,,6713,,11955,,22956,,20049,,26216,,19363,,14692,,14479,,10527,,3159,,3871,,4407,,3692,,5143,,3898,,4504,,5137,,0,-,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -French Polynesia,All,Export,Value (USD 1000),USD 1000,31,,134,,64,,128,,8,,25,,18,,31,,20,,7,,30,,70,,113,,17,,427,,370,F,363,,814,,732,,731,,545,,7008,,4726,,5033,,7275,,12090,,10721,,8929,,6585,,8538,,10903,,9834,,9024,,8720,,10309,,11982,,18166,,16535,,16976,,17796,,14623,F,10311,F,20219,F -French Polynesia,All,Import,Value (USD 1000),USD 1000,2882,,2587,,3499,,4719,,4840,,4757,,4809,,4646,,4258,,4159,,6919,,6239,,6650,,7154,,7071,,6390,F,8579,,8934,,8689,,10461,,10302,,8938,,10374,,8944,,9499,,9782,,10928,,13593,,13554,,17224,,19166,,18438,,18741,,19037,,17907,,15094,,18335,,15335,,17901,,13587,,12088,F,9998,F,12895,F -Gabon,All,Export,Value (USD 1000),USD 1000,51,,759,,1073,,4023,,630,,6845,,4493,,4171,,3472,,4039,,6305,,7933,,5390,,8175,F,3859,,4400,F,4070,F,2840,,1780,,6649,F,1423,,5040,,8745,,8923,,12587,,9769,,10552,,9827,,19405,,24340,,15882,,14859,,7461,,2181,,1610,F,1643,F,3126,F,2732,F,3014,F,3317,F,2919,F,3116,F,2782,F -Gabon,All,Import,Value (USD 1000),USD 1000,3296,,6188,,5680,,8105,,10117,,11240,,10792,,9210,,10249,,9922,,13443,,14531,,12696,,11590,F,12144,,14670,,14618,,16094,,8386,,5500,F,13591,,11870,,11318,,9484,,9840,,10552,,1717,,7521,,9926,,11432,,13212,,13816,,17607,,20014,,21661,F,33334,F,38058,F,41115,F,60155,F,53847,F,57842,F,43669,F,68176,F -Gambia,All,Export,Value (USD 1000),USD 1000,1468,F,1870,F,1257,F,500,,370,,290,,416,,271,,0,.,690,,776,,1400,F,2394,,2275,,3977,,3620,,2176,F,2658,,3061,,4440,F,2585,,3831,,2378,,3214,,3619,,1349,,1118,,345,,310,,919,,355,,3453,,2775,,5168,,6785,,2891,,2302,,1802,,1987,,2673,F,2710,,3360,,3029, -Gambia,All,Import,Value (USD 1000),USD 1000,2410,F,3030,F,1970,F,140,F,150,F,549,,502,,959,,1080,F,500,F,500,F,350,F,0,.,7,F,15,F,140,F,200,F,493,,202,,275,F,413,,296,,1231,,427,,495,,1105,,455,,290,,484,,431,,579,,907,,808,,792,,1486,,772,,448,,403,,345,,572,F,335,,462,,269, -Gambia,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,64,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1923,,1747,,301,,157,,0,-,0,-,0,-,0,- -Georgia,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,89,,513,,99,,311,,439,,166,,230,,136,,358,,124,,362,,382,,1396,,903,,2529,,6718,,5297,,10461,,6241,,2758,,22014,,24621,,23881,,32146,,21297,,28639, -Georgia,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,347,,295,,1378,,3149,,4366,,2809,,1431,,1395,,1201,,1589,,2203,,6453,,11925,,27341,,34800,,43494,,32367,,35965,,43729,,45528,,48255,,44187,,40387,,41434,,43900,,36794, -Georgia,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,9,,0,.,5,,24,,0,.,0,.,0,.,0,.,0,.,631,,659,,199,,1721,,731,,311,,279,,241, -Germany,All,Export,Value (USD 1000),USD 1000,173295,,226430,,243062,,281551,,317827,,279577,,316615,,306926,,300555,,287243,,361717,,442805,,500330,,559915,,669193,,719879,,697840,,657163,,798397,,907123,,1064947,,986642,,1062577,,975261,,1110897,,1044266,,1170688,,1292083,,1430582,,1517773,,1844327,,2295294,,2496847,,2387536,,2507327,,2931913,,2790309,,2964039,,3299473,,2803877,,3002643,,2918369,,3024392, -Germany,All,Import,Value (USD 1000),USD 1000,539999,,671454,,770389,,891246,,1031110,,826632,,829944,,838094,,808321,,829114,,1124101,,1282188,,1447527,,1497610,,1914175,,2129105,,2206691,,1899940,,2340571,,2504124,,2566535,,2385003,,2649313,,2309380,,2282399,,2370057,,2440391,,2658455,,2830918,,3262805,,3778631,,4323840,,4544772,,4610989,,4762512,,5564571,,5241026,,5475657,,6095489,,5189958,,5661524,,5780943,,6048383, -Ghana,All,Export,Value (USD 1000),USD 1000,1351,,4052,,34276,F,46687,F,41961,F,55681,F,47166,F,22690,F,16560,F,20195,,19868,,15207,,20976,,13232,,21592,,23657,F,17201,F,21810,F,29187,F,43950,F,65129,,96944,,69300,,72233,,78472,,83513,,76255,F,120460,,93785,,97439,,51956,,61001,,44134,,53923,F,64392,F,72832,F,85406,F,80253,F,90997,F,107260,F,133152,F,209776,,226858, -Ghana,All,Import,Value (USD 1000),USD 1000,28377,,20953,,63575,,39880,,34241,,25065,,15191,,15907,F,9674,F,17298,F,17590,F,11584,,6820,,11300,F,11100,F,13138,,22924,,19983,,22536,F,30442,F,68970,,63659,,105001,,103226,,83646,,92308,,125347,,58357,,119773,F,190725,,125321,,170804,,128738,,121399,,146384,,281436,,244230,,373200,,262109,,257222,,323623,,278177,,285956, -Greece,All,Export,Value (USD 1000),USD 1000,13196,,15139,,17743,,22855,,20027,,16307,,15019,,14733,,19723,,22004,,33376,,47039,,46824,,65640,,65374,,74048,,98798,,122078,,147888,,156680,,173927,,208014,,221558,,269125,,220815,,204682,,221408,,313130,,409196,,427025,,510560,,616609,,636428,,656395,,710621,,836541,,788655,,745424,,764322,,661958,,738906,,777505,,821540, -Greece,All,Import,Value (USD 1000),USD 1000,29759,,32144,,45373,,60994,,63264,,82862,,85823,,75625,,80335,,82174,,108180,,125034,,140506,,172563,,194330,,175413,,185329,,165405,,178926,,218222,,288545,,303415,,298511,,317208,,288303,,312434,,386471,,447207,,483961,,527013,,597886,,765119,,789508,,708464,,654963,,708790,,642209,,643680,,678392,,567082,,631504,,702752,,790152, -Greenland,All,Export,Value (USD 1000),USD 1000,40160,,48357,,55482,,96862,,137556,,147019,,104322,,116473,,101903,,135385,,211020,,288854,,307723,,326165,,375906,,325742,,315769,,290037,,267041,,345768,,336131,,269524,,237043,,265461,,266542,,239413,,247013,,352124,,342160,,364237,,352751,,295302,,418318,,319517,,355624,,451459,,432283,,446046,,515395,,377753,,512847,,540928,,594356, -Greenland,All,Import,Value (USD 1000),USD 1000,451,,607,,969,,581,,778,,702,,700,,696,,3881,,597,,962,,3005,,1413,,3628,,1829,,5883,,14117,,4270,,3513,,4104,,1772,,720,,1567,,1516,,2205,,2930,,2466,,3955,,4358,,3770,,5333,,2618,,5245,,6671,,6036,,5989,,5588,,9262,,6229,,6517,,5033,,4582,,5772, -Grenada,All,Export,Value (USD 1000),USD 1000,0,.,0,.,15,,17,,25,,19,,0,.,0,.,0,.,0,.,0,.,68,,257,,162,,59,,162,,365,F,675,F,1260,F,3739,,3097,,4293,,3708,,3531,,3408,,4052,,3863,,3258,,3086,,3517,,3734,,4115,,2957,,4919,F,6466,F,5402,F,5559,F,6629,F,8081,F,5360,F,7738,F,5188,F,7975,F -Grenada,All,Import,Value (USD 1000),USD 1000,0,.,0,.,779,,835,,958,,1114,,984,,680,,940,,1062,,1005,,1365,,1259,,1507,,1625,,2008,,1568,F,1224,F,1302,F,1783,,2214,,2190,,2391,,2533,,2181,,2556,,2558,,2550,,2676,,2966,,3922,,4704,,4622,,3807,,2980,F,2815,F,3233,F,2499,F,3008,F,2294,F,2780,F,2668,F,3329,F -Grenada,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,18,,18,,0,.,1,,1,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,14,,13,,23,,16,,0,0,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Guadeloupe,All,Export,Value (USD 1000),USD 1000,0,.,55,,28,,44,,35,,68,,54,,66,,80,,401,,243,,220,,131,,269,,250,,200,,121,,268,,230,,266,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -Guadeloupe,All,Import,Value (USD 1000),USD 1000,4048,,4752,,5686,,7076,,8138,,8140,,7130,,8190,,6977,,7645,,10736,,14512,,16337,,15746,,18712,,22063,,24291,,21199,,25244,,30428,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -Guam,All,Export,Value (USD 1000),USD 1000,0,-,2760,F,6500,F,11940,F,8870,F,0,.,0,.,9290,F,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,,0,,0, -Guam,All,Import,Value (USD 1000),USD 1000,0,.,1951,,3238,,6513,,5501,,0,.,0,.,6611,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,,0, -Guatemala,All,Export,Value (USD 1000),USD 1000,4964,,5687,,8073,,6176,,8854,,7294,,12843,,9729,,12727,,10057,,8138,,11404,,20169,,18767,,14659,,18749,,21384,,27127,,31366,,23253,,27518,,16213,,26407,,28127,,35063,,22415,,33517,,42969,,53070,,49248,,67595,,89669,,77898,,86747,,98423,,106199,,123443,,123496,,130405,,91985,,97956,,114787,,114782, -Guatemala,All,Import,Value (USD 1000),USD 1000,1089,,1353,,1418,,2643,,2446,,2301,,2213,,1917,,2200,,1905,,1468,,3318,,3037,,5446,,4827,,3657,,4210,,5848,,7960,,8800,,3917,,6285,,7902,,6966,,8334,,11579,,18987,,23484,,33907,,35293,,39373,,44253,,37258,,43912,,75730,,77009,,96603,,99159,,132659,,91389,,95233,,105554,,105454, -Guinea,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,5260,F,5910,F,650,,2442,,1738,,680,,1718,,4778,F,4630,F,2834,,4402,,7068,F,10046,F,7042,,4583,,5482,,7146,F,7442,F,7544,F,12625,F,10433,,15316,,13628,,13689,F,13054,,13920,F -Guinea,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,3411,,4205,,3563,,4249,,5130,,5310,,3091,,6535,,4480,,5050,F,4090,,6385,,8430,F,11439,,12115,,11784,,12295,,13295,,13133,,12848,F,13533,F,317,,1186,,864,,1426,,518,,634,,324,,606,,917,,1600,F,1915,F,2014,F,2564,F,2515,,2228,,1984,,2536,F,1687,,2446,F -Guinea,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,12,,0,.,33,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Guinea-Bissau,All,Export,Value (USD 1000),USD 1000,548,,2273,,2254,,4175,,3545,,4725,,504,,400,F,855,,3416,F,1158,,500,,1400,,2160,,3340,,2560,,850,,700,,740,F,2473,F,3991,F,2902,F,2788,F,2021,F,2887,F,3012,F,5307,F,5100,F,5717,F,4721,F,4246,F,2883,F,2232,F,2059,F,3438,F,2681,F,2230,F,3102,F,3960,F,4748,F,5664,F,4654,F,5669,F -Guinea-Bissau,All,Import,Value (USD 1000),USD 1000,461,,140,,159,,260,,298,,233,,107,,160,F,231,,467,,403,,370,F,320,F,350,F,540,F,420,F,420,F,430,F,400,F,505,F,552,F,671,F,533,F,487,F,283,F,155,F,140,F,229,F,289,,404,F,389,F,1452,F,1684,F,1456,F,708,F,1247,F,1212,F,1266,F,944,F,782,F,1844,F,2983,F,999,F -Guyana,All,Export,Value (USD 1000),USD 1000,3395,,4837,,4451,,6206,,25535,F,16670,F,32699,,29242,,22571,,19682,,27322,,28403,,25021,,25947,,26674,,25988,,19963,,17222,,16685,,9731,F,19490,,23491,,30996,,42589,,51304,,61975,,54497,,54242,,62028,,64264,,64863,,63743,,67931,,53327,,49184,,53619,,76536,,89556,,73391,,87300,,99324,,111224,,110836, -Guyana,All,Import,Value (USD 1000),USD 1000,50,,10,,3,,3,,17,,6,,2,,2,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1347,F,740,F,1221,F,633,F,168,F,2099,F,1166,,863,,2475,,2040,,2319,,2715,,1073,,1476,,1430,,1461,,2669,,1248,,1632,,1686,,2557,,2964,,2854,,2905,,2647,,3547,,3297,,3634, -Guyana,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,0,0,0,0,.,0,0,0,.,6,,34,,0,.,0,.,0,0,2,,2,,0,.,0,.,0,.,0,.,54, -Haiti,All,Export,Value (USD 1000),USD 1000,411,,651,,822,,517,,612,,637,F,948,,697,,930,,796,,1390,F,2510,F,2160,F,1950,F,2025,F,2130,F,1650,F,1920,F,2094,F,2632,F,2521,F,2918,,3344,F,3906,F,3969,F,3868,F,4449,F,4130,F,3586,F,4269,F,3818,F,5029,F,4875,F,5018,F,6809,F,10227,F,8222,F,9683,F,13958,F,13715,F,11506,F,10059,F,10544,F -Haiti,All,Import,Value (USD 1000),USD 1000,1950,,1727,,2694,,3118,,4784,,2050,,3560,F,3650,F,6580,F,5760,F,5290,F,5790,F,5503,F,5650,F,5802,F,3719,F,3650,F,4005,F,3353,F,4213,F,4099,F,6709,F,8333,F,7990,F,5949,F,7088,F,4702,F,7951,F,8646,F,9830,F,16512,F,17014,F,21885,F,26629,F,20318,F,34609,F,21889,F,42616,F,42958,F,45748,F,33170,F,60433,F,53933,F -Honduras,All,Export,Value (USD 1000),USD 1000,12306,,15230,,15887,,24985,,18700,,26441,,28228,,36144,,33455,,27353,,59284,,82248,,51158,,54042,,30739,,68582,,21654,F,33914,,89223,,153459,,159493,,160653,,194478,,198436,,188693,,213179,,137028,,152544,,159863,,188459,,213676,,186934,,186016,,170550,,169245,,144222,,264909,,359728,,426927,,395309,,426166,,452547,,363745, -Honduras,All,Import,Value (USD 1000),USD 1000,1050,,1525,,1130,,1437,,1997,,1955,,1271,,734,,936,,980,,1803,,1577,,1860,F,1527,,1421,,2485,,2313,,6047,,33926,,7593,,6538,,9725,,15292,,15600,,16395,,13409,,11661,,11722,,14633,,14893,,19331,,19224,,26842,,21298,,27187,,26148,,23186,,27676,,25630,,29366,,30448,,23006,,29558, -Honduras,All,Reexports,Value (USD 1000),USD 1000,0,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,546,,11246,,0,.,0,.,0,.,0,.,0,.,32141,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,889,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Hungary,All,Export,Value (USD 1000),USD 1000,3420,,4481,,6187,,8305,,8860,,10218,,9072,,6146,,5761,,5272,,6455,,7355,,5579,,5161,,6271,,6612,,8174,,4745,,9326,,9010,,9004,,6894,,10682,,7514,,6050,,5806,,4813,,6945,,4518,,18234,,3899,,2466,,1990,,3265,,9104,,13414,,15660,,15273,,25935,,21680,,24106,,22816,,22195, -Hungary,All,Import,Value (USD 1000),USD 1000,28327,,42818,,38765,,32155,,30283,,31177,,38623,,45031,,41457,,34547,,36870,,42779,,63603,,54657,,43733,,27241,,33322,,31372,,41851,,46354,,44835,,46919,,57898,,40183,,39994,,49733,,56848,,57902,,51363,,71639,,58713,,60413,,76050,,67569,,77185,,87534,,79840,,85955,,102927,,97229,,103324,,108419,,125301, -Iceland,All,Export,Value (USD 1000),USD 1000,290333,,381064,,506787,,594902,,708628,,712635,,538734,,527167,,509245,,617355,,857994,,1071067,,1059371,,1026990,,1240603,,1280153,,1253058,,1138132,,1264633,,1342674,,1425898,,1360616,,1437211,,1383251,,1236612,,1280499,,1438364,,1521163,,1782756,,1793579,,1822671,,2034892,,2207660,,1815800,,1849340,,2217437,,2204471,,2300147,,2156878,,2075235,,2025863,,2018878,,2379402, -Iceland,All,Import,Value (USD 1000),USD 1000,172,,253,,564,,432,,934,,730,,1912,,1996,,3711,,4698,,1607,,3379,,9011,,13682,,17056,,13981,,14232,,23374,,25280,,40459,,42611,,36168,,83933,,82841,,74348,,65271,,83762,,87643,,117367,,102880,,98485,,120565,,130891,,78473,,101917,,137010,,104179,,111837,,124655,,184791,,123933,,111318,,140655, -India,All,Export,Value (USD 1000),USD 1000,192600,,197253,,248201,,311278,,268539,,317677,,356069,,353691,,333452,,298804,,362256,,377555,,420270,,388327,,467426,,647762,,673435,,836119,,1125806,,1041226,,1121977,,1233938,,1056658,,1190011,,1417853,,1249689,,1423707,,1311250,,1412334,,1597722,,1768732,,1678841,,1632314,,2029287,,2566916,,3550886,,3416838,,4622306,,5613712,,4883100,,5555430,,7183336,,6940493, -India,All,Import,Value (USD 1000),USD 1000,256,,363,,475,,665,,5807,,6433,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,197,,608,,1671,,2092,,5124,,7468,,14423,,12893,,20914,,35435,,20995,,17285,,24024,,40268,,50490,,50122,,59777,,52549,,53362,,64370,,65474,,96110,,129310,,95251,,84109,,86579,,103005,,114716,,113732,,153008, -Indonesia,All,Export,Value (USD 1000),USD 1000,124224,,153126,,180504,,221255,,211299,,203590,,231326,,234953,,228013,,236620,,340619,,441079,,664483,,767422,,997458,,1197725,,1190807,,1433155,,1616557,,1691803,,1705692,,1637419,,1639366,,1550356,,1608609,,1559076,,1515404,,1579368,,1734507,,1841643,,2017273,,2167839,,2598922,,2350376,,2718099,,3360923,,3752294,,4025024,,4467564,,3788795,,4009356,,4383228,,4703142, -Indonesia,All,Import,Value (USD 1000),USD 1000,10118,,10336,,10914,,8987,,15013,,38008,,44820,,33671,,28324,,22940,,26315,,26519,,19376,,30850,,43203,,47501,,56975,,100696,,125786,,110148,,118686,,114489,,49384,,68412,,101644,,93730,,79095,,75903,,143669,,106330,,142742,,118966,,202029,,234531,,326108,,411209,,358946,,379626,,355529,,318615,,365836,,398007,,423664, -Iran (Islamic Rep. of),All,Export,Value (USD 1000),USD 1000,3454,,15813,,14996,,21511,,17428,,16856,,18215,F,24723,,28595,,26525,,19668,,42801,,47294,,53690,,50625,,63395,,50736,,50551,,73053,F,59990,F,36523,F,44462,,52288,,42007,,50366,,43474,,50800,,80573,,77775,,43166,,60966,,35917,F,57627,,165726,,210265,,211147,,253926,,243636,,256579,,212275,,350048,,414291,,386484, -Iran (Islamic Rep. of),All,Import,Value (USD 1000),USD 1000,19556,,36973,,26670,,29003,,44150,,81515,,37619,,32156,,35591,,20883,,20205,,30870,,20841,,23610,,14100,F,43490,F,56840,F,45700,F,32781,F,41208,,56991,F,24242,,52113,,80923,,35781,,27437,,30010,,69217,,31541,,24655,,12635,,18540,F,26707,,38375,,87600,,71836,,65304,,102433,,173625,,150863,,184308,,142653,,89879, -Iraq,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,242,,193,,99,,100,F,75,F,10,,40,,70,,70,,70,,90,F,115,F,94,,96,F,60,F,34,,42,F,80,F,54,F -Iraq,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1231,F,1104,F,650,F,1277,F,99,,25,,177,,2660,F,9208,F,9542,F,21602,F,22512,F,31833,F,25548,,45563,F,40090,F,42658,F,67202,F,72908,,60718,F,212204,F,153141,F,139616,F -Ireland,All,Export,Value (USD 1000),USD 1000,37448,,44653,,61254,,72985,,98160,,88728,,103929,,106488,,96576,,110086,,141549,,200029,,216921,,223177,,271674,,308733,,326242,,272464,,298159,,350415,,407256,,345060,,336695,,315704,,308384,,392804,,405459,,429106,,478088,,444128,,449561,,507703,,505761,,464232,,505577,,602831,,714964,,696472,,755517,,682087,,669156,,766790,,816844, -Ireland,All,Import,Value (USD 1000),USD 1000,15943,,23439,,27851,,33633,,41967,,42272,,42610,,37747,,35417,,40605,,51503,,62592,,69349,,66865,,101680,,100550,,97584,,79406,,86509,,95541,,109851,,107714,,115026,,120988,,113407,,127784,,132909,,121064,,137581,,178693,,211480,,270089,,257903,,237491,,246406,,304195,,313360,,320448,,373401,,348206,,349419,,381877,,421878, -Israel,All,Export,Value (USD 1000),USD 1000,2960,,4575,,3051,,1893,,907,,1079,,1040,,4201,,1450,,1747,,2505,,2914,,5280,,5032,,7648,,9685,,8691,,10014,,9246,,10569,,10281,,10107,,9004,,9932,,8619,,9486,,8690,,13520,,16074,,15445,,17490,,21249,,25000,,25992,,31100,,31636,,28736,,37074,,37216,,28791,,25895,,22923,,32364, -Israel,All,Import,Value (USD 1000),USD 1000,16813,,22613,,21065,,27255,,24983,,29884,,26277,,34280,,30306,,32216,,49235,F,61210,F,71949,,75845,,82751,,88456,,91408,,101842,,112923,,135191,,118295,,138277,,140673,,130923,,129351,,146547,,135218,,142377,,148414,,165480,,165485,,196165,,247948,,227982,,306696,,387701,,385798,,453577,,484950,,446584,,508595,,628923,,665688, -Italy,All,Export,Value (USD 1000),USD 1000,48798,,56514,,72162,,123623,,111546,,108189,,102837,,106296,,107385,,144273,,170124,,144089,,188644,,209134,,242090,,251253,,260531,,264729,,295079,,320691,,381338,,385346,,378986,,363639,,380377,,386657,,430759,,459709,,542205,,606698,,726162,,778525,,806873,,716420,,714973,,796320,,689929,,774412,,843326,,765120,,791940,,845968,,906474, -Italy,All,Import,Value (USD 1000),USD 1000,397425,,437203,,557742,,737632,,846883,,733228,,765834,,744634,,751121,,997500,,1274912,,1753253,,1919925,,2009015,,2482251,,2709363,,2663528,,2148410,,2281313,,2309039,,2620098,,2597954,,2833765,,2748663,,2555491,,2732804,,2922447,,3579947,,3928154,,4250071,,4745607,,5173638,,5483243,,5086303,,5403656,,6250387,,5537243,,5778741,,6146079,,5579026,,6197286,,6588912,,7108200, -Jamaica,All,Export,Value (USD 1000),USD 1000,28,,104,,68,,134,,36,,340,,95,,330,,388,,1986,,2309,,2706,,2204,,1823,,3386,F,6995,,9270,,13230,,1592,,17983,,25800,,15513,,14459,,14687,,10001,,11817,,5531,,8177,,7411,,9545,,10820,,8447,,8241,,4749,,7996,,9991,,8967,,11192,,12587,,10596,,11843,,12883,,12728,F -Jamaica,All,Import,Value (USD 1000),USD 1000,19365,,10777,,18041,,17754,,17736,,23158,,29052,,24983,,26296,F,18369,,25665,,22602,,31452,,34872,,31836,F,27348,,23528,,31400,,28597,,32275,,50446,,51408,,57155,,59298,,51611,,59740,,58252,,59384,,59803,,76895,,81244,,94500,,102889,,88734,,90344,,105210,,110112,,105859,,109764,,103392,,104572,,116803,,131929,F -Jamaica,All,Reexports,Value (USD 1000),USD 1000,8,,1,,2,,5,,0,.,0,.,0,.,0,.,0,.,0,.,80,,0,.,13,,0,.,0,.,0,.,22,,0,.,0,.,0,.,3,,0,.,0,.,0,.,0,.,30,,62,,122,,94,,88,,0,.,789,,1193,,1804,,2148,,2000,,2049,,1969,,1939,,1969,,2519,,1806,,0,- -Japan,All,Export,Value (USD 1000),USD 1000,662469,,647213,,773500,,743676,,937067,,897279,,840744,,822036,,917559,,854365,,927430,,919989,,1084089,,960160,,854170,,890690,,850772,,807984,,821925,,754930,,745137,,921195,,739799,,745812,,832088,,794897,,817593,,952419,,1111634,,1290505,,1456604,,1704297,,1744996,,1629408,,2014055,,1939186,,1897374,,2059862,,1950359,,1986418,,2113853,,2112314,,2392716, -Japan,All,Import,Value (USD 1000),USD 1000,1850360,,2382860,,3137205,,4145893,,3253210,,3880590,,4114203,,4106540,,4300982,,4852280,,6701242,,8463279,,10839671,,10359444,,10904945,,12332615,,13066174,,14411847,,16580070,,18146582,,17287999,,15779953,,13032751,,14991704,,15742561,,13649228,,13862980,,12623644,,14830080,,14728775,,14258699,,13439646,,15230877,,13509183,,15175785,,17728059,,18355836,,15654784,,15204333,,13752060,,14216106,,15352351,,15713511, -Jordan,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,39,,1687,,223,,238,,339,,39,,43,,10,,0,.,151,,0,.,0,.,0,.,6,,10,,4,,1,,0,.,5,,179, -Jordan,All,Import,Value (USD 1000),USD 1000,3484,,5503,,6838,,5165,,6641,,9477,,8744,,10232,,9021,,10720,,9312,,14818,,9682,,9707,,9810,,15004,,16402,,19690,,19779,,24197,,34629,,26889,,27365,,21022,,21816,,27569,,27251,,29260,,39113,,38542,,49110,,56922,,79318,,85983,,80235,,83333,,116048,,105565,,115655,,112085,,114164,,114328,,113774, -Jordan,All,Reexports,Value (USD 1000),USD 1000,422,,249,,303,,156,,64,,148,,156,,63,,0,.,28,,0,.,71,,132,,5,,79,,968,,1971,,845,,810,,503,,2596,,493,,505,,1192,,0,.,1090,,477,,1183,,1472,,1746,,2114,,956,,4886,,7122,,4841,,6523,,11117,,4292,,1969,,2918,,900,,2691,,2644, -Kazakhstan,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,8175,F,14116,,19017,,22411,,15052,,12257,,13490,,19074,,17879,,21778,,33286,,53929,,50644,,81472,,84093,,86502,,93370,,80825,,70089,,81108,,83973,,55549,,58618,,60163,,70294, -Kazakhstan,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2950,F,8845,,16881,,12826,,8255,,11909,,19869,,16700,,12228,,14720,,16822,,24520,,34700,,56574,,78277,,66995,,57445,,88627,,91106,,90549,,86529,,72379,,69986,,81565,,94479, -Kenya,All,Export,Value (USD 1000),USD 1000,1005,,1053,,1118,,2436,,792,,2353,,2054,,1849,,1161,,1347,,3093,,6854,,10119,,16967,F,26918,,27539,,25777,,25610,,41056,,36301,,50431,,52307,,38993,,32415,,38874,,49181,,56263,,57706,,53142,,61873,,55938,,61869,,75594,,57389,,64110,,55522,,64291,,40528,,49639,,37697,,19984,,24153,,31653, -Kenya,All,Import,Value (USD 1000),USD 1000,1205,,1635,,3043,,1591,,2712,,1180,,772,,968,,1308,,977,,752,,1275,,1402,,1000,F,720,,1036,,2654,,2790,,5526,,4076,,6872,,12228,,8993,,5339,,4614,,5219,,2955,,3474,,5112,,7329,,8108,,11189,,9937,,10091,,13255,,15955,,18731,,17800,,22884,,20640,,23417,,25049,,29846, -Kenya,All,Reexports,Value (USD 1000),USD 1000,5,,140,,31,,151,,60,,2,,2,,0,0,64,,0,0,12,,1,,0,.,0,.,0,.,0,.,0,.,0,.,15,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,370,,0,.,0,.,0,.,0,.,0,0,0,.,0,.,0,.,97,,30, -Kiribati,All,Export,Value (USD 1000),USD 1000,0,.,10,,13,,197,,238,,822,,555,,1373,,1554,,738,,1206,,627,,1285,,2161,,1343,,1024,,1026,,1409,,3679,F,3950,F,4027,F,3226,F,4429,F,6017,F,5897,F,3566,F,3872,F,3691,F,4169,F,4857,F,5568,F,13120,F,15663,F,25763,F,28456,F,47226,F,60719,F,68310,F,108606,F,120140,F,133066,F,145169,F,118868,F -Kiribati,All,Import,Value (USD 1000),USD 1000,86,,80,,200,,247,,217,,300,,276,,150,,196,,102,,112,,206,,214,,227,,256,,235,,293,,280,F,338,,418,,418,,318,,397,,387,,369,F,128,F,310,F,809,F,396,F,1123,,798,,1110,,648,,572,,534,,1083,,1068,,659,,1021,F,872,F,1602,,2298,F,2147,F -"Korea, Dem. People's Rep",All,Export,Value (USD 1000),USD 1000,9125,F,8232,F,17304,F,39138,F,29290,F,28491,F,40175,F,38461,F,33336,F,27954,F,35930,F,79307,F,102755,F,84390,F,65520,F,61400,F,71980,F,65815,F,55718,F,70223,F,58104,F,53503,F,50257,F,72319,F,87763,F,100179,F,228009,F,253319,F,271835,F,117995,F,71794,F,35251,F,45837,F,60458,F,66775,F,83756,F,100947,F,106813,F,141496,F,111770,F,139577,F,171874,F,10023,F -"Korea, Dem. People's Rep",All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,423,,258,,389,,543,,2405,F,4390,F,3739,F,2447,F,1269,F,2731,F,13132,F,24540,F,40100,F,42697,F,36574,F,36187,F,52802,F,73291,F,85602,F,94160,F,48076,F,59169,F,79029,F,114062,F,102524,F,98780,F,94235,F,112683,F,51090,F -"Korea, Republic of",All,Export,Value (USD 1000),USD 1000,329114,,706329,,646333,,800832,,681607,,850521,,774928,,751498,,791371,,858623,,1185409,,1608079,,1901817,,1677897,,1496453,,1625157,,1500322,,1479378,,1572858,,1713534,,1625277,,1484770,,1361044,,1509602,,1491441,,1255426,,1141133,,1104758,,1248031,,1153744,,1050859,,1185837,,1398483,,1443866,,1710983,,2173124,,2190066,,1949520,,1845805,,1681150,,1869301,,2006989,,2024606, -"Korea, Republic of",All,Import,Value (USD 1000),USD 1000,17165,,16516,,42577,,65737,,35681,,70542,,69229,,70395,,77770,,99443,,128753,,226891,,334870,,342776,,392294,,599121,,523538,,558158,,745093,,862075,,1093932,,1055862,,585121,,1175910,,1411994,,1660275,,1895632,,1974474,,2275535,,2398051,,2791106,,3124928,,2964196,,2724146,,3226918,,3976331,,3775679,,3678607,,4310293,,4384055,,4635561,,5137626,,5960492, -Kuwait,All,Export,Value (USD 1000),USD 1000,3439,,7071,,5294,,8213,,11039,,14948,,11720,,14066,,9398,,6347,,3701,,7686,,15067,,9898,,10851,,795,,2850,,7259,,3011,,3364,,5806,,10042,,7492,,4721,,5027,,3145,,4466,,1922,,2165,,790,F,4027,,2279,,2710,,2588,,1961,,1406,,824,F,1118,,31,,1364,,484,,1828,,2446, -Kuwait,All,Import,Value (USD 1000),USD 1000,4850,,11191,,8651,,14035,,28369,,27246,,29526,,25560,,22724,,21350,,23347,,28266,,30141,,27723,,9966,,7743,,15786,,15538,,15270,,20109,,20508,,15858,,17695,,22111,,25744,,23535,,23559,F,42796,F,33766,F,39134,F,58305,,75426,,77077,,93673,F,91097,,111316,,124208,F,138327,,172747,,202412,,208020,,230092,,229299, -Kuwait,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,147,,0,.,40,,305,,912,,0,.,1057,,482,,695,,652,,0,.,0,.,0,.,0,.,1691,,0,-,0,-,0,-,0,- -Kyrgyzstan,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,30,,7,,48,,5,,1,,0,0,18,,6,,18,,218,,363,,407,,123,,350,,0,.,0,.,124,F,87,,138,,1282,,1515, -Kyrgyzstan,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,425,F,651,F,1435,F,2390,F,2948,,1934,,1937,,1219,,1431,,2447,,2573,,2669,,3963,,5642,,7232,,8016,,9270,,11909,,15080,,16305,,18202,F,10224,,5645,,8330,,6893, -Kyrgyzstan,All,Reexports,Value (USD 1000),USD 1000,0,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,16,,0,-,0,-,168,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Lao People's Dem. Rep.,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,3,F,3,F,36,F,4,F,3,F,29,F,78,F,27,F,26,F,12,F,17,F,3,F,3,F,6,F,7,F,9,,0,.,247,,107,,355,,138,,73,,45,,22, -Lao People's Dem. Rep.,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,335,F,559,F,665,F,924,F,999,F,652,F,575,F,1023,F,1306,F,1459,F,1847,F,2280,F,2001,F,1599,F,1735,F,2499,F,1611,F,1519,,2072,,1451,,1634,,2347,,3811,,2042,,4416,,4313, -Latvia,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,12385,F,30686,,54342,,90244,,113054,,82116,,78296,,51873,,49964,,106535,,122933,,131327,,108289,,133065,,163401,,161229,,217122,,170928,,173477,,205489,,247193,,282653,,240848,,189160,,207964,,231220,,252924, -Latvia,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2320,F,3232,,12108,,20783,,29695,,39994,,48973,,36374,,37796,,45924,,48930,,56299,,37354,,59586,,76236,,110744,,145646,,119328,,134507,,166777,,194759,,208404,,194514,,155001,,184600,,186431,,197657, -Lebanon,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,377,,209,,353,,131,,117,,246,,725,,660,,992,,1651,,2740,,4486,,6264,,2700,,2641,,2378,,2770,,2631,,2620,F,1867,,2248,,2864, -Lebanon,All,Import,Value (USD 1000),USD 1000,3300,F,8018,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,9192,F,7579,F,9585,F,14538,F,14688,F,22470,,44549,,47907,,48183,,43876,,55140,,46102,,46282,,56008,,57217,,63159,,74011,,87068,,97700,,114340,,124174,,134915,,141254,,156844,,146294,F,150356,,155770,,163902, -Lesotho,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1,,16,,9,,9,,0,.,0,.,0,.,64,,370,,699,,718,,488,,722,F,2022,F,2040,,1645,F,3553,,3411,F -Lesotho,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,954,,1899,,2000,F,2414,,3399,,2580,,2500,F,2450,F,2500,F,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,9285,,6219,,6990,,5206,,3441,,1989,F,1972,F,1986,F,3055,,4425,,4145,,7028,,6365,,5714,F,10735,F,6690,,5283,F,6440,,8469,F -Liberia,All,Export,Value (USD 1000),USD 1000,2810,,2434,,2574,,2093,,1634,,647,F,471,,624,,780,,1635,,2137,,2294,,1115,,1430,,597,,1563,,614,,963,,908,,211,F,3,F,18,F,11,F,64,F,49,F,85,F,80,F,178,F,383,F,391,F,639,F,630,F,642,F,996,F,1199,F,693,F,640,F,541,F,422,F,445,F,524,F,597,F,981,F -Liberia,All,Import,Value (USD 1000),USD 1000,4420,F,5763,,6109,,7391,F,6623,,5263,,6656,F,8557,F,9632,F,6969,,6322,,6090,,7584,,6501,,6285,,5263,F,2393,,1078,,1785,,1730,F,2419,F,2199,F,1428,F,1412,F,2766,F,2215,F,1401,F,2207,F,2156,F,3117,F,4009,F,2936,F,6154,F,3583,F,5413,F,6328,F,10013,F,11718,F,17262,F,12289,F,10323,F,7262,F,8257,F -Libya,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,632,F,1480,F,0,.,0,.,0,.,260,F,520,F,330,F,380,F,22710,,23100,F,25000,F,31260,,32000,F,6369,F,6932,F,8870,F,5397,F,12462,F,8557,F,11230,F,10617,F,12023,F,14631,F,7647,F,8987,F,7685,F,5533,F,6582,F,4076,F,8959,F,10812,F,14322,F,17199,F,24676,F,36220,F,35811,F -Libya,All,Import,Value (USD 1000),USD 1000,3922,,838,,17180,,21497,,35345,,27965,,18498,,15335,F,18970,F,12000,F,7605,,11118,,20628,,25033,,15505,,39504,,30305,F,19520,F,10593,F,14379,F,13734,F,10852,F,12284,F,12785,F,9100,F,9612,F,9752,F,6931,,7677,,20093,F,33416,F,33137,,35543,,39634,,70795,,81395,F,146883,F,151333,F,161805,F,144150,F,176867,F,173488,F,213808,F -Lithuania,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,8444,,5585,F,19336,,39565,,60094,,68740,,52405,,33534,,34013,,61147,,77906,,115948,,137707,,189219,,210248,,254741,,285395,,326850,,380178,,414542,,387102,,458991,,558647,,520918,,583701,,647165,,682843, -Lithuania,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,952,,6292,F,27210,,31067,,71188,,72536,,62508,,50016,,56199,,92244,,88935,,113601,,127716,,179176,,188928,,237931,,289548,,297265,,350886,,358448,,365310,,439357,,511753,,447501,,539026,,573406,,641894, -Luxembourg,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,22516,,18188,,23990,,22152,,19873,,15511,,15274,,13259,,17550,,18806,,19379,,18968,,14940,,16511,,17987,,13978,,14762,,16849,,18944, -Luxembourg,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,65506,,62860,,63165,,77282,,80115,,77052,,83660,,88461,,101260,,98358,,100945,,111712,,109000,,115549,,126938,,108849,,117838,,123163,,139468, -Madagascar,All,Export,Value (USD 1000),USD 1000,12935,,18532,,13835,,17333,,18551,,16817,,21531,,23975,,23106,,22681,,25940,,44169,,36336,,37050,,46191,,49415,,52767,,68339,,104661,,93161,,100682,,77222,,55046,F,39630,F,38075,,127809,,152828,,86152,,73499,,132840,,162275,,186358,,160537,,116874,,114686,,159905,,112552,,130964,,158831,,113017,,136045,,177070,,151313, -Madagascar,All,Import,Value (USD 1000),USD 1000,218,,428,,48,,136,,241,,368,,90,,157,,132,,462,,298,,227,,558,,463,,345,,405,,8681,,7323,,6519,,8570,,5345,,14084,F,15744,F,11654,F,6745,F,8631,F,13651,F,16819,F,14190,F,18791,,33727,,58881,,28093,,19079,,28151,,44501,,36085,,41977,,36622,,21878,,20645,,27478,,16393, -Madagascar,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,4804,,1010,,1228,,619,,116,,36,,473,,310,,3160,,1636,,1536,,2207,,4802,,1217, -Malawi,All,Export,Value (USD 1000),USD 1000,967,,1158,,940,,1027,,2881,,1858,,1265,,902,,409,,379,,300,,433,,393,,346,F,428,,366,,214,,227,,349,,257,,424,,294,,105,,193,,143,,71,,122,,560,,733,,357,,1554,,259,,294,,198,,276,,451,,270,,254,,156,,136,F,209,,239,,348,F -Malawi,All,Import,Value (USD 1000),USD 1000,391,,278,,571,,442,,453,,432,,627,,688,,251,,333,,149,,213,,635,,748,,1345,,1403,,1272,,687,,1031,,627,,368,,921,,357,,671,,585,,392,,305,,714,,1017,,480,,1384,,1208,,1765,,2738,,2045,,1757,,1489,,2242,,2826,F,2622,,3094,,4119,,4326,F -Malawi,All,Reexports,Value (USD 1000),USD 1000,5,,0,.,0,.,1,,5,,0,.,3,,0,0,1,,29,,0,.,0,.,0,.,0,.,5,,18,,5,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Malaysia,All,Export,Value (USD 1000),USD 1000,106819,,83330,,103875,,167539,,126824,,129754,,118276,,104248,,105552,,106657,,132547,,191261,,191242,,210212,,230108,,265954,,295782,,307216,,325540,,335809,,328695,,338437,,187554,,184012,,200469,,220126,,381983,,256197,,573238,,619653,,624015,,738535,,770273,,657479,,827565,,916456,,846169,,800030,,866051,,688272,,712732,,718629,,762883, -Malaysia,All,Import,Value (USD 1000),USD 1000,38570,,43811,,54055,,67786,,71455,,80684,,99990,,88281,,123243,,110946,,126598,,157503,,143508,,164966,,146747,,171234,,245373,,266059,,309725,,333743,,349325,,351539,,234019,,270471,,307340,,336705,,400345,,377504,,538112,,530863,,580337,,644881,,594255,,683818,,790291,,998720,,1071037,,1070210,,1131857,,945414,,955990,,999274,,1059755, -Malaysia,All,Reexports,Value (USD 1000),USD 1000,0,,0,,0,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,125251,,118848,,153268,,137083,,0,.,182600,,14533,,18492,,17379,,17023,,26874,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Maldives,All,Export,Value (USD 1000),USD 1000,2832,,3236,,3860,,4365,,6857,,6997,,6150,,6579,,10397,,15453,,15777,,17693,,30158,,33873,,37843,,36969,,31521,,28707,,36503,,36770,,48928,,56405,,56732,,38943,,40923,,43975,,55987,,76471,,90255,,102531,,133598,,106030,,124346,,74859,,71343,,118325,,158645,,138986,,141554,,140428,,137568,,196178,,178386, -Maldives,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,373,F,0,.,2533,,2955,,3336,,4297,,3235,,5802,,8626,,8687,,8394,,11252,,16992,,20117,,23769,,27611,,26119,,27722,,32086,,30699, -Mali,All,Export,Value (USD 1000),USD 1000,1920,,1800,,1290,,924,,1990,,2079,F,1963,,754,,626,,879,,1383,,255,,1053,,1103,,721,,735,,550,,514,,370,F,412,F,3715,,191,,388,F,488,F,422,F,259,,200,,92,,115,,62,,281,,926,,130,,165,F,87,,251,,60,,155,F,335,F,319,F,500,,311,,275,F -Mali,All,Import,Value (USD 1000),USD 1000,272,,303,,381,,222,,387,,0,.,1253,,180,,408,,1336,,3019,,5097,,1900,F,1770,F,3461,,2670,F,2490,F,3700,F,758,F,1651,F,835,,2175,,1862,,2886,,1747,,1193,,699,,2434,,3474,,5255,,6513,,6852,,7875,,9721,F,11696,,11166,,11973,,13344,F,25148,F,27282,F,30445,,36374,,38707,F -Mali,All,Reexports,Value (USD 1000),USD 1000,0,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,3,,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Malta,All,Export,Value (USD 1000),USD 1000,12,,0,.,0,.,0,.,238,,238,,274,,254,,1388,,3627,,140,,475,,369,,405,,458,,1008,,1802,,5910,,6509,,2615,,5364,,6118,,5710,,6409,,7708,,13205,,40937,,36275,,17128,,11771,,64421,,49822,,93992,,20608,,89256,,77525,,104426,,139568,,130017,,132857,,176378,,156942,,177005, -Malta,All,Import,Value (USD 1000),USD 1000,2514,,3278,,4288,,4608,F,8542,,6416,,3919,,4565,,4912,,5693,,6237,,7529,,9406,,13909,,13941,,19362,,21189,,15639,,14606,,17552,,19225,,18785,,20646,,19605,,17158,,22981,,21148,,32092,,32035,,30414,,38398,,43748,,55010,,60630,,51052,,58224,,96420,,64269,,98907,,69966,,137028,,175959,,196120, -Malta,All,Reexports,Value (USD 1000),USD 1000,9,,2,,0,0,0,0,0,.,0,.,4,,0,.,9,,4,,0,.,6,,0,.,0,.,1928,,803,,277,,339,,0,.,0,.,0,.,374,,96,,162,,254,,417,,576,,152,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Marshall Islands,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,73,,0,.,0,.,0,.,0,.,0,.,0,.,110,F,220,F,170,F,580,F,530,F,625,F,690,F,523,F,2893,F,1683,F,222,F,256,F,2703,F,8109,,16273,F,16395,F,29130,F,30238,F,18022,F,27893,F,44800,F,45186,F,54056,F,89737,F,126846,F,120392,F,96552,F,72676,F,75213,F,82184,F,71696,F -Marshall Islands,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,60,F,335,,240,F,130,F,285,F,350,F,300,F,230,F,210,F,212,F,210,F,230,F,230,F,250,F,199,F,255,F,433,F,232,F,120,F,191,F,189,F,420,F,735,F,824,F,632,F,527,F,1047,F,2198,F,1726,F,2458,F,1921,F,5420,F,3747,F,5791,F,2686,F,2262,F,4037,F,5364,F -Martinique,All,Export,Value (USD 1000),USD 1000,0,.,48,,25,,123,,61,,103,,68,,102,,76,,173,,107,,157,,969,,217,,460,,238,,93,,261,,161,,169,,0,-,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -Martinique,All,Import,Value (USD 1000),USD 1000,5987,,8296,,10714,,12864,,12299,,13979,,10581,,12725,,10050,,12839,,17242,,21625,,23893,,24327,,26257,,31096,,34199,,31406,,34245,,38680,,0,-,0,-,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -Mauritania,All,Export,Value (USD 1000),USD 1000,14463,,16699,,19459,F,10257,F,11071,F,36003,F,34800,F,50962,F,55234,F,74762,F,133964,F,133622,F,115399,F,137344,F,120337,F,111352,F,101198,F,97292,F,99916,F,160097,F,175000,F,124131,F,128800,,95607,F,87190,,91302,,95916,,107103,,114437,,124305,F,130981,F,154617,,143340,,136302,F,209004,F,313120,,386053,,353003,,464593,,548560,,612080,,828183,,1311130,F -Mauritania,All,Import,Value (USD 1000),USD 1000,123,,0,.,0,.,0,.,66,,0,.,300,,131,,621,,400,F,270,F,160,F,250,F,455,,480,F,700,,446,,786,,910,F,1039,F,1068,F,2532,F,1011,F,594,F,29,,29,,57,,61,,24,,260,,171,,161,,191,,430,,827,,722,,737,,958,,1350,,1393,F,1749,,1189,,1948,F -Mauritius,All,Export,Value (USD 1000),USD 1000,2827,,4814,,5227,,4049,,5468,,6774,,5842,,6637,,8016,,9905,,10055,,10703,,16190,,14470,,10772,,20217,,20106,,23108,,30392,,38141,,42190,,45416,,42795,,38559,,36659,,63193,,68414,,75027,,84202,,109424,,160250,,196799,,214987,,217495,,259046,,278001,,348678,,385284,,369769,,289374,,300198,,450648,,473387, -Mauritius,All,Import,Value (USD 1000),USD 1000,5203,,8025,,11149,,10286,,11648,,11757,,8542,,7260,,7158,,6689,,8011,,7436,,7978,,11302,,18046,,15851,,31371,,20172,,29939,,40142,,44168,,44852,,47454,,32829,,41885,,53485,,129642,,93759,,117233,,146111,,214748,,229956,,306052,,240627,,238068,,305504,,398377,,395572,,354144,,276991,,330525,,389894,,327032, -Mauritius,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,427,,0,.,0,.,0,.,0,.,0,.,68224,,35484,,37943,,54864,,65903,,67670,,67985,,70907,,79878,,70540,,91461,,119842,,117988,,129245,,129770,,0,-,0,- -Mayotte,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,6,,5,,2,,0,.,0,.,3,F,0,.,0,.,0,.,10,,165,,681,,1057,,827,,690,,851,,837,,868,,748,,741,F,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -Mayotte,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,99,F,158,F,488,F,50,F,0,.,1078,,1498,,1790,,2154,,2156,,2480,,3046,,3101,,4666,,3415,,2785,F,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -Mexico,All,Export,Value (USD 1000),USD 1000,205220,,192663,,490404,,429363,,580038,,494479,,472472,,410599,,437371,,378299,,457308,,569639,,439194,,494995,,363880,,401349,,320669,,435701,,482984,,710733,,740980,,828510,,721810,,655274,,710620,,673430,,607786,,638192,,634779,,624098,,731619,,831043,,833537,,807350,,773770,,1122897,,1082752,,1093127,,1171429,,1054073,,1035726,,1298053,,1468076, -Mexico,All,Import,Value (USD 1000),USD 1000,12517,,10023,,24263,,34599,,35214,,33866,,27566,,3672,,8792,,11290,,5981,,6277,,37914,,46275,,63098,,53881,,74129,,128454,,159690,,90936,,83544,,115729,,102088,,131431,,149985,,173830,,190765,,228918,,310271,,365518,,447318,,549476,,598756,,393350,,539852,,641667,,677155,,803372,,955754,,807179,,837914,,938190,,927069, -"Micronesia, Fed.States of",All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,17443,,25005,,30049,F,34160,F,12281,F,9631,F,18202,F,21731,F,18027,F,27181,F,27098,F,26291,F,28062,F,29347,F,25060,F,10151,F,20979,F,26724,F,24345,F,28159,F,39395,F,63866,F,36347,F,54945,F,67900,F,54233,F,85523,F,115687,F -"Micronesia, Fed.States of",All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,933,,1718,,1474,,1261,,2526,,6209,,4172,F,6465,F,3919,F,4825,F,2538,F,3280,F,2265,F,2157,F,2462,F,3297,F,3823,F,2666,F,1907,F,1946,F,3145,F,4049,F,5238,F,7961,F,15424,F,13194,F,9187,F,7891,F,4607,F,4132,F,8564,F -"Moldova, Republic of",All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,194,,88,,88,,33,,0,.,2,,5,,15,,440,,0,.,0,0,41,,3,,7,,6,,7,,8,,5,,52,,0,0,0,0,0,0,13,,17,,9, -"Moldova, Republic of",All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2569,,6594,,13197,,11409,,9104,,2832,,3578,,8512,,9726,,9021,,13533,,18892,,22984,,32243,,49687,,39922,,42669,,48015,,53323,,56004,,53560,,39360,,45487,,49092,,54105, -"Moldova, Republic of",All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,89,,471,,1731,,163,,381,,6,,0,.,0,.,0,.,43,,51,,15,,33,,14,,172,,27,,0,.,0,.,16,,93,,64,,9,,23,,20, -Mongolia,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,183,F,136,,90,,230,,265,,222,,76,,147,,162,,95,,249,,231,,118,,89,F,70,F,205,F,193,F,216,F,75,,109,,134,,48,,86,F,12, -Mongolia,All,Import,Value (USD 1000),USD 1000,1800,,660,,1290,,1497,,1408,,1144,,903,,1040,,1310,,1150,F,2040,F,2480,F,2400,F,2140,F,2370,F,0,.,0,.,20,F,0,.,55,,52,,241,,104,,96,,239,,357,,534,,346,,246,,333,,405,,786,,1209,F,1273,F,2832,F,4173,F,6381,F,5443,,3646,,3469,,3741,,3917,,5186, -Mongolia,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,16,,0,.,4,,0,-,0,-,0,- -Montenegro,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,972,,846,,409,,1488,,631,,260,,41,,14,,44,,3,,109,,87,,65, -Montenegro,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,7802,,12473,,14947,,13222,,13106,,15064,,14445,,17282,,17615,,15273,,17954,,20938,,22453, -Montserrat,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,235,,245,,213,,212,,228,,206,,267,,334,,318,,362,,386,,325,,343,,372,,390,,343,,312,,397,,429,,439,F -Morocco,All,Export,Value (USD 1000),USD 1000,80045,,70882,,90160,,107947,,118642,,192669,,154568,,198995,,203894,,220235,,306724,,356577,,415703,,421568,,522700,,608946,,554738,,538833,,638009,,806377,,765652,,705521,,764351,,774979,,976427,,874408,,964134,,1014499,,831813,,1094479,,1266340,,1410329,,1728515,,1578773,,1518011,,1461548,,1767536,,1867981,,2038349,,2005351,,2170107,,2268179,,2358896, -Morocco,All,Import,Value (USD 1000),USD 1000,111,,128,,80,,73,,80,,51,,68,,38,,246,,93,,833,,1339,,2335,,3707,,6722,,4630,,5993,,9899,,9351,,8492,,8065,,12802,,14749,,10951,,9871,,9704,,13407,,21604,,33087,,36340,,61272,,62109,,72882,,92378,,120673,,150651,,143670,,164774,,211209,,185691,,170198,,177308,,245598, -Mozambique,All,Export,Value (USD 1000),USD 1000,12434,,9613,,12150,,15400,,20592,,52449,,38524,,31200,,28264,,33402,,34423,,38243,,39867,,32575,,50629,,63240,,66453,,70802,,68763,,65110,,86343,,84315,,91256,F,75006,,99889,,99716,,122840,,96018,,100469,,85036,,96698,,70190,,76913,,66626,,56802,,51857,,27456,,43809,,61530,,38562,,46368,,42409,,72421, -Mozambique,All,Import,Value (USD 1000),USD 1000,3876,,1950,F,3730,F,5094,,4216,,3576,,5950,F,9315,F,7810,F,6620,F,9922,,14926,,14000,F,9365,F,9635,F,11140,F,10500,F,10266,F,2249,,2023,F,1623,,3919,,2483,F,4758,F,9403,,8559,,10992,,33391,,28918,,33561,,31781,,26693,,38898,,39820,,35486,,58264,,50371,,94399,,92688,,85836,,61863,,74043,,78909, -Myanmar,All,Export,Value (USD 1000),USD 1000,2470,,3033,,4425,F,7323,F,10020,,11015,,14808,,17343,,8225,,8463,,12502,F,3033,F,11764,,9844,,15689,F,12893,F,12966,F,51000,F,69738,,120600,,114405,,163000,,167090,,201328,,183707,,218291,,251534,,317382,,318514,,456919,,382951,,376315,,560568,,483230,,495454,,555515,,654129,,652840,,536255,,482237,,561826,,696180,,708945, -Myanmar,All,Import,Value (USD 1000),USD 1000,2,F,2,F,8,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2395,F,3374,F,3125,F,2935,F,4018,F,4900,F,3540,F,1179,F,739,F,1089,F,742,F,1389,,642,F,1685,F,2789,F,3186,F,2533,F,2914,F,5189,F,6376,F,10996,F,13666,F,13505,F,10944,F,15187,F,10563,,9147,,9724,,11704, -Namibia,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,54496,F,57882,F,313053,,329621,,198906,,247829,,386023,,291992,,283931,,330327,,291749,,334518,,361817,,376924,,458543,,502720,,576978,,715263,,783413,,772824,,758602,,784628,,724474,,620159,,643843,,747738,,772015, -Namibia,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,7605,,11419,,13615,,5984,,24214,,13132,,9375,,9423,,19685,,22063,,20204,,35351,,39425,,56011,,51604,,49009,,40504,,46413,,49754,,41524,,59227,,48909,,57497, -Namibia,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,4649,,3067,,4113,,0,- -Nepal,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,90,,171,,156,,108,,68,,35,,45,,0,.,0,.,39,,69,F,0,.,5,F,152,F,39,,71,F,28,F,3,F,16,,42,F,33,F,0,.,0,0,0,.,3,,5,,5,,14,,9,,5,,5,,10,,21,,13,F -Nepal,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,81,,102,,108,,214,,224,,110,,0,.,0,.,0,.,416,,581,F,512,F,620,F,206,F,254,,362,,353,F,364,F,481,,333,F,332,F,650,F,1072,F,1972,F,2986,,2745,,6522,,7934,,7944,,12899,,8548,,11128,,12956,,14166,F -Netherlands,All,Export,Value (USD 1000),USD 1000,267130,,318514,,401601,,507976,,529085,,515512,,507915,,515074,,505673,,549945,,775091,,962711,,959556,,1001982,,1317675,,1361016,,1410776,,1300608,,1442416,,1464034,,1488695,,1434646,,1373510,,1754731,,1351828,,1427251,,1812577,,2196412,,2468384,,2837525,,2827177,,3300086,,3414376,,3162079,,3231463,,3579226,,3481287,,3493042,,4062591,,3634252,,4209954,,5297877,,5670057, -Netherlands,All,Import,Value (USD 1000),USD 1000,200825,,258660,,317250,,370038,,390649,,331578,,310975,,273828,,289215,,309415,,389314,,511041,,579781,,615944,,770793,,868946,,890389,,793869,,1020389,,1200293,,1148335,,1114143,,1238887,,1314307,,1172233,,1238817,,1343724,,1712736,,1850165,,2093559,,2296876,,2629196,,2940671,,2792668,,2810201,,3305452,,3199207,,3208476,,3698793,,3071269,,3341043,,4310968,,4535928, -Netherlands Antilles,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,69,,0,.,1,,11,,16,,10,,401,,176,,130,,88,,162,,414,,527,,240,,160,,148,,144,,1074,F,833,F,5056,F,5718,F,5696,F,5952,F,5285,F,9168,F,9987,F,2604,F,3262,F,2534,F,18956,F,17230,F,14817,F,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0, -Netherlands Antilles,All,Import,Value (USD 1000),USD 1000,4562,,4707,,4710,F,6695,,6900,F,7944,,8857,,8685,,8793,,5385,,7021,,8522,,8469,,9298,,9511,,7169,,8222,,8323,,7672,,10561,,4106,F,5728,F,10191,,7683,F,12653,F,7019,F,4555,F,6143,F,7328,F,10078,,11803,,11637,,14021,,18983,F,18513,F,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0, -New Caledonia,All,Export,Value (USD 1000),USD 1000,24,,165,,66,,125,,86,,338,,909,,558,,1097,,800,F,1387,,3288,,3640,F,4920,F,7211,F,8288,F,8261,,7724,,8179,,10873,,9559,,13662,,15754,,18938,,19052,,22732,,22059,,26076,,30202,,31435,,34025,,26017,,29310,,25221,,20993,,19088,,20912,,22882,,24233,,18541,,18991,,18065,F,15199,F -New Caledonia,All,Import,Value (USD 1000),USD 1000,2231,,2453,,2597,,2769,,3222,,2853,,2701,,2461,,2108,,2005,,3440,,4451,,4036,F,3854,,4231,F,4428,,4606,,5966,,6275,,6163,,7065,,7012,,7037,,5683,,5715,,7570,,7338,,9138,,10632,,11812,,13160,,14775,,17133,,11770,,16541,,16255,,18875,,16518,,17636,,15649,,14739,,12688,F,16004,F -New Caledonia,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,179,,3,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -New Zealand,All,Export,Value (USD 1000),USD 1000,37678,,48811,,66010,,99787,,157992,,165451,,190854,,206972,,254949,,271008,,344393,,402948,,474178,,477042,,440202,,558283,,656513,,649773,,695569,,818062,,816501,,749982,,666139,,713948,,666947,,640318,,712238,,705476,,843125,,885787,,875883,,923351,,896966,,906018,,1078938,,1213396,,1249990,,1213473,,1252928,,1112486,,1214043,,1240166,,1230786, -New Zealand,All,Import,Value (USD 1000),USD 1000,10808,,9790,,11227,,18504,,16250,,21352,,21597,,19968,,20103,,17743,,23237,,27023,,31373,,38756,,35820,,38772,,35242,,36238,,42481,,60273,,59306,,57002,,54745,,54774,,62439,,68765,,71146,,72807,,86114,,99339,,106185,,109730,,129716,,107934,,128692,,153244,,163225,,166730,,201705,,194932,,176508,,203283,,221596, -New Zealand,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,9,,3,,0,.,0,.,34,,78,,469,,232,,0,.,0,.,698,,0,.,1159,,973,,1440,,1417,,1546,,6932,,3505,,2299,,0,.,3266,,2131,,1632,,2220,,3888,,7864,,4522,,2270, -Nicaragua,All,Export,Value (USD 1000),USD 1000,21492,,23584,,16190,,22387,,27531,,18506,,21930,,16959,F,12608,,12876,,8662,,12383,,9579,,12705,,10124,,18079,,24199,,28545,,53158,,80786,,78305,,76250,,54947,,78597,,127792,,42518,,47195,,67648,,98488,,106411,,89231,,96543,,127440,,107960,,136690,,143992,,205975,,264922,,313139,,271901,,289458,,282990,,297603, -Nicaragua,All,Import,Value (USD 1000),USD 1000,915,,1272,,718,,712,,1427,,220,,1157,,760,F,64,,2,,26,,0,.,0,.,0,.,0,.,2118,,2650,,1460,,1338,,1941,,1835,,2284,,4451,,7845,,7226,,6094,,6488,,3766,,3588,,4516,,5039,,6662,,7356,,5834,,7172,,9342,,12680,,13425,,15544,,19654,,20813,,17826,,16616, -Niger,All,Export,Value (USD 1000),USD 1000,5,,571,,95,,1331,,6,,96,,37,,33,,7,,52,,35,,13,,3,,6,,0,.,0,.,0,.,340,F,740,F,586,F,0,.,100,,221,,1594,,1599,,4330,,4344,,4841,,3309,,2229,,547,,309,,391,,326,,563,,789,,773,,1123,,1037,,267,,404,,283,F,331,F -Niger,All,Import,Value (USD 1000),USD 1000,92,F,203,F,253,,390,,437,,502,,639,F,487,,289,,285,F,748,F,787,F,869,,2108,F,1332,,1835,,1256,,280,F,780,F,1259,F,1216,,1342,,760,,400,,504,,622,,254,,432,,502,,579,,797,,763,,1303,,1369,,1822,,1188,,2025,,2278,,2053,,5876,,7451,,9973,F,9351,F -Niger,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,93,,0,.,155,,31,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,10,,0,-,0,-,0,-,101,,0,-,0,-,0,-,0,-,0,-,0,- -Nigeria,All,Export,Value (USD 1000),USD 1000,2610,,382,,2137,,3598,,3670,F,10424,,4502,,3810,,4435,,3291,,4020,F,4831,,7606,,10498,,14070,,15590,F,15717,,10169,,14090,,21546,,18322,,13856,,2054,,10698,,12473,,12792,F,17179,F,17933,F,30675,F,56940,F,43740,,49486,,72300,,153267,,160866,,100766,,100143,F,263603,,93901,,78783,,60791,,51180,,53969, -Nigeria,All,Import,Value (USD 1000),USD 1000,157543,,176170,,271826,,345424,,455399,F,522091,,416319,,366208,F,167954,F,113033,F,89984,F,165856,F,119705,F,150208,F,179641,F,190330,F,277711,F,278057,F,159748,,146780,F,250366,,221137,,198445,,196725,,188485,,457108,,382795,,513272,,530636,F,640244,F,807418,,959487,,665436,,820632,,991148,,2061935,,1457188,,1215091,,1334535,,1237111,,682868,,780362,,831766, -North Macedonia,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,877,,398,,260,,143,,133,,983,F,698,,594,,463,,146,,191,,418,,428,,271,,258,,563,,1239,,1105,,1400,,1255,,391,,1798,,516,,786,,960,,2629,,4637, -North Macedonia,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,5344,,6696,,7095,,8574,,10205,,8522,,10034,,9818,,7705,,7421,,9944,,10734,,12493,,13345,,16799,,20755,,24213,,21131,,20356,,24417,,22092,,22620,,25711,,22383,,21460,,24566,,29176, -Northern Mariana Is.,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,20,F,25,F,18,F,22,F,30,F,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,,0,,0,,0,,0,,0,,0, -Norway,All,Export,Value (USD 1000),USD 1000,654703,,805397,,759771,,890904,,974666,,1001679,,888348,,977937,,902866,,922460,,1171170,,1474930,,1608071,,1563496,,2066298,,2296292,,2455952,,2315639,,2738030,,3139873,,3434073,,3422070,,3682575,,3781100,,3550369,,3385263,,3601215,,3669067,,4170996,,4921788,,5543705,,6290039,,6993982,,7107237,,8852961,,9484237,,8921085,,10392246,,10830773,,9210984,,10798031,,11311852,,12014212, -Norway,All,Import,Value (USD 1000),USD 1000,26840,,28450,,34897,,57148,,69963,,58865,,48443,,47801,,46334,,70871,,105217,,120748,,157582,,177849,,238771,,307581,,347783,,311802,,325667,,494205,,539989,,575901,,685935,,620802,,613890,,667882,,655077,,583268,,681941,,720616,,851543,,1108277,,1233521,,1190447,,1103778,,1364543,,1384000,,1303177,,1391188,,1262440,,1270837,,1233323,,1317642, -Oman,All,Export,Value (USD 1000),USD 1000,0,0,185,,1665,,4307,,5808,,7678,,10845,,12703,,18211,,25374,,25465,,30795,,50173,,38992,,45027,,34505,,35025,,51462,,49467,,61431,,63404,,67651,,50209,,38250,,51371,,53155,,82737,,80768,,105855,,102590,,100639,,92493,,81890,,85936,,127342,,156927,,158219,,143131,,141598,,144118,,179275,,115059,,324278, -Oman,All,Import,Value (USD 1000),USD 1000,1172,,1210,,1708,,1896,,1321,,2538,,2378,,2421,,2898,,2750,,2845,,2397,,2292,,1943,,2037,,3951,,3185,,3819,,4654,,4293,,4475,,5070,,6184,,5963,,5551,,7933,,10834,,10666,,11096,,13801,,20866,,26849,,32018,,31975,,35315,,45021,,53911,,41965,,60216,,71286,,79347,,62967,,66645, -Oman,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1784,,1048,,221,,208,,3079,,904,,843,,1152,,286,,1670,,1313,,1325,,0,.,0,.,0,.,8854,,7551, -Pakistan,All,Export,Value (USD 1000),USD 1000,39462,,41319,,42053,,59692,,50062,,66063,,79287,,69280,,79491,,79808,,97032,,120885,,125177,,93417,,93509,,108890,,115012,,171090,,153741,,149601,,131629,,176772,,135173,,142127,,149820,,130551,,131360,,137390,,152594,,140614,,196849,,192535,,193665,,228859,,266248,,331085,,332323,,361778,,382867,,348158,,352141,,430855,,459272, -Pakistan,All,Import,Value (USD 1000),USD 1000,0,.,0,.,113,,162,,117,,350,,224,,114,,339,,79,,89,,230,,239,,539,,266,,125,,188,,414,,178,,161,,78,,166,F,215,,816,,297,,233,,240,,510,,1187,,1719,,1943,,3811,,2358,,2310,,3549,,4954,,6600,,12800,,17144,,18973,,18525,,23992,,12490, -Pakistan,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1,,0,.,0,.,0,.,36,,0,.,0,.,0,.,0,.,0,.,132,,0,.,30,,2,,6,,0,0,0,0,0,0,0,0,0,.,0,.,0,.,0,.,0,.,77, -Palau,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,661,,400,F,0,.,0,.,0,.,200,,137,F,165,F,540,F,550,F,0,.,0,.,0,.,0,.,50,F,296,F,699,F,347,F,294,F,344,F,388,F,343,F,223,F,244,F,646,F,696,F,712,F,277,F,141,F,264,F,196,F,355,,426,F,622,F,508,,430,,1291,,964,F -Palau,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,190,F,195,F,197,F,2070,F,1036,F,500,F,87,F,170,F,235,F,285,F,228,F,366,F,341,F,715,F,1453,,1612,,1497,,1417,,1573,,1987,,1903,,2154,,2360,,2361,,2475,,2047, -Palestine,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,244,,162,,188,,83,,350,,188,,370,,0,.,546,,428,,462,F,1102, -Palestine,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,7246,,10662,,12602,,11425,,14265,,10478,,15229,,18582,,18630,,19506,,18563,F,24440, -Palestine,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,231,,155,,184,,83,,350,,186,,370,,556,,0,-,0,-,0,-,0,- -Panama,All,Export,Value (USD 1000),USD 1000,43984,,47944,,38784,,56958,,66042,,57899,,64118,,73302,,67061,,92093,,117175,,117478,,78824,,91121,,66935,,74762,,82203,,93600,,106293,,124768,,115534,,162593,,239158,,196470,,262730,,328847,,331705,,412708,,448551,,435599,,381873,,365708,,410079,,381935,,210259,,126122,,125203,,178952,,220618,,188122,,176947,,131417,,162861, -Panama,All,Import,Value (USD 1000),USD 1000,2748,,2827,,3967,,5471,,6333,,6537,,7500,,9015,,9040,,7999,,7417,,8277,,6547,,9300,,9679,,10611,,12776,,11951,,11157,,10891,,14523,,13167,,15728,,15208,,15401,,12317,,16060,,18184,,16120,,22377,,21231,,25166,,32273,,33025,,38282,,46965,,57419,,60340,,69924,,75336,,73052,,66340,,95186, -Panama,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,72,,0,.,0,.,0,.,112,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Papua New Guinea,All,Export,Value (USD 1000),USD 1000,11156,,25534,,35347,,29300,,47286,,40715,,11205,,10925,,9758,,14320,,11624,,12827,,10069,,10995,,10735,,14161,,13323,,10832,,12307,,12688,F,16719,,12708,F,44442,,42980,F,57745,,80978,,92232,,120783,,89360,,86906,F,103020,F,139935,F,156597,F,161894,,127949,F,167724,,117181,,154282,F,137712,F,152882,F,169507,F,290873,F,260245,F -Papua New Guinea,All,Import,Value (USD 1000),USD 1000,10447,,11700,F,15910,F,19300,F,28407,,34528,,28791,,28472,,26952,,26053,,36588,,36660,,44074,,41053,,36992,,35189,F,35166,F,33064,F,33765,F,22398,,29013,,12904,F,15529,,7890,F,11304,,6641,,9484,,10186,,12652,,11063,F,17923,F,21629,F,28782,F,27628,F,25084,F,25709,,22557,,32911,F,38740,F,46511,F,47755,F,64553,F,56935,F -Papua New Guinea,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1,,0,.,2,,494,,0,.,2,F,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Paraguay,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,50,F,20,F,20,F,20,F,10,F,0,.,0,.,6,,19,,15,,8,,10,,92,,13,,24,,19,,14,,31,,85,,99,,36,,45,,70,,39,,98,,206,,201,,230,,170,,95,,45,,19,,14,,15,,14,,14,,12,,117,,134,,160, -Paraguay,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,384,,399,,270,,302,,299,,276,,179,,231,,375,,370,,622,,909,,948,,1672,,2626,,2292,,3661,,3080,,3550,,1611,,1660,,1673,,1038,,927,,1537,,1651,,1581,,2547,,3925,,4255,,5959,,6866,,7050,,7725,,9228,,8130,,8051,,9884,,11754, -Peru,All,Export,Value (USD 1000),USD 1000,214666,,225705,,250352,,277600,,320611,,298822,,282266,,163356,,233167,,221595,,258083,,287451,,402400,,478997,,399950,,499040,,517320,,685344,,980130,,869762,,1121565,,1343598,,640497,,789672,,1129350,,1229574,,1068048,,1032725,,1389162,,1636291,,1773198,,1966878,,2438291,,2218406,,2547302,,3164417,,3327259,,2773824,,2918608,,2387275,,2189709,,2875635,,3309988, -Peru,All,Import,Value (USD 1000),USD 1000,415,,388,,136,,501,,3744,,2779,,741,,20275,,8340,F,2230,F,170,F,7505,,8438,,1203,,1558,,1122,,2412,,1704,,2422,,4117,,5329,,6010,,13358,,16879,,16159,,22580,,24643,,25984,,27535,,48077,,30432,,36785,,73758,,79825,,164521,,146353,,147732,,221822,,189159,,248145,,254826,,311286,,325355, -Philippines,All,Export,Value (USD 1000),USD 1000,27869,,42176,,63272,,95493,,141605,,146163,,120141,,133687,,116782,,151748,,200099,,267895,,407504,,409879,,395960,,492725,,417921,,499511,,559635,,545650,,482309,,472464,,482268,,418844,,455984,,420184,,453030,,464463,,454384,,380094,,419552,,499539,,672813,,585044,,680905,,711155,,850344,,1185788,,1054800,,805286,,735786,,883537,,912387, -Philippines,All,Import,Value (USD 1000),USD 1000,35779,,27651,,31220,,28106,,36570,,36535,,52322,,11158,,2717,,6325,,19019,,31254,,63063,,65730,,84809,,96550,,111447,,95432,,110337,,136314,,141738,,137170,,95698,,123888,,111596,,71362,,92524,,86405,,73892,,103680,,103126,,132922,,176815,,203336,,148552,,193314,,263038,,278737,,266158,,369746,,398264,,585047,,605809, -Poland,All,Export,Value (USD 1000),USD 1000,51056,,46256,F,38461,F,59813,F,54775,F,65168,F,88547,F,83692,F,93089,F,100147,F,104926,F,182972,F,168007,F,146654,F,184851,F,189938,F,249397,,203158,,251654,,262285,,272023,,228574,,274738,,282491,,243282,,246765,,253715,,313951,,439784,,633650,,815516,,961380,,1180015,,1110234,,1380165,,1569975,,1556453,,1780386,,2002304,,1736433,,1958034,,2203749,,2540709, -Poland,All,Import,Value (USD 1000),USD 1000,71260,,76129,,95370,,102780,,78712,,52352,,33644,,53978,,50396,,55428,,60173,,77926,,98600,,81627,,42418,,39100,,102455,,128633,,170956,,191622,,252375,,268987,,322744,,260737,,297715,,369919,,334818,,384005,,584961,,718728,,871522,,1009388,,1264389,,1211458,,1535352,,1621625,,1609904,,1996734,,2164501,,1857786,,2213691,,2337030,,2591800, -Portugal,All,Export,Value (USD 1000),USD 1000,65010,,74199,,91821,,104377,,129512,,94733,,95922,,94212,,94447,,108709,,147816,,159005,,182988,,229966,,281867,,292379,,260369,,211556,,206467,,279132,,278700,,263650,,271194,,285283,,286238,,276167,,303917,,349087,,435228,,452539,,569851,,651196,,734885,,631061,,918119,,1087982,,1023311,,1084160,,1193339,,1117586,,1062377,,1190020,,1301215, -Portugal,All,Import,Value (USD 1000),USD 1000,110269,,88019,,65604,,78419,,101267,,158570,,186194,,141532,,139606,,202257,,256694,,425236,,457700,,391240,,607845,,761241,,739571,,630534,,670801,,764480,,784715,,752435,,929640,,1019601,,863407,,937333,,949424,,1103819,,1264862,,1339629,,1543282,,1848161,,1888653,,1584654,,1797964,,2030384,,1877740,,1933667,,2085827,,1947992,,2119660,,2396614,,2578487, -Puerto Rico,All,Import,Value (USD 1000),USD 1000,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,,0, -Qatar,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,5,,23,,0,.,0,.,3,,12,,33,,1,F,8,,1615,,1586,,1907,,1983,,2019,,1908,,3350,,1993,,1463,,1325,,2292,,5534,,3028,,1318,,2411,,1623,,1962,,1374,,825,,735,F -Qatar,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,462,,1149,,1318,,2268,,1088,,1081,,1201,,1348,F,1615,F,1587,F,1879,F,1812,,2112,,2470,,2977,,3680,,4386,,3789,,1321,F,4949,,5296,,5740,,6257,,7457,,6654,,7047,,13084,,19231,,24507,,40935,,49565,F,59632,,60798,,80134,,71073,,83574,,100141,,102151,,95779,,104152, -Qatar,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,30,,313,,38,,37,,35,F,42,,35,,0,.,3,,96,,52,,0,.,22,,33,,75,,212,,82,,209,,438,,591,,542,,351,,184,,15,,18,,0,-,0,-,0,-,0,- -Romania,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,3330,,337,,866,,3059,,786,,2061,,41,,661,,1770,,4998,,7727,,3459,,3084,,2806,,2034,,4894,,6090,,12730,,2282,,3003,,6346,,8947,,18681,,13454,,16850,,17776,,16519,,16724,,18069,,23642, -Romania,All,Import,Value (USD 1000),USD 1000,26200,F,27950,F,28000,F,41600,F,56200,F,36800,F,28400,F,29000,F,22550,F,18230,F,20580,F,30030,F,35430,F,9130,,37600,,9413,,5493,,14813,,21919,,17115,,41039,,29512,,54443,,32313,,30728,,37618,,49586,,56499,,75981,,105740,,139102,,154480,,214271,,196756,,185752,,184983,,200087,,222584,,245514,,230065,,262845,,305350,,363461, -Russian Federation,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,826144,,1471446,,1714642,,1627395,,1673626,,1348891,,1138920,,1209247,,1520173,,1552095,,1422860,,1477412,,1528172,,1954022,,2129338,,2374618,,2632330,,2325335,,2854792,,3295517,,3262822,,3593516,,3829024,,3678728,,3877332,,4523637,,5302419, -Russian Federation,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,34687,,19074,,253797,,346190,,418977,,403046,,272149,,201103,,194859,,356406,,457526,,588352,,812242,,1183734,,1447639,,2027547,,2440276,,2004538,,2388215,,2735411,,2849912,,3379557,,3058971,,1685904,,1703393,,2024489,,2278455, -Rwanda,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,57,,1,,21,,94,,289,,512,,83,,231,,178,,1207,,2064,,1526,,1514,F,1361,F -Rwanda,All,Import,Value (USD 1000),USD 1000,103,,149,,168,,269,,617,,330,,158,F,233,,479,,977,,394,,1341,,296,,221,,117,,243,,263,,196,,55,F,55,F,116,,190,,198,,73,,113,F,120,,173,,136,,28,,37,,455,,3031,,1787,,2389,F,3814,,6698,,8291,,8793,,18128,,22016,,24662,,30746,F,49335,F -Rwanda,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,408,,1176,,1787,,1629,,1023,,1000,F,1000,F -Réunion,All,Export,Value (USD 1000),USD 1000,0,.,821,,1422,,1584,,1448,,3028,,2674,,2857,,3181,,4213,,4628,,5127,,5308,,4970,,6254,,9115,,10428,,7995,,13477,,16434,,0,-,0,-,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -Réunion,All,Import,Value (USD 1000),USD 1000,6746,,8417,,10968,,12316,,17629,,13444,,11093,,14534,,10723,,13543,,18532,,23562,,27172,,25891,,30241,,32053,,40282,,34922,,38567,,44620,,0,-,0,-,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0, -Saint Helena,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,670,F,238,F,53,F,126,F,383,F,1435,F,2516,F,2393,F,2662,F,2720,F,3778,F,4550,F,5380,F,3149,F,2680,F,3578,F,3636,F,3975,F,3323,F,2157,F,3995,F,6172,F,6929,F,5592,F,5411,F,5002,F,5228,F,7783,F,13310,F,9468,F,9695,F,11223,F,10033,F,9779,F,10879,F,17615,F,19573,F,18685,F,19325,F -Saint Helena,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,9,,7,,8,,7,,10,,12,,0,.,12,,0,.,0,.,0,.,0,.,0,.,0,.,23,,54,F,16,F,31,F,98,F,224,F,145,F,94,F,398,F,193,F,242,F,516,F,115,F,66,F,42,F,80,F,51,F,34,F,117,F,199,F,125,F,496,F,791,F,136,F -Saint Kitts and Nevis,All,Export,Value (USD 1000),USD 1000,111,,157,,155,F,132,,151,,133,,58,,98,,239,,161,,151,,123,,203,,121,,110,F,108,,176,,276,F,197,F,153,,119,,69,,13,F,206,,245,,131,,149,,267,,196,,45,,242,,422,,483,,492,,493,,689,,390,,406,,407,F,376,F,362,F,252,,286,F -Saint Kitts and Nevis,All,Import,Value (USD 1000),USD 1000,363,,258,,283,,351,,459,,486,,585,,563,,661,,578,,588,,693,,937,,856,,930,F,620,F,620,F,433,F,328,F,2176,,2170,,2305,,795,F,2199,,2807,,2063,,1946,,1864,,2666,,995,,4128,,3931,,3717,,3785,,3854,,3241,,2521,F,3814,F,3160,F,3704,F,2675,F,4577,,3060,F -Saint Kitts and Nevis,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,0,2,,0,0,0,.,12,,22,,24,,26,,30,,0,.,0,.,0,.,0,.,0,.,98,,0,- -Saint Lucia,All,Export,Value (USD 1000),USD 1000,0,.,24,,1,,6,,1,,17,,2,,0,0,0,.,16,,86,,156,,139,,14,,77,,46,,18,,7,,0,.,0,.,0,.,1,,1,,0,.,28,,47,,106,,40,,10,,1,,1,,0,0,107,,0,.,1,F,0,0,31,F,0,.,29,,14,,13,,8,,8,F -Saint Lucia,All,Import,Value (USD 1000),USD 1000,634,,679,,1364,,1298,,1473,,1202,,1431,,1522,,1567,,1260,,1340,,2028,,2316,,2800,,2600,,3679,,3583,,3291,,3977,,4347,,4922,,4207,,4832,,5193,,4806,,4165,,4237,,5070,,5630,,5990,,6322,,6814,,8341,,6326,F,6582,F,7404,F,9609,F,7010,F,10589,,9893,,8922,,9815,,7846,F -Saint Lucia,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,13,,0,.,10,,9,,15,,69,,0,.,0,.,0,.,11,,0,.,0,.,0,.,0,.,0,.,0,.,0,0,0,0,0,-,0,-,0,- -Saint Vincent/Grenadines,All,Export,Value (USD 1000),USD 1000,70,,94,,341,,1,,0,0,9,,229,,62,,56,,176,,230,,317,,9549,F,12450,F,19603,F,17238,F,2573,F,1614,F,654,,1097,,273,,710,,470,,931,,961,,630,,712,,510,,410,,434,,241,,269,,510,,405,,466,,266,,276,,351,,675,,578,,631,,1235,,2491, -Saint Vincent/Grenadines,All,Import,Value (USD 1000),USD 1000,446,,353,,416,,460,,523,,615,,688,,527,,557,,563,,452,,531,,608,,756,,787,,856,F,863,,782,,569,,960,,1032,F,1223,,1206,,1537,,1156,,1297,,1367,,1408,,1610,,1890,,2033,,2228,,2524,,1942,,1963,,2027,,2295,,1963,,2305,,2339,,2391,,2766,,2354, -Saint Vincent/Grenadines,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,41,,0,.,0,.,0,0,7,,0,.,0,.,0,.,4,,0,.,0,.,35,,3,,0,-,0,-,0,0 -Samoa,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2,,0,.,0,.,0,.,20,,25,F,10,F,123,,110,F,130,F,25,F,35,F,129,,1132,,4667,,11534,,11860,,7826,F,15319,,12796,,7085,,10580,,9517,,8126,,8735,,5582,,7624,,13147,,7793,,7989,,5544,,2520,,13428,,17011,,13171,,13125, -Samoa,All,Import,Value (USD 1000),USD 1000,903,,1668,,1085,,2534,,1555,,1986,,1244,,1076,,1090,F,1610,F,1230,F,2018,,2400,F,2900,F,3288,,3300,F,3500,F,3960,F,3147,F,3355,F,6096,F,5748,F,4481,F,6045,F,8201,F,3601,,3959,,4832,,4599,,4608,,6109,,6568,,6670,,5115,,5940,,6753,,7195,,7559,,6726,,8617,,8931,,6548,,8775, -Sao Tome and Principe,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,277,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,39,F,47,F,24,F,17,F,26,,96,F,270,,77,,2,,0,0,0,0,18,,24,,11,,9,,7,,0,0,1,,1,,19,,50,,29,,2,,2, -Sao Tome and Principe,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,76,,0,.,65,,0,.,0,.,0,.,534,,428,,394,,680,F,750,F,1200,F,680,F,1430,F,390,F,240,F,648,F,213,F,220,F,49,F,43,,82,,11,,15,,39,,20,,17,,82,,148,,164,,162,,151,,319,,327,,375,,355,,271,,329,,307,,307, -Saudi Arabia,All,Export,Value (USD 1000),USD 1000,1256,,1447,,1777,,1763,,3279,,2988,,6904,,7653,,1302,,3326,,2607,,5316,,5532,,8999,,1584,,4068,,4064,,2754,,735,F,2066,F,4235,,5701,,8244,,9105,,8366,,10322,,9238,,25507,,28305,,45212,,47389,,60569,,64013,,73095,,119200,,72839,,54524,,40123,,70469,,90077,,132047,,272516,,296270, -Saudi Arabia,All,Import,Value (USD 1000),USD 1000,9678,,24204,,30158,,45949,,72242,,78513,,92687,,91223,,74282,,73428,,59065,,58612,,63550,,66896,,56392,,78170,,57201,,54882,,52135,,90022,,88795,,95790,,105952,,110322,,109301,,131053,,130148,,136690,,182285,,205053,,243816,,250746,,239119,,232318,,385526,,490206,,637777,,644418,,678599,,636807,,629861,,614779,,654138, -Saudi Arabia,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,968,,2410,,1143,,447,,65,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,588,,908,,558,,136,,230,,1189,,1760,,989,,1256,,1269,,1169,,0,.,13,,3025,,3481,,5683,,7534,,10322,,10057,,0,.,0,.,12543, -Senegal,All,Export,Value (USD 1000),USD 1000,52480,,63433,,75533,,93093,,103712,,105758,,146666,,138002,,142059,,166231,,259110,,178163,,171284,,209113,,216489,,218073,,188320,,127068,,231600,F,264373,F,310541,,286667,,309763,,301529,,260373,,251324,,223227,F,279039,,316182,,255200,,260784,,315995,,225067,,243885,,240235,,310643,,266103,,288817,,396437,,352903,,377692,,408330,,498289, -Senegal,All,Import,Value (USD 1000),USD 1000,6446,F,11916,,15557,,12420,,20575,,23200,,13604,,21110,,16468,,19484,,18550,,18892,,38119,,37042,,47412,,24539,,21276,,11752,,11970,F,17215,F,11258,F,21801,F,15760,F,3789,F,1651,F,872,,666,,680,,1504,,1277,,1085,,2080,,2827,,1160,,5800,,17589,,14926,,19287,,15018,,20039,,22552,,36929,,38547, -Senegal,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,50,,0,.,0,.,0,.,83074,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Serbia,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1405,,2077,,3270,,3344,,3434,,3357,,3712,,4522,,7075,,5763,,13357,,15875,,18003, -Serbia,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,71376,,91691,,107866,,81475,,69837,,102856,,100492,,88328,,93405,,70689,,70516,,95194,,108361, -Serbia,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1,,0,.,111,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Serbia and Montenegro,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,13814,F,338,F,71,F,504,F,1588,,1163,,787,,940,,713,,815,,521,,285,F,1023,,2449,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0, -Serbia and Montenegro,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,32768,F,11885,F,14598,F,19835,F,57391,,52768,,61689,,38444,,33679,,39884,,52647,,45672,F,69679,,66091,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0, -Seychelles,All,Export,Value (USD 1000),USD 1000,506,,417,,519,,1002,,1555,,704,,1119,,1406,,1511,,1533,,1045,,5494,,13236,,11592,,13252,,16119,,17446,,14323,,21694,,22713,,39715,,68838,,88895,,108443,,113465,,150712,,171871,,297351,,270683,,299618,,292249,,286175,,324558,,316036,F,284969,,362417,,507702,,493396,,431036,,375923,,483672,,536270,,496801, -Seychelles,All,Import,Value (USD 1000),USD 1000,87,,122,,79,,151,,88,,84,,56,,82,,92,,289,,179,,3108,,7619,,4827,,4698,,5372,,4076,,6987,,7987,,8942,,19206,,18949,,16280,,3128,,11517,,15453,,23619,,67469,,75821,,78510,,95755,,81959,,63583,,86995,F,97211,,132057,,148123,,142709,,129936,,97001,,124745,,164990,,163825, -Seychelles,All,Reexports,Value (USD 1000),USD 1000,0,.,3,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,5,,0,.,31,,27,,3,,0,.,0,.,0,.,0,.,0,.,18,,0,.,0,.,0,.,0,.,12,,0,0,1,,1,,6,,0,0,57,,53,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Sierra Leone,All,Export,Value (USD 1000),USD 1000,3,,22,,0,.,353,F,2380,F,5660,F,7370,F,4472,,2004,,6480,F,8292,,7650,F,8220,F,11200,F,16260,F,10500,F,10770,F,45854,,47805,,48100,,33093,,31162,F,13366,F,12295,F,11055,F,10887,F,13435,F,15290,F,12474,F,13056,F,11081,F,10592,F,10018,F,10399,F,10685,F,10602,F,11402,F,10812,F,9858,F,10305,F,10116,F,8180,,8413,F -Sierra Leone,All,Import,Value (USD 1000),USD 1000,1405,,1484,,2060,F,2610,,5698,,7416,,1896,,4235,,3140,,3600,F,2653,F,800,F,1225,F,1565,F,1850,F,2040,F,2030,F,1788,F,3609,F,3312,F,2829,F,3562,F,715,F,767,F,1015,F,2129,F,825,,3755,F,1944,F,1971,F,1670,F,3591,F,3397,F,4052,F,2862,F,3325,F,2734,F,3391,F,4786,,3943,,2982,,2849,,3704,F -Sierra Leone,All,Reexports,Value (USD 1000),USD 1000,0,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,19,,0,- -Singapore,All,Export,Value (USD 1000),USD 1000,40655,,60924,,76830,,101136,,117648,,134844,,139454,,152111,,163586,,161943,,204268,,289354,,356193,,359071,,422428,,502918,,497009,,484233,,577443,,511324,,450097,,424464,,340326,,394943,,457105,,388184,,325267,,335331,,422195,,427544,,396388,,385455,,398016,,321098,,384518,,416370,,367196,,339621,,323114,,376654,,365690,,342992,,357504, -Singapore,All,Import,Value (USD 1000),USD 1000,68016,,88799,,103169,,112845,,141325,,161704,,183421,,200335,,224133,,204376,,257666,,312955,,370311,,366126,,365617,,461712,,544633,,567114,,630661,,440698,,424137,,397357,,267531,,468044,,544165,,473241,,497176,,599269,,706016,,776389,,757944,,818704,,914863,,824248,,971041,,1162463,,1074992,,1073334,,1109339,,1093000,,1129644,,1096665,,1162525, -Slovakia,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2449,,2114,,2091,,2175,,2550,,3006,,1862,,1798,,1630,,2335,,3270,,5470,,4699,,5614,,6530,,7743,,5136,,10054,,12811,,9767,,16656,,25904,,11648,,10106,,11942,,19395, -Slovakia,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,25459,,33639,,34820,,42100,,44169,,47687,,33184,,30479,,32655,,37611,,37469,,44030,,50670,,70092,,62412,,72795,,69217,,80379,,92681,,89856,,97770,,123461,,104749,,104361,,110551,,124724, -Slovenia,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,8701,,5914,,6227,,4394,,5794,,5120,,6244,,6605,,6249,,5856,,6131,,7443,,10981,,33046,,20370,,19114,,25894,,26641,,24878,,28006,,26110,,28014,,32104,,28547,,39539,,39327,,44548, -Slovenia,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,19313,,19653,,23536,,27052,,29199,,28846,,30294,,29467,,27034,,28376,,31947,,37817,,50317,,72259,,66505,,74059,,89131,,83589,,80661,,95798,,87153,,92163,,98870,,92381,,112237,,116623,,129020, -Solomon Islands,All,Export,Value (USD 1000),USD 1000,9121,,9283,,8440,,19594,,28072,,25080,,14686,,18609,,22875,,21625,,30662,,26801,,38494,,29161,,22080,,39595,,33967,,26746,,30856,,42984,,30566,,41407,,28100,F,25955,F,12555,F,15468,F,9952,F,14914,F,15458,F,13275,F,23425,,22826,,20808,F,20866,,19945,,22286,,41012,,37006,,53897,F,62132,F,80543,,59026,F,60296, -Solomon Islands,All,Import,Value (USD 1000),USD 1000,201,,270,,287,,286,,476,,434,,566,,319,,476,,679,,808,,253,,626,,733,,197,,260,,141,,18,,22,,83,,113,,110,F,92,F,79,F,336,F,273,F,1093,,338,,567,,636,,1831,,2356,,2429,F,2188,F,3658,F,2517,,2015,,4647,,2433,,2137,,2315,,2688,,2724, -Solomon Islands,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,9,,1,,0,-,0,- -Somalia,All,Export,Value (USD 1000),USD 1000,2483,,1493,,409,,589,,442,,2526,,2558,,1331,,3710,F,3350,F,2950,F,3250,F,7800,F,11490,F,14770,F,11615,F,13795,F,7166,F,6421,F,10161,F,7349,F,10938,F,4599,F,4058,F,2298,,3444,F,3479,F,3395,F,9790,F,4592,F,4602,F,3276,F,4597,F,3076,F,1411,F,2104,F,1030,F,1349,F,1751,F,3915,F,5059,F,5932,F,7956,F -Somalia,All,Import,Value (USD 1000),USD 1000,0,0,69,,0,0,2,,213,,0,.,7,,35,,0,.,0,.,0,.,0,.,0,.,5,F,10,F,10,F,95,F,270,F,80,F,0,.,71,F,0,.,0,.,170,F,100,F,29,F,313,F,392,F,906,F,835,F,4068,F,2669,F,3745,F,5502,F,2857,F,6164,F,6398,F,11489,F,12044,F,15315,F,13328,F,18851,F,13811,F -South Africa,All,Export,Value (USD 1000),USD 1000,121500,,108327,,97918,,131795,,104556,,100977,,93812,,102685,,96324,,90276,,115530,F,148268,F,115776,F,99587,F,117393,,154559,,181239,,199030,,256182,,242284,,201620,,219699,,246968,,261846,,272550,,284536,,321485,,395004,,419420,,444585,,406069,,512968,,521013,,443281,,567178,,601605,,573223,,518898,,644975,,518708,,624126,,601300,,716641, -South Africa,All,Import,Value (USD 1000),USD 1000,14797,,17977,,28726,,27013,,62846,,127656,,75750,,96464,,105649,,70095,,75863,,80620,,66819,,105851,F,130815,,141433,,115443,,90197,,134784,,155406,,126823,,153643,,77547,,57597,,60296,,61788,,49262,,78606,,104911,,126648,,152952,,194927,,241331,,263168,,243644,,274069,,362589,,367019,,428205,,325655,,364274,,427851,,509992, -South Sudan,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,374,F,1012,F,575,F,605,F,327,F,48,F,617,F -Spain,All,Export,Value (USD 1000),USD 1000,244969,,236417,,282334,,410210,,344403,,453559,,290697,,295054,,308800,,354982,,410958,,490548,,670473,,794573,,748792,,779202,,718022,,816157,,1036133,,1207652,,1472136,,1493780,,1552359,,1619411,,1615229,,1837238,,1903305,,2241793,,2581893,,2603184,,2871910,,3258101,,3492514,,3168096,,3339477,,4216974,,3938973,,3990355,,4096581,,3807001,,4155816,,4711052,,5116540, -Spain,All,Import,Value (USD 1000),USD 1000,151876,,155758,,245674,,409615,,544113,,485275,,528319,,400573,,391077,,413801,,701527,,1289906,,1693398,,1775555,,2302710,,2676974,,2808686,,2628501,,2647321,,3118660,,3151581,,3082821,,3559700,,3305921,,3372480,,3733478,,3867431,,4918928,,5238660,,5648733,,6377813,,7004624,,7125534,,5929660,,6542267,,7340003,,6412813,,6443506,,7049188,,6502993,,7176867,,8032957,,8641041, -Sri Lanka,All,Export,Value (USD 1000),USD 1000,8857,,11159,,14884,,19688,,14991,,18098,,21302,,18232,,24013,,17682,,22690,,20433,,26296,,23298,,22838,,21786,,31044,,31797,,32407,,56679,,67039,,75914,,101389,,73378,,135691,,101535,,84337,,100826,,96250,,107946,,140611,,173199,,177049,,183301,,175275,,198304,,206777,,246723,,267484,,181682,,183973,,256934,,286658, -Sri Lanka,All,Import,Value (USD 1000),USD 1000,3480,,2330,,2133,,12388,,18270,,5860,,16356,,15516,,25361,,29020,,30974,,33895,,37197,,22643,,45089,,53116,,58391,,34463,,32089,,58066,,64917,,78398,,66222,,59954,,73382,,76085,,71911,,70172,,65139,,77328,,102239,,113877,,126218,,131340,,141610,,163117,,152145,,179640,,158906,,241075,,254920,,234396,,213416, -Sri Lanka,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,4,,26,,0,.,86,,114,,0,.,0,.,107,,0,.,0,.,90,,264,,623,,93,,2090,,4,,207,,16,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,440,,0,.,58,,1165,,523,,332,,0,- -St. Pierre and Miquelon,All,Export,Value (USD 1000),USD 1000,2179,,3412,,3799,,4478,,5539,,7115,,6167,,5898,,7836,,11868,,16352,,22414,,12435,,18628,,25325,,30383,,25375,,299,,242,,583,,3160,F,2695,F,4739,F,6279,F,7054,F,4844,F,4174,F,3399,F,5339,F,6311,F,7130,F,8883,F,9387,F,5250,F,3958,F,2543,F,3079,F,2574,F,1951,F,3137,F,1963,F,2737,F,3630,F -St. Pierre and Miquelon,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,129,,106,,111,,121,,257,,278,,239,,407,,441,,480,F,490,F,370,F,544,,456,,517,,860,F,1718,F,1000,F,1166,F,550,F,832,F,188,F,607,F,165,F,191,F,748,F,917,F,594,F,829,F,166,F,120,F,194,F,222,F,285,F,272,F,336,F,376,F,283,F,217,F,239,F -Sudan,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,248,,1043,F,874,F,1302,,1479,F,1753,,2699, -Sudan,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,5429,,6786,F,5920,F,6465,,7112,F,5582,,4733, -Sudan (former),All,Export,Value (USD 1000),USD 1000,17,,0,.,67,,190,,422,,389,,320,F,777,,11,,0,.,95,,63,,625,,33,,25,F,20,F,15,F,172,F,190,F,67,,214,,76,,134,,410,,594,,588,,806,,814,,1108,,932,,330,,1176,F,674,,321,,178,,368,,0,,0,,0,,0,,0,,0,,0, -Sudan (former),All,Import,Value (USD 1000),USD 1000,34,,57,,61,,47,,820,,787,,771,,701,,127,,860,F,48,,145,,393,,2411,,2500,F,2300,F,500,F,500,F,250,F,192,,148,,105,,113,,896,,855,,449,,584,,304,,296,,572,,2269,,2899,,1970,,4633,,3865,,8400,,0,,0,,0,,0,,0,,0,,0, -Sudan (former),All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,32,,0,-,0,-,0,,0,,0,,0,,0,,0,,0, -Suriname,All,Export,Value (USD 1000),USD 1000,3450,F,5070,F,1470,F,1770,F,2240,F,5695,F,4841,F,5132,F,4235,F,4141,F,4250,F,5273,F,1468,,1340,,2024,,763,,2482,,3440,F,49812,,40465,,55763,,45834,,36251,F,37247,,40858,,39701,,37549,F,45973,F,52030,F,46060,F,46271,F,49909,F,53301,F,63288,F,62641,F,64925,F,79279,F,86670,F,89544,F,90518,F,94737,F,107046,F,105868,F -Suriname,All,Import,Value (USD 1000),USD 1000,0,.,0,.,3156,,1770,F,2228,,2621,,2443,,2577,,317,,230,F,41,,98,,164,,71,,92,,215,,513,,520,F,1475,,1355,,5055,,5075,,2837,,1898,,6211,,2932,,2781,,3580,,3306,,4092,,3060,,3863,,3230,,4100,,4737,,2981,,4303,,5248,,4414,,5551,F,4095,,5727,,5218, -Suriname,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,34,,56,,276,,280,,517,,738,,819,,426,,0,-,31,,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Sweden,All,Export,Value (USD 1000),USD 1000,44231,,52458,,63534,,91636,,108571,,95443,,89519,,89193,,86500,,80829,,96852,,117792,,154675,,127846,,175077,,169529,,209543,,211242,,266655,,400142,,297969,,346268,,399399,,456978,,476258,,472833,,528310,,709094,,926985,,1185087,,1560884,,1666705,,1914641,,2045915,,2670699,,2865464,,2877978,,3588328,,3885019,,3680508,,4428092,,4143061,,4839445, -Sweden,All,Import,Value (USD 1000),USD 1000,194589,,218430,,245916,,291971,,325164,,269928,,267591,,261981,,257376,,245222,,333934,,404897,,418409,,408307,,450294,,442464,,468626,,372555,,451285,,546421,,590168,,595455,,638714,,718387,,711688,,733624,,806868,,1049904,,1303654,,1600288,,2029669,,2534071,,2769300,,2620772,,3298179,,3637371,,3622942,,4490103,,4787656,,4429795,,5191746,,4934766,,5630244, -Switzerland,All,Export,Value (USD 1000),USD 1000,3824,,4506,,2326,,2723,,2357,,2469,,3474,,2960,,4446,,4176,,5646,,7899,,10077,,11144,,11085,,7517,,6804,,5786,,5913,,7246,,4791,,3881,,3275,,3137,,3039,,3021,,4384,,6234,,9241,,10740,,16531,,16806,,24872,,22036,,21451,,25260,,23673,,26851,,23439,,20863,,19647,,20928,,21826, -Switzerland,All,Import,Value (USD 1000),USD 1000,107549,,138059,,172243,,189195,,210901,,204868,,192535,,193621,,188246,,192653,,263901,,331538,,362960,,345236,,393743,,395896,,393268,,356361,,394790,,421786,,399442,,364064,,389849,,379121,,357029,,369654,,358027,,406002,,452141,,473378,,531383,,596812,,653692,,631826,,669993,,773329,,736568,,831133,,861755,,768208,,811132,,847858,,880022, -Syrian Arab Republic,All,Export,Value (USD 1000),USD 1000,163,,113,,183,,177,,182,,140,,46,,83,,255,,43,,0,.,21,,78,,42,,0,.,47,,22,,36,,105,,360,,37,F,183,F,113,F,23,F,105,F,268,F,138,F,29,,133,F,186,,260,,270,F,266,,676,,137,,617,F,266,F,185,F,227,F,302,F,497,F,604,F,691,F -Syrian Arab Republic,All,Import,Value (USD 1000),USD 1000,1770,,5300,,10072,,10703,,14136,,19672,,15239,,19351,,13970,,13026,,745,,2747,,419,,978,,577,,451,,546,,1891,,1452,F,61996,,24869,,39394,,50881,,55056,,41012,,12512,,24756,,25858,,25599,,23608,,37226,,42411,,41085,,68202,,58747,,65650,F,48483,F,50712,F,75244,F,41375,F,36672,F,32643,F,47277,F -Taiwan Province of China,All,Export,Value (USD 1000),USD 1000,431893,,514740,,694741,,742012,,809734,,828592,,753738,,848179,,869976,,997328,,1450476,,1868842,,1790373,,1801174,,1274576,,1329660,,1623773,,2143528,,1822389,,1830215,,1778588,,1789085,,1592779,,1713258,,1762576,,1810205,,1611806,,1291189,,1806042,,1650913,,1459089,,1650535,,1568305,,1576963,,1941524,,2265881,,2252813,,1976873,,1827474,,1619492,,1662472,,1870699,,1977776, -Taiwan Province of China,All,Import,Value (USD 1000),USD 1000,180479,,182942,F,305459,,496353,,485550,,857724,,1107061,,825107,,932500,,894416,,1130798,,1163679,,1447763,,1420226,,425720,,458830,,491029,,544243,,609970,,643177,,662633,,677768,,552476,,594881,,578933,,565893,,496541,,494222,,517812,,559838,,579513,,625345,,781133,,825277,,964896,,1093057,,1156653,,1141613,,1306131,,1345835,,1380212,,1508568,,1674523, -Taiwan Province of China,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,2352,,4893,,6679,,6493,,2561,,2647,,1538,,1498,,2741,,1874,,1505,,1662,,1395,,1368,,1439,,5207,,1717,,1843,,2314,,4396,,2179,,3665,,8249,,0,-,0,-,0,-,0,- -Tajikistan,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1,F -Tajikistan,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,150,F,288,F,114,F,242,F,177,F,143,F,212,F,199,F,260,F,377,F,426,F,519,F,954,F,1016,F,1454,F,1133,F,3111,F,2957,F,3045,F,2181,F,2652,F,2357,F,1586,F,2899,F,2712,F -"Tanzania, United Rep. of",All,Export,Value (USD 1000),USD 1000,386,,741,F,473,,604,,409,,484,,397,,1969,,2837,,1260,F,3176,F,4490,,6888,F,4245,F,6383,,7922,,7024,,12775,,19118,,20381,,41344,,64691,,83643,,62658,,99012,,114327,,119513,,134345,,117569,,144646,,137325,F,168640,,188218,,148408,,154121,,153327,,169837,,133246,,167963,,161718,,142622,,187368,,213131,F -"Tanzania, United Rep. of",All,Import,Value (USD 1000),USD 1000,2581,,1730,F,3220,,1917,,1479,,1430,,592,,731,F,575,F,459,F,299,F,63,F,864,F,162,,413,,325,,791,F,1290,F,311,F,355,,817,,483,,202,,190,,450,,771,,164,,613,,615,,545,,1077,,2321,,3959,,4005,,4607,,3545,,6322,,9447,,18179,,17337,,21780,,14608,,10423, -"Tanzania, United Rep. of",All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,86,,0,0,0,.,0,.,0,0,0,.,15,,43,,0,-,0,-,3,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Thailand,All,Export,Value (USD 1000),USD 1000,150378,,176783,,252895,,362760,,358259,,412451,,482012,,544929,,632940,,675063,,1011896,,1261066,,1630891,,1959428,,2267284,,2904036,,3074023,,3406282,,4192574,,4454186,,4120443,,4334222,,4038054,,4122627,,4384437,,4075341,,3713299,,3943194,,4079407,,4502821,,5275349,,5721525,,6547742,,6248891,,7166020,,8159613,,8144920,,7067700,,6657459,,5701788,,5914988,,6041469,,6077436, -Thailand,All,Import,Value (USD 1000),USD 1000,7248,,6682,,8608,,20996,,23378,,22223,,28206,,42821,,85813,F,138312,,283658,,267149,,537918,,726846,,803364,,1063425,,955812,,842827,,844421,,875033,,841085,,897715,,864580,,868077,,826699,,1072925,,1079930,,1134471,,1255346,,1457936,,1573958,,1750024,,2447759,,2026369,,2195932,,2788193,,3205504,,3238545,,2840219,,2615969,,3179168,,3669194,,4068859, -Thailand,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,200,,577,,717,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,3201,,3878,,0,-,0,-,0,-,0,- -Timor-Leste,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,5,,2,,65,F,60,F,279,F,156,F,0,.,6,F,39,F,78,,49,F,40,F,161,F,105,F,5,F -Timor-Leste,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,327,,267,,332,F,455,F,767,F,1080,F,3378,F,4876,F,5549,F,5103,F,6601,F,5029,F,5719,F,6513,,7734,F -Timor-Leste,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,5,,2,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Togo,All,Export,Value (USD 1000),USD 1000,7,,24,,25,,7,,11,,8,,14,,54,,23,,347,F,14,,280,,1213,,543,,817,,564,,498,,617,,725,,3005,F,2149,F,650,,1019,,1498,,3011,,5674,,10339,,3037,,10322,,4187,,4227,F,2327,F,3634,F,5362,F,4401,F,4335,F,6675,F,6731,F,10014,F,8123,F,4499,F,3793,F,6322,F -Togo,All,Import,Value (USD 1000),USD 1000,3375,,3383,,3265,,6290,,6367,,5032,,6071,,3474,,3791,,6947,,8084,,11480,,16937,,14312,,15309,,17342,,16453,,9202,,15557,,16095,F,27427,,22285,,18485,,12498,,11975,,13625,,7156,,7917,,5296,,8858,,7831,F,7827,,7157,,10087,,24367,,32592,,32310,,32119,F,30100,,37386,,26252,,31934,,42707, -Togo,All,Reexports,Value (USD 1000),USD 1000,0,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,3228,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,322,,163,,165,,35,,31,,229,,0,- -Tonga,All,Export,Value (USD 1000),USD 1000,0,.,0,.,146,,8,,7,,17,,28,,34,,76,,515,,512,,876,,1868,,931,,1074,,1199,,1558,,2466,,2987,,1775,,1645,,3088,,1688,,2806,F,3758,,1312,,5749,,5036,,5880,,3370,,4413,,2931,,2807,,4352,,5134,,4944,,5226,,2665,,6735,,4574,F,4674,F,3918,F,4118,F -Tonga,All,Import,Value (USD 1000),USD 1000,0,.,0,.,267,,1240,,359,,396,,633,,241,,358,,381,,269,,313,,252,,365,,233,,364,,333,,479,,542,,653,,832,,839,,870,,896,F,1077,,1294,,1252,,1296,,1406,,1594,,1508,,2391,,1470,,1704,,1656,,2249,,1498,,1814,,1872,,1756,F,1753,F,3039,F,2779,F -Tonga,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,8,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Trinidad and Tobago,All,Export,Value (USD 1000),USD 1000,1123,,885,,871,,1076,,2059,,2471,,1940,,842,,778,,492,,2076,,2865,,4240,,2628,,2423,,2795,,3835,,6128,,8813,,10010,,11806,,9946,,12429,,12326,,10630,,10503,,11438,,9977,,6894,,8946,,10442,,9272,,12220,,12141,,10614,,11698,F,11865,F,14260,F,16802,F,26339,F,26687,F,27417,F,25276,F -Trinidad and Tobago,All,Import,Value (USD 1000),USD 1000,5298,,4553,,7621,,7657,,10348,,14930,,12831,,15441,,15367,,12792,,13408,,14007,,7687,,7646,,4038,,5583,,3978,,4219,,5126,,5558,,6351,,6817,,8068,,8044,,7159,,8781,,11805,,12133,,13798,,22072,,19602,,25760,,27076,,29195,,28231,,36776,F,40778,F,50345,F,49111,F,49832,F,38661,F,51358,F,44038,F -Trinidad and Tobago,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,0,7,,259,,16,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,5,,6,,0,.,1036,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Tunisia,All,Export,Value (USD 1000),USD 1000,9196,,10130,,16644,,22111,,27187,,21569,,19949,F,35888,F,31482,,34001,,63732,,81504,,103069,,89287,,110591,,84402,,73656,,87890,,83656,,80330,,93183,,100048,,110321,,85009,,88862,,89028,,97054,,110031,,125687,,161404,,156043,,189662,,204005,,154335,,160194,,231763,,187812,,174657,,177512,,166954,,159730,,171434,,192023,F -Tunisia,All,Import,Value (USD 1000),USD 1000,369,,764,,783,,270,,415,,351,,485,,1303,,701,,688,,70,,150,,1640,,1316,,2679,,1561,,1757,,2246,,7702,,10751,,7946,,16761,,14328,,13412,,11705,,18856,,18637,,36377,,29062,,41258,,47667,,53496,,71500,,67065,,67087,,73776,,82391,,76033,,79463,,60376,,73656,,70151,,71675,F -Turkey,All,Export,Value (USD 1000),USD 1000,16678,,19742,,19293,,29219,,37415,,48298,,46771,,43117,,62374,,49060,,81015,,74217,,77379,,69214,,73355,,64216,,64616,,33496,,75330,,92318,,105250,,127049,,96861,,100364,,92363,,74841,,117437,,150667,,214069,,245510,,206518,,223887,,439705,,346259,,361239,,437265,,448180,,575415,,695579,,698910,,806592,,862127,,976347, -Turkey,All,Import,Value (USD 1000),USD 1000,394,,544,,429,,40,,160,,434,,251,,108,,2771,,534,,1637,,3445,,1871,,11310,,27828,,24770,,30557,,18483,,38526,,51233,,61291,,85305,,76684,,59577,,52534,,30676,,29671,,46586,,93828,,105286,,151694,,181172,,205033,,191505,,245043,,279805,,321554,,381371,,384305,,437777,,399080,,456745,,461975, -Turkmenistan,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,300,F,265,F,293,F,57,,439,F,1035,F,782,F,140,F,176,F,19,F,214,F,103,F,27,F,34,F,115,F,0,0,0,.,0,.,1,F,0,.,3,F,9,F,11,F,0,.,0,. -Turkmenistan,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,305,F,174,F,223,F,439,F,152,F,99,F,119,F,206,F,164,F,424,F,522,F,867,F,617,F,859,F,1928,F,1529,F,3673,F,4060,F,5592,F,6940,F,8994,F,7636,F,4631,F,2339,F,2988,F -Turks and Caicos Is.,All,Export,Value (USD 1000),USD 1000,450,F,800,F,820,F,1100,F,1170,F,2640,F,2060,F,2950,F,2820,F,2620,F,3780,F,3440,F,3095,F,2065,F,2150,F,3560,F,5630,F,5970,F,6290,F,6418,F,4339,F,3587,F,3426,F,3101,,3864,F,5081,,4493,,3897,,5933,,6942,F,6757,F,5664,,4588,F,4563,F,3658,F,1964,,1490,,1865,F,3662,F,3586,F,3152,F,1907,F,2414,F -Turks and Caicos Is.,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,120,F,170,F,220,F,160,F,25,F,65,F,70,F,209,F,237,F,55,F,284,F,1536,,2008,,2170,,2133,,2329,,2296,,2426,,3573,,4063,,4009,,3968,,2850,F,3706,,3813,,4012,F,4829,F,5156,F,5019,F,5521,F,6590,F -Tuvalu,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,389,,394,F,361,F,357,F,300,F,326,F,312,F,300,F,623,F,1121,F,1282,F,1786,F,2910,F,3437,F,2925,F,2937,F,11671,F,8890,F,14378,F,18561,F,8317,F,5486,F,7949,F,6974,F,12370,F -Tuvalu,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,46,,33,,42,,11,F,2,F,29,,14,,78,,95,,57,,103,F,120,,46,F,168,F,204,F,62,F,171,F,86,F,308,F,76,F,108,F,250,F -Uganda,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1386,,5313,,6489,,15794,,16331,,26462,,39780,,28800,,42207,,28288,,30986,,51020,,87955,,88362,,103670,,143258,,146945,,125872,,133490,,114330,,130499,,133116,,118881,,125212,,134508,,117063,,121151,,135911,,171671, -Uganda,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,100,F,3,F,73,,79,,101,,53,,109,,1068,,561,,850,,374,,798,,1051,,874,,1565,,2806,,4068,,2501,,3676,,2294,,2723,,5943,,9169,F -Uganda,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,253,,2,,952,,0,.,48,,328,,33,,174,,946,,1584,,1411,,1564,,1219,,1291,,3011,,0,- -Ukraine,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,32230,F,63458,F,82123,F,67555,F,84594,,52148,F,75079,F,42123,F,32237,,20814,,17675,,18257,,23524,,22039,,33500,,39610,,60549,,55029,,61153,,66360,,66488,,50037,,18795,,21797,,33541,,37316, -Ukraine,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,38340,F,40831,F,129791,F,76684,F,91624,,121251,F,96776,F,111248,F,99064,,94964,,114402,,139940,,281371,,457996,,546204,,746529,,544567,,460366,,611037,,804951,,995992,,690247,,332027,,470507,,532254,,641746, -Un. Sov. Soc. Rep.,All,Export,Value (USD 1000),USD 1000,198774,,203976,,245461,,310979,,307916,,257435,,229438,,368698,,369544,,383908,,587079,,637287,,799633,,718407,,933448,,818566,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0, -Un. Sov. Soc. Rep.,All,Import,Value (USD 1000),USD 1000,28259,,45389,,43690,,51859,,91190,,76812,,71151,,133951,,157517,,157099,,155909,,155951,,208628,,166227,,162985,,45044,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0, -United Arab Emirates,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1135,,1081,,1211,,5484,,5557,,8000,,9894,,9102,,8500,F,8116,,8300,F,7670,,14195,F,30465,F,34707,F,36818,F,30524,,40451,,38827,,24521,F,29254,,50149,F,180455,,83696,F,57941,,57159,,70260,,89008,,107816,F,86432,,86192,,87702,,101623,,114201,,119818,,269454, -United Arab Emirates,All,Import,Value (USD 1000),USD 1000,0,.,0,.,7367,,9279,,19500,,20125,,18581,,17882,,13983,,15380,,14080,F,13216,,13200,,13845,,14698,,20860,,23446,,16818,,70248,,30492,F,27543,F,18392,F,22624,F,67299,,86919,,107073,,121525,,188694,,158184,F,123761,,194236,F,261542,,303292,,314380,,377056,,440194,F,538463,,577281,,723781,,626345,,656055,,740680,,778105, -United Arab Emirates,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,5133,,7374,,6163,,6213,,6711,,10333,,2534,,2620,,2880,F,10097,F,12000,F,13646,,13773,,11620,F,11630,F,4883,,11402,,0,.,0,.,0,.,0,.,19987,,27126,,22770,,26273,,36925,,96,,134918,,145,,33788,,33123,,22463,,28128,,46146,,55819,,94201,,74483,,69524,,67644,,62959,,143975, -United Kingdom,All,Export,Value (USD 1000),USD 1000,153471,,198547,,285251,,335672,,373390,,312004,,305840,,324452,,320952,,368016,,518254,,727350,,744242,,803118,,971625,,1131096,,1155921,,1042815,,1191949,,1209268,,1316075,,1279973,,1563690,,1438555,,1269848,,1325352,,1363609,,1683704,,1833866,,1890085,,1959705,,2232702,,2138359,,2148529,,2433461,,2803765,,2588480,,2721518,,3103872,,2515271,,2674742,,2930105,,2871605, -United Kingdom,All,Import,Value (USD 1000),USD 1000,518737,,563210,,695028,,913926,,1046030,,1002521,,892869,,917397,,882158,,949471,,1227987,,1397616,,1628007,,1655958,,1934402,,1932977,,1925868,,1650507,,1901439,,1934274,,2075666,,2169033,,2409858,,2305521,,2209877,,2263407,,2355587,,2535957,,2843021,,3209370,,3751920,,4183957,,4257176,,3630058,,3747624,,4295777,,4280019,,4541440,,4594760,,4141346,,4257050,,4222259,,4420872, -United States of America,All,Export,Value (USD 1000),USD 1000,371899,,508064,,895709,,1072165,,1001725,,1163031,,1068649,,1047901,,1002932,,1162372,,1480990,,1824788,,2441176,,2532468,,3110459,,3385635,,3649348,,3240126,,3295275,,3493612,,3263358,,2912870,,2451559,,3003763,,3118839,,3379748,,3318519,,3457908,,3693079,,4286889,,4190110,,4499115,,4533441,,4225019,,4774879,,5900623,,5483758,,5684621,,5851186,,5670390,,5588117,,6246034,,5787552, -United States of America,All,Import,Value (USD 1000),USD 1000,1890921,,2085849,,2228172,,2674167,,2633160,,2988195,,3174633,,3621363,,3702490,,4051794,,4748692,,5662329,,5389345,,5756932,,5619229,,6044065,,6067973,,6335816,,7111006,,7220604,,7162307,,8224936,,8667431,,9499500,,10553850,,10384571,,10730217,,11755792,,12076165,,12887256,,14201329,,14577194,,15103882,,13991947,,15641328,,17632876,,17741663,,19174348,,21512467,,20052419,,20762702,,21842536,,23943195, -United States of America,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,217545,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,372112,,409389,,435114,,383199,,360279,,0,.,368920, -Uruguay,All,Export,Value (USD 1000),USD 1000,5153,,10299,,22443,,36328,,50892,,61295,,47500,,45694,,48859,,54149,,65141,,82772,,63880,,71183,,71226,,111738,,101500,,74476,,82445,,95535,,96035,,110671,,134357,,99410,,111280,,99517,,102335,,107967,,130290,,140298,,149467,,173318,,207973,,174352,,186543,,234559,,186833,,143581,,154934,,113794,,90532,,101801,,116694, -Uruguay,All,Import,Value (USD 1000),USD 1000,659,,773,,996,,2798,,4842,,1470,,1328,,476,,756,,1029,,1449,,2746,,1618,,3168,,3417,,3381,,5958,,6013,,7128,,7826,,9561,,11674,,13786,,13334,,12494,,17035,,12536,,16654,,16969,,20238,,28446,,44270,,51447,,51204,,62071,,62252,,54226,,58565,,60246,,46428,,40232,,45024,,49691, -Uzbekistan,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,510,F,1223,F,534,F,96,F,83,F,44,F,2,F,71,F,195,F,9,F,28,F,337,F,684,F,3248,F,2820,F,3296,F,4871,F,3195,F,694,F,218,F,258,F,318,F,813,F,1495,F,2444, -Uzbekistan,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,930,F,1553,F,1326,F,1832,F,2689,F,1454,F,1363,F,1534,F,1161,F,1562,F,2047,F,1443,F,2177,F,4703,F,1984,F,3957,F,5829,F,7822,F,12102,F,10203,F,11115,F,5956,F,5653,F,8827, -Vanuatu,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,31,,17,,5360,F,13962,F,18736,F,31252,F,30400,F,46899,F,48270,F,47712,F,45185,F,52361,F,59288,F,51369,F,79174,F,81646,F,77037,F,58730,F,33943,F,45042,F,52134,F,74919,F,83698,F,72644,F,84137,F,67578,F,69458,F,60341,F,45442,F,67734,F,61987,F,77895,F,100595,F,73168,F,94413,F,122774,F -Vanuatu,All,Import,Value (USD 1000),USD 1000,6683,F,12182,F,12329,F,11660,F,15924,F,10411,,7623,,8870,F,7393,,7362,F,4648,F,742,,817,,647,,1105,,579,,1020,,1182,,1307,,1021,F,1817,,1015,F,601,F,684,F,1489,,501,F,740,F,1655,F,1458,F,1746,F,2343,,2781,,3472,,3497,,3464,,5018,,4410,F,5208,F,5641,F,5169,F,5603,F,4549,F,6679,F -Vanuatu,All,Reexports,Value (USD 1000),USD 1000,5992,,11958,,11965,,10879,,15623,,9567,,7203,,8073,,7233,,7389,,3553,,0,.,1,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,28,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -"Venezuela, Boliv Rep of",All,Export,Value (USD 1000),USD 1000,25883,,13989,,13544,F,9447,,4890,F,12427,F,24099,F,55449,F,80396,,127794,,192914,,50756,,27608,,111880,,97604,,90129,,70717,,90070,,99569,,76754,,84172,,114799,,92968,,134478,,153127,,137755,,100656,,66576,,86985,,157537,,24048,,33262,,27938,,30736,,11338,,14090,,13277,,14489,,18838,,29945,,51019,,65436,F,78602,F -"Venezuela, Boliv Rep of",All,Import,Value (USD 1000),USD 1000,7538,,11188,,17933,,21452,,25618,,29303,,31057,,9966,,5920,,4199,,637,,958,,2713,,1553,,3217,,4575,,7035,,16756,,14988,,31516,,17519,,23794,,39603,,40506,,56711,,65207,,44596,,16911,,53925,,61321,,70553,,180029,,378389,,484519,,240099,,276915,,369270,,340711,,353762,,76319,,29189,,30871,F,27053,F -"Venezuela, Boliv Rep of",All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,14,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,- -Viet Nam,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,15500,F,10691,,19770,F,45235,F,45060,F,64555,F,73989,F,90493,F,140883,F,186352,F,125289,F,175832,F,278888,F,305163,F,368235,F,483677,F,512987,F,503274,F,764492,,822832,,943355,,1484316,,1823150,,2044630,,2202443,,2450112,,2765366,,3379955,,3790167,,4559252,,4311738,,5122710,,6259788,,6291141,,6900612,,8046560,,6774148,,7344113,,8586492,,8911638, -Viet Nam,All,Import,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,1153,,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,620,F,1275,F,3579,F,6416,F,11840,F,4349,F,16963,F,36242,,60145,,116141,,151622,,218636,,276576,,302425,,373470,,461125,,433337,,529849,,726215,,837929,,916980,,1289819,,1300954,,1366351,,1765991,,1859331, -Yemen,All,Export,Value (USD 1000),USD 1000,12738,,17660,,7238,,10571,,19177,,6300,,7003,,6097,,11928,,15860,,13660,F,12005,F,13522,,17321,,16124,,10587,,7533,,6880,,11229,,17471,F,22503,,44449,F,18013,F,20533,F,21353,F,57178,F,118545,F,179869,,106230,,124516,,154280,,176836,,218899,,195861,,236170,,263407,,225461,,216531,,211943,,134139,,122713,F,109147,F,124030,F -Yemen,All,Import,Value (USD 1000),USD 1000,1551,,1551,F,4620,,5467,,7970,,4684,,5187,,5026,,4091,,3845,F,3880,F,2015,F,2370,F,3570,,2017,,3472,,3658,,4047,,5958,,2448,,3931,,7170,,3826,,4313,,5064,,5758,F,6892,,4516,,3445,,5592,,9219,,9424,,10153,,8916,,14583,,13676,,21359,,31475,,30186,,20523,,26556,F,36083,F,48505,F -Yemen,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,1,,0,.,52,,0,.,173,,5,,191,,1381,,1,,1107,,1016,,257,,274,,0,.,411,,0,.,0,.,0,.,0,. -Yugoslavia SFR,All,Export,Value (USD 1000),USD 1000,18618,,17763,,20283,,30246,,27957,,33193,,36712,,32984,,22483,,21375,,23163,,33835,,35704,,36040,,36761,,2582,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0, -Yugoslavia SFR,All,Import,Value (USD 1000),USD 1000,35042,,55107,,63521,,64266,,70071,,80463,,66788,,95457,,60895,,58534,,72708,,75190,,95506,,105785,,114165,,33567,,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0,-,0, -Zambia,All,Export,Value (USD 1000),USD 1000,11,,9,,0,.,0,.,0,.,7,,0,.,0,.,6,,0,.,138,,278,,521,,440,F,336,F,285,F,234,,250,F,88,,237,F,399,F,421,,278,F,1681,,465,,320,,892,,912,,1862,,3523,,362,,399,,1295,,1160,,1069,,1531,,1109,,860,,745,,531,,538,,1004,,994, -Zambia,All,Import,Value (USD 1000),USD 1000,1792,,2594,,7525,,7321,,2499,,2431,,2380,,1777,,350,,110,,482,,234,,485,,679,,421,,760,F,1571,F,2080,,2078,,1446,F,1596,F,1704,,600,F,1208,,1700,,2066,,2467,,4463,,5561,,7234,,7598,,10411,,8935,,8369,,11716,,19144,,35773,,55182,,81393,,116824,,101229,,129529,,115547, -Zambia,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,446,,0,- -Zimbabwe,All,Export,Value (USD 1000),USD 1000,0,.,0,.,0,.,40,,72,,136,,124,,1,,90,,88,,71,,81,,94,,0,.,77,,96,,183,,1057,,93,,653,,880,,3684,,971,,1464,,4308,,3479,F,2434,,2596,F,2480,,1819,,4673,,3021,,1453,,2157,,3246,,4841,,6599,,8776,,15321,,12518,,7112,,8067,,6533, -Zimbabwe,All,Import,Value (USD 1000),USD 1000,2153,,2359,,1443,,1615,,3989,,3524,,2020,F,1662,,972,,7,,1514,,1665,,1070,F,872,F,759,,1125,,2890,,7729,,10275,,14499,,18001,,18683,,9511,,9945,,8621,,3896,,2221,,1681,F,3378,,2658,,5165,,3486,,4776,,5968,,21340,,27382,,27373,,30804,,27986,,28568,,24435,,21034,,16175, -Zimbabwe,All,Reexports,Value (USD 1000),USD 1000,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,0,.,15,,0,0,0,.,0,.,0,.,27,,14,,0,-,0,-,0,-,0,-,0,- -Totals - Value (USD 1000),,,,,16878861,,20160223,,24835221,,30487955,,32223649,,33613401,,33590633,,33899153,,34361446,,36833277,,48423478,,59831917,,68717332,,69534549,,75860383,,83087921,,86420540,,86928844,,100575902,,109711402,,111520850,,111432747,,108035569,,112406536,,117521858,,117817783,,122302638,,133352162,,149520716,,163520520,,179472603,,195135215,,213304602,,198899184,,224727422,,263171330,,263001608,,276698637,,294106792,,264656049,,281612957,,306546908,,329599600, -"FAO. 2020. Fishery and Aquaculture Statistics. Global Fisheries commodities production and trade 1976-2018 (FishstatJ). In: FAO Fisheries Division [online]. Rome. Updated 2020. www.fao.org/fishery/statistics/software/fishstatj/en"">" diff --git a/inst/extdata/sectoral/FAO_livestock_carcass_price_factor.csv b/inst/extdata/sectoral/FAO_livestock_carcass_price_factor.csv deleted file mode 100644 index 49afcc55..00000000 --- a/inst/extdata/sectoral/FAO_livestock_carcass_price_factor.csv +++ /dev/null @@ -1,17 +0,0 @@ -FAO_live_weigth,FAO_carcass,MAgPIE,Price_factor -"973|Meat live weight, buffalo","947|Meat, buffalo",livst_rum,0.5937625 -"945|Meat live weight, cattle","867|Meat, cattle",livst_rum,0.5323081 -"1013|Meat live weight, sheep","977|Meat, sheep",livst_rum,0.5035164 -"1095|Meat live weight, chicken","1058|Meat, chicken",livst_chick,0.720224 -"1071|Meat live weight, duck","1069|Meat, duck",livst_chick,0.7809208 -"1033|Meat live weight, goat","1017|Meat, goat",livst_rum,0.5255308 -"1078|Meat live weight, goose","1073|Meat, goose and guinea fowl",livst_chick,0.7749782 -"1121|Meat live weight, horse","1097|Meat, horse",livst_rum,0.5116796 -"1056|Meat live weight, pig","1035|Meat, pig",livst_pig,0.7129732 -"1145|Meat live weight, rabbit","1141|Meat, rabbit",livst_rum,0.5581514 -"1088|Meat live weight, turkey","1080|Meat, turkey",livst_chick,0.7855886 -"1138|Meat live weight, camel","1127|Meat, camel",livst_rum,0.543236 -"1085|Meat live weight, poultry, other","1089|Meat, bird nes",livst_chick,0.658163 -"1123|Meat live weight, ass","1108|Meat, ass",livst_rum,0.5116796 -"1162|Meat live weight, camelids, other","1158|Meat, other camelids",livst_rum,0.4520733 -"1125|Meat live weight, mule","1111|Meat, mule",livst_rum,0.5116796 diff --git a/inst/extdata/sectoral/newFAOitems_online_DRAFT.csv b/inst/extdata/sectoral/newFAOitems_online_DRAFT.csv deleted file mode 100755 index 9c2ad078..00000000 --- a/inst/extdata/sectoral/newFAOitems_online_DRAFT.csv +++ /dev/null @@ -1,746 +0,0 @@ -,FAOoriginalItem_fromWebsite,new_FAOoriginalItem_fromWebsite,Item,FAOaggregatedItem_fromWebsite,ProductionItem,FoodBalanceItem,newFoodBalanceItem,k,k_ICP,comment -1,564|Wine,564|Wine,Wine,2655|Wine,564|Wine,2655|Wine,2655|Wine,alcohol,alcohol, -2,51|Beer of barley,"51|Beer of barley, malted",Beer of barley,2656|Beer,51|Beer of barley,2656|Beer,2656|Beer,alcohol,alcohol, -3,"26|Beverages, fermented wheat",,"Beverages, fermented wheat","2657|Beverages, Fermented",,"2657|Beverages, Fermented","2657|Beverages, Fermented",alcohol,alcohol, -4,"39|Beverages, fermented rice",39|Rice-fermented beverages,"Beverages, fermented rice","2657|Beverages, Fermented",,"2657|Beverages, Fermented","2657|Beverages, Fermented",alcohol,alcohol, -5,517|Cider etc,517|Cider and other fermented beverages,Cider etc,"2657|Beverages, Fermented",,"2657|Beverages, Fermented","2657|Beverages, Fermented",alcohol,alcohol, -6,66|Beer of maize,,Beer of maize,"2657|Beverages, Fermented",,"2657|Beverages, Fermented","2657|Beverages, Fermented",alcohol,alcohol, -7,82|Beer of millet,,Beer of millet,"2657|Beverages, Fermented",,"2657|Beverages, Fermented","2657|Beverages, Fermented",alcohol,alcohol, -8,86|Beer of sorghum,,Beer of sorghum,"2657|Beverages, Fermented",,"2657|Beverages, Fermented","2657|Beverages, Fermented",alcohol,alcohol, -9,"634|Beverages, distilled alcoholic","634|Undenatured ethyl alcohol of an alcoholic strength by volume of less than 80% vol; spirits, liqueurs and other spirituous beverages","Beverages, distilled alcoholic","2658|Beverages, Alcoholic",,"2658|Beverages, Alcoholic","2658|Beverages, Alcoholic",alcohol,alcohol, -10,632|Alcohol non food,,Alcohol non food,"2659|Alcohol, Non-Food",,"2659|Alcohol, Non-Food","2659|Alcohol, Non-Food",alcohol,alcohol, -11,,,,2600|Brans,,2600|Brans,2600|Brans,brans,brans, -12,125|Cassava,"125|Cassava, fresh",Cassava,2532|Cassava and products,125|Cassava,2532|Cassava and products,2532|Cassava and products,cassav_sp,cassav_sp, -13,122|Sweet potatoes,122|Sweet potatoes,Sweet potatoes,2533|Sweet potatoes,122|Sweet potatoes,2533|Sweet potatoes,2533|Sweet potatoes,cassav_sp,cassav_sp, -14,135|Yautia (cocoyam),,Yautia (cocoyam),"2534|Roots, Other",135|Yautia (cocoyam),"2534|Roots, Other","2534|Roots, Other",cassav_sp,cassav_sp, -15,136|Taro (cocoyam),,Taro (cocoyam),"2534|Roots, Other",136|Taro (cocoyam),"2534|Roots, Other","2534|Roots, Other",cassav_sp,cassav_sp, -16,"149|Roots and tubers, nes","149|Edible roots and tubers with high starch or inulin content, n.e.c., fresh","Roots and tubers, nes","2534|Roots, Other","149|Roots and tubers, nes","2534|Roots, Other","2534|Roots, Other",cassav_sp,cassav_sp, -17,"149|Roots and tubers, nes","149|Edible roots and tubers with high starch or inulin content, n.e.c., fresh","Roots and tubers, nes","2534|Roots, Other",149|Roots and tubers nes,"2534|Roots, Other","2534|Roots, Other",cassav_sp,cassav_sp, -18,137|Yams,,Yams,2535|Yams,137|Yams,2535|Yams,2535|Yams,cassav_sp,cassav_sp, -19,329|Cottonseed,329|Cotton seed,Cottonseed,2559|Cottonseed,329|Cottonseed,2559|Cottonseed,2559|Cottonseed,cottn_pro,cottn_pro,"used for extracting oil, no area information" -20,,,,,,X002|Distillers_grain,X002|Distillers_grain,distillers_grain,distillers_grain, -21,,,,,,X004|Brewers_grain,X004|Brewers_grain,distillers_grain,distillers_grain, -22,,,,,,X001|Ethanol,X001|Ethanol,ethanol,ethanol, -23,,,,2661|Cotton lint,767|Cotton lint,2661|Cotton lint,2661|Cotton lint,fibres,fibres,no area information -24,,,,2662|Jute,780|Jute,2662|Jute,2662|Jute,remaining,remaining, -25,,,,2663|Jute-Like Fibres,"782|Bastfibres, other",2663|Jute-Like Fibres,2663|Jute-Like Fibres,remaining,remaining, -26,,,,"2664|Soft-Fibres, Other",,"2664|Soft-Fibres, Other","2664|Soft-Fibres, Other",remaining,remaining, -27,,,,2665|Sisal,789|Sisal,2665|Sisal,2665|Sisal,remaining,remaining, -28,,,,2666|Abaca,809|Manila fibre (abaca),2666|Abaca,2666|Abaca,remaining,remaining, -29,,,,"2667|Hard Fibres, Other",,"2667|Hard Fibres, Other","2667|Hard Fibres, Other",remaining,remaining, -30,1501|Frwtr Diad F,,Frwtr Diad F,2761|Freshwater Fish,,2761|Freshwater Fish,2761|Freshwater Fish,fish,fish, -31,1502|Frwtr Fz Whl,,Frwtr Fz Whl,2761|Freshwater Fish,,2761|Freshwater Fish,2761|Freshwater Fish,fish,fish, -32,1503|Frwtr Fillet,,Frwtr Fillet,2761|Freshwater Fish,,2761|Freshwater Fish,2761|Freshwater Fish,fish,fish, -33,1504|Frwtr Fz Flt,,Frwtr Fz Flt,2761|Freshwater Fish,,2761|Freshwater Fish,2761|Freshwater Fish,fish,fish, -34,1505|Frwtr Cured,,Frwtr Cured,2761|Freshwater Fish,,2761|Freshwater Fish,2761|Freshwater Fish,fish,fish, -35,1506|Frwtr Canned,,Frwtr Canned,2761|Freshwater Fish,,2761|Freshwater Fish,2761|Freshwater Fish,fish,fish, -36,1507|Frwtr Pr nes,,Frwtr Pr nes,2761|Freshwater Fish,,2761|Freshwater Fish,2761|Freshwater Fish,fish,fish, -37,1508|Frwtr Meals,,Frwtr Meals,2761|Freshwater Fish,,2761|Freshwater Fish,2761|Freshwater Fish,fish,fish, -38,1514|Dmrsl Fresh,,Dmrsl Fresh,2762|Demersal Fish,,2762|Demersal Fish,2762|Demersal Fish,fish,fish, -39,1515|Dmrsl Fz Whl,,Dmrsl Fz Whl,2762|Demersal Fish,,2762|Demersal Fish,2762|Demersal Fish,fish,fish, -40,1516|Dmrsl Fillet,,Dmrsl Fillet,2762|Demersal Fish,,2762|Demersal Fish,2762|Demersal Fish,fish,fish, -41,1517|Dmrsl Fz Flt,,Dmrsl Fz Flt,2762|Demersal Fish,,2762|Demersal Fish,2762|Demersal Fish,fish,fish, -42,1518|Dmrsl Cured,,Dmrsl Cured,2762|Demersal Fish,,2762|Demersal Fish,2762|Demersal Fish,fish,fish, -43,1519|Dmrsl Canned,,Dmrsl Canned,2762|Demersal Fish,,2762|Demersal Fish,2762|Demersal Fish,fish,fish, -44,1520|Dmrsl Pr nes,,Dmrsl Pr nes,2762|Demersal Fish,,2762|Demersal Fish,2762|Demersal Fish,fish,fish, -45,1521|Dmrsl Meals,,Dmrsl Meals,2762|Demersal Fish,,2762|Demersal Fish,2762|Demersal Fish,fish,fish, -46,1527|Pelagic Frsh,,Pelagic Frsh,2763|Pelagic Fish,,2763|Pelagic Fish,2763|Pelagic Fish,fish,fish, -47,1528|Pelgc Fz Whl,,Pelgc Fz Whl,2763|Pelagic Fish,,2763|Pelagic Fish,2763|Pelagic Fish,fish,fish, -48,1529|Pelgc Fillet,,Pelgc Fillet,2763|Pelagic Fish,,2763|Pelagic Fish,2763|Pelagic Fish,fish,fish, -49,1530|Pelgc Fz Flt,,Pelgc Fz Flt,2763|Pelagic Fish,,2763|Pelagic Fish,2763|Pelagic Fish,fish,fish, -50,1531|Pelgc Cured,,Pelgc Cured,2763|Pelagic Fish,,2763|Pelagic Fish,2763|Pelagic Fish,fish,fish, -51,1532|Pelgc Canned,,Pelgc Canned,2763|Pelagic Fish,,2763|Pelagic Fish,2763|Pelagic Fish,fish,fish, -52,1533|Pelgc Pr nes,,Pelgc Pr nes,2763|Pelagic Fish,,2763|Pelagic Fish,2763|Pelagic Fish,fish,fish, -53,1534|Pelgc Meals,,Pelgc Meals,2763|Pelagic Fish,,2763|Pelagic Fish,2763|Pelagic Fish,fish,fish, -54,1540|Marine nes F,,Marine nes F,"2764|Marine Fish, Other",,"2764|Marine Fish, Other","2764|Marine Fish, Other",fish,fish, -55,1541|Marin Fz Whl,,Marin Fz Whl,"2764|Marine Fish, Other",,"2764|Marine Fish, Other","2764|Marine Fish, Other",fish,fish, -56,1542|Marin Fillet,,Marin Fillet,"2764|Marine Fish, Other",,"2764|Marine Fish, Other","2764|Marine Fish, Other",fish,fish, -57,1543|Marin Fz Flt,,Marin Fz Flt,"2764|Marine Fish, Other",,"2764|Marine Fish, Other","2764|Marine Fish, Other",fish,fish, -58,1544|Marin Cured,,Marin Cured,"2764|Marine Fish, Other",,"2764|Marine Fish, Other","2764|Marine Fish, Other",fish,fish, -59,1545|Marin Canned,,Marin Canned,"2764|Marine Fish, Other",,"2764|Marine Fish, Other","2764|Marine Fish, Other",fish,fish, -60,1546|Marin Pr nes,,Marin Pr nes,"2764|Marine Fish, Other",,"2764|Marine Fish, Other","2764|Marine Fish, Other",fish,fish, -61,1547|Marin Meals,,Marin Meals,"2764|Marine Fish, Other",,"2764|Marine Fish, Other","2764|Marine Fish, Other",fish,fish, -62,1553|Crstaceans F,,Crstaceans F,2765|Crustaceans,,2765|Crustaceans,2765|Crustaceans,fish,fish, -63,1554|Crstc Frozen,,Crstc Frozen,2765|Crustaceans,,2765|Crustaceans,2765|Crustaceans,fish,fish, -64,1555|Crstc Cured,,Crstc Cured,2765|Crustaceans,,2765|Crustaceans,2765|Crustaceans,fish,fish, -65,1556|Crstc Canned,,Crstc Canned,2765|Crustaceans,,2765|Crustaceans,2765|Crustaceans,fish,fish, -66,1557|Crstc Pr nes,,Crstc Pr nes,2765|Crustaceans,,2765|Crustaceans,2765|Crustaceans,fish,fish, -67,1558|Crstc Meals,,Crstc Meals,2765|Crustaceans,,2765|Crustaceans,2765|Crustaceans,fish,fish, -68,1570|Cephlp Fresh,,Cephlp Fresh,2766|Cephalopods,,2766|Cephalopods,2766|Cephalopods,fish,fish, -69,1571|Cphlp Frozen,,Cphlp Frozen,2766|Cephalopods,,2766|Cephalopods,2766|Cephalopods,fish,fish, -70,1572|Cphlp Cured,,Cphlp Cured,2766|Cephalopods,,2766|Cephalopods,2766|Cephalopods,fish,fish, -71,1573|Cphlp Canned,,Cphlp Canned,2766|Cephalopods,,2766|Cephalopods,2766|Cephalopods,fish,fish, -72,1574|Cphlp Pr nes,,Cphlp Pr nes,2766|Cephalopods,,2766|Cephalopods,2766|Cephalopods,fish,fish, -73,1575|Cphlp Meals,,Cphlp Meals,2766|Cephalopods,,2766|Cephalopods,2766|Cephalopods,fish,fish, -74,1562|Mlluscs Frsh,,Mlluscs Frsh,"2767|Molluscs, Other",,"2767|Molluscs, Other","2767|Molluscs, Other",fish,fish, -75,1563|Molsc Frozen,,Molsc Frozen,"2767|Molluscs, Other",,"2767|Molluscs, Other","2767|Molluscs, Other",fish,fish, -76,1564|Molsc Cured,,Molsc Cured,"2767|Molluscs, Other",,"2767|Molluscs, Other","2767|Molluscs, Other",fish,fish, -77,1565|Molsc Canned,,Molsc Canned,"2767|Molluscs, Other",,"2767|Molluscs, Other","2767|Molluscs, Other",fish,fish, -78,1566|Molsc Meals,,Molsc Meals,"2767|Molluscs, Other",,"2767|Molluscs, Other","2767|Molluscs, Other",fish,fish, -79,1580|Aq M Meat,,Aq M Meat,"2768|Meat, Aquatic Mammals",,"2768|Meat, Aquatic Mammals","2768|Meat, Aquatic Mammals",fish,fish, -80,1583|Aq M Prep Ns,,Aq M Prep Ns,"2768|Meat, Aquatic Mammals",,"2768|Meat, Aquatic Mammals","2768|Meat, Aquatic Mammals",fish,fish, -81,1587|Aqutc Anim F,,Aqutc Anim F,"2769|Aquatic Animals, Others",,"2769|Aquatic Animals, Others","2769|Aquatic Animals, Others",fish,fish, -82,1588|Aq A Cured,,Aq A Cured,"2769|Aquatic Animals, Others",,"2769|Aquatic Animals, Others","2769|Aquatic Animals, Others",fish,fish, -83,1589|Aquatic Animals Meals,,Aquatic Animals Meals,"2769|Aquatic Animals, Others",,"2769|Aquatic Animals, Others","2769|Aquatic Animals, Others",fish,fish, -84,1590|Aq A Prep Ns,,Aq A Prep Ns,"2769|Aquatic Animals, Others",,"2769|Aquatic Animals, Others","2769|Aquatic Animals, Others",fish,fish, -85,1509|Frwt Bdy Oil,,Frwt Bdy Oil,"2781|Fish, Body Oil",,"2781|Fish, Body Oil","2781|Fish, Body Oil",fish,fish, -86,1522|Dmrs Bdy Oil,,Dmrs Bdy Oil,"2781|Fish, Body Oil",,"2781|Fish, Body Oil","2781|Fish, Body Oil",fish,fish, -87,1535|Pelg Bdy Oil,,Pelg Bdy Oil,"2781|Fish, Body Oil",,"2781|Fish, Body Oil","2781|Fish, Body Oil",fish,fish, -88,1548|Marn Bdy Oil,,Marn Bdy Oil,"2781|Fish, Body Oil",,"2781|Fish, Body Oil","2781|Fish, Body Oil",fish,fish, -89,1582|Aq M Oils,,Aq M Oils,"2781|Fish, Body Oil",,"2781|Fish, Body Oil","2781|Fish, Body Oil",fish,fish, -90,1510|Frwt Lvr Oil,,Frwt Lvr Oil,"2782|Fish, Liver Oil",,"2782|Fish, Liver Oil","2782|Fish, Liver Oil",fish,fish, -91,1523|Demersal Liver Oils,,Demersal Liver Oils,"2782|Fish, Liver Oil",,"2782|Fish, Liver Oil","2782|Fish, Liver Oil",fish,fish, -92,1536|Pelg Lvr Oil,,Pelg Lvr Oil,"2782|Fish, Liver Oil",,"2782|Fish, Liver Oil","2782|Fish, Liver Oil",fish,fish, -93,1549|Marine nes Liver Oils,,Marine nes Liver Oils,"2782|Fish, Liver Oil",,"2782|Fish, Liver Oil","2782|Fish, Liver Oil",fish,fish, -94,,,,2855|Fish Meal,,2855|Fish Meal,2855|Fish Meal,fish,fish, -95,,,,,"636|Forage and silage, maize",X005|Fodder_and_forage,X005|Fodder_and_forage,foddr,foddr, -96,,,,,"637|Forage and silage, sorghum",X005|Fodder_and_forage,X005|Fodder_and_forage,foddr,foddr, -97,,,,,"638|Forage and silage, rye grass",X005|Fodder_and_forage,X005|Fodder_and_forage,foddr,foddr, -98,,,,,"639|Forage and silage, grasses nes",X005|Fodder_and_forage,X005|Fodder_and_forage,foddr,foddr, -99,,,,,"640|Forage and silage, clover",X005|Fodder_and_forage,X005|Fodder_and_forage,foddr,foddr, -100,,,,,"641|Forage and silage, alfalfa",X005|Fodder_and_forage,X005|Fodder_and_forage,foddr,foddr, -101,,,,,"642|Forage and silage, green oilseeds",X005|Fodder_and_forage,X005|Fodder_and_forage,foddr,foddr, -102,,,,,"643|Forage and silage, legumes",X005|Fodder_and_forage,X005|Fodder_and_forage,foddr,foddr, -103,,,,,644|Cabbage for fodder,X005|Fodder_and_forage,X005|Fodder_and_forage,foddr,foddr, -104,,,,,645|Pumpkins for Fodder,X005|Fodder_and_forage,X005|Fodder_and_forage,foddr,foddr, -105,,,,,646|Turnips for fodder,X005|Fodder_and_forage,X005|Fodder_and_forage,foddr,foddr, -106,,,,,647|Beets for fodder,X005|Fodder_and_forage,X005|Fodder_and_forage,foddr,foddr, -107,,,,,648|Carrots for fodder,X005|Fodder_and_forage,X005|Fodder_and_forage,foddr,foddr, -108,,,,,649|Swedes for fodder,X005|Fodder_and_forage,X005|Fodder_and_forage,foddr,foddr, -109,,,,,651|Forage products,X005|Fodder_and_forage,X005|Fodder_and_forage,foddr,foddr, -110,,,,,655|Vegetables and roots fodder,X005|Fodder_and_forage,X005|Fodder_and_forage,foddr,foddr, -111,,,,2820|Groundnuts (in Shell Eq),"242|Groundnuts, with shell",2820|Groundnuts (in Shell Eq),2820|Groundnuts (in Shell Eq),groundnut,groundnut, -,,,,,,,2552|Groundnuts,groundnut,groundnut,only existing in new FBS -112,"1058|Meat, chicken","1058|Meat of chickens, fresh or chilled","Meat, chicken",2734|Poultry Meat,"1058|Meat, chicken",2734|Poultry Meat,2734|Poultry Meat,livst_chick,livst_chick, -113,"1069|Meat, duck","1069|Meat of ducks, fresh or chilled","Meat, duck",2734|Poultry Meat,"1069|Meat, duck",2734|Poultry Meat,2734|Poultry Meat,livst_chick,livst_chick, -114,"1073|Meat, goose and guinea fowl","1073|Meat of geese, fresh or chilled","Meat, goose and guinea fowl",2734|Poultry Meat,"1073|Meat, goose and guinea fowl",2734|Poultry Meat,2734|Poultry Meat,livst_chick,livst_chick, -115,"1080|Meat, turkey","1080|Meat of turkeys, fresh or chilled","Meat, turkey",2734|Poultry Meat,"1080|Meat, turkey",2734|Poultry Meat,2734|Poultry Meat,livst_chick,livst_chick, -116,"1062|Eggs, hen, in shell","1062|Hen eggs in shell, fresh","Eggs, hen, in shell",2744|Eggs,"1062|Eggs, hen, in shell",2744|Eggs,2744|Eggs,livst_egg,livst_egg, -117,"1091|Eggs, other bird, in shell","1091|Eggs from other birds in shell, fresh, n.e.c.","Eggs, other bird, in shell",2744|Eggs,"1091|Eggs, other bird, in shell",2744|Eggs,2744|Eggs,livst_egg,livst_egg, -118,"886|Butter, cow milk",886|Butter of cow milk,"Butter, cow milk","2740|Butter, Ghee","886|Butter, cow milk","2740|Butter, Ghee","2740|Butter, Ghee",livst_milk,livst_milk, -119,"887|Ghee, butteroil of cow milk",,"Ghee, butteroil of cow milk","2740|Butter, Ghee","887|Ghee, butteroil of cow milk","2740|Butter, Ghee","2740|Butter, Ghee",livst_milk,livst_milk, -120,"952|Butter, buffalo milk",,"Butter, buffalo milk","2740|Butter, Ghee","952|Butter, buffalo milk","2740|Butter, Ghee","2740|Butter, Ghee",livst_milk,livst_milk, -121,"953|Ghee, of buffalo milk",953|Ghee from buffalo milk,"Ghee, of buffalo milk","2740|Butter, Ghee","953|Ghee, of buffalo milk","2740|Butter, Ghee","2740|Butter, Ghee",livst_milk,livst_milk, -122,"983|Butter and ghee, sheep milk",,"Butter and ghee, sheep milk","2740|Butter, Ghee","983|Butter and ghee, sheep milk","2740|Butter, Ghee","2740|Butter, Ghee",livst_milk,livst_milk, -123,885|Cream fresh,"885|Cream, fresh",Cream fresh,2743|Cream,885|Cream fresh,2743|Cream,2743|Cream,livst_milk,livst_milk, -124,"1020|Milk, whole fresh goat",,"Milk, whole fresh goat",2848|Milk - Excluding Butter,"1020|Milk, whole fresh goat",2848|Milk - Excluding Butter,2848|Milk - Excluding Butter,livst_milk,livst_milk, -125,1021|Cheese of goat mlk,,Cheese of goat mlk,2848|Milk - Excluding Butter,1021|Cheese of goat mlk,2848|Milk - Excluding Butter,2848|Milk - Excluding Butter,livst_milk,livst_milk, -126,"1130|Milk, whole fresh camel",,"Milk, whole fresh camel",2848|Milk - Excluding Butter,"1130|Milk, whole fresh camel",2848|Milk - Excluding Butter,2848|Milk - Excluding Butter,livst_milk,livst_milk, -127,"882|Milk, whole fresh cow",882|Raw milk of cattle,"Milk, whole fresh cow",2848|Milk - Excluding Butter,"882|Milk, whole fresh cow",2848|Milk - Excluding Butter,2848|Milk - Excluding Butter,livst_milk,livst_milk, -128,"888|Milk, skimmed cow",888|Skim milk of cows,"Milk, skimmed cow",2848|Milk - Excluding Butter,"888|Milk, skimmed cow",2848|Milk - Excluding Butter,2848|Milk - Excluding Butter,livst_milk,livst_milk, -129,"889|Milk, whole condensed","889|Whole milk, condensed","Milk, whole condensed",2848|Milk - Excluding Butter,"889|Milk, whole condensed",2848|Milk - Excluding Butter,2848|Milk - Excluding Butter,livst_milk,livst_milk, -130,"890|Whey, condensed","890|Whey, condensed","Whey, condensed",2848|Milk - Excluding Butter,"890|Whey, condensed",2848|Milk - Excluding Butter,2848|Milk - Excluding Butter,livst_milk,livst_milk, -131,891|Yoghurt,891|Yoghurt,Yoghurt,2848|Milk - Excluding Butter,891|Yoghurt,2848|Milk - Excluding Butter,2848|Milk - Excluding Butter,livst_milk,livst_milk, -132,"894|Milk, whole evaporated","894|Whole milk, evaporated","Milk, whole evaporated",2848|Milk - Excluding Butter,"894|Milk, whole evaporated",2848|Milk - Excluding Butter,2848|Milk - Excluding Butter,livst_milk,livst_milk, -133,"895|Milk, skimmed evaporated",,"Milk, skimmed evaporated",2848|Milk - Excluding Butter,"895|Milk, skimmed evaporated",2848|Milk - Excluding Butter,2848|Milk - Excluding Butter,livst_milk,livst_milk, -134,"896|Milk, skimmed condensed",,"Milk, skimmed condensed",2848|Milk - Excluding Butter,"896|Milk, skimmed condensed",2848|Milk - Excluding Butter,2848|Milk - Excluding Butter,livst_milk,livst_milk, -135,"897|Milk, whole dried",897|Whole milk powder,"Milk, whole dried",2848|Milk - Excluding Butter,"897|Milk, whole dried",2848|Milk - Excluding Butter,2848|Milk - Excluding Butter,livst_milk,livst_milk, -136,"898|Milk, skimmed dried",898|Skim milk and whey powder,"Milk, skimmed dried",2848|Milk - Excluding Butter,"898|Milk, skimmed dried",2848|Milk - Excluding Butter,2848|Milk - Excluding Butter,livst_milk,livst_milk, -137,"899|Milk, dry buttermilk",,"Milk, dry buttermilk",2848|Milk - Excluding Butter,"899|Milk, dry buttermilk",2848|Milk - Excluding Butter,2848|Milk - Excluding Butter,livst_milk,livst_milk, -138,"900|Whey, dry","900|Whey, dry","Whey, dry",2848|Milk - Excluding Butter,"900|Whey, dry",2848|Milk - Excluding Butter,2848|Milk - Excluding Butter,livst_milk,livst_milk, -139,"901|Cheese, whole cow milk",901|Cheese from whole cow milk,"Cheese, whole cow milk",2848|Milk - Excluding Butter,"901|Cheese, whole cow milk",2848|Milk - Excluding Butter,2848|Milk - Excluding Butter,livst_milk,livst_milk, -140,"904|Cheese, skimmed cow milk",,"Cheese, skimmed cow milk",2848|Milk - Excluding Butter,"904|Cheese, skimmed cow milk",2848|Milk - Excluding Butter,2848|Milk - Excluding Butter,livst_milk,livst_milk, -141,"951|Milk, whole fresh buffalo",,"Milk, whole fresh buffalo",2848|Milk - Excluding Butter,"951|Milk, whole fresh buffalo",2848|Milk - Excluding Butter,2848|Milk - Excluding Butter,livst_milk,livst_milk, -142,"955|Cheese, buffalo milk",,"Cheese, buffalo milk",2848|Milk - Excluding Butter,"955|Cheese, buffalo milk",2848|Milk - Excluding Butter,2848|Milk - Excluding Butter,livst_milk,livst_milk, -143,"982|Milk, whole fresh sheep",982|Raw milk of sheep,"Milk, whole fresh sheep",2848|Milk - Excluding Butter,"982|Milk, whole fresh sheep",2848|Milk - Excluding Butter,2848|Milk - Excluding Butter,livst_milk,livst_milk, -144,"984|Cheese, sheep milk","984|Cheese from milk of sheep, fresh or processed","Cheese, sheep milk",2848|Milk - Excluding Butter,"984|Cheese, sheep milk",2848|Milk - Excluding Butter,2848|Milk - Excluding Butter,livst_milk,livst_milk, -145,"1035|Meat, pig","1035|Meat of pig with the bone, fresh or chilled","Meat, pig",2733|Pigmeat,"1035|Meat, pig",2733|Pigmeat,2733|Pigmeat,livst_pig,livst_pig, -146,"1089|Meat, bird nes",,"Meat, bird nes","2735|Meat, Other","1089|Meat, bird nes","2735|Meat, Other","2735|Meat, Other",livst_rum,livst_rum, -147,"1097|Meat, horse","1097|Horse meat, fresh or chilled","Meat, horse","2735|Meat, Other","1097|Meat, horse","2735|Meat, Other","2735|Meat, Other",livst_rum,livst_rum, -148,"1108|Meat, ass","1108|Meat of asses, fresh or chilled","Meat, ass","2735|Meat, Other","1108|Meat, ass","2735|Meat, Other","2735|Meat, Other",livst_rum,livst_rum, -149,"1111|Meat, mule",,"Meat, mule","2735|Meat, Other","1111|Meat, mule","2735|Meat, Other","2735|Meat, Other",livst_rum,livst_rum, -150,"1127|Meat, camel",,"Meat, camel","2735|Meat, Other","1127|Meat, camel","2735|Meat, Other","2735|Meat, Other",livst_rum,livst_rum, -151,"1141|Meat, rabbit","1141|Meat of rabbits and hares, fresh or chilled","Meat, rabbit","2735|Meat, Other","1141|Meat, rabbit","2735|Meat, Other","2735|Meat, Other",livst_rum,livst_rum, -152,"1151|Meat, other rodents",,"Meat, other rodents","2735|Meat, Other","1151|Meat, other rodents","2735|Meat, Other","2735|Meat, Other",livst_rum,livst_rum, -153,"1158|Meat, other camelids",,"Meat, other camelids","2735|Meat, Other","1158|Meat, other camelids","2735|Meat, Other","2735|Meat, Other",livst_rum,livst_rum, -154,"1163|Meat, game","1163|Game meat, fresh, chilled or frozen","Meat, game","2735|Meat, Other","1163|Meat, game","2735|Meat, Other","2735|Meat, Other",livst_rum,livst_rum, -155,"1166|Meat, nes","1166|Other meat n.e.c. (excluding mammals), fresh, chilled or frozen","Meat, nes","2735|Meat, Other","1166|Meat, nes","2735|Meat, Other","2735|Meat, Other",livst_rum,livst_rum, -156,"1176|Snails, not sea",,"Snails, not sea","2735|Meat, Other","1176|Snails, not sea","2735|Meat, Other","2735|Meat, Other",livst_rum,livst_rum, -157,"1167|Offals, nes",,"Offals, nes","2736|Offals, Edible","1167|Offals, nes","2736|Offals, Edible","2736|Offals, Edible",livst_rum,livst_rum, -158,1043|Lard,"1043|Pig fat, rendered",Lard,"2737|Fats, Animals, Raw",1043|Lard,"2737|Fats, Animals, Raw","2737|Fats, Animals, Raw",livst_rum,livst_rum, -159,1225|Tallow,1225|Tallow,Tallow,"2737|Fats, Animals, Raw",1225|Tallow,"2737|Fats, Animals, Raw","2737|Fats, Animals, Raw",livst_rum,livst_rum, -160,"867|Meat, cattle","867|Meat of cattle with the bone, fresh or chilled","Meat, cattle",2731|Bovine Meat,"867|Meat, cattle",2731|Bovine Meat,2731|Bovine Meat,livst_rum,livst_rum, -161,"947|Meat, buffalo",,"Meat, buffalo",2731|Bovine Meat,"947|Meat, buffalo",2731|Bovine Meat,2731|Bovine Meat,livst_rum,livst_rum, -162,"1017|Meat, goat","1017|Meat of goat, fresh or chilled","Meat, goat",2732|Mutton & Goat Meat,"1017|Meat, goat",2732|Mutton & Goat Meat,2732|Mutton & Goat Meat,livst_rum,livst_rum, -163,"977|Meat, sheep","977|Meat of sheep, fresh or chilled","Meat, sheep",2732|Mutton & Goat Meat,"977|Meat, sheep",2732|Mutton & Goat Meat,2732|Mutton & Goat Meat,livst_rum,livst_rum, -164,,,,,,2749|Meat Meal,2749|Meat Meal,livst_rum,livst_rum, -165,56|Maize,56|Maize (corn),Maize,2514|Maize and products,56|Maize,2514|Maize and products,2514|Maize and products,maiz,maiz, -166,,,,2544|Molasses,165|Molasses,2544|Molasses,2544|Molasses,molasses,molasses, -167,256|Palm kernels,,Palm kernels,2562|Palm kernels,256|Palm kernels,2562|Palm kernels,2562|Palm kernels,not_clear,not_clear,what to do with these?? -168,,,,2590|Soyabean Cake,,2590|Soyabean Cake,2590|Soyabean Cake,oilcakes,oilcakes, -169,,,,2591|Groundnut Cake,,2591|Groundnut Cake,2591|Groundnut Cake,oilcakes,oilcakes, -170,,,,2592|Sunflowerseed Cake,,2592|Sunflowerseed Cake,2592|Sunflowerseed Cake,oilcakes,oilcakes, -171,,,,2593|Rape and Mustard Cake,,2593|Rape and Mustard Cake,2593|Rape and Mustard Cake,oilcakes,oilcakes, -172,,,,2594|Cottonseed Cake,,2594|Cottonseed Cake,2594|Cottonseed Cake,oilcakes,oilcakes, -173,,,,2595|Palmkernel Cake,,2595|Palmkernel Cake,2595|Palmkernel Cake,oilcakes,oilcakes, -174,,,,2596|Copra Cake,,2596|Copra Cake,2596|Copra Cake,oilcakes,oilcakes, -175,,,,2597|Sesameseed Cake,,2597|Sesameseed Cake,2597|Sesameseed Cake,oilcakes,oilcakes, -176,,,,"2598|Oilseed Cakes, Other",,"2598|Oilseed Cakes, Other","2598|Oilseed Cakes, Other",oilcakes,oilcakes, -177,,,,,,X003|Palmoil_Kerneloil_Kernelcake,X003|Palmoil_Kerneloil_Kernelcake,oilpalm,oilpalm, -178,"237|Oil, soybean",237|Soya bean oil,"Oil, soybean",2571|Soyabean Oil,"237|Oil, soybean",2571|Soyabean Oil,2571|Soyabean Oil,oils,oils, -179,"244|Oil, groundnut",244|Groundnut oil,"Oil, groundnut",2572|Groundnut Oil,"244|Oil, groundnut",2572|Groundnut Oil,2572|Groundnut Oil,oils,oils, -180,"268|Oil, sunflower","268|Sunflower-seed oil, crude","Oil, sunflower",2573|Sunflowerseed Oil,"268|Oil, sunflower",2573|Sunflowerseed Oil,2573|Sunflowerseed Oil,oils,oils, -181,"271|Oil, rapeseed","271|Rapeseed or canola oil, crude","Oil, rapeseed",2574|Rape and Mustard Oil,"271|Oil, rapeseed",2574|Rape and Mustard Oil,2574|Rape and Mustard Oil,oils,oils, -182,"331|Oil, cottonseed",331|Cottonseed oil,"Oil, cottonseed",2575|Cottonseed Oil,"331|Oil, cottonseed",2575|Cottonseed Oil,2575|Cottonseed Oil,oils,oils, -183,"258|Oil, palm kernel",258|Oil of palm kernel,"Oil, palm kernel",2576|Palmkernel Oil,"258|Oil, palm kernel",2576|Palmkernel Oil,2576|Palmkernel Oil,oils,oils, -184,"257|Oil, palm",257|Palm oil,"Oil, palm",2577|Palm Oil,"257|Oil, palm",2577|Palm Oil,2577|Palm Oil,oils,oils, -185,"252|Oil, coconut (copra)",252|Coconut oil,"Oil, coconut (copra)",2578|Coconut Oil,"252|Oil, coconut (copra)",2578|Coconut Oil,2578|Coconut Oil,oils,oils, -186,"290|Oil, sesame",290|Oil of sesame seed,"Oil, sesame",2579|Sesameseed Oil,"290|Oil, sesame",2579|Sesameseed Oil,2579|Sesameseed Oil,oils,oils, -187,"261|Oil, olive, virgin",261|Olive oil,"Oil, olive, virgin",2580|Olive Oil,"261|Oil, olive, virgin",2580|Olive Oil,2580|Olive Oil,oils,oils, -188,"36|Oil, rice bran",36|Oil of rice bran,"Oil, rice bran",2581|Ricebran Oil,,2581|Ricebran Oil,2581|Ricebran Oil,oils,oils,no production in ProdSTAT -189,"60|Oil, maize",60|Oil of maize,"Oil, maize",2582|Maize Germ Oil,"60|Oil, maize",2582|Maize Germ Oil,2582|Maize Germ Oil,oils,oils, -190,"1242|Margarine, short",1242|Margarine and shortening,"Margarine, short","2586|Oilcrops Oil, Other","1242|Margarine, short","2586|Oilcrops Oil, Other","2586|Oilcrops Oil, Other",oils,oils, -191,"281|Oil, safflower","281|Safflower-seed oil, crude","Oil, safflower","2586|Oilcrops Oil, Other","281|Oil, safflower","2586|Oilcrops Oil, Other","2586|Oilcrops Oil, Other",oils,oils, -192,"334|Oil, linseed",334|Oil of linseed,"Oil, linseed","2586|Oilcrops Oil, Other","334|Oil, linseed","2586|Oilcrops Oil, Other","2586|Oilcrops Oil, Other",oils,oils, -193,"216|Brazil nuts, with shell",,"Brazil nuts, with shell",2551|Nuts and products,"216|Brazil nuts, with shell",2551|Nuts and products,2551|Nuts and products,others,others, -194,"217|Cashew nuts, with shell","217|Cashew nuts, in shell","Cashew nuts, with shell",2551|Nuts and products,"217|Cashew nuts, with shell",2551|Nuts and products,2551|Nuts and products,others,others, -195,220|Chestnut,"220|Chestnuts, in shell",Chestnut,2551|Nuts and products,220|Chestnut,2551|Nuts and products,2551|Nuts and products,others,others, -196,"221|Almonds, with shell","221|Almonds, in shell","Almonds, with shell",2551|Nuts and products,"221|Almonds, with shell",2551|Nuts and products,2551|Nuts and products,others,others, -197,"222|Walnuts, with shell","222|Walnuts, in shell","Walnuts, with shell",2551|Nuts and products,"222|Walnuts, with shell",2551|Nuts and products,2551|Nuts and products,others,others, -198,223|Pistachios,"223|Pistachios, in shell",Pistachios,2551|Nuts and products,223|Pistachios,2551|Nuts and products,2551|Nuts and products,others,others, -199,224|Kola nuts,224|Kola nuts,Kola nuts,2551|Nuts and products,224|Kola nuts,2551|Nuts and products,2551|Nuts and products,others,others, -200,"225|Hazelnuts, with shell","225|Hazelnuts, in shell","Hazelnuts, with shell",2551|Nuts and products,"225|Hazelnuts, with shell",2551|Nuts and products,2551|Nuts and products,others,others, -201,226|Areca nuts,226|Areca nuts,Areca nuts,2551|Nuts and products,226|Areca nuts,2551|Nuts and products,2551|Nuts and products,others,others, -202,"234|Nuts, nes","234|Other nuts (excluding wild edible nuts and groundnuts), in shell, n.e.c.","Nuts, nes",2551|Nuts and products,"234|Nuts, nes",2551|Nuts and products,2551|Nuts and products,others,others, -203,"234|Nuts, nes","234|Other nuts (excluding wild edible nuts and groundnuts), in shell, n.e.c.","Nuts, nes",2551|Nuts and products,234|Nuts nes,2551|Nuts and products,2551|Nuts and products,others,others, -204,388|Tomatoes,388|Tomatoes,Tomatoes,2601|Tomatoes and products,388|Tomatoes,2601|Tomatoes and products,2601|Tomatoes and products,others,Vegetables, -205,"403|Onions, dry","403|Onions and shallots, dry (excluding dehydrated)","Onions, dry",2602|Onions,"403|Onions, dry",2602|Onions,2602|Onions,others,Vegetables, -206,358|Cabbages and other brassicas,358|Cabbages,Cabbages and other brassicas,"2605|Vegetables, Other",358|Cabbages and other brassicas,"2605|Vegetables, Other","2605|Vegetables, other",others,Vegetables, -207,366|Artichokes,366|Artichokes,Artichokes,"2605|Vegetables, Other",366|Artichokes,"2605|Vegetables, Other","2606|Vegetables, other",others,Vegetables, -208,367|Asparagus,367|Asparagus,Asparagus,"2605|Vegetables, Other",367|Asparagus,"2605|Vegetables, Other","2607|Vegetables, other",others,Vegetables, -209,372|Lettuce and chicory,372|Lettuce and chicory,Lettuce and chicory,"2605|Vegetables, Other",372|Lettuce and chicory,"2605|Vegetables, Other","2608|Vegetables, other",others,Vegetables, -210,373|Spinach,373|Spinach,Spinach,"2605|Vegetables, Other",373|Spinach,"2605|Vegetables, Other","2609|Vegetables, other",others,Vegetables, -211,378|Cassava leaves,,Cassava leaves,"2605|Vegetables, Other",378|Cassava leaves,"2605|Vegetables, Other","2610|Vegetables, other",others,Vegetables, -212,393|Cauliflowers and broccoli,393|Cauliflowers and broccoli,Cauliflowers and broccoli,"2605|Vegetables, Other",393|Cauliflowers and broccoli,"2605|Vegetables, Other","2611|Vegetables, other",others,Vegetables, -213,"394|Pumpkins, squash and gourds","394|Pumpkins, squash and gourds","Pumpkins, squash and gourds","2605|Vegetables, Other","394|Pumpkins, squash and gourds","2605|Vegetables, Other","2612|Vegetables, other",others,Vegetables, -214,397|Cucumbers and gherkins,397|Cucumbers and gherkins,Cucumbers and gherkins,"2605|Vegetables, Other",397|Cucumbers and gherkins,"2605|Vegetables, Other","2613|Vegetables, other",others,Vegetables, -215,399|Eggplants (aubergines),399|Eggplants (aubergines),Eggplants (aubergines),"2605|Vegetables, Other",399|Eggplants (aubergines),"2605|Vegetables, Other","2614|Vegetables, other",others,Vegetables, -216,"401|Chillies and peppers, green","401|Chillies and peppers, green (Capsicum spp. and Pimenta spp.)","Chillies and peppers, green","2605|Vegetables, Other","401|Chillies and peppers, green","2605|Vegetables, Other","2615|Vegetables, other",others,Vegetables, -217,"402|Onions, shallots, green","402|Onions and shallots, green","Onions, shallots, green","2605|Vegetables, Other","402|Onions, shallots, green","2605|Vegetables, Other","2616|Vegetables, other",others,Vegetables, -218,406|Garlic,406|Green garlic,Garlic,"2605|Vegetables, Other",406|Garlic,"2605|Vegetables, Other","2617|Vegetables, other",others,Vegetables, -219,"407|Leeks, other alliaceous vegetables",407|Leeks and other alliaceous vegetables,"Leeks, other alliaceous vegetables","2605|Vegetables, Other","407|Leeks, other alliaceous vegetables","2605|Vegetables, Other","2618|Vegetables, other",others,Vegetables, -220,"414|Beans, green","414|Other beans, green","Beans, green","2605|Vegetables, Other","414|Beans, green","2605|Vegetables, Other","2619|Vegetables, other",others,Vegetables, -221,"417|Peas, green","417|Peas, green","Peas, green","2605|Vegetables, Other","417|Peas, green","2605|Vegetables, Other","2620|Vegetables, other",others,Vegetables, -222,"420|Vegetables, leguminous nes",,"Vegetables, leguminous nes","2605|Vegetables, Other","420|Vegetables, leguminous nes","2605|Vegetables, Other","2621|Vegetables, other",others,Vegetables, -223,423|String beans,,String beans,"2605|Vegetables, Other",423|String beans,"2605|Vegetables, Other","2622|Vegetables, other",others,Vegetables, -224,426|Carrots and turnips,426|Carrots and turnips,Carrots and turnips,"2605|Vegetables, Other",426|Carrots and turnips,"2605|Vegetables, Other","2623|Vegetables, other",others,Vegetables, -225,430|Okra,,Okra,"2605|Vegetables, Other",430|Okra,"2605|Vegetables, Other","2624|Vegetables, other",others,Vegetables, -226,"446|Maize, green",446|Green corn (maize),"Maize, green","2605|Vegetables, Other","446|Maize, green","2605|Vegetables, Other","2625|Vegetables, other",others,Vegetables, -227,449|Mushrooms and truffles,449|Mushrooms and truffles,Mushrooms and truffles,"2605|Vegetables, Other",449|Mushrooms and truffles,"2605|Vegetables, Other","2626|Vegetables, other",others,Vegetables, -228,459|Chicory roots,,Chicory roots,"2605|Vegetables, Other",459|Chicory roots,"2605|Vegetables, Other","2627|Vegetables, other",others,Vegetables, -229,461|Carobs,,Carobs,"2605|Vegetables, Other",461|Carobs,"2605|Vegetables, Other","2628|Vegetables, other",others,Vegetables, -230,"463|Vegetables, fresh nes","463|Other vegetables, fresh n.e.c.","Vegetables, fresh nes","2605|Vegetables, Other","463|Vegetables, fresh nes","2605|Vegetables, Other","2629|Vegetables, other",others,Vegetables, -231,567|Watermelons,567|Watermelons,Watermelons,"2605|Vegetables, Other",567|Watermelons,"2605|Vegetables, Other","2630|Vegetables, other",others,others, -232,"568|Melons, other (inccantaloupes)",568|Cantaloupes and other melons,"Melons, other (inccantaloupes)","2605|Vegetables, Other","568|Melons, other (inccantaloupes)","2605|Vegetables, Other","2631|Vegetables, other",others,others, -233,490|Oranges,490|Oranges,Oranges,"2611|Oranges, Mandarines",490|Oranges,"2611|Oranges, Mandarines","2611|Oranges, Mandarines",others,others, -234,"495|Tangerines, mandarins, clementines, satsumas","495|Tangerines, mandarins, clementines","Tangerines, mandarins, clementines, satsumas","2611|Oranges, Mandarines","495|Tangerines, mandarins, clementines, satsumas","2611|Oranges, Mandarines","2611|Oranges, Mandarines",others,others, -235,497|Lemons and limes,497|Lemons and limes,Lemons and limes,"2612|Lemons, Limes and products",497|Lemons and limes,"2612|Lemons, Limes and products","2612|Lemons, Limes and products",others,others, -236,507|Grapefruit (inc pomelos),507|Pomelos and grapefruits,Grapefruit (inc pomelos),2613|Grapefruit and products,507|Grapefruit (inc pomelos),2613|Grapefruit and products,2613|Grapefruit and products,others,others, -237,"512|Fruit, citrus nes",,"Fruit, citrus nes","2614|Citrus, Other","512|Fruit, citrus nes","2614|Citrus, Other","2614|Citrus, Other",others,others, -238,486|Bananas,486|Bananas,Bananas,2615|Bananas,486|Bananas,2615|Bananas,2615|Bananas,cassav_sp,cassav_sp, -239,489|Plantains,489|Plantains and cooking bananas,Plantains,2616|Plantains,489|Plantains,2616|Plantains,2616|Plantains,cassav_sp,cassav_sp, -240,489|Plantains and others,489|Plantains and cooking bananas,Plantains and others,2616|Plantains,489|Plantains and others,2616|Plantains,2616|Plantains,cassav_sp,cassav_sp, -241,515|Apples,515|Apples,Apples,2617|Apples and products,515|Apples,2617|Apples and products,2617|Apples and products,others,others, -242,574|Pineapples,574|Pineapples,Pineapples,2618|Pineapples and products,574|Pineapples,2618|Pineapples and products,2618|Pineapples and products,others,others, -243,577|Dates,577|Dates,Dates,2619|Dates,577|Dates,2619|Dates,2619|Dates,others,others, -244,560|Grapes,560|Grapes,Grapes,2620|Grapes and products (excl wine),560|Grapes,2620|Grapes and products (excl wine),2620|Grapes and products (excl wine),others,others, -245,521|Pears,521|Pears,Pears,"2625|Fruits, Other",521|Pears,"2625|Fruits, Other","2625|Fruits, other",others,others, -246,523|Quinces,523|Quinces,Quinces,"2625|Fruits, Other",523|Quinces,"2625|Fruits, Other","2626|Fruits, other",others,others, -247,526|Apricots,526|Apricots,Apricots,"2625|Fruits, Other",526|Apricots,"2625|Fruits, Other","2627|Fruits, other",others,others, -248,"530|Cherries, sour",530|Sour cherries,"Cherries, sour","2625|Fruits, Other","530|Cherries, sour","2625|Fruits, Other","2628|Fruits, other",others,others, -249,531|Cherries,531|Cherries,Cherries,"2625|Fruits, Other",531|Cherries,"2625|Fruits, Other","2629|Fruits, other",others,others, -250,534|Peaches and nectarines,534|Peaches and nectarines,Peaches and nectarines,"2625|Fruits, Other",534|Peaches and nectarines,"2625|Fruits, Other","2630|Fruits, other",others,others, -251,536|Plums and sloes,536|Plums and sloes,Plums and sloes,"2625|Fruits, Other",536|Plums and sloes,"2625|Fruits, Other","2631|Fruits, other",others,others, -252,"541|Fruit, stone nes",,"Fruit, stone nes","2625|Fruits, Other","541|Fruit, stone nes","2625|Fruits, Other","2632|Fruits, other",others,others, -253,"542|Fruit, pome nes",,"Fruit, pome nes","2625|Fruits, Other","542|Fruit, pome nes","2625|Fruits, Other","2633|Fruits, other",others,others, -254,544|Strawberries,544|Strawberries,Strawberries,"2625|Fruits, Other",544|Strawberries,"2625|Fruits, Other","2634|Fruits, other",others,others, -255,547|Raspberries,,Raspberries,"2625|Fruits, Other",547|Raspberries,"2625|Fruits, Other","2635|Fruits, other",others,others, -256,549|Gooseberries,549|Gooseberries,Gooseberries,"2625|Fruits, Other",549|Gooseberries,"2625|Fruits, Other","2636|Fruits, other",others,others, -257,550|Currants,550|Currants,Currants,"2625|Fruits, Other",550|Currants,"2625|Fruits, Other","2637|Fruits, other",others,others, -258,552|Blueberries,552|Blueberries,Blueberries,"2625|Fruits, Other",552|Blueberries,"2625|Fruits, Other","2638|Fruits, other",others,others, -259,554|Cranberries,554|Cranberries,Cranberries,"2625|Fruits, Other",554|Cranberries,"2625|Fruits, Other","2639|Fruits, other",others,others, -260,558|Berries nes,,Berries nes,"2625|Fruits, Other",558|Berries nes,"2625|Fruits, Other","2640|Fruits, other",others,others, -261,569|Figs,569|Figs,Figs,"2625|Fruits, Other",569|Figs,"2625|Fruits, Other","2641|Fruits, other",others,others, -262,"571|Mangoes, mangosteens, guavas","571|Mangoes, guavas and mangosteens","Mangoes, mangosteens, guavas","2625|Fruits, Other","571|Mangoes, mangosteens, guavas","2625|Fruits, Other","2642|Fruits, other",others,others, -263,572|Avocados,572|Avocados,Avocados,"2625|Fruits, Other",572|Avocados,"2625|Fruits, Other","2643|Fruits, other",others,others, -264,587|Persimmons,587|Persimmons,Persimmons,"2625|Fruits, Other",587|Persimmons,"2625|Fruits, Other","2644|Fruits, other",others,others, -265,591|Cashewapple,591|Cashewapple,Cashewapple,"2625|Fruits, Other",591|Cashewapple,"2625|Fruits, Other","2645|Fruits, other",others,others, -266,592|Kiwi fruit,592|Kiwi fruit,Kiwi fruit,"2625|Fruits, Other",592|Kiwi fruit,"2625|Fruits, Other","2646|Fruits, other",others,others, -267,600|Papayas,600|Papayas,Papayas,"2625|Fruits, Other",600|Papayas,"2625|Fruits, Other","2647|Fruits, other",others,others, -268,"603|Fruit, tropical fresh nes","603|Other tropical fruits, n.e.c.","Fruit, tropical fresh nes","2625|Fruits, Other","603|Fruit, tropical fresh nes","2625|Fruits, Other","2648|Fruits, other",others,others, -269,"619|Fruit, fresh nes","619|Other fruits, n.e.c.","Fruit, fresh nes","2625|Fruits, Other","619|Fruit, fresh nes","2625|Fruits, Other","2649|Fruits, other",others,others, -270,"656|Coffee, green","656|Coffee, green","Coffee, green",2630|Coffee and products,"656|Coffee, green",2630|Coffee and products,2630|Coffee and products,others,others, -271,"661|Cocoa, beans",661|Cocoa beans,"Cocoa, beans",2633|Cocoa Beans and products,"661|Cocoa, beans",2633|Cocoa Beans and products,2633|Cocoa Beans and products,others,others, -272,667|Tea,667|Tea leaves,Tea,2635|Tea (including mate),667|Tea,2635|Tea (including mate),2635|Tea (including mate),others,others, -273,671|Mate,671|Mate leaves,Mate,2635|Tea (including mate),671|Mate,2635|Tea (including mate),2635|Tea (including mate),others,others, -274,687|Pepper (piper spp),"687|Pepper (Piper spp.), raw",Pepper (piper spp),2640|Pepper,687|Pepper (piper spp),2640|Pepper,2640|Pepper,others,Vegetables, -275,"689|Chillies and peppers, dry","689|Chillies and peppers, dry (Capsicum spp., Pimenta spp.), raw","Chillies and peppers, dry",2641|Pimento,"689|Chillies and peppers, dry",2641|Pimento,2641|Pimento,others,Vegetables, -276,698|Cloves,"698|Cloves (whole stems), raw",Cloves,2642|Cloves,698|Cloves,2642|Cloves,2642|Cloves,others,others, -277,692|Vanilla,"692|Vanilla, raw",Vanilla,"2645|Spices, Other",692|Vanilla,"2645|Spices, Other","2645|Spices, Other",others,others, -278,693|Cinnamon (canella),"693|Cinnamon and cinnamon-tree flowers, raw",Cinnamon (canella),"2645|Spices, Other",693|Cinnamon (canella),"2645|Spices, Other","2645|Spices, Other",others,others, -279,693|Cinnamon (cannella),"693|Cinnamon and cinnamon-tree flowers, raw",Cinnamon (cannella),"2645|Spices, Other",693|Cinnamon (cannella),"2645|Spices, Other","2645|Spices, Other",others,others, -280,"702|Nutmeg, mace and cardamoms","702|Nutmeg, mace, cardamoms, raw","Nutmeg, mace and cardamoms","2645|Spices, Other","702|Nutmeg, mace and cardamoms","2645|Spices, Other","2645|Spices, Other",others,others, -281,"711|Anise, badian, fennel, coriander","711|Anise, badian, coriander, cumin, caraway, fennel and juniper berries, raw","Anise, badian, fennel, coriander","2645|Spices, Other","711|Anise, badian, fennel, coriander","2645|Spices, Other","2645|Spices, Other",others,others, -282,720|Ginger,"720|Ginger, raw",Ginger,"2645|Spices, Other",720|Ginger,"2645|Spices, Other","2645|Spices, Other",others,others, -283,"723|Spices, nes","723|Other stimulant, spice and aromatic crops, n.e.c.","Spices, nes","2645|Spices, Other","723|Spices, nes","2645|Spices, Other","2645|Spices, Other",others,others, -284,"723|Spices, nes","723|Other stimulant, spice and aromatic crops, n.e.c.","Spices, nes","2645|Spices, Other",723|Spices nes,"2645|Spices, Other","2645|Spices, Other",others,others, -285,109|Infant food,109|Infant food,Infant food,2680|Infant food,,2680|Infant food,2680|Infant food,others,others,no production in ProdSTAT -286,"1594|Aquatic plants, fresh",,"Aquatic plants, fresh",2775|Aquatic Plants,,2775|Aquatic Plants,2775|Aquatic Plants,others,Vegetables,no production in ProdSTAT -287,"1595|Aquatic plants, dried",,"Aquatic plants, dried",2775|Aquatic Plants,,2775|Aquatic Plants,2775|Aquatic Plants,others,Vegetables,no production in ProdSTAT -288,"1596|Aquatic plants, other preparations",,"Aquatic plants, other preparations",2775|Aquatic Plants,,2775|Aquatic Plants,2775|Aquatic Plants,others,Vegetables,no production in ProdSTAT -289,,,,2899|Miscellaneous,,2899|Miscellaneous,2899|Miscellaneous,livst_rum,livst_rum,no production in ProdSTAT -290,116|Potatoes,116|Potatoes,Potatoes,2531|Potatoes and products,116|Potatoes,2531|Potatoes and products,2531|Potatoes and products,potato,potato, -291,"176|Beans, dry","176|Beans, dry","Beans, dry",2546|Beans,"176|Beans, dry",2546|Beans,2546|Beans,puls_pro,puls_pro, -292,"187|Peas, dry","187|Peas, dry","Peas, dry",2547|Peas,"187|Peas, dry",2547|Peas,2547|Peas,puls_pro,puls_pro, -293,"181|Broad beans, horse beans, dry","181|Broad beans and horse beans, dry","Broad beans, horse beans, dry","2549|Pulses, Other and products","181|Broad beans, horse beans, dry","2549|Pulses, Other and products","2549|Pulses, Other and products",puls_pro,puls_pro, -294,191|Chick peas,"191|Chick peas, dry",Chick peas,"2549|Pulses, Other and products",191|Chick peas,"2549|Pulses, Other and products","2549|Pulses, Other and products",puls_pro,puls_pro, -295,"195|Cow peas, dry",,"Cow peas, dry","2549|Pulses, Other and products","195|Cow peas, dry","2549|Pulses, Other and products","2549|Pulses, Other and products",puls_pro,puls_pro, -296,197|Pigeon peas,,Pigeon peas,"2549|Pulses, Other and products",197|Pigeon peas,"2549|Pulses, Other and products","2549|Pulses, Other and products",puls_pro,puls_pro, -297,201|Lentils,"201|Lentils, dry",Lentils,"2549|Pulses, Other and products",201|Lentils,"2549|Pulses, Other and products","2549|Pulses, Other and products",puls_pro,puls_pro, -298,203|Bambara beans,"203|Bambara beans, dry",Bambara beans,"2549|Pulses, Other and products",203|Bambara beans,"2549|Pulses, Other and products","2549|Pulses, Other and products",puls_pro,puls_pro, -299,205|Vetches,205|Vetches,Vetches,"2549|Pulses, Other and products",205|Vetches,"2549|Pulses, Other and products","2549|Pulses, Other and products",puls_pro,puls_pro, -300,210|Lupins,,Lupins,"2549|Pulses, Other and products",210|Lupins,"2549|Pulses, Other and products","2549|Pulses, Other and products",puls_pro,puls_pro, -301,"211|Pulses, nes",,"Pulses, nes","2549|Pulses, Other and products","211|Pulses, nes","2549|Pulses, Other and products","2549|Pulses, Other and products",puls_pro,puls_pro, -302,"211|Pulses, nes",,"Pulses, nes","2549|Pulses, Other and products",211|Pulses nes,"2549|Pulses, Other and products","2549|Pulses, Other and products",puls_pro,puls_pro, -303,270|Rapeseed,270|Rape or colza seed,Rapeseed,2558|Rape and Mustardseed,270|Rapeseed,2558|Rape and Mustardseed,2558|Rape and Mustardseed,rapeseed,rapeseed, -304,292|Mustard seed,292|Mustard seed,Mustard seed,2558|Rape and Mustardseed,292|Mustard seed,2558|Rape and Mustardseed,2558|Rape and Mustardseed,rapeseed,rapeseed, -305,249|Coconuts,"249|Coconuts, in shell",Coconuts,2560|Coconuts - Incl Copra,249|Coconuts,2560|Coconuts - Incl Copra,2560|Coconuts - Incl Copra,rapeseed,rapeseed, -306,289|Sesame seed,289|Sesame seed,Sesame seed,2561|Sesame seed,289|Sesame seed,2561|Sesame seed,2561|Sesame seed,rapeseed,rapeseed, -307,260|Olives,260|Olives,Olives,2563|Olives (including preserved),260|Olives,2563|Olives (including preserved),2563|Olives (including preserved),rapeseed,rapeseed, -308,263|Karite nuts (sheanuts),263|Karite nuts (sheanuts),Karite nuts (sheanuts),"2570|Oilcrops, Other",263|Karite nuts (sheanuts),"2570|Oilcrops, Other","2570|Oilcrops, Other",rapeseed,rapeseed, -309,265|Castor oil seed,,Castor oil seed,"2570|Oilcrops, Other",265|Castor oil seed,"2570|Oilcrops, Other","2570|Oilcrops, Other",rapeseed,rapeseed, -310,275|Tung nuts,,Tung nuts,"2570|Oilcrops, Other",275|Tung nuts,"2570|Oilcrops, Other","2570|Oilcrops, Other",rapeseed,rapeseed, -311,277|Jojoba seed,,Jojoba seed,"2570|Oilcrops, Other",277|Jojoba seed,"2570|Oilcrops, Other","2570|Oilcrops, Other",rapeseed,rapeseed, -312,280|Safflower seed,,Safflower seed,"2570|Oilcrops, Other",280|Safflower seed,"2570|Oilcrops, Other","2570|Oilcrops, Other",rapeseed,rapeseed, -313,296|Poppy seed,296|Poppy seed,Poppy seed,"2570|Oilcrops, Other",296|Poppy seed,"2570|Oilcrops, Other","2570|Oilcrops, Other",rapeseed,rapeseed, -314,299|Melonseed,,Melonseed,"2570|Oilcrops, Other",299|Melonseed,"2570|Oilcrops, Other","2570|Oilcrops, Other",rapeseed,rapeseed, -315,305|Tallowtree seed,,Tallowtree seed,"2570|Oilcrops, Other",305|Tallowtree seed,"2570|Oilcrops, Other","2570|Oilcrops, Other",rapeseed,rapeseed, -316,310|Kapok fruit,,Kapok fruit,"2570|Oilcrops, Other",310|Kapok fruit,"2570|Oilcrops, Other","2570|Oilcrops, Other",rapeseed,rapeseed, -317,311|Kapokseed in shell,311|Kapokseed in shell,Kapokseed in shell,"2570|Oilcrops, Other",311|Kapokseed in shell,"2570|Oilcrops, Other","2570|Oilcrops, Other",rapeseed,rapeseed, -318,333|Linseed,333|Linseed,Linseed,"2570|Oilcrops, Other",333|Linseed,"2570|Oilcrops, Other","2570|Oilcrops, Other",rapeseed,rapeseed, -319,336|Hempseed,,Hempseed,"2570|Oilcrops, Other",336|Hempseed,"2570|Oilcrops, Other","2570|Oilcrops, Other",rapeseed,rapeseed, -320,339|Oilseeds nes,"339|Other oil seeds, n.e.c.",Oilseeds nes,"2570|Oilcrops, Other",339|Oilseeds nes,"2570|Oilcrops, Other","2570|Oilcrops, Other",rapeseed,rapeseed, -321,162|Sugar Raw Centrifugal,162|Raw cane or beet sugar (centrifugal only),Sugar Raw Centrifugal,2542|Sugar (Raw Equivalent),162|Sugar Raw Centrifugal,2542|Sugar (Raw Equivalent),,remaining,remaining, -,,,,,,,2542|Sugar (Raw Equivalent),sugar,sugar,New FBS: put this in sugar for now as only using FBS and not SUA . FBS only reports raw eq sugar -322,,,,2671|Tobacco,"826|Tobacco, unmanufactured",2671|Tobacco,2671|Tobacco,remaining,remaining, -323,,,,2672|Rubber,"836|Rubber, natural",2672|Rubber,2672|Rubber,remaining,remaining, -324,,,,,"1012|Meat indigenous, sheep",remaining,remaining,remaining,remaining, -325,,,,,"1025|Skins, goat, fresh",remaining,remaining,remaining,remaining, -326,,,,,"1032|Meat indigenous, goat",remaining,remaining,remaining,remaining, -327,,,,,"1055|Meat indigenous, pig",remaining,remaining,remaining,remaining, -328,,,,,"1067|Eggs, hen, in shell (number)",remaining,remaining,remaining,remaining, -329,,,,,"1070|Meat indigenous, duck",remaining,remaining,remaining,remaining, -330,,,,,"1077|Meat indigenous, geese",remaining,remaining,remaining,remaining, -331,,,,,"1084|Meat indigenous, bird nes",remaining,remaining,remaining,remaining, -332,,,,,"1087|Meat indigenous, turkey",remaining,remaining,remaining,remaining, -333,,,,,"1092|Eggs, other bird, in shell (number)",remaining,remaining,remaining,remaining, -334,,,,,"1094|Meat indigenous, chicken",remaining,remaining,remaining,remaining, -335,,,,,"1100|Hair, horse",remaining,remaining,remaining,remaining, -336,,,,,"1120|Meat indigenous, horse",remaining,remaining,remaining,remaining, -337,,,,,"1122|Meat indigenous, ass",remaining,remaining,remaining,remaining, -338,,,,,"1124|Meat indigenous, mule",remaining,remaining,remaining,remaining, -339,,,,,"1137|Meat indigenous, camel",remaining,remaining,remaining,remaining, -340,,,,,"1144|Meat indigenous, rabbit",remaining,remaining,remaining,remaining, -341,,,,,"1154|Meat indigenous, rodents",remaining,remaining,remaining,remaining, -342,,,,,"1161|Meat indigenous, other camelids",remaining,remaining,remaining,remaining, -343,,,,,1183|Beeswax,remaining,remaining,remaining,remaining, -344,,,,,"1185|Silk-worm cocoons, reelable",remaining,remaining,remaining,remaining, -345,,,,,1186|Silk raw,remaining,remaining,remaining,remaining, -346,,,,,328|Seed cotton,2559|Cottonseed,2559|Cottonseed,cottn_pro,cottn_pro,only cotton item with area information -347,,,,,677|Hops,remaining,remaining,remaining,remaining, -348,,,,,748|Peppermint,remaining,remaining,remaining,remaining, -349,,,,,"754|Pyrethrum, dried",remaining,remaining,remaining,remaining, -350,,,,,773|Flax fibre and tow,remaining,remaining,remaining,remaining, -351,,,,,777|Hemp tow waste,remaining,remaining,remaining,remaining, -352,,,,,778|Kapok fibre,remaining,remaining,remaining,remaining, -353,,,,,788|Ramie,remaining,remaining,remaining,remaining, -354,,,,,800|Agave fibres nes,remaining,remaining,remaining,remaining, -355,,,,,813|Coir,remaining,remaining,remaining,remaining, -356,,,,,821|Fibre crops nes,remaining,remaining,remaining,remaining, -357,,,,,"839|Gums, natural",remaining,remaining,remaining,remaining, -358,,,,,"919|Hides, cattle, fresh",remaining,remaining,remaining,remaining, -359,,,,,"944|Meat indigenous, cattle",remaining,remaining,remaining,remaining, -360,,,,,"957|Hides, buffalo, fresh",remaining,remaining,remaining,remaining, -361,,,,,"972|Meat indigenous, buffalo",remaining,remaining,remaining,remaining, -362,,,,,"987|Wool, greasy",remaining,remaining,remaining,remaining, -363,,,,,"995|Skins, sheep, fresh",remaining,remaining,remaining,remaining, -364,,,,,"999|Skins, sheep, with wool",remaining,remaining,remaining,remaining, -365,,,,2804|Rice (Paddy Equivalent),"27|Rice, paddy",2804|Rice (Paddy Equivalent),2804|Rice (Paddy Equivalent),rice_pro,rice_pro,only existing in new FBS -,,,,,,,2807|Rice and products,rice_pro,rice_pro, -366,236|Soybeans,236|Soya beans,Soybeans,2555|Soyabeans,236|Soybeans,2555|Soyabeans,2555|Soyabeans,soybean,soybean, -367,163|Sugar non-centrifugal,"163|Cane sugar, non-centrifugal",Sugar non-centrifugal,2541|Sugar non-centrifugal,,2541|Sugar non-centrifugal,2541|Sugar non-centrifugal,sugar,sugar, -368,"161|Sugar crops, nes",161|Other sugar crops n.e.c.,"Sugar crops, nes",,"161|Sugar crops, nes",remaining,remaining,remaining,remaining, -369,"161|Sugar crops, nes",161|Other sugar crops n.e.c.,"Sugar crops, nes",,161|Sugar crops nes,remaining,remaining,remaining,remaining, -370,,,,"2543|Sweeteners, Other",,"2543|Sweeteners, Other","2543|Sweeteners, Other",sugar,sugar, -371,"1182|Honey, natural",1182|Natural honey,"Honey, natural",2745|Honey,"1182|Honey, natural",2745|Honey,2745|Honey,sugar,sugar, -372,,,,"2818|Sugar, Refined Equiv",,"2818|Sugar, Refined Equiv","2818|Sugar, Refined Equiv",sugar,sugar, -373,157|Sugar beet,157|Sugar beet,Sugar beet,2537|Sugar beet,157|Sugar beet,2537|Sugar beet,2537|Sugar beet,sugr_beet,sugr_beet, -374,156|Sugar cane,,Sugar cane,2536|Sugar cane,156|Sugar cane,2536|Sugar cane,2536|Sugar cane,sugr_cane,sugr_cane, -375,267|Sunflower seed,267|Sunflower seed,Sunflower seed,2557|Sunflower seed,267|Sunflower seed,2557|Sunflower seed,2557|Sunflower seed,sunflower,sunflower, -376,15|Wheat,15|Wheat,Wheat,2511|Wheat and products,15|Wheat,2511|Wheat and products,2511|Wheat and products,tece,tece, -377,44|Barley,44|Barley,Barley,2513|Barley and products,44|Barley,2513|Barley and products,2513|Barley and products,tece,tece, -378,71|Rye,71|Rye,Rye,2515|Rye and products,71|Rye,2515|Rye and products,2515|Rye and products,tece,tece, -379,75|Oats,75|Oats,Oats,2516|Oats,75|Oats,2516|Oats,2516|Oats,tece,tece, -380,101|Canary seed,101|Canary seed,Canary seed,"2520|Cereals, Other",101|Canary seed,"2520|Cereals, Other","2520|Cereals, Other",tece,tece, -381,"103|Grain, mixed",103|Mixed grain,"Grain, mixed","2520|Cereals, Other","103|Grain, mixed","2520|Cereals, Other","2520|Cereals, Other",tece,tece, -382,"108|Cereals, nes",,"Cereals, nes","2520|Cereals, Other","108|Cereals, nes","2520|Cereals, Other","2520|Cereals, Other",tece,tece, -383,"108|Cereals, nes",,"Cereals, nes","2520|Cereals, Other",108|Cereals nes,"2520|Cereals, Other","2520|Cereals, Other",tece,tece, -384,68|Popcorn,,Popcorn,"2520|Cereals, Other",68|Popcorn,"2520|Cereals, Other","2520|Cereals, Other",tece,tece, -385,89|Buckwheat,89|Buckwheat,Buckwheat,"2520|Cereals, Other",89|Buckwheat,"2520|Cereals, Other","2520|Cereals, Other",tece,tece, -386,92|Quinoa,92|Quinoa,Quinoa,"2520|Cereals, Other",92|Quinoa,"2520|Cereals, Other","2520|Cereals, Other",tece,tece, -387,94|Fonio,94|Fonio,Fonio,"2520|Cereals, Other",94|Fonio,"2520|Cereals, Other","2520|Cereals, Other",tece,tece, -388,97|Triticale,97|Triticale,Triticale,"2520|Cereals, Other",97|Triticale,"2520|Cereals, Other","2520|Cereals, Other",tece,tece, -389,79|Millet,79|Millet,Millet,2517|Millet and products,79|Millet,2517|Millet and products,2517|Millet and products,trce,trce, -390,83|Sorghum,83|Sorghum,Sorghum,2518|Sorghum and products,83|Sorghum,2518|Sorghum and products,2518|Sorghum and products,trce,trce, -391,,,,"2739|Milk, Skimmed",,,,,, -392,"27|Rice, paddy",27|Rice,"Rice, paddy",2805|Rice (Milled Equivalent),,,,,, -393,"28|Rice, husked",28|Husked rice,"Rice, husked",2805|Rice (Milled Equivalent),,,,,, -394,"29|Rice, milled/husked","29|Rice, milled (husked)","Rice, milled/husked",2805|Rice (Milled Equivalent),,,,,, -395,"31|Rice, milled","31|Rice, milled","Rice, milled",2805|Rice (Milled Equivalent),,,,,, -396,"32|Rice, broken","32|Rice, broken","Rice, broken",2805|Rice (Milled Equivalent),,,,,, -397,"33|Gluten, rice",,"Gluten, rice",2805|Rice (Milled Equivalent),,,,,, -398,"34|Starch, rice",,"Starch, rice",2805|Rice (Milled Equivalent),,,,,, -399,"35|Bran, rice",,"Bran, rice",2805|Rice (Milled Equivalent),,,,,, -400,"38|Flour, rice",38|Flour of rice,"Flour, rice",2805|Rice (Milled Equivalent),,,,,, -401,110|Wafers,"110|Communion wafers, empty cachets of a kind suitable for pharmaceutical use, sealing wafers, rice paper and similar products.",Wafers,2511|Wheat and products,,,,,, -402,114|Mixes and doughs,114|Mixes and doughs for the preparation of bakers' wares,Mixes and doughs,2511|Wheat and products,,,,,, -403,"115|Food preparations, flour, malt extract","115|Food preparations of flour, meal or malt extract","Food preparations, flour, malt extract",2511|Wheat and products,,,,,, -404,"16|Flour, wheat",16|Wheat and meslin flour,"Flour, wheat",2511|Wheat and products,,,,,, -405,"17|Bran, wheat",17|Bran of wheat,"Bran, wheat",2511|Wheat and products,,,,,, -406,18|Macaroni,"18|Uncooked pasta, not stuffed or otherwise prepared",Macaroni,2511|Wheat and products,,,,,, -407,"19|Germ, wheat",,"Germ, wheat",2511|Wheat and products,,,,,, -408,20|Bread,20|Bread,Bread,2511|Wheat and products,,,,,, -409,21|Bulgur,21|Bulgur,Bulgur,2511|Wheat and products,,,,,, -410,22|Pastry,22|Pastry,Pastry,2511|Wheat and products,,,,,, -411,"23|Starch, wheat",,"Starch, wheat",2511|Wheat and products,,,,,, -412,"24|Gluten, wheat",,"Gluten, wheat",2511|Wheat and products,,,,,, -413,"41|Cereals, breakfast",41|Breakfast cereals,"Cereals, breakfast",2511|Wheat and products,,,,,, -414,"45|Barley, pot",,"Barley, pot",2513|Barley and products,,,,,, -415,"46|Barley, pearled","46|Barley, pearled","Barley, pearled",2513|Barley and products,,,,,, -416,"47|Bran, barley",,"Bran, barley",2513|Barley and products,,,,,, -417,"48|Flour, barley and grits",,"Flour, barley and grits",2513|Barley and products,,,,,, -418,49|Malt,"49|Malt, whether or not roasted",Malt,2513|Barley and products,,,,,, -419,50|Malt extract,,Malt extract,2513|Barley and products,,,,,, -420,"57|Germ, maize",57|Germ of maize,"Germ, maize",2514|Maize and products,,,,,, -421,"58|Flour, maize",58|Flour of maize,"Flour, maize",2514|Maize and products,,,,,, -422,"59|Bran, maize",59|Bran of maize,"Bran, maize",2514|Maize and products,,,,,, -423,"63|Gluten, maize",,"Gluten, maize",2514|Maize and products,,,,,, -424,"64|Starch, maize",,"Starch, maize",2514|Maize and products,,,,,, -425,"846|Feed and meal, gluten",846|Gluten feed and meal,"Feed and meal, gluten",2514|Maize and products,,,,,, -426,"72|Flour, rye",,"Flour, rye",2515|Rye and products,,,,,, -427,"73|Bran, rye",,"Bran, rye",2515|Rye and products,,,,,, -428,76|Oats rolled,"76|Oats, rolled",Oats rolled,2516|Oats,,,,,, -429,"77|Bran, oats",,"Bran, oats",2516|Oats,,,,,, -430,"80|Flour, millet",,"Flour, millet",2517|Millet and products,,,,,, -431,"81|Bran, millet",81|Bran of millet,"Bran, millet",2517|Millet and products,,,,,, -432,"84|Flour, sorghum",,"Flour, sorghum",2518|Sorghum and products,,,,,, -433,"85|Bran, sorghum",85|Bran of sorghum,"Bran, sorghum",2518|Sorghum and products,,,,,, -434,"104|Flour, mixed grain",104|Flour of mixed grain,"Flour, mixed grain","2520|Cereals, Other",,,,,, -435,"105|Bran, mixed grains",,"Bran, mixed grains","2520|Cereals, Other",,,,,, -436,"111|Flour, cereals",111|Flour of cereals n.e.c.,"Flour, cereals","2520|Cereals, Other",,,,,, -437,"112|Bran, cereals nes",,"Bran, cereals nes","2520|Cereals, Other",,,,,, -438,"113|Cereal preparations, nes",113|Cereal preparations,"Cereal preparations, nes","2520|Cereals, Other",,,,,, -439,"90|Flour, buckwheat",,"Flour, buckwheat","2520|Cereals, Other",,,,,, -440,"91|Bran, buckwheat",91|Bran of buckwheat,"Bran, buckwheat","2520|Cereals, Other",,,,,, -441,"95|Flour, fonio",,"Flour, fonio","2520|Cereals, Other",,,,,, -442,"96|Bran, fonio",,"Bran, fonio","2520|Cereals, Other",,,,,, -443,"98|Flour, triticale",,"Flour, triticale","2520|Cereals, Other",,,,,, -444,"99|Bran, triticale",,"Bran, triticale","2520|Cereals, Other",,,,,, -445,"117|Flour, potatoes","117|Flour, meal, powder, flakes, granules and pellets of potatoes","Flour, potatoes",2531|Potatoes and products,,,,,, -446,"118|Potatoes, frozen","118|Potatoes, frozen","Potatoes, frozen",2531|Potatoes and products,,,,,, -447,"119|Starch, potatoes",,"Starch, potatoes",2531|Potatoes and products,,,,,, -448,"121|Tapioca, potatoes",,"Tapioca, potatoes",2531|Potatoes and products,,,,,, -449,"126|Flour, cassava",126|Flour of cassava,"Flour, cassava",2532|Cassava and products,,,,,, -450,"127|Tapioca, cassava",,"Tapioca, cassava",2532|Cassava and products,,,,,, -451,128|Cassava dried,"128|Cassava, dry",Cassava dried,2532|Cassava and products,,,,,, -452,"129|Starch, cassava",129|Starch of cassava,"Starch, cassava",2532|Cassava and products,,,,,, -453,"150|Flour, roots and tubers nes",150|Flour of roots and tubers n.e.c.,"Flour, roots and tubers nes","2534|Roots, Other",,,,,, -454,151|Roots and tubers dried,,Roots and tubers dried,"2534|Roots, Other",,,,,, -455,"158|Sugar, cane, raw, centrifugal",,"Sugar, cane, raw, centrifugal",2542|Sugar (Raw Equivalent),,,,,, -456,"159|Sugar, beet, raw, centrifugal",,"Sugar, beet, raw, centrifugal",2542|Sugar (Raw Equivalent),,,,,, -457,164|Sugar refined,164|Refined sugar,Sugar refined,2542|Sugar (Raw Equivalent),,,,,, -458,168|Sugar confectionery,168|Sugar confectionery,Sugar confectionery,2542|Sugar (Raw Equivalent),,,,,, -459,171|Sugar flavoured,,Sugar flavoured,2542|Sugar (Raw Equivalent),,,,,, -460,154|Fructose chemically pure,,Fructose chemically pure,"2543|Sweeteners, Other",,,,,, -461,155|Maltose chemically pure,,Maltose chemically pure,"2543|Sweeteners, Other",,,,,, -462,160|Maple sugar and syrups,"160|Refined cane or beet sugar, in solid form, containing added flavouring or colouring matter; maple sugar and maple syrup",Maple sugar and syrups,"2543|Sweeteners, Other",,,,,, -463,165|Molasses,165|Molasses,Molasses,"2543|Sweeteners, Other",,,,,, -464,"166|Fructose and syrup, other",166|Other fructose and syrup,"Fructose and syrup, other","2543|Sweeteners, Other",,,,,, -465,"167|Sugar, nes",167|Sugar and syrups n.e.c.,"Sugar, nes","2543|Sweeteners, Other",,,,,, -466,172|Glucose and dextrose,172|Glucose and dextrose,Glucose and dextrose,"2543|Sweeteners, Other",,,,,, -467,173|Lactose,173|Lactose,Lactose,"2543|Sweeteners, Other",,,,,, -468,175|Isoglucose,,Isoglucose,"2543|Sweeteners, Other",,,,,, -469,"633|Beverages, non alcoholic",633|Other non-alcoholic caloric beverages,"Beverages, non alcoholic","2543|Sweeteners, Other",,,,,, -470,"212|Flour, pulses",212|Flour of pulses,"Flour, pulses","2549|Pulses, Other and products",,,,,, -471,"213|Bran, pulses",,"Bran, pulses","2549|Pulses, Other and products",,,,,, -472,"229|Brazil nuts, shelled","229|Brazil nuts, shelled","Brazil nuts, shelled",2551|Nuts and products,,,,,, -473,"230|Cashew nuts, shelled","230|Cashew nuts, shelled","Cashew nuts, shelled",2551|Nuts and products,,,,,, -474,231|Almonds shelled,"231|Almonds, shelled",Almonds shelled,2551|Nuts and products,,,,,, -475,"232|Walnuts, shelled","232|Walnuts, shelled","Walnuts, shelled",2551|Nuts and products,,,,,, -476,"233|Hazelnuts, shelled","233|Hazelnuts, shelled","Hazelnuts, shelled",2551|Nuts and products,,,,,, -477,"235|Nuts, prepared (exc groundnuts)",235|Prepared nuts,"Nuts, prepared (exc groundnuts)",2551|Nuts and products,,,,,, -478,239|Soya sauce,239|Soya sauce,Soya sauce,2555|Soyabeans,,,,,, -479,240|Soya paste,240|Soya paste,Soya paste,2555|Soyabeans,,,,,, -480,241|Soya curd,,Soya curd,2555|Soyabeans,,,,,, -481,"242|Groundnuts, with shell",,"Groundnuts, with shell",2556|Groundnuts (Shelled Eq),,,,,, -482,"243|Groundnuts, shelled","243|Groundnuts, shelled","Groundnuts, shelled",2556|Groundnuts (Shelled Eq),,,,groundnut,groundnut, -483,"246|Groundnuts, prepared",246|Prepared groundnuts,"Groundnuts, prepared",2556|Groundnuts (Shelled Eq),,,,groundnut,groundnut, -484,247|Peanut butter,247|Peanut butter,Peanut butter,2556|Groundnuts (Shelled Eq),,,,,, -485,"295|Flour, mustard",295|Flour of mustard seed,"Flour, mustard",2558|Rape and Mustardseed,,,,,, -486,"250|Coconuts, desiccated","250|Coconuts, desiccated","Coconuts, desiccated",2560|Coconuts - Incl Copra,,,,,, -487,251|Copra,251|Copra,Copra,2560|Coconuts - Incl Copra,,,,,, -488,"254|Oil, palm fruit",,"Oil, palm fruit",2562|Palm kernels,"254|Oil, palm fruit",oilpalm,oilpalm,oilpalm,oilpalm, -489,"254|Oil, palm fruit",,"Oil, palm fruit",2562|Palm kernels,254|Oil palm fruit,oilpalm,oilpalm,oilpalm,oilpalm, -490,262|Olives preserved,262|Olives preserved,Olives preserved,2563|Olives (including preserved),,,,,, -491,312|Kapokseed shelled,,Kapokseed shelled,"2570|Oilcrops, Other",,,,,, -492,"343|Flour, oilseeds",,"Flour, oilseeds","2570|Oilcrops, Other",,,,,, -493,"293|Oil, mustard",,"Oil, mustard",2574|Rape and Mustard Oil,,,,,, -494,1276|Fatty acids,1276|Industrial monocarboxylic fatty acids, acid oils from refining,Fatty acids,2577|Palm Oil,,,,, -495,1277|Fatty substance residues,1277|Residues of fatty substances,Fatty substance residues,2577|Palm Oil,,,,,, -496,"274|Oil, olive residues",274|Oil of olive residues,"Oil, olive residues",2580|Olive Oil,,,,,, -497,"1241|Margarine, liquid",1241|Liquid margarine,"Margarine, liquid","2586|Oilcrops Oil, Other",,,,,, -498,"1273|Castor oil, hydrogenated (opal wax)",,"Castor oil, hydrogenated (opal wax)","2586|Oilcrops Oil, Other",,,,,, -499,"1274|Oil, boiled etc","1274|Animal or vegetable fats and oils and their fractions, chemically modified, except those hydrogenated, inter-esterified, re-esterified or elaidinized; inedible mixtures or preparations of animal or vegetable fats or oils","Oil, boiled etc","2586|Oilcrops Oil, Other",,,,,, -500,"1275|Oil, hydrogenated",,"Oil, hydrogenated","2586|Oilcrops Oil, Other",,,,,, -501,264|Butter of karite nuts,264|Butter of karite nuts,Butter of karite nuts,"2586|Oilcrops Oil, Other",,,,,, -502,"266|Oil, castor beans",266|Oil of castor beans,"Oil, castor beans","2586|Oilcrops Oil, Other",,,,,, -503,"276|Oil, tung nuts",,"Oil, tung nuts","2586|Oilcrops Oil, Other",,,,,, -504,"278|Oil, jojoba",,"Oil, jojoba","2586|Oilcrops Oil, Other",,,,,, -505,"297|Oil, poppy",297|Oil of poppy seed,"Oil, poppy","2586|Oilcrops Oil, Other",,,,,, -506,306|Vegetable tallow,306|Vegetable tallow,Vegetable tallow,"2586|Oilcrops Oil, Other",,,,,, -507,"307|Oil, stillingia",,"Oil, stillingia","2586|Oilcrops Oil, Other",,,,,, -508,"313|Oil, kapok",313|Oil of kapok,"Oil, kapok","2586|Oilcrops Oil, Other",,,,,, -509,"337|Oil, hempseed",,"Oil, hempseed","2586|Oilcrops Oil, Other",,,,,, -510,"340|Oil, vegetable origin nes","340|Other oil of vegetable origin, crude n.e.c.","Oil, vegetable origin nes","2586|Oilcrops Oil, Other",,,,,, -511,"664|Cocoa, butter","664|Cocoa butter, fat and oil","Cocoa, butter","2586|Oilcrops Oil, Other",,,,,, -512,"389|Juice, tomato, concentrated",,"Juice, tomato, concentrated",2601|Tomatoes and products,,,,,, -513,"390|Juice, tomato",390|Tomato juice,"Juice, tomato",2601|Tomatoes and products,,,,,, -514,"391|Tomatoes, paste",391|Paste of tomatoes,"Tomatoes, paste",2601|Tomatoes and products,,,,,, -515,"392|Tomatoes, peeled","392|Tomatoes, peeled (o/t vinegar)","Tomatoes, peeled",2601|Tomatoes and products,,,,,, -516,447|Sweet corn frozen,"447|Sweet corn, frozen",Sweet corn frozen,"2605|Vegetables, Other",,,,,, -517,448|Sweet corn prep or preserved,"448|Sweet corn, prepared or preserved",Sweet corn prep or preserved,"2605|Vegetables, Other",,,,,, -518,"450|Mushrooms, dried",,"Mushrooms, dried","2605|Vegetables, Other",,,,,, -519,"451|Mushrooms, canned",451|Canned mushrooms,"Mushrooms, canned","2605|Vegetables, Other",,,,,, -520,"464|Vegetables, dried nes",,"Vegetables, dried nes","2605|Vegetables, Other",,,,,, -521,"465|Vegetables, canned nes","465|Other vegetables and pulses, preserved other than by vinegar, acetic acid or sugar, n.e.c.","Vegetables, canned nes","2605|Vegetables, Other",,,,,, -522,"466|Juice, vegetables nes",,"Juice, vegetables nes","2605|Vegetables, Other",,,,,, -523,"469|Vegetables, dehydrated","469|Vegetables, dehydrated","Vegetables, dehydrated","2605|Vegetables, Other",,,,,, -524,471|Vegetables in vinegar,"471|Vegetables, pulses and potatoes, preserved by vinegar or acetic acid",Vegetables in vinegar,"2605|Vegetables, Other",,,,,, -525,"472|Vegetables, preserved nes",472|Vegetables preserved nes (o/t vinegar),"Vegetables, preserved nes","2605|Vegetables, Other",,,,,, -526,"473|Vegetables, frozen",473|Vegetables frozen,"Vegetables, frozen","2605|Vegetables, Other",,,,,, -527,"474|Vegetables, temporarily preserved",474|Other vegetables provisionally preserved,"Vegetables, temporarily preserved","2605|Vegetables, Other",,,,,, -528,"475|Vegetables, preserved, frozen",475|Vegetables preserved (frozen),"Vegetables, preserved, frozen","2605|Vegetables, Other",,,,,, -529,"476|Vegetables, homogenized preparations",476|Homogenized vegetable preparations,"Vegetables, homogenized preparations","2605|Vegetables, Other",,,,,, -530,"658|Coffee, substitutes containing coffee",658|Coffee substitutes,"Coffee, substitutes containing coffee","2605|Vegetables, Other",,,,,, -531,"491|Juice, orange, single strength",491|Orange juice,"Juice, orange, single strength","2611|Oranges, Mandarines",,,,,, -532,"492|Juice, orange, concentrated","492|Orange juice, concentrated","Juice, orange, concentrated","2611|Oranges, Mandarines",,,,,, -533,"496|Juice, tangerine",,"Juice, tangerine","2611|Oranges, Mandarines",,,,,, -534,"498|Juice, lemon, single strength",498|Juice of lemon,"Juice, lemon, single strength","2612|Lemons, Limes and products",,,,,, -535,"499|Juice, lemon, concentrated","499|Lemon juice, concentrated","Juice, lemon, concentrated","2612|Lemons, Limes and products",,,,,, -536,"509|Juice, grapefruit",509|Grapefruit juice,"Juice, grapefruit",2613|Grapefruit and products,,,,,, -537,"510|Juice, grapefruit, concentrated","510|Grapefruit juice, concentrated","Juice, grapefruit, concentrated",2613|Grapefruit and products,,,,,, -538,"513|Juice, citrus, single strength",513|Juice of citrus fruit n.e.c.,"Juice, citrus, single strength","2614|Citrus, Other",,,,,, -539,"514|Juice, citrus, concentrated","514|Citrus juice, concentrated n.e.c.","Juice, citrus, concentrated","2614|Citrus, Other",,,,,, -540,"518|Juice, apple, single strength",518|Apple juice,"Juice, apple, single strength",2617|Apples and products,,,,,, -541,"519|Juice, apple, concentrated","519|Apple juice, concentrated","Juice, apple, concentrated",2617|Apples and products,,,,,, -542,575|Pineapples canned,"575|Pineapples, otherwise prepared or preserved",Pineapples canned,2618|Pineapples and products,,,,,, -543,"576|Juice, pineapple",576|Pineapple juice,"Juice, pineapple",2618|Pineapples and products,,,,,, -544,"580|Juice, pineapple, concentrated","580|Juice of pineapples, concentrated","Juice, pineapple, concentrated",2618|Pineapples and products,,,,,, -545,561|Raisins,561|Raisins,Raisins,2620|Grapes and products (excl wine),,,,,, -546,"562|Juice, grape",562|Grape juice,"Juice, grape",2620|Grapes and products (excl wine),,,,,, -547,"563|Grapes, must",,"Grapes, must",2620|Grapes and products (excl wine),,,,,, -548,"527|Apricots, dry","527|Apricots, dried","Apricots, dry","2625|Fruits, Other",,,,,, -549,537|Plums dried (prunes),"537|Plums, dried",Plums dried (prunes),"2625|Fruits, Other",,,,,, -550,"538|Juice, plum, single strength",,"Juice, plum, single strength","2625|Fruits, Other",,,,,, -551,"539|Juice, plum, concentrated","539|Juice of plum, concentrated","Juice, plum, concentrated","2625|Fruits, Other",,,,,, -552,570|Figs dried,"570|Figs, dried",Figs dried,"2625|Fruits, Other",,,,,, -553,"583|Juice, mango",,"Juice, mango","2625|Fruits, Other",,,,,, -554,"604|Fruit, tropical dried nes",,"Fruit, tropical dried nes","2625|Fruits, Other",,,,,, -555,"620|Fruit, dried nes","620|Other fruit n.e.c., dried","Fruit, dried nes","2625|Fruits, Other",,,,,, -556,"622|Juice, fruit nes",622|Juice of fruits n.e.c.,"Juice, fruit nes","2625|Fruits, Other",,,,,, -557,"623|Fruit, prepared nes",623|Fruit prepared n.e.c.,"Fruit, prepared nes","2625|Fruits, Other",,,,,, -558,"624|Flour, fruit",,"Flour, fruit","2625|Fruits, Other",,,,,, -559,"625|Fruits, nuts, peel, sugar preserved",,"Fruits, nuts, peel, sugar preserved","2625|Fruits, Other",,,,,, -560,"626|Fruit, cooked, homogenized preparations","626|Homogenized cooked fruit, prepared","Fruit, cooked, homogenized preparations","2625|Fruits, Other",,,,,, -561,"657|Coffee, roasted","657|Coffee, decaffeinated or roasted","Coffee, roasted",2630|Coffee and products,,,,,, -562,"659|Coffee, extracts",659|Coffee extracts,"Coffee, extracts",2630|Coffee and products,,,,,, -563,"662|Cocoa, paste",662|Cocoa paste not defatted,"Cocoa, paste",2633|Cocoa Beans and products,,,,,, -564,"665|Cocoa, powder and cake",665|Cocoa powder and cake,"Cocoa, powder and cake",2633|Cocoa Beans and products,,,,,, -565,666|Chocolate products nes,666|Chocolate products nes,Chocolate products nes,2633|Cocoa Beans and products,,,,,, -566,"672|Tea, mate extracts","672|Extracts, essences and concentrates of tea or mate, and preparations with a basis thereof or with a basis of tea or mate","Tea, mate extracts",2635|Tea (including mate),,,,,, -567,565|Vermouths and similar,565|Vermouth and other wine of fresh grapes flavoured with plats or aromatic substances,Vermouths and similar,2655|Wine,,,,,, -568,,,,"2738|Milk, Whole",,,,,, -569,,,,2741|Cheese,,,,,, -570,,,,2742|Whey,,,,,, -571,"1063|Eggs, liquid","1063|Eggs, liquid","Eggs, liquid",2744|Eggs,,,,,, -572,"1064|Eggs, dried","1064|Eggs, dried","Eggs, dried",2744|Eggs,,,,,, -573,916|Egg albumine,,Egg albumine,2744|Eggs,,,,,, -574,,,,2746|Wool (Clean Eq),,,,,, -575,,,,2747|Silk,,,,,, -576,,,,2748|Hides and skins,,,,,, -577,,,,2749|Meat Meal,,,,,, -578,,,,2815|Roots & Tuber Dry Equiv,,,,,, -579,,,,"2827|Sugar, Raw Equivalent",,,,,, -580,"1023|Milk, skimmed goat",,"Milk, skimmed goat",2848|Milk - Excluding Butter,,,,,, -581,"892|Yoghurt, concentrated or not","892|Yoghurt, with additives","Yoghurt, concentrated or not",2848|Milk - Excluding Butter,,,,,, -582,"893|Buttermilk, curdled, acidified milk","893|Buttermilk, curdled and acidified milk","Buttermilk, curdled, acidified milk",2848|Milk - Excluding Butter,,,,,, -583,"903|Whey, fresh",,"Whey, fresh",2848|Milk - Excluding Butter,,,,,, -584,"905|Whey, cheese",,"Whey, cheese",2848|Milk - Excluding Butter,,,,,, -585,"907|Cheese, processed",907|Processed cheese,"Cheese, processed",2848|Milk - Excluding Butter,,,,,, -586,"908|Milk, reconstituted",,"Milk, reconstituted",2848|Milk - Excluding Butter,,,,,, -587,"909|Milk, products of natural constituents nes",909|Dairy products n.e.c.,"Milk, products of natural constituents nes",2848|Milk - Excluding Butter,,,,,, -588,910|Ice cream and edible ice,910|Ice cream and other edible ice,Ice cream and edible ice,2848|Milk - Excluding Butter,,,,,, -589,917|Casein,,Casein,2848|Milk - Excluding Butter,,,,,, -590,"954|Milk, skimmed buffalo",,"Milk, skimmed buffalo",2848|Milk - Excluding Butter,,,,,, -591,"985|Milk, skimmed sheep",,"Milk, skimmed sheep",2848|Milk - Excluding Butter,,,,,, -592,"1018|Offals, edible, goats","1018|Edible offal of goat, fresh, chilled or frozen","Offals, edible, goats",,,,,,, -593,"1019|Fat, goats",,"Fat, goats",,,,,,, -594,1022|Butter of goat mlk,,Butter of goat mlk,,,,,,, -595,"1036|Offals, pigs, edible","1036|Edible offal of pigs, fresh, chilled or frozen","Offals, pigs, edible",,,,,,, -596,"1037|Fat, pigs",1037|Fat of pigs,"Fat, pigs",,,,,,, -597,"1038|Meat, pork","1038|Meat of pig boneless, fresh or chilled","Meat, pork",,,,,livst_pig,livst_pig, -598,1039|Bacon and ham,"1039|Pig meat, cuts, salted, dried or smoked (bacon and ham)",Bacon and ham,,,,,,, -599,"1040|Fat, pig butcher",,"Fat, pig butcher",,,,,,, -600,"1041|Meat, pig sausages","1041|Sausages and similar products of meat, offal or blood of pig","Meat, pig sausages",,,,,,, -601,"1042|Meat, pig, preparations",1042|Pig meat preparations,"Meat, pig, preparations",,,,,,, -602,"1059|Offals, liver chicken","1059|Edible offals and liver of chickens and guinea fowl, fresh, chilled or frozen","Offals, liver chicken",,,,,,, -603,"1060|Fat, liver prepared (foie gras)",1060|Fatty liver preparations,"Fat, liver prepared (foie gras)",,,,,,, -604,"1061|Meat, chicken, canned",1061|Poultry meat preparations,"Meat, chicken, canned",,,,,,, -605,"1065|Fat, poultry",,"Fat, poultry",,,,,,, -606,"1066|Fat, poultry, rendered",,"Fat, poultry, rendered",,,,,,, -607,"1074|Offals, liver geese","1074|Edible offals and liver of geese, fresh, chilled or frozen","Offals, liver geese",,,,,,, -608,"1075|Offals, liver duck","1075|Edible offals and liver of ducks, fresh, chilled or frozen","Offals, liver duck",,,,,,, -609,"1081|Offals, liver turkeys",,"Offals, liver turkeys",,,,,,, -610,"1098|Offals, horses",,"Offals, horses",,,,,,, -611,"1128|Offals, edible, camels",,"Offals, edible, camels",,,,,,, -612,"1129|Fat, camels",1129|Fat of camels,"Fat, camels",,,,,,, -613,"1159|Offals, other camelids",,"Offals, other camelids",,,,,,, -614,"1160|Fat, other camelids",,"Fat, other camelids",,,,,,, -615,"1164|Meat, dried nes","1164|Other meat and edible meat offal, salted, in brine, dried or smoked; edible flours and meals of meat or meat offal","Meat, dried nes",,,,,,, -616,"1168|Oils, fats of animal nes",1168|Animal oils and fats n.e.c.,"Oils, fats of animal nes",,,,,,, -617,"1172|Meat, nes, preparations",,"Meat, nes, preparations",,,,,,, -618,1221|Lard stearine oil,,Lard stearine oil,,,,,,, -619,1222|Degras,,Degras,,,,,,, -620,"1243|Fat, nes, prepared",1243|Fat preparations n.e.c.,"Fat, nes, prepared",,,,,,, -621,"868|Offals, edible, cattle","868|Edible offal of cattle, fresh, chilled or frozen","Offals, edible, cattle",,,,,,, -622,"869|Fat, cattle","869|Cattle fat, unrendered","Fat, cattle",,,,,,, -623,"870|Meat, cattle, boneless (beef and veal)","870|Meat of cattle boneless, fresh or chilled","Meat, cattle, boneless (beef and veal)",2731|Bovine Meat,"867|Meat, cattle",2731|Bovine Meat,2731|Bovine Meat,livst_rum,livst_rum, -624,"871|Fat, cattle butcher",,"Fat, cattle butcher",,,,,,, -625,"872|Meat, beef, dried, salted, smoked",,"Meat, beef, dried, salted, smoked",,,,,,, -626,"873|Meat, extracts",,"Meat, extracts",,,,,,, -627,"874|Meat, beef and veal sausages","874|Sausages and similar products of meat, offal or blood of beef and veal","Meat, beef and veal sausages",,,,,,, -628,"875|Meat, beef, preparations",875|beef and veal preparations nes,"Meat, beef, preparations",,,,,,, -629,"876|Meat, beef, canned",,"Meat, beef, canned",,,,,,, -630,"877|Meat, homogenized preparations",,"Meat, homogenized preparations",,,,,,, -631,878|Liver prep,,Liver prep,,,,,,, -632,"948|Offals, edible, buffaloes",,"Offals, edible, buffaloes",,,,,,, -633,"949|Fat, buffaloes",,"Fat, buffaloes",,,,,,, -634,"978|Offals, sheep,edible","978|Edible offal of sheep, fresh, chilled or frozen","Offals, sheep,edible",,,,,,, -635,"979|Fat, sheep",,"Fat, sheep",,,,,,, -636,994|Grease incl lanolin wool,994|Wool grease and lanolin,Grease incl lanolin wool,,,,,,, -637,,828|Cigarettes,,,,,,,, -638,,1293|Crude organic material n.e.c.,,,,,,,, -639,,"831|Other manufactured tobacco and manufactured tobacco substitutes; ""homogenized"" or ""reconstituted"" tobacco; tobacco extracts and essences",,,,,,,, -640,,1232|Food preparations n.e.c.,,,,,,,, -641,,238|Cake of soya beans,,,,,,,, -642,,"1169|Other live animals non food, n.e.c.",,,,,,,, -643,,677|Hop cones,,,,,,,, -644,,"767|Cotton lint, ginned",,,,,,,, -645,,826|Unmanufactured tobacco,,,,,,,, -646,,332|Cake of cottonseed,,,,,,,, -647,,652|Vegetable products for feed n.e.c.,,,,,,,, -648,,1057|Chickens,,,,,,,, -649,,"30|Rice, paddy (rice milled equivalent)",,,,,,rice_pro,rice_pro,new bilateral trade matrix product -650,,"843|Dog or cat food, put up for retail sale",,,,,,,, -651,,"1171|Other live animals, n.e.c.",,,,,,,, -652,,"988|Wool, degreased or carbonized, not carded or combed",,,,,,,, -653,,829|Cigars and cheroots,,,,,,,, -654,,"768|Cotton, carded or combed",,,,,,,, -655,,753|Essential oils n.e.c.,,,,,,,, -656,,866|Cattle,,,,,,,, -657,,1150|Other rodents,,,,,,,, -658,,653|Food wastes,,,,,,,, -659,,"999|Raw hides and skins of sheep or lambs, with wool",,,,,,,, -660,,"460|Vegetable products, fresh or dry n.e.c.",,,,,,,, -661,,"920|Hides, wet-salted of cattle",,,,,,,, -662,,1107|Asses,,,,,,,, -663,,"1218|Fine animal hair, n.e.c.",,,,,,,, -664,,836|Natural rubber in primary forms,,,,,,,, -665,,631|Ice and snow,,,,,,,, -666,,1183|Beeswax,,,,,,,, -667,,1181|Bees,,,,,,,, -668,,837|Natural rubber in other forms,,,,,,,, -669,,660|Coffee husks and skins,,,,,,,, -670,,1016|Goats,,,,,,,, -671,,1187|Silk waste,,,,,,,, -672,,769|Cotton waste,,,,,,,, -673,,"651|Other forage products, n.e.c.",,,,,,,, -674,,"628|Pulp, waste of fruit for feed",,,,,,,, -675,,245|Cake of groundnuts,,,,,,,, -676,,"987|Shorn wool, greasy, including fleece-washed shorn wool",,,,,,,, -677,,"754|Pyrethrum, dried flowers",,,,,,,, -678,,"780|Jute, raw or retted",,,,,,,, -679,,845|Compound feed n.e.c.,,,,,,,, -680,,862|Lucerne (alfalfa) meal and pellets,,,,,,,, -681,,1096|Horses,,,,,,,, -682,,335|Cake of linseed,,,,,,,, -683,,"1216|Other raw skins of other animals, preserved",,,,,,,, -684,,169|Beet pulp,,,,,,,, -685,,"1009|Wool, hair waste",,,,,,,, -686,,1034|Swine / pigs,,,,,,,, -687,,"928|Skins, wet-salted of calves",,,,,,,, -688,,253|Cake of copra,,,,,,,, -689,,"821|Other fibre crops, raw, n.e.c.",,,,,,,, -690,,269|Cake of sunflower seed,,,,,,,, -691,,946|Buffalo,,,,,,,, -692,,"809|Abaca, manila hemp, raw",,,,,,,, -693,,1126|Camels,,,,,,,, -694,,"748|Peppermint, spearmint",,,,,,,, -695,,259|Cake of palm kernel,,,,,,,, -696,,"813|Coir, raw",,,,,,,, -697,,"1026|Skins, wet-salted of goats",,,,,,,, -698,,"773|Flax, processed but not spun",,,,,,,, -699,,"771|Flax, raw or retted",,,,,,,, -700,,272|Cake of rapeseed,,,,,,,, -701,,1173|Meat meal,,,,,,,, -702,,1068|Ducks,,,,,,,, -703,,770|Cotton linters,,,,,,,, -704,,"997|Skins, dry-salted of sheep",,,,,,,, -705,,654|Brewing or distilling dregs and waste,,,,,,,, -706,,"1296|Vegetable waxes (other than triglycerides), whether or not refined or coloured",,,,,,,, -707,,976|Sheep,,,,,,,, -708,,"635|Cereal straw, husks, unprepared, ground, pressed, or in the form of pellets",,,,,,,, -709,,850|Feed supplements,,,,,,,, -710,,1186|Raw silk (not thrown),,,,,,,, -711,,737|Oil of citronella,,,,,,,, -712,,1140|Rabbits and hares,,,,,,,, -713,,755|Pyrethrum extract,,,,,,,, -714,,"774|Flax, tow and waste",,,,,,,, -715,,61|Cake of maize,,,,,,,, -716,,1110|Mules and hinnies,,,,,,,, -717,,1079|Turkeys,,,,,,,, -718,,1083|Other birds,,,,,,,, -719,,"996|Skins, wet-salted of sheep",,,,,,,, -720,,1185|Silk-worm cocoons suitable for reeling,,,,,,,, -721,,294|Cake of mustard seed,,,,,,,, -722,,1031|Coarse goat hair,,,,,,,, -723,,37|Cake of rice bran,,,,,,,, -724,,"1104|Hides, dry-salted of horses",,,,,,,, -725,,1157|Other camelids,,,,,,,, -726,,"859|Hay for forage, from other crops n.e.c.",,,,,,,, -727,,855|Feed minerals,,,,,,,, -728,,291|Cake of sesame seed,,,,,,,, -729,,120|Potato offals,,,,,,,, -730,,"858|Hay for forage, from legumes",,,,,,,, -731,,"958|Hides, wet-salted of buffalo",,,,,,,, -732,,919|Raw hides and skins of cattle,,,,,,,, -733,,"959|Hides, dry-salted of buffalo",,,,,,,, -734,,"1213|Other raw skins of other animals, fresh",,,,,,,, -735,,"778|Kapok fibre, raw",,,,,,,, -736,,853|Vitamins,,,,,,,, -737,,338|Cake of hempseed,,,,,,,, -738,,"646|Turnips, for forage",,,,,,,, -739,,"1134|Hides, wet-salted of camels",,,,,,,, -740,,282|Cake of safflowerseed,,,,,,,, -741,,314|Cake of kapok,,,,,,,, -742,,630|Cane tops,,,,,,,, diff --git a/man/calcAttributes.Rd b/man/calcAttributes.Rd deleted file mode 100644 index 6e2cf51c..00000000 --- a/man/calcAttributes.Rd +++ /dev/null @@ -1,29 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calcAttributes.R -\name{calcAttributes} -\alias{calcAttributes} -\title{calcAttributes} -\usage{ -calcAttributes(subtype = "Products") -} -\arguments{ -\item{subtype}{subtype of readProductAttributes function.} -} -\value{ -List of magpie objects with results on global level, empty weight, unit and description. -} -\description{ -provides attributes of different products -} -\examples{ -\dontrun{ -calcOutput("Attributes") -} - -} -\seealso{ -\code{\link[=readProductAttributes]{readProductAttributes()}} -} -\author{ -Benjamin Leon Bodirsky -} diff --git a/man/calcCroparea.Rd b/man/calcCroparea.Rd deleted file mode 100644 index e867d3b5..00000000 --- a/man/calcCroparea.Rd +++ /dev/null @@ -1,46 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calcCroparea.R -\name{calcCroparea} -\alias{calcCroparea} -\title{calcCroparea} -\usage{ -calcCroparea( - sectoral = "kcr", - physical = TRUE, - cellular = FALSE, - cells = "lpjcell", - irrigation = FALSE -) -} -\arguments{ -\item{sectoral}{"area_harvested" returns croparea aggregated to FAO products, -"ProductionItem" unaggregated ProdSTAT items, -"FoodBalanceItem" Food Balance Sheet categories, -"kcr" MAgPIE items, and "lpj" LPJmL items} - -\item{physical}{if TRUE the sum over all crops agrees with the cropland area per country} - -\item{cellular}{if TRUE: calculates cellular MAgPIE crop area for all magpie croptypes. -Crop area from LUH2 crop types (c3ann, c4ann, c3per, c4per, cnfx) -are mapped to MAgpIE crop types using mappingLUH2cropsToMAgPIEcrops.csv. -Harvested areas of FAO weight area within a specific LUH crop type -to divide into MAgPIE crop types.} - -\item{cells}{Switch between "magpiecell" (59199) and "lpjcell" (67420)} - -\item{irrigation}{If true: cellular areas are returned separated -into irrigated and rainfed (see setup in calcLUH2v2)} -} -\value{ -areas of individual crops from FAOSTAT and weight -} -\description{ -Returns harvested areas of individual crops from FAOSTAT. -Total harvested areas can be lower or higher than arable -land because of multicropping or fallow land. -Rice areas are distributed to flooded LUH areas. Additional FAOSTAT -rice areas are distributed based on country shares. -} -\author{ -Ulrich Kreidenweis, Kristine Karstens, Felicitas Beier -} diff --git a/man/calcCropareaLandInG.Rd b/man/calcCropareaLandInG.Rd deleted file mode 100644 index ae57607e..00000000 --- a/man/calcCropareaLandInG.Rd +++ /dev/null @@ -1,52 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calcCropareaLandInG.R -\name{calcCropareaLandInG} -\alias{calcCropareaLandInG} -\title{calcCropareaLandInG} -\usage{ -calcCropareaLandInG( - sectoral = "kcr", - physical = TRUE, - cellular = FALSE, - cells = "magpiecell", - irrigation = FALSE, - selectyears = "all", - lpjml = c(natveg = "LPJmL4_for_MAgPIE_44ac93de", crop = - "ggcmi_phase3_nchecks_bft_e511ac58"), - climatetype = "GSWP3-W5E5:historical" -) -} -\arguments{ -\item{sectoral}{"kcr" MAgPIE items, and "lpj" LPJmL items} - -\item{physical}{if TRUE the sum over all crops plus fallow land (of calcFallowLand) -agrees with the physical cropland of readLandInG(subtype = physical)} - -\item{cellular}{if TRUE: calculates cellular crop area for all magpie croptypes. -Option FALSE is not (yet) available.} - -\item{cells}{Switch between "magpiecell" (59199) and "lpjcell" (67420)} - -\item{irrigation}{If true: cellular areas are returned separated -into irrigated and rainfed} - -\item{selectyears}{extract certain years from the data} - -\item{lpjml}{LPJmL version used to determine multiple cropping suitability} - -\item{climatetype}{Climate scenario or historical baseline "GSWP3-W5E5:historical" -used to determine multiple cropping suitability} -} -\value{ -MAgPIE object with cropareas -} -\description{ -This function uses total physical area and -crop-specific harvested area data from LandInG -to calculate crop-specific physical and harvested -areas considering special rules -for the allocation of perennial and annual crops. -} -\author{ -David Hoetten, Felicitas Beier -} diff --git a/man/calcFAOBilateralTrade.Rd b/man/calcFAOBilateralTrade.Rd deleted file mode 100644 index b381b0fd..00000000 --- a/man/calcFAOBilateralTrade.Rd +++ /dev/null @@ -1,38 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calcFAOBilateralTrade.R -\name{calcFAOBilateralTrade} -\alias{calcFAOBilateralTrade} -\title{calcFAOBilateralTrade} -\usage{ -calcFAOBilateralTrade( - output = "value", - products = "kcr", - prodAgg = TRUE, - fiveYear = TRUE -) -} -\arguments{ -\item{output}{"value", "qty", or "price"} - -\item{products}{"kcr", "kli", or "kothers"} - -\item{prodAgg}{binary to keep FAO product level or magpie} - -\item{fiveYear}{only 5 year steps due to memory load} -} -\value{ -List of magpie objects with results on bilateral country level, -weight on bilateral country level, unit and description. -} -\description{ -Calculates bilateral trade values based on FAO trade matrix -} -\examples{ -\dontrun{ -calcOutput("FAOBilateralTrade", output = "qty", products = "kcr") -} - -} -\author{ -David M Chen -} diff --git a/man/calcFAOLand.Rd b/man/calcFAOLand.Rd deleted file mode 100644 index 8bcdc214..00000000 --- a/man/calcFAOLand.Rd +++ /dev/null @@ -1,17 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calcFAOLand.R -\name{calcFAOLand} -\alias{calcFAOLand} -\title{calcFAOLand} -\usage{ -calcFAOLand() -} -\value{ -land areas from FAOSTAT and weight -} -\description{ -Returns physical land areas from FAOSTAT -} -\author{ -Ulrich Kreidenweis, Kristine Karstens -} diff --git a/man/calcFAOTradePrices.Rd b/man/calcFAOTradePrices.Rd deleted file mode 100644 index 582bb57c..00000000 --- a/man/calcFAOTradePrices.Rd +++ /dev/null @@ -1,29 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calcFAOTradePrices.R -\name{calcFAOTradePrices} -\alias{calcFAOTradePrices} -\title{calcFAOTradePrices} -\usage{ -calcFAOTradePrices(aggregation = "k") -} -\arguments{ -\item{aggregation}{"none", "k", "fbs" or "springmann" -for the last uses Marco Springmann's custom product mapping} -} -\value{ -List of magpie objects with results on country level, -weight on country level, unit and description. -} -\description{ -calculates USD per kg of FAOSTAT Trade data -for import and export prices -} -\examples{ -\dontrun{ -calcOutput("calcFAOTradePrices") -} - -} -\author{ -David M Chen -} diff --git a/man/calcFAOharmonized.Rd b/man/calcFAOharmonized.Rd deleted file mode 100644 index 1562fce1..00000000 --- a/man/calcFAOharmonized.Rd +++ /dev/null @@ -1,23 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calcFAOharmonized.R -\name{calcFAOharmonized} -\alias{calcFAOharmonized} -\title{calcFAOharmonized} -\usage{ -calcFAOharmonized() -} -\value{ -FAO harmonized data, weight as NULL, and a description as as a list of MAgPIE objects -} -\description{ -Calculate harmonized FAO Commodity Balance and Food Supply data based on CB, only harvested areas -are taken from ProdSTAT. This functions adds the CBCrop, CBLive, FSCrop and FSLive data together. -} -\examples{ -\dontrun{ -a <- calcOutput("FAOharmonized") -} -} -\author{ -Ulrich Kreidenweis, David Chen, Kristine Karstens -} diff --git a/man/calcFAOmassbalance_pre.Rd b/man/calcFAOmassbalance_pre.Rd deleted file mode 100644 index 1e2814d2..00000000 --- a/man/calcFAOmassbalance_pre.Rd +++ /dev/null @@ -1,32 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calcFAOmassbalance_pre.R -\name{calcFAOmassbalance_pre} -\alias{calcFAOmassbalance_pre} -\title{calcFAOmassbalance_pre} -\usage{ -calcFAOmassbalance_pre(years = NULL) -} -\arguments{ -\item{years}{years to be estimated, if null, then all years in FAOharmonized are returned} -} -\value{ -List of magpie objects with results on country level, weight on country level, unit and description. -This is an intermediary result, which is used e.g. for estimating the feed baskets. For most uses, it is more -appropriate to use the FAOmasbalance instead of the FAOmassbalance_pre. -} -\description{ -Calculates an extended version of the Food Balance Sheets. Makes explicit the conversion processes that -convert one type of product into another. Includes processes like milling, distilling, extraction etc. Adds certain -byproducts like distillers grains or ethanol. -} -\examples{ -\dontrun{ -calcOutput("FAOmassbalance_pre") -} -} -\seealso{ -\code{\link[=calcFAOmassbalance]{calcFAOmassbalance()}} -} -\author{ -Benjamin Leon Bodirsky -} diff --git a/man/calcFallowLand.Rd b/man/calcFallowLand.Rd deleted file mode 100644 index 55d73de4..00000000 --- a/man/calcFallowLand.Rd +++ /dev/null @@ -1,36 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calcFallowLand.R -\name{calcFallowLand} -\alias{calcFallowLand} -\title{calcFallowLand} -\usage{ -calcFallowLand(cellular = TRUE) -} -\arguments{ -\item{cellular}{TRUE for cellular outputs.} -} -\value{ -MAgPIE object containing fallow land in Mha -} -\description{ -Calculates fallow land on grid cell level, -based on physical cropland extend and harvested area output -of LandInG data. -The formula -"fallow land are = max( physical cropland area - harvested cropland area, 0)" -is used. -Due to multiple cropping, harvested cropland area can be greater than non-fallow land area -and even greater than physical cropland area. -Thus, the results can only be considered a rough estimate of fallow land area. -} -\examples{ -\dontrun{ -calcOutput("FallowLand") -} -} -\seealso{ -\code{\link{readLandInG}} -} -\author{ -David Hoetten, Felicitas Beier -} diff --git a/man/calcFertilizerPricesFAO.Rd b/man/calcFertilizerPricesFAO.Rd deleted file mode 100644 index a4d61c21..00000000 --- a/man/calcFertilizerPricesFAO.Rd +++ /dev/null @@ -1,29 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calcFertilizerPricesFAO.R -\name{calcFertilizerPricesFAO} -\alias{calcFertilizerPricesFAO} -\title{calcFertilizerPricesFAO} -\usage{ -calcFertilizerPricesFAO(subtype = "N", by = "nutrient") -} -\arguments{ -\item{subtype}{"N" for fertilizer containing nitrogen, "P" for fertilizer containing phosphorus} - -\item{by}{"nutrient" if referring to price per amount of nutrients (N or P) within the fertilizer products, or -"product" if referring to price per amount of fertilizer product} -} -\value{ -List of magpie objects with results on country level, weight on country level, unit and description. -} -\description{ -calculates dataset of fertilizer prices in US$MER05/tonne (either referring to the amount of fertilizer -product, or to the amount of nutrients within the fertilizer) based on FAO data -} -\examples{ -\dontrun{ -calcOutput("FertilizerPricesFAO", subtype = "N", by = "nutrient") -} -} -\author{ -Debbora Leip -} diff --git a/man/calcFertilizerUseFAO.Rd b/man/calcFertilizerUseFAO.Rd deleted file mode 100644 index e9bb4a9f..00000000 --- a/man/calcFertilizerUseFAO.Rd +++ /dev/null @@ -1,31 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calcFertilizerUseFAO.R -\name{calcFertilizerUseFAO} -\alias{calcFertilizerUseFAO} -\title{calcFertilizerUseFAO} -\usage{ -calcFertilizerUseFAO(subtype = "N", by = "nutrient") -} -\arguments{ -\item{subtype}{"N" for fertilizer containing nitrogen, "P" for fertilizer containing phosphorus (note that there -is an overlap between those categories, as some fertilizers include both nutrients)} - -\item{by}{"nutrient" if referring to amount of nutrients (N or P) in total used fertilizer, or "product" if -referring to total amount of fertilizer used} -} -\value{ -List of magpie objects with results on country level, weight on country level, unit and description. -} -\description{ -calculates dataset of fertilizer use in tonnes (either referring to the amount of fertilizer products -used, or to the amount of nutrients within the fertilizer used) based on FAO data -} -\examples{ -\dontrun{ -calcOutput("FertilizerUseFAO", subtype = "N", by = "nutrient") -} - -} -\author{ -Debbora Leip -} diff --git a/man/calcForestArea.Rd b/man/calcForestArea.Rd deleted file mode 100644 index 3c275923..00000000 --- a/man/calcForestArea.Rd +++ /dev/null @@ -1,26 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calcForestArea.R -\name{calcForestArea} -\alias{calcForestArea} -\title{calcForestArea} -\usage{ -calcForestArea(selectyears = "past") -} -\arguments{ -\item{selectyears}{defaults to past} -} -\value{ -List of magpie object with results on country level, weight, unit and description. -} -\description{ -Calculates consistent forest area and its subcategories based on FAO_FRA2015 -and LanduseInitialisation data. -} -\examples{ -\dontrun{ -calcOutput("ForestArea") -} -} -\author{ -Kristine Karstens, Jan Philipp Dietrich -} diff --git a/man/calcGrassGPP.Rd b/man/calcGrassGPP.Rd deleted file mode 100644 index fb7db3f8..00000000 --- a/man/calcGrassGPP.Rd +++ /dev/null @@ -1,35 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calcGrassGPP.R -\name{calcGrassGPP} -\alias{calcGrassGPP} -\title{calcGrassGPP} -\usage{ -calcGrassGPP(selectyears, lpjml, climatetype, season) -} -\arguments{ -\item{selectyears}{Years to be returned} - -\item{lpjml}{LPJmL version required for respective inputs: natveg or crop} - -\item{climatetype}{Switch between different climate scenarios or historical baseline "GSWP3-W5E5:historical"} - -\item{season}{"wholeYear": grass GPP in the entire year (main + off season) -"mainSeason": grass GPPP in the crop-specific growing -period of LPJmL (main season)} -} -\value{ -magpie object in cellular resolution -} -\description{ -Calculates gross primary production (GPP) of grassland -under irrigated and rainfed conditions based on LPJmL inputs. -} -\examples{ -\dontrun{ -calcOutput("GrassGPP", aggregate = FALSE) -} - -} -\author{ -Felicitas Beier -} diff --git a/man/calcGrowingPeriodMonths.Rd b/man/calcGrowingPeriodMonths.Rd deleted file mode 100644 index 0394ee4c..00000000 --- a/man/calcGrowingPeriodMonths.Rd +++ /dev/null @@ -1,42 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calcGrowingPeriodMonths.R -\name{calcGrowingPeriodMonths} -\alias{calcGrowingPeriodMonths} -\title{calcGrowingPeriodMonths} -\usage{ -calcGrowingPeriodMonths(selectyears, lpjml, climatetype, minThreshold = 100) -} -\arguments{ -\item{selectyears}{Years to be returned} - -\item{lpjml}{LPJmL version required for respective inputs: natveg or crop} - -\item{climatetype}{Switch between different climate scenarios or -historical baseline "GSWP3-W5E5:historical"} - -\item{minThreshold}{Threshold of monthly grass GPP to be classified as -growing period month -Unit of the threshold is gC/m^2. -Default: 100gC/m^2 -Note: the default value is chosen based on LPJmL version 5 -to reflect multiple cropping suitability as shown in GAEZ-4. -An update of LPJmL5 with regards to grass management may -require an adjustment of the threshold.} -} -\value{ -magpie object in cellular resolution -} -\description{ -Calculates which gridcell-specific months in which -growing conditions are favorable for crop growth -based on monthly grass GPP -} -\examples{ -\dontrun{ -calcOutput("GrowingPeriodMonths", aggregate = FALSE) -} - -} -\author{ -Felicitas Beier, Jens Heinke -} diff --git a/man/calcLPJmLClimateInput.Rd b/man/calcLPJmLClimateInput.Rd deleted file mode 100644 index 5abb7e2e..00000000 --- a/man/calcLPJmLClimateInput.Rd +++ /dev/null @@ -1,42 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calcLPJmLClimateInput.R -\name{calcLPJmLClimateInput} -\alias{calcLPJmLClimateInput} -\title{calcLPJmLClimateInput} -\usage{ -calcLPJmLClimateInput( - climatetype = "MRI-ESM2-0:ssp370", - variable = "temperature:annualMean", - stage = "harmonized2020", - lpjmlVersion = "LPJmL4_for_MAgPIE_44ac93de" -) -} -\arguments{ -\item{climatetype}{Switch between different climate scenario} - -\item{variable}{Switch between different climate inputs and temporal resolution} - -\item{stage}{Degree of processing: raw, smoothed - raw or smoothed data from 1930|1951 -raw1901, smoothed1901 - raw or smoothed data from 1901 -harmonized, harmonized2020 - based on toolLPJmLVersion} - -\item{lpjmlVersion}{LPJmL Version hand over} -} -\value{ -magpie object in cellular resolution -} -\description{ -Handle LPJmL climate input data and its time behaviour -(smoothing and harmonizing to baseline) -} -\examples{ -\dontrun{ -calcOutput("LPJmLClimateInput", - climatetype = "MRI-ESM2-0:ssp370", - variable = "temperature:annualMean") -} - -} -\author{ -Marcos Alves, Kristine Karstens, Felicitas Beier -} diff --git a/man/calcLPJmL_new.Rd b/man/calcLPJmL_new.Rd deleted file mode 100644 index 319eb881..00000000 --- a/man/calcLPJmL_new.Rd +++ /dev/null @@ -1,45 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calcLPJmL_new.R -\name{calcLPJmL_new} -\alias{calcLPJmL_new} -\title{calcLPJmL_new} -\usage{ -calcLPJmL_new( - version = "LPJmL4_for_MAgPIE_44ac93de", - climatetype = "MRI-ESM2-0:ssp370", - subtype = "soilc", - subdata = NULL, - stage = "harmonized2020" -) -} -\arguments{ -\item{version}{Switch between LPJmL versions (including addons for further version specification)} - -\item{climatetype}{Switch between different climate scenarios} - -\item{subtype}{Switch between different lpjml input as specified in readLPJmL} - -\item{subdata}{Switch between data dimension subitems} - -\item{stage}{Degree of processing: raw, smoothed - raw or smoothed data from 1930|1951 -raw1901, smoothed1901 - raw or smoothed data from 1901 -harmonized, harmonized2020 - based on toolLPJmLVersion} -} -\value{ -List of magpie objects with results on cellular level, weight, unit and description. -} -\description{ -Handle LPJmL data and its time behaviour (smoothing and harmonizing to baseline) -} -\examples{ -\dontrun{ -calcOutput("LPJmL_new", subtype = "soilc", aggregate = FALSE) -} - -} -\seealso{ -\code{\link[=readLPJmL]{readLPJmL()}} -} -\author{ -Kristine Karstens, Felicitas Beier -} diff --git a/man/calcLUH2MAgPIE.Rd b/man/calcLUH2MAgPIE.Rd deleted file mode 100644 index 9b1c9f2c..00000000 --- a/man/calcLUH2MAgPIE.Rd +++ /dev/null @@ -1,44 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calcLUH2MAgPIE.R -\name{calcLUH2MAgPIE} -\alias{calcLUH2MAgPIE} -\title{calcLUH2MAgPIE} -\usage{ -calcLUH2MAgPIE( - share = "total", - bioenergy = "ignore", - rice = "non_flooded", - selectyears = "past", - missing = "ignore" -) -} -\arguments{ -\item{share}{total (for total numbers), LUHofMAG (for share of LUH within kcr types), -MAGofLUH (for share of kcr within LUH types)} - -\item{bioenergy}{"ignore": 0 for share and totals, -"fix": fixes betr and begr shares in LUHofMAG to 1 for c3per and c4per} - -\item{rice}{rice category: "non_flooded" or "total"} - -\item{selectyears}{years to be returned (default: "past")} - -\item{missing}{"ignore" will leave data as is, -"fill" will add proxy values for data gaps of FAO} -} -\value{ -List of magpie objects with results on country level, weight on country level, unit and description -} -\description{ -Calculates the real aggregation of LUH croptypes to MAgPIE croptypes -out of LUH2FAO and FAO2MAgPIE mappings -} -\examples{ -\dontrun{ -calcOutput("LUH2MAgPIE") -} - -} -\author{ -Kristine Karstens, Felicitas Beier -} diff --git a/man/calcLUH2v2.Rd b/man/calcLUH2v2.Rd deleted file mode 100644 index 2786e27b..00000000 --- a/man/calcLUH2v2.Rd +++ /dev/null @@ -1,47 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calcLUH2v2.R -\name{calcLUH2v2} -\alias{calcLUH2v2} -\title{calcLUH2v2} -\usage{ -calcLUH2v2( - landuse_types = "magpie", - irrigation = FALSE, - cellular = FALSE, - cells = "lpjcell", - selectyears = "past" -) -} -\arguments{ -\item{landuse_types}{magpie: magpie landuse classes, -LUH2v2: original landuse classes -flooded: flooded areas as reported by LUH} - -\item{irrigation}{if true: areas are returned separated by irrigated and rainfed, -if false: total areas} - -\item{cellular}{if true: dataset is returned on 0.5 degree resolution} - -\item{cells}{Switch between "magpiecell" (59199) and "lpjcell" (67420) -NOTE: This setting also affects the sums on country level!} - -\item{selectyears}{years to be returned (default: "past")} -} -\value{ -List of magpie objects with results on country level, -weight on country level, unit and description -} -\description{ -Integrates the LUH2v2 landuse-dataset -} -\examples{ -\dontrun{ -calcOutput("LUH2v2") -} -} -\seealso{ -\code{\link[=calcLanduseInitialisation]{calcLanduseInitialisation()}} -} -\author{ -Benjamin Leon Bodirsky, Florian Humpenoeder, Jens Heinke, Felicitas Beier -} diff --git a/man/calcLanduseInitialisation.Rd b/man/calcLanduseInitialisation.Rd deleted file mode 100644 index 88b8621b..00000000 --- a/man/calcLanduseInitialisation.Rd +++ /dev/null @@ -1,52 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calcLanduseInitialisation.R -\name{calcLanduseInitialisation} -\alias{calcLanduseInitialisation} -\title{calcLanduseInitialisation} -\usage{ -calcLanduseInitialisation( - cellular = FALSE, - nclasses = "seven", - cells = "lpjcell", - selectyears = "past", - input_magpie = FALSE -) -} -\arguments{ -\item{cellular}{cellular (TRUE) or country-level/regional (FALSE) data? -For country-level vs regional data: remember to set "aggregate" to FALSE.} - -\item{nclasses}{options are either "six", "seven" or "nine". -\itemize{ -\item "six" includes the original land use classes "crop", "past", "forestry", "forest", "urban" and "other" -\item "seven" separates primary and secondary forest and includes "crop", "past", "forestry", "primforest", -"secdforest", "urban" and "other" -\item "nine" adds the separation of pasture and rangelands, as well as a differentiation of primary -and secondary non-forest vegetation and therefore returns "crop", "past", "range", "forestry", "primforest", -"secdforest", "urban", "primother" and "secdother" -}} - -\item{cells}{if cellular is TRUE: "magpiecell" for 59199 cells or "lpjcell" for 67420 cells} - -\item{selectyears}{default on "past"} - -\item{input_magpie}{applies area fix (set cells with zero area to minimal value to -not disturb aggregating to clusters)} -} -\value{ -List of magpie object with results on country or cellular level, weight on cellular level, -unit and description. -} -\description{ -Calculates the cellular MAgPIE landuse initialisation area. -Data from FAO on forestry is used to split the secondary forest pool -of the LU2v2 dataset into forestry and secd_forest. -} -\examples{ -\dontrun{ -calcOutput("LanduseInitialisation") -} -} -\author{ -Jan Philipp Dietrich, Benjamin Leon Bodirsky, Kristine Karstens, Felcitas Beier, Patrick v. Jeetze -} diff --git a/man/calcLanduseInitialisationBase.Rd b/man/calcLanduseInitialisationBase.Rd deleted file mode 100644 index 40e4f614..00000000 --- a/man/calcLanduseInitialisationBase.Rd +++ /dev/null @@ -1,30 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calcLanduseInitialisationBase.R -\name{calcLanduseInitialisationBase} -\alias{calcLanduseInitialisationBase} -\title{calcLanduseInitialisationBase} -\usage{ -calcLanduseInitialisationBase(cells = "lpjcell", selectyears = "past") -} -\arguments{ -\item{cells}{"magpiecell" for 59199 cells or "lpjcell" for 67420 cells} - -\item{selectyears}{Years to be computed (default on "past")} -} -\value{ -Cellular landuse initialisation in its base configuration -} -\description{ -Calculates the cellular MAgPIE landuse initialisation area. Data from FAO on forestry is used -to split the secondary forest pool of the LU2v2 dataset into forestry and secd_forest. This function -returns the data set in a basic configuration. Use \code{\link{calcLanduseInitialisation}} for -more settings. -} -\examples{ -\dontrun{ -calcOutput("LanduseInitialisationBase") -} -} -\author{ -Jan Philipp Dietrich, Benjamin Leon Bodirsky, Kristine Karstens, Felcitas Beier, Patrick v. Jeetze -} diff --git a/man/calcMulticropping.Rd b/man/calcMulticropping.Rd deleted file mode 100644 index 658a1394..00000000 --- a/man/calcMulticropping.Rd +++ /dev/null @@ -1,39 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calcMulticropping.R -\name{calcMulticropping} -\alias{calcMulticropping} -\title{calcMulticropping} -\usage{ -calcMulticropping(extend_future = FALSE, factortype = "CI") -} -\arguments{ -\item{extend_future}{if TRUE} - -\item{factortype}{CI: cropping intensity factor calculated as ratio of -harvested to physical area where values above one -indicate multicropping, below one fallow land (default) -MC: multiple cropping factor indicating areas that are -harvested more than once in one year calculated taking -fallow land into account explicitly: -harvestedArea / (physicalArea - fallowLand)} -} -\value{ -List of magpie objects with results on country level, weight on country level, unit and description. -} -\description{ -calculates a multiple cropping factor based on area harvested, -physical cropland area (and optionally fallow land). -} -\examples{ -\dontrun{ -calcOutput("Multicropping") -} - -} -\seealso{ -\code{\link[=calcFAOLand]{calcFAOLand()}}, -\code{\link[=calcCroparea]{calcCroparea()}} -} -\author{ -Benjamin Leon Bodirsky, David Chen, Felicitas Beier -} diff --git a/man/calcMulticroppingSuitability.Rd b/man/calcMulticroppingSuitability.Rd deleted file mode 100644 index 8db44c73..00000000 --- a/man/calcMulticroppingSuitability.Rd +++ /dev/null @@ -1,47 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calcMulticroppingSuitability.R -\name{calcMulticroppingSuitability} -\alias{calcMulticroppingSuitability} -\title{calcMulticroppingSuitability} -\usage{ -calcMulticroppingSuitability( - selectyears, - lpjml, - climatetype, - suitability = "endogenous", - sectoral = "kcr" -) -} -\arguments{ -\item{selectyears}{Years to be returned} - -\item{lpjml}{LPJmL version required for respective inputs: natveg or crop} - -\item{climatetype}{Switch between different climate scenarios or -historical baseline "GSWP3-W5E5:historical"} - -\item{suitability}{"endogenous": suitability for multiple cropping determined -by rules based on grass and crop productivity -"exogenous": suitability for multiple cropping given by -GAEZ data set} - -\item{sectoral}{"kcr" MAgPIE crops, and "lpj" LPJmL crops} -} -\value{ -magpie object in cellular resolution -} -\description{ -Calculates which grid cells are potentially suitable for -multiple cropping activities under rainfed and irrigated conditions. -Calculation is based on the length of the growing period determined by -monthly grassland gross primary production (GPP). -} -\examples{ -\dontrun{ -calcOutput("MulticroppingSuitability", aggregate = FALSE) -} - -} -\author{ -Felicitas Beier, Jens Heinke -} diff --git a/man/calcMultipleCroppingZones.Rd b/man/calcMultipleCroppingZones.Rd deleted file mode 100644 index 631182bf..00000000 --- a/man/calcMultipleCroppingZones.Rd +++ /dev/null @@ -1,31 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calcMultipleCroppingZones.R -\name{calcMultipleCroppingZones} -\alias{calcMultipleCroppingZones} -\title{\if{html}{\out{
}}\preformatted{ calcMultipleCroppingZones -}\if{html}{\out{
}}} -\usage{ -calcMultipleCroppingZones(layers = 2) -} -\arguments{ -\item{layers}{8 for original GAEZ layers, -3 for aggregated multiple cropping zones with -1 = single cropping, 2 = double cropping, 3 = triple cropping -2 for aggregated boolean multicropping potential with -0 = no multicropping (single cropping), 1 = multiple cropping} -} -\value{ -magpie object in cellular resolution -} -\description{ -This function returns multiple cropping zones at 0.5 degree resolution -} -\examples{ -\dontrun{ -calcOutput("MultipleCroppingZones", layers = 3, aggregate = FALSE) -} - -} -\author{ -Felicitas Beier -} diff --git a/man/calcRicearea.Rd b/man/calcRicearea.Rd deleted file mode 100644 index a8b97475..00000000 --- a/man/calcRicearea.Rd +++ /dev/null @@ -1,26 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calcRicearea.R -\name{calcRicearea} -\alias{calcRicearea} -\title{calcRicearea} -\usage{ -calcRicearea(cellular = FALSE, cells = "lpjcell", share = TRUE) -} -\arguments{ -\item{cellular}{If TRUE: calculates cellular rice area} - -\item{cells}{Switch between "magpiecell" (59199) and "lpjcell" (67420)} - -\item{share}{If TRUE: non-flooded share is returned. -If FALSE: rice area (flooded, non-flooded, total) in Mha is returned} -} -\value{ -rice areas or rice area shares of flooded and non-flooded category -} -\description{ -calculates rice area based on LUH flooded areas and -physical rice areas reported by FAOSTAT. -} -\author{ -Felicitas Beier, Kristine Karstens -} diff --git a/man/convertFAO.Rd b/man/convertFAO.Rd deleted file mode 100644 index 926a996a..00000000 --- a/man/convertFAO.Rd +++ /dev/null @@ -1,38 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/convertFAO.R -\name{convertFAO} -\alias{convertFAO} -\title{Convert FAO data} -\usage{ -convertFAO(x, subtype) -} -\arguments{ -\item{x}{MAgPIE object containing original values} - -\item{subtype}{The FAO file type, e.g.: CBCrop} -} -\value{ -Data as MAgPIE object with common country list -} -\description{ -Converts FAO data to fit to the common country list and removes or converts -relative values where possible. Yields (Hg/ha) are for instance removed -since they can later easily be calculated from production and area but might -be problematic in the spatial aggregation. Per capita demand values are -transformed into absolute values using population estimates from the -calcPopulationPast function. -} -\details{ -Update 23-Jan-2017 - Added FAO Forestry production and trade data (Abhi) -} -\examples{ -\dontrun{ -a <- readSource("FAO", "Crop", convert = TRUE) -} -} -\seealso{ -\code{\link[=readFAO]{readFAO()}}, \code{\link[=readSource]{readSource()}}, -} -\author{ -Ulrich Kreidenweis, Abhijeet Mishra, Mishko Stevanovic -} diff --git a/man/convertFAOTradeMatrix.Rd b/man/convertFAOTradeMatrix.Rd deleted file mode 100644 index 5f1a9f3e..00000000 --- a/man/convertFAOTradeMatrix.Rd +++ /dev/null @@ -1,33 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/convertFAOTradeMatrix.R -\name{convertFAOTradeMatrix} -\alias{convertFAOTradeMatrix} -\title{Convert FAOTradeMatrix} -\usage{ -convertFAOTradeMatrix(x, subtype) -} -\arguments{ -\item{x}{output from read function} - -\item{subtype}{subsets of the detailed trade matrix to read in. Very large csv needs to be read in chunks -separated by export/import quantities and values, as well as kcr, kli and kothers (not in kcr nor kli) -Options are all combinations of c("import_value", -"import_qty", "export_value", "export_quantity") X c("kcr", "kli", "kothers")) -import is import side reporting while export is export-sde reporting} -} -\value{ -FAO data as MAgPIE object -} -\description{ -Convert FAOSTAT detailed trade matrix. -FAOSTAT does not balance or harmonize the import/export side reporting. -Furthermore, in terms of trade value, exporters are "usuallY" reporting FOB, -while importers report CIF. Difference in value, -given identical qty, is thus the transport margin mixed with unharmonized reporting. -} -\seealso{ -\code{\link[=readSource]{readSource()}} -} -\author{ -David C -} diff --git a/man/convertFAO_FRA2015.Rd b/man/convertFAO_FRA2015.Rd deleted file mode 100644 index a937823c..00000000 --- a/man/convertFAO_FRA2015.Rd +++ /dev/null @@ -1,32 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/convertFAO_FRA2015.R -\name{convertFAO_FRA2015} -\alias{convertFAO_FRA2015} -\title{Convert FRA 2015 data -Update dd-Jmm-jjjj - Please add comment if changes made here (Abhi)} -\usage{ -convertFAO_FRA2015(x, subtype) -} -\arguments{ -\item{x}{MAgPIE object containing original values} - -\item{subtype}{The FAO FRA 2015 file type, e.g.: fac, production, biodiversity or anndat.} -} -\value{ -Data as MAgPIE object with common country list -} -\description{ -Convert FRA 2015 data -Update dd-Jmm-jjjj - Please add comment if changes made here (Abhi) -} -\examples{ -\dontrun{ -a <- readSource("FRA2015", "production", convert = TRUE) -} -} -\seealso{ -\code{\link[=readSource]{readSource()}}, -} -\author{ -Abhijeet Mishra -} diff --git a/man/convertFAO_online.Rd b/man/convertFAO_online.Rd deleted file mode 100644 index ccc5360f..00000000 --- a/man/convertFAO_online.Rd +++ /dev/null @@ -1,38 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/convertFAO_online.R -\name{convertFAO_online} -\alias{convertFAO_online} -\title{Convert FAO data} -\usage{ -convertFAO_online(x, subtype) -} -\arguments{ -\item{x}{MAgPIE object containing original values} - -\item{subtype}{The FAO file type, e.g.: CBCrop} -} -\value{ -Data as MAgPIE object with common country list -} -\description{ -Converts FAO data to fit to the common country list and removes or converts -relative values where possible. Yields (Hg/ha) are for instance removed -since they can later easily be calculated from production and area but might -be problematic in the spatial aggregation. Per capita demand values are -transformed into absolute values using population estimates from the -calcPopulationPast function. -} -\details{ -Update 23-Jan-2017 - Added FAO Forestry production and trade data (Abhi) -} -\examples{ -\dontrun{ -a <- readSource("FAO_online", "Crop", convert = TRUE) -} -} -\seealso{ -\code{\link[=readFAO]{readFAO()}}, \code{\link[=readSource]{readSource()}}, -} -\author{ -Ulrich Kreidenweis, Abhijeet Mishra, Mishko Stevanovic, David Klein, Daivd Chen, Edna Molina Bacca -} diff --git a/man/convertFRA2020.Rd b/man/convertFRA2020.Rd deleted file mode 100644 index b37aae11..00000000 --- a/man/convertFRA2020.Rd +++ /dev/null @@ -1,30 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/convertFRA2020.R -\name{convertFRA2020} -\alias{convertFRA2020} -\title{Convert FRA 2020 data} -\usage{ -convertFRA2020(x, subtype) -} -\arguments{ -\item{x}{MAgPIE object containing original values} - -\item{subtype}{The FAO FRA 2020 subtype.} -} -\value{ -Data as MAgPIE object with common country list -} -\description{ -Convert FRA 2020 data -} -\examples{ -\dontrun{ -a <- readSource("FRA2020", "growing_stock", convert = TRUE) -} -} -\seealso{ -\code{\link[=readSource]{readSource()}}, -} -\author{ -Abhijeet Mishra -} diff --git a/man/convertIEA_EEI.Rd b/man/convertIEA_EEI.Rd new file mode 100644 index 00000000..43cce7e1 --- /dev/null +++ b/man/convertIEA_EEI.Rd @@ -0,0 +1,17 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/convertIEA_EEI.R +\name{convertIEA_EEI} +\alias{convertIEA_EEI} +\title{Convert IEA End Uses and Efficiency Indicators data to data on ISO country level.} +\usage{ +convertIEA_EEI(x) +} +\arguments{ +\item{x}{MAgPIE object to be converted} +} +\description{ +Convert IEA End Uses and Efficiency Indicators data to data on ISO country level. +} +\author{ +Falk Benke +} diff --git a/man/convertLPJmL.Rd b/man/convertLPJmL.Rd deleted file mode 100644 index ee6c78fa..00000000 --- a/man/convertLPJmL.Rd +++ /dev/null @@ -1,29 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/convertLPJmL.R -\name{convertLPJmL} -\alias{convertLPJmL} -\title{convertLPJmL} -\usage{ -convertLPJmL(x) -} -\arguments{ -\item{x}{magpie object provided by the read function} -} -\value{ -List of magpie objects with results on cellular level, weight, unit and description. -} -\description{ -Convert LPJmL content -} -\examples{ -\dontrun{ -readSource("LPJmL", subtype = "soilc", convert = TRUE) -} - -} -\seealso{ -\code{\link[=readLPJmL]{readLPJmL()}} -} -\author{ -Kristine Karstens -} diff --git a/man/correctFAO.Rd b/man/correctFAO.Rd deleted file mode 100644 index 5eb0cef6..00000000 --- a/man/correctFAO.Rd +++ /dev/null @@ -1,31 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/correctFAO.R -\name{correctFAO} -\alias{correctFAO} -\title{correctFAO} -\usage{ -correctFAO(x, subtype) -} -\arguments{ -\item{x}{MAgPIE object containing original values} - -\item{subtype}{The FAO file type, e.g.: CBCrop} -} -\value{ -Data as MAgPIE object -} -\description{ -Corrects FAO data for known mismatches or insufficiencies -} -\examples{ -\dontrun{ -a <- readSource("FAO", "Crop", convert = TRUE) -} - -} -\seealso{ -\code{\link[=readFAO]{readFAO()}}, \code{\link[=readSource]{readSource()}}, -} -\author{ -Kristine Karstens -} diff --git a/man/correctFAO_online.Rd b/man/correctFAO_online.Rd deleted file mode 100644 index 5af2d487..00000000 --- a/man/correctFAO_online.Rd +++ /dev/null @@ -1,31 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/correctFAO_online.R -\name{correctFAO_online} -\alias{correctFAO_online} -\title{correctFAO_online} -\usage{ -correctFAO_online(x, subtype) -} -\arguments{ -\item{x}{MAgPIE object containing original values} - -\item{subtype}{The FAO file type, e.g.: CBCrop} -} -\value{ -Data as MAgPIE object -} -\description{ -Corrects FAO data for known mismatches or insufficiencies -} -\examples{ -\dontrun{ -a <- readSource("FAO_online", "Crop", convert = TRUE) -} - -} -\seealso{ -\code{\link[=readFAO]{readFAO()}}, \code{\link[=readSource]{readSource()}}, -} -\author{ -Kristine Karstens -} diff --git a/man/correctGAEZv4.Rd b/man/correctGAEZv4.Rd deleted file mode 100644 index 31440cf8..00000000 --- a/man/correctGAEZv4.Rd +++ /dev/null @@ -1,26 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/correctGAEZv4.R -\name{correctGAEZv4} -\alias{correctGAEZv4} -\title{correctGAEZv4} -\usage{ -correctGAEZv4(x) -} -\arguments{ -\item{x}{MAgPIE object provided by readGAEZv4 function} -} -\value{ -MAgPIE object at 0.5 cellular level -} -\description{ -Correct Global Agro-ecological Zones (GAEZ) data -} -\examples{ -\dontrun{ -readSource("GAEZv4", convert = "onlycorrect") -} - -} -\author{ -Felicitas Beier -} diff --git a/man/correctLPJmL.Rd b/man/correctLPJmL.Rd deleted file mode 100644 index a821b9f4..00000000 --- a/man/correctLPJmL.Rd +++ /dev/null @@ -1,29 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/correctLPJmL.R -\name{correctLPJmL} -\alias{correctLPJmL} -\title{correctLPJmL} -\usage{ -correctLPJmL(x) -} -\arguments{ -\item{x}{magpie object provided by the read function} -} -\value{ -List of magpie objects with results on cellular level, weight, unit and description. -} -\description{ -Correct LPJmL content -} -\examples{ -\dontrun{ -readSource("LPJmL", subtype = "soilc", convert = "onlycorrect") -} - -} -\seealso{ -\code{\link[=correctLPJmL]{correctLPJmL()}} -} -\author{ -Kristine Karstens, Felicitas Beier -} diff --git a/man/correctLPJmLClimateInput.Rd b/man/correctLPJmLClimateInput.Rd deleted file mode 100644 index 2dada20f..00000000 --- a/man/correctLPJmLClimateInput.Rd +++ /dev/null @@ -1,30 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/correctLPJmLClimateInput.R -\name{correctLPJmLClimateInput} -\alias{correctLPJmLClimateInput} -\title{correctLPJmLClimateInput} -\usage{ -correctLPJmLClimateInput(x) -} -\arguments{ -\item{x}{magpie object provided by the read function} -} -\value{ -Magpie objects with results on cellular level, weight, unit and description. -} -\description{ -Correct LPJmL climate input variables -} -\examples{ - -\dontrun{ -readSource("LPJmLClimateInput", subtype, convert="onlycorrect") -} - -} -\seealso{ -\code{\link{readLPJmLClimateInput}} -} -\author{ -Marcos Alves, Felicitas Beier -} diff --git a/man/correctLPJmLInputs.Rd b/man/correctLPJmLInputs.Rd deleted file mode 100644 index 79cac1ca..00000000 --- a/man/correctLPJmLInputs.Rd +++ /dev/null @@ -1,24 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/correctLPJmLInputs.R -\name{correctLPJmLInputs} -\alias{correctLPJmLInputs} -\title{\if{html}{\out{
}}\preformatted{ correctLPJmLInputs -}\if{html}{\out{
}}} -\usage{ -correctLPJmLInputs(x) -} -\arguments{ -\item{x}{magpie object provided by the read function} -} -\description{ -correct LPJmLInputs content (dummy function) -} -\examples{ -\dontrun{ -readSource("LPJmLInputs", convert = "onlycorrect") -} - -} -\author{ -Felicitas Beier -} diff --git a/man/correctLPJmL_new.Rd b/man/correctLPJmL_new.Rd deleted file mode 100644 index f083292b..00000000 --- a/man/correctLPJmL_new.Rd +++ /dev/null @@ -1,26 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/correctLPJmL_new.R -\name{correctLPJmL_new} -\alias{correctLPJmL_new} -\title{correctLPJmL_new} -\usage{ -correctLPJmL_new(x) -} -\arguments{ -\item{x}{magpie object provided by the read function} -} -\description{ -Convert LPJmL content (dummy function) -} -\examples{ -\dontrun{ -readSource("LPJmL", convert = "onlycorrect") -} - -} -\seealso{ -\code{\link[=readLPJmL]{readLPJmL()}} -} -\author{ -Kristine Karstens -} diff --git a/man/correctLUH2v2.Rd b/man/correctLUH2v2.Rd deleted file mode 100644 index 7b76ed2e..00000000 --- a/man/correctLUH2v2.Rd +++ /dev/null @@ -1,23 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/correctLUH2v2.R -\name{correctLUH2v2} -\alias{correctLUH2v2} -\title{correctLUH2v2} -\usage{ -correctLUH2v2(x, subtype) -} -\arguments{ -\item{x}{magpie object provided by the read function} - -\item{subtype}{switch between different inputs} -} -\value{ -List of magpie object with results on cellular level -} -\description{ -Correct LUH2v2 content -} -\author{ -Florian Humpenoeder, Stephen Wirth, Kristine Karstens, Felicitas Beier, Jan Philipp Dietrich, -Edna J. Molina Bacca -} diff --git a/man/correctLandInG.Rd b/man/correctLandInG.Rd deleted file mode 100644 index 88e6289b..00000000 --- a/man/correctLandInG.Rd +++ /dev/null @@ -1,29 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/correctLandInG.R -\name{correctLandInG} -\alias{correctLandInG} -\title{correctLandInG} -\usage{ -correctLandInG(x) -} -\arguments{ -\item{x}{magpie object provided by the read function} -} -\value{ -corrected magpie object -} -\description{ -correct LandInG data. Convert unit from ha to mio ha -} -\examples{ -\dontrun{ -a <- readSource("LandInG", convert = "onlycorrect") -} - -} -\seealso{ -\code{\link{readLandInG}} -} -\author{ -David Hoetten, Felicitas Beier -} diff --git a/man/downloadFAO_online.Rd b/man/downloadFAO_online.Rd deleted file mode 100644 index 5e92f277..00000000 --- a/man/downloadFAO_online.Rd +++ /dev/null @@ -1,14 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/downloadFAO_online.R -\name{downloadFAO_online} -\alias{downloadFAO_online} -\title{Download FAO data} -\usage{ -downloadFAO_online(subtype) -} -\arguments{ -\item{subtype}{Type of FAO data that should be read.} -} -\description{ -Downloads the latest data and meta data form the FAOStat website. -} diff --git a/man/downloadLPJmLClimateInput.Rd b/man/downloadLPJmLClimateInput.Rd deleted file mode 100644 index 3039bd15..00000000 --- a/man/downloadLPJmLClimateInput.Rd +++ /dev/null @@ -1,28 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/downloadLPJmLClimateInput.R -\name{downloadLPJmLClimateInput} -\alias{downloadLPJmLClimateInput} -\title{downloadLPJmLClimateInput} -\usage{ -downloadLPJmLClimateInput(subtype = "ISIMIP3bv2:MRI-ESM2-0:ssp370:temperature") -} -\arguments{ -\item{subtype}{Switch between different inputs (e.g. "ISIMIP3b:IPSL-CM6A-LR:historical:1850-2014:temperature") -Argument consists of GCM version, climate model, scenario and variable, -separated by ":"} -} -\value{ -metadata entry -} -\description{ -Download GCM climate input used for LPJmL runs -} -\examples{ -\dontrun{ -readSource("LPJmLClimateInput", convert = "onlycorrect") -} - -} -\author{ -Marcos Alves, Kristine Karstens -} diff --git a/man/downloadLPJmL_new.Rd b/man/downloadLPJmL_new.Rd deleted file mode 100644 index 09f0dda6..00000000 --- a/man/downloadLPJmL_new.Rd +++ /dev/null @@ -1,31 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/downloadLPJmL_new.R -\name{downloadLPJmL_new} -\alias{downloadLPJmL_new} -\title{downloadLPJmL_new} -\usage{ -downloadLPJmL_new( - subtype = "LPJmL4_for_MAgPIE_44ac93de:GSWP3-W5E5:historical:soilc" -) -} -\arguments{ -\item{subtype}{Switch between different input -It consists of LPJmL version, climate model, scenario and variable. -For pasture lpjml runs, the scenario variable is used to navigate the output folder structure -(e.g. 'LPJmL4_for_MAgPIE_3dda0615:GSWP3-W5E5:historical:soilc' or -"LPJmL5.2_Pasture:IPSL_CM6A_LR:ssp126_co2_limN_00:soilc_past_hist")} -} -\value{ -metadata entry -} -\description{ -Download LPJmL content by version, climate model and scenario -} -\examples{ -\dontrun{ -readSource("LPJmL_new", convert = FALSE) -} -} -\author{ -Kristine Karstens, Marcos Alves, Felicitas Beier -} diff --git a/man/readFAO.Rd b/man/readFAO.Rd deleted file mode 100644 index 54ba6f21..00000000 --- a/man/readFAO.Rd +++ /dev/null @@ -1,56 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/readFAO.R -\name{readFAO} -\alias{readFAO} -\title{Read FAO} -\usage{ -readFAO(subtype) -} -\arguments{ -\item{subtype}{Type of FAO data that should be read. Available types are: -\itemize{ -\item \code{CBCrop}: Commodity Balance Crop (CommodityBalances_Crops_E_All_Data.zip) -\item \code{CBLive}: Commoditiy Balance Livestock (CommodityBalances_LivestockFish_E_All_Data.zip) -\item \code{Crop}: Production Crops ("Production_Crops_E_All_Data.zip") -\item \code{CropProc}: Production Crops Processed ("Production_CropsProcessed_E_All_Data.zip") -\item \code{Fbs}: Food Balance Sheet ("FoodBalanceSheets_E_All_Data.zip") -\item \code{Fertilizer}: Fertilizer ("Resources_Fertilizers_E_All_Data.zip") -\item \code{Fodder}: Fodder (data that has been manually downloaded from the FAOSTAT website as -seperate .xls files via a search for "forage" and "fodder" withing -Production-Crops. These datasets have been added together to a "Fodder.csv" file) -\item \code{FoodSecurity}: Food Security Data ("Food_Security_Data_E_All_Data.zip") -\item \code{FSCrop}: Food Supply Crops ("FoodSupply_Crops_E_All_Data.zip") -\item \code{FSLive}: Food Supply Livestock ("FoodSupply_LivestockFish_E_All_Data.zip") -\item \code{Land}: Land ("Resources_Land_E_All_Data.zip") -\item \code{LiveHead}: Production Live Animals ("Production_Livestock_E_All_Data.zip") -\item \code{LivePrim}: Production Livestock Primary ("Production_LivestockPrimary_E_All_Data.zip") -\item \code{LiveProc}: Production Livestock Processed ("Production_LivestockProcessed_E_All_Data.zip") -\item \code{Pop}: Population ("Population_E_All_Data.zip") -\item \code{ForestProdTrade}: Forestry Production and Trade ("Forestry_E_All_Data_(Normalized).zip") -\item \code{PricesProducerAnnual}: Producer Prices - Annual ("Prices_E_All_Data.zip") -\item \code{PricesProducerAnnualLCU}: Producer Prices - Annual in LCU ("Prices_E_All_Data.zip") -\item \code{ValueOfProd}: Value of Agricultural Production ("Value_of_Production_E_All_Data.zip") -}} -} -\value{ -FAO data as MAgPIE object -} -\description{ -Read in FAO data that has been bulk downloaded from the FAOSTAT website. -Files with exception of fodder.csv are aquired from: -http://faostat.fao.org/Portals/_Faostat/Downloads/zip_files/ -} -\details{ -Update 23-Jan-2017 - Added FAO Forestry production and trade data (Abhi) -} -\examples{ -\dontrun{ -a <- readSource("FAO", "Crop") -} -} -\seealso{ -\code{\link[=readSource]{readSource()}} -} -\author{ -Ulrich Kreidenweis, Abhijeet Mishra, Mishko Stevanovic -} diff --git a/man/readFAOTradeMatrix.Rd b/man/readFAOTradeMatrix.Rd deleted file mode 100644 index 42584e63..00000000 --- a/man/readFAOTradeMatrix.Rd +++ /dev/null @@ -1,36 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/readFAOTradeMatrix.R -\name{readFAOTradeMatrix} -\alias{readFAOTradeMatrix} -\title{Read FAOTradeMatrix} -\usage{ -readFAOTradeMatrix(subtype) -} -\arguments{ -\item{subtype}{subsets of the detailed trade matrix to read in. Very large csv needs to be read in chunks -separated by export/import quantities and values, as well as kcr, kli and kothers (not in kcr nor kli) -Options are all combinations of c("import_value", "import_qty", "export_value", -"export_quantity" X c("kcr", "kli", "kothers")) -import is import side reporting while export is export-sde reporting} -} -\value{ -FAO data as MAgPIE object -} -\description{ -Read in FAOSTAT detail trade matrix. -FAOSTAT does not balance or harmonize the import/export side reporting. -Furthermore, in terms of trade value, exporters are "usuallY" reporting FOB, while importers report CIF. -Difference in value, given identical qty, -is thus the transport margin and any unharmonized reporting combined. -} -\examples{ -\dontrun{ -a <- readSource("FAOTradeMatrix", "import_value_kcr") -} -} -\seealso{ -\code{\link[=readSource]{readSource()}} -} -\author{ -David C -} diff --git a/man/readFAO_FRA2015.Rd b/man/readFAO_FRA2015.Rd deleted file mode 100644 index 2d8906a5..00000000 --- a/man/readFAO_FRA2015.Rd +++ /dev/null @@ -1,30 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/readFAO_FRA2015.R -\name{readFAO_FRA2015} -\alias{readFAO_FRA2015} -\title{Read FAO_FRA2015} -\usage{ -readFAO_FRA2015(subtype) -} -\arguments{ -\item{subtype}{data subtype. Either "production" or "fac" (forest area and characteristics) -or "biodiversity" or "anndat" (Annual Data)} -} -\value{ -magpie object of the FRA 2015 data -} -\description{ -Read-in an FRA data from 2015 (forest resource assessment) -} -\examples{ -\dontrun{ -a <- readSource("FAO_FRA2015", "production") -} - -} -\seealso{ -\code{\link[=readSource]{readSource()}} -} -\author{ -Abhijeet Mishra -} diff --git a/man/readFAO_WHO_UNU1985.Rd b/man/readFAO_WHO_UNU1985.Rd deleted file mode 100644 index 3a501081..00000000 --- a/man/readFAO_WHO_UNU1985.Rd +++ /dev/null @@ -1,27 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/readFAO_WHO_UNU1985.R -\name{readFAO_WHO_UNU1985} -\alias{readFAO_WHO_UNU1985} -\title{Read parameters of Schofield equations} -\usage{ -readFAO_WHO_UNU1985() -} -\value{ -MAgPIE object -} -\description{ -Food and Agriculture Organization of the United Nations, World Health Organization, and United Nations University. -1985. "Energy and protein requirements." http://www.who.int/iris/handle/10665/39527. -} -\examples{ -\dontrun{ -a <- readSource("Schofield") -} - -} -\seealso{ -\code{\link[=readSource]{readSource()}} -} -\author{ -Benjamin Bodirsky -} diff --git a/man/readFAO_online.Rd b/man/readFAO_online.Rd deleted file mode 100644 index 0d314ea8..00000000 --- a/man/readFAO_online.Rd +++ /dev/null @@ -1,59 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/readFAO_online.R -\name{readFAO_online} -\alias{readFAO_online} -\title{Read FAO_online} -\usage{ -readFAO_online(subtype) -} -\arguments{ -\item{subtype}{Type of FAO data that should be read. Available types are: -\itemize{ -\item \code{CBCrop}: Commodity Balance Crop (CommodityBalances_Crops_E_All_Data.zip) -\item \code{CBLive}: Commoditiy Balance Livestock (CommodityBalances_LivestockFish_E_All_Data.zip) -\item \code{Crop}: Production Crops ("Production_Crops_E_All_Data.zip") -\item \code{CropProc}: Production Crops Processed ("Production_CropsProcessed_E_All_Data.zip") -\item \code{Fbs}: Food Balance Sheet ("FoodBalanceSheets_E_All_Data.zip") -\item \code{Fertilizer}: Fertilizer ("Resources_Fertilizers_E_All_Data.zip") -\item \code{FertilizerProducts}: Fertilizer by product ("Inputs_FertilizersProduct_E_All_Data_(Normalized).zip") -\item \code{FertilizerNutrients}: Fertilizer by nutrient ("Inputs_FertilizersNutrient_E_All_Data_(Normalized).zip") -\item \code{Fodder}: Fodder (data that has been manually downloaded from the FAOSTAT website as -seperate .xls files via a search for "forage" and "fodder" withing -Production-Crops. These datasets have been added together to a "Fodder.csv" file) -\item \code{FoodSecurity}: Food Security Data ("Food_Security_Data_E_All_Data.zip") -\item \code{FSCrop}: Food Supply Crops ("FoodSupply_Crops_E_All_Data.zip") -\item \code{FSLive}: Food Supply Livestock ("FoodSupply_LivestockFish_E_All_Data.zip") -\item \code{Land}: Land ("Resources_Land_E_All_Data.zip") -\item \code{LiveHead}: Production Live Animals ("Production_Livestock_E_All_Data.zip") -\item \code{LivePrim}: Production Livestock Primary ("Production_LivestockPrimary_E_All_Data.zip") -\item \code{LiveProc}: Production Livestock Processed ("Production_LivestockProcessed_E_All_Data.zip") -\item \code{Pop}: Population ("Population_E_All_Data.zip") -\item \code{ForestProdTrade}: Forestry Production and Trade ("Forestry_E_All_Data_(Normalized).zip") -\item \code{PricesProducerAnnual}: Producer Prices - Annual ("Prices_E_All_Data.zip") -\item \code{PricesProducerAnnualLCU}: Producer Prices - Annual in LCU ("Prices_E_All_Data.zip") -\item \code{ValueOfProd}: Value of Agricultural Production ("Value_of_Production_E_All_Data.zip") -\item \code{ValueShares}: Value shares by industry and primary factors -\item \code{Trade}: Trade quantities and values -}} -} -\value{ -FAO data as MAgPIE object -} -\description{ -Read in FAO data that has been downloaded from the FAOSTAT website. -Files with exception of fodder.csv are aquired according to downloadFAO. -} -\details{ -Update 23-Jan-2017 - Added FAO Forestry production and trade data (Abhi) -} -\examples{ -\dontrun{ -a <- readSource("FAO_online", "Crop") -} -} -\seealso{ -\code{\link[=readSource]{readSource()}} -} -\author{ -Ulrich Kreidenweis, Abhijeet Mishra, Mishko Stevanovic, David Klein, Edna Molina Bacca -} diff --git a/man/readFRA2020.Rd b/man/readFRA2020.Rd deleted file mode 100644 index 2eb5e72f..00000000 --- a/man/readFRA2020.Rd +++ /dev/null @@ -1,30 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/readFRA2020.R -\name{readFRA2020} -\alias{readFRA2020} -\title{Read FRA2020} -\usage{ -readFRA2020(subtype) -} -\arguments{ -\item{subtype}{data subtype. Available subtypes: forest_area, deforestation, growing_stock, biomass_stock, -carbon_stock, management, disturbance, forest_fire} -} -\value{ -Magpie object of the FRA 2020 data -} -\description{ -Read-in an FRA (forest resource assessment) dataset from 2020. -} -\examples{ -\dontrun{ -a <- readSource("FRA2020", "growing_stock") -} - -} -\seealso{ -\code{\link[=readSource]{readSource()}} -} -\author{ -Abhijeet Mishra -} diff --git a/man/readFishstatJ_FAO.Rd b/man/readFishstatJ_FAO.Rd deleted file mode 100644 index 86c3f976..00000000 --- a/man/readFishstatJ_FAO.Rd +++ /dev/null @@ -1,32 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/readFishstatJ_FAO.R -\name{readFishstatJ_FAO} -\alias{readFishstatJ_FAO} -\title{readFishstatJ_FAO} -\usage{ -readFishstatJ_FAO(subtype = "Production") -} -\arguments{ -\item{subtype}{data subtype needed. Either "exportsValue", "exportsQuantity", or "Production"} -} -\value{ -magpie object of either tonnes of liveweight or 1000 current USD -} -\description{ -Reads data of fisheries generated using the FishstatJ app of FAO. -Read-in specifically, exports_value, exports_quantity, and/or overall production of fish/aquatic products. -} -\examples{ -\dontrun{ -a <- readSource("FishstatJ_FAO", "Production") -a <- readSource("FishstatJ_FAO", "exportsQuantity") -a <- readSource("FishstatJ_FAO", "exportsValue") -} - -} -\seealso{ -\code{\link[=readSource]{readSource()}} -} -\author{ -Edna J. Molina Bacca -} diff --git a/man/readGAEZv4.Rd b/man/readGAEZv4.Rd deleted file mode 100644 index 30c5eb03..00000000 --- a/man/readGAEZv4.Rd +++ /dev/null @@ -1,26 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/readGAEZv4.R -\name{readGAEZv4} -\alias{readGAEZv4} -\title{readGAEZv4} -\usage{ -readGAEZv4(subtype = "MCzones") -} -\arguments{ -\item{subtype}{Subtype to be read} -} -\value{ -MAgPIE object at 0.5 cellular level -} -\description{ -Read in data from the Global Agro-ecological Zones (GAEZ) data set version 4 -} -\examples{ -\dontrun{ -readSource("GAEZv4", convert = "onlycorrect") -} - -} -\author{ -Felicitas Beier -} diff --git a/man/readIEA_EEI.Rd b/man/readIEA_EEI.Rd new file mode 100644 index 00000000..8b2dde57 --- /dev/null +++ b/man/readIEA_EEI.Rd @@ -0,0 +1,14 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/readIEA_EEI.R +\name{readIEA_EEI} +\alias{readIEA_EEI} +\title{Read-in data from IEA End Uses and Efficiency Indicators Database} +\usage{ +readIEA_EEI() +} +\description{ +Read-in data from IEA End Uses and Efficiency Indicators Database +} +\author{ +Falk Benke +} diff --git a/man/readLPJmL.Rd b/man/readLPJmL.Rd deleted file mode 100644 index 5b44765f..00000000 --- a/man/readLPJmL.Rd +++ /dev/null @@ -1,29 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/readLPJmL.R -\name{readLPJmL} -\alias{readLPJmL} -\title{readLPJmL} -\usage{ -readLPJmL(subtype = "LPJmL5:CRU4p02.soilc") -} -\arguments{ -\item{subtype}{Switch between different input} -} -\value{ -List of magpie objects with results on cellular level, weight, unit and description. -} -\description{ -Read LPJmL content -} -\examples{ -\dontrun{ -readSource("LPJmL", subtype = "LPJmL5:CRU4p02.soilc", convert = "onlycorrect") -} - -} -\seealso{ -\code{\link[=readLPJ]{readLPJ()}} -} -\author{ -Kristine Karstens, Abhijeet Mishra, Felicitas Beier -} diff --git a/man/readLPJmLClimateInput.Rd b/man/readLPJmLClimateInput.Rd deleted file mode 100644 index 2fbb75ba..00000000 --- a/man/readLPJmLClimateInput.Rd +++ /dev/null @@ -1,42 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/readLPJmLClimateInput.R -\name{readLPJmLClimateInput} -\alias{readLPJmLClimateInput} -\title{readLPJmLClimateInput} -\usage{ -readLPJmLClimateInput( - subtype = "ISIMIP3bv2:MRI-ESM2-0:ssp370:temperature", - subset = "annualMean" -) -} -\arguments{ -\item{subtype}{Switch between different inputs, -e.g. "ISIMIP3bv2:MRI-ESM2-0:ssp370:1850-2014:tas" -Available variables are: * tas - -* wet - -* per -} - -\item{subset}{Switch between different subsets of the same subtype -Available options are: "annualMean", "annualSum", -"monthlyMean", "monthlySum", -"wetDaysMonth" -Note that not all subtype-subset combinations make sense} -} -\value{ -MAgPIE objects with results on cellular level. -} -\description{ -Read Climate data used as LPJmL inputs into MAgPIE objects -} -\examples{ -\dontrun{ -readSource("LPJmLClimateInput", subtype, convert = "onlycorrect") -} - -} -\seealso{ -\code{\link{readLPJmLClimateInput}} -} -\author{ -Marcos Alves, Kristine Karstens, Felicitas Beier -} diff --git a/man/readLPJmLInputs.Rd b/man/readLPJmLInputs.Rd deleted file mode 100644 index 77d1d4d8..00000000 --- a/man/readLPJmLInputs.Rd +++ /dev/null @@ -1,27 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/readLPJmLInputs.R -\name{readLPJmLInputs} -\alias{readLPJmLInputs} -\title{\if{html}{\out{
}}\preformatted{ readLPJmLInputs -}\if{html}{\out{
}}} -\usage{ -readLPJmLInputs(subtype = "lakeshare") -} -\arguments{ -\item{subtype}{Switch between different inputs} -} -\value{ -List of magpie objects with results on cellular level, weight, unit and description. -} -\description{ -This function reads in LPJmL inputs (inputs to LPJmL) -} -\examples{ -\dontrun{ -readSource("LPJmLInputs", subtype = "lakeshare", convert = FALSE) -} - -} -\author{ -Felicitas Beier -} diff --git a/man/readLPJmL_new.Rd b/man/readLPJmL_new.Rd deleted file mode 100644 index 9907ff33..00000000 --- a/man/readLPJmL_new.Rd +++ /dev/null @@ -1,32 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/readLPJmL_new.R -\name{readLPJmL_new} -\alias{readLPJmL_new} -\title{readLPJmL_new} -\usage{ -readLPJmL_new( - subtype = "LPJmL4_for_MAgPIE_44ac93de:GSWP3-W5E5:historical:soilc" -) -} -\arguments{ -\item{subtype}{Switch between different inputs -(eg. "LPJmL5.2_Pasture:IPSL_CM6A_LR:ssp126_co2_limN_00:soilc_past_hist")} -} -\value{ -List of magpie objects with results on cellular level, weight, unit and description. -} -\description{ -Read in LPJmL outputs -} -\examples{ -\dontrun{ -readSource("LPJmL_new", convert = FALSE) -} - -} -\seealso{ -\code{\link[=readLPJ]{readLPJ()}} -} -\author{ -Kristine Karstens, Abhijeet Mishra, Felicitas Beier, Marcos Alves -} diff --git a/man/readLUH2v2.Rd b/man/readLUH2v2.Rd deleted file mode 100644 index da599c53..00000000 --- a/man/readLUH2v2.Rd +++ /dev/null @@ -1,21 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/readLUH2v2.R -\name{readLUH2v2} -\alias{readLUH2v2} -\title{readLUH2v2} -\usage{ -readLUH2v2(subtype) -} -\arguments{ -\item{subtype}{switch between different inputs} -} -\value{ -List of magpie objects with results on cellular level, weight, unit and description -} -\description{ -read LUH inputs -} -\author{ -Florian Humpenoeder, Stephen Wirth, Kristine Karstens, Felicitas Beier, -Jan Philipp Dietrich, Patrick v. Jeetze -} diff --git a/man/readLandInG.Rd b/man/readLandInG.Rd deleted file mode 100644 index 7253bdbd..00000000 --- a/man/readLandInG.Rd +++ /dev/null @@ -1,33 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/readLandInG.R -\name{readLandInG} -\alias{readLandInG} -\title{readLandInG} -\usage{ -readLandInG(subtype = "physicalArea") -} -\arguments{ -\item{subtype}{Type of LandInG data that should be read: -\itemize{ -\item \code{physicalArea}: Cropland extend/ physical cropping area separated in irrigated and rainfed -\item \code{harvestedArea}: Harvested area separated in different crop types -}} -} -\value{ -magpie object -} -\description{ -Reads in LandInG data -} -\examples{ -\dontrun{ -A <- readSource("LandInG", subtype = "harvestedArea", aggregate = FALSE) -} - -} -\seealso{ -\code{\link{readSource}} -} -\author{ -Felicitas Beier -} diff --git a/man/readProductAttributes.Rd b/man/readProductAttributes.Rd deleted file mode 100644 index 5334f77a..00000000 --- a/man/readProductAttributes.Rd +++ /dev/null @@ -1,35 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/readProductAttributes.R -\name{readProductAttributes} -\alias{readProductAttributes} -\title{Read product attributes} -\usage{ -readProductAttributes(subtype = "Products") -} -\arguments{ -\item{subtype}{Available subtypes: "Products", MAgPIE products "AgResidues" -Aboveground crop residues and "BgResidues" Belowground crop residues} -} -\value{ -magpie object with the dimension crops and attributes -} -\description{ -Read-in a file containing the attributes of MAgPIE products. Currently -Covers dry matter (DM), reactive nitrogen (Nr), Phosphorus (P), -Generalizable Energy (GE) and wet matter (WM). Values are assembled from -various literature sources, and the weighting and allocation is done in the -spreadsheet crop_specifications_06_2011.ods and -livestock_specifications_2012_06_14.ods in the svn folder /tools/Nutrients . -Values standardized on DM. -} -\examples{ -\dontrun{ -a <- readSource("ProductAttributes") -} -} -\seealso{ -\code{\link[=readSource]{readSource()}} -} -\author{ -Benjamin Leon Bodrisky -} diff --git a/man/toolAggregateCell2Country.Rd b/man/toolAggregateCell2Country.Rd deleted file mode 100644 index 983148b3..00000000 --- a/man/toolAggregateCell2Country.Rd +++ /dev/null @@ -1,24 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/toolAggregateCell2Country.R -\name{toolAggregateCell2Country} -\alias{toolAggregateCell2Country} -\title{toolAggregateCell2Country} -\usage{ -toolAggregateCell2Country(x, weight = NULL, ...) -} -\arguments{ -\item{x}{cellular magpie object with coordinates} - -\item{weight}{aggregation weight} - -\item{...}{additional options forwarded to \code{toolCountryFill}} -} -\value{ -return country ISO level data -} -\description{ -Aggregate cellular data (with coordinate information) to countries and perform consistency checks -} -\author{ -Jan Philipp Dietrich -} diff --git a/man/toolCell2isoCell.Rd b/man/toolCell2isoCell.Rd deleted file mode 100644 index 3d42e83a..00000000 --- a/man/toolCell2isoCell.Rd +++ /dev/null @@ -1,22 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/toolCell2isoCell.R -\name{toolCell2isoCell} -\alias{toolCell2isoCell} -\title{toolCell2isoCell} -\usage{ -toolCell2isoCell(x, cells = "magpiecell") -} -\arguments{ -\item{x}{magpie object on cellular level} - -\item{cells}{switch between magpie cells (59199) and lpj cells (67420)} -} -\value{ -return changed input data -} -\description{ -Sets cell names to "iso country code"."cell number" -} -\author{ -Kristine Karstens -} diff --git a/man/toolClimateInputVersion.Rd b/man/toolClimateInputVersion.Rd deleted file mode 100644 index 7e176645..00000000 --- a/man/toolClimateInputVersion.Rd +++ /dev/null @@ -1,22 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/toolClimateInputVersion.R -\name{toolClimateInputVersion} -\alias{toolClimateInputVersion} -\title{toolClimateInputVersion} -\usage{ -toolClimateInputVersion(lpjmlVersion, climatetype) -} -\arguments{ -\item{lpjmlVersion}{Add-ons (+*) for further version specification for LPJmL version} - -\item{climatetype}{Switch between different climate scenarios} -} -\value{ -configuration as list -} -\description{ -Specify default settings for LPJmL climate input version and baseline settings -} -\author{ -Kristine Karstens -} diff --git a/man/toolConv2CountryByCelltype.Rd b/man/toolConv2CountryByCelltype.Rd deleted file mode 100644 index 6235f2fa..00000000 --- a/man/toolConv2CountryByCelltype.Rd +++ /dev/null @@ -1,24 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/toolConv2CountryByCelltype.R -\name{toolConv2CountryByCelltype} -\alias{toolConv2CountryByCelltype} -\title{toolConv2CountryByCelltype} -\usage{ -toolConv2CountryByCelltype(x, cells) -} -\arguments{ -\item{x}{magpie object on cellular level} - -\item{cells}{switch between 59199 ("magpiecell") and 67420 ("lpjcell") cells} -} -\value{ -return selected input data on ISO country level -} -\description{ -Aggregates cellular data to ISO country level after conversion of cellular -data to a specific cell setup (this type is relevant as some settings, -such as "magpiecell" remove some cells and therby affect country sums) -} -\author{ -Jan Philipp Dietrich -} diff --git a/man/toolCoord2Isocell.Rd b/man/toolCoord2Isocell.Rd deleted file mode 100644 index 28969c44..00000000 --- a/man/toolCoord2Isocell.Rd +++ /dev/null @@ -1,34 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/toolCoord2Isocell.R -\name{toolCoord2Isocell} -\alias{toolCoord2Isocell} -\title{\if{html}{\out{
}}\preformatted{ toolCoord2Isocell -}\if{html}{\out{
}}} -\usage{ -toolCoord2Isocell( - x, - cells = "magpiecell", - fillMissing = NULL, - warnMissing = TRUE -) -} -\arguments{ -\item{x}{Object to be transformed from coordinates to (old) magpie isocell standard} - -\item{cells}{Switch between "magpiecell" (59199) and "lpjcell" (67420)} - -\item{fillMissing}{if NULL cells missing from the total 59199 are just being ignore. If set to a value -missing cells will be added with this value (e.g. all set to 0 if fillMissing is 0)} - -\item{warnMissing}{Switch which controls whether missing cells should trigger a warning or not} -} -\value{ -magpie object with 59199 cells in isocell naming -} -\description{ -Transforms an object with coordinate spatial data (on half-degree) -to isocell (59199) standard -} -\author{ -Kristine Karstens, Felicitas Beier, Jan Philipp Dietrich -} diff --git a/man/toolCoord2Isocoord.Rd b/man/toolCoord2Isocoord.Rd deleted file mode 100644 index d4aff4f3..00000000 --- a/man/toolCoord2Isocoord.Rd +++ /dev/null @@ -1,22 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/toolCoord2Isocoord.R -\name{toolCoord2Isocoord} -\alias{toolCoord2Isocoord} -\title{\if{html}{\out{
}}\preformatted{ toolCoord2Isocoord -}\if{html}{\out{
}}} -\usage{ -toolCoord2Isocoord(x) -} -\arguments{ -\item{x}{object to be transformed from coordinates to iso-coordinate object} -} -\value{ -magpie object with 67420 cells in x.y.iso naming -} -\description{ -Transforms an object with coordinate spatial data (on half-degree) -to object with 67420 cells and coordinate and iso country information -} -\author{ -Felicitas Beier -} diff --git a/man/toolCountryFillBilateral.Rd b/man/toolCountryFillBilateral.Rd deleted file mode 100644 index 71307cf5..00000000 --- a/man/toolCountryFillBilateral.Rd +++ /dev/null @@ -1,16 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/toolCountryFillBilateral.R -\name{toolCountryFillBilateral} -\alias{toolCountryFillBilateral} -\title{toolCountryFillBilateral} -\usage{ -toolCountryFillBilateral(x, fill = NA) -} -\arguments{ -\item{x}{input variable, a bilateral magclass object} - -\item{fill}{fill value, default NA} -} -\description{ -Fills bilateral iso-level magpie objects to 249 x 249 countries -} diff --git a/man/toolExtrapolateFodder.Rd b/man/toolExtrapolateFodder.Rd deleted file mode 100644 index f4d1fbd7..00000000 --- a/man/toolExtrapolateFodder.Rd +++ /dev/null @@ -1,27 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/toolExtrapolateFodder.R -\name{toolExtrapolateFodder} -\alias{toolExtrapolateFodder} -\title{\if{html}{\out{
}}\preformatted{ toolExtrapolateFodder -}\if{html}{\out{
}}} -\usage{ -toolExtrapolateFodder(x, exyears = c(2004, 2009), average = 5, endyear = 2015) -} -\arguments{ -\item{x}{input data} - -\item{exyears}{two years} - -\item{average}{the averaging_range in toolTimeInterpolate} - -\item{endyear}{year till when it should be extrapolated} -} -\value{ -magpie object including extrapolated years -} -\description{ -Extrapolate fodder data, based on two time steps (5-averages around this years) -} -\author{ -Kristine Karstens -} diff --git a/man/toolFAOcombine.Rd b/man/toolFAOcombine.Rd deleted file mode 100644 index 906a51c6..00000000 --- a/man/toolFAOcombine.Rd +++ /dev/null @@ -1,34 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/toolFAOcombine.R -\name{toolFAOcombine} -\alias{toolFAOcombine} -\title{Combine FAO datasets} -\usage{ -toolFAOcombine(..., combine = "Item") -} -\arguments{ -\item{...}{two magpie objects with FAO data} - -\item{combine}{"Item" to combine datasets that for instance both contain -palm oil data} -} -\value{ -MAgPIE object with data from both inputs but dublicates removed -} -\description{ -Allows to combine two similar FAO datasets with dublicates being removed. -For instance combine Production:Crops and Production: Crops Processed to one -magpie object -} -\examples{ -\dontrun{ -a <- toolFAOcombine(Crop, CropPro, combine = "Item") -} - -} -\seealso{ -\code{\link[=readSource]{readSource()}} -} -\author{ -Ulrich Kreidenweis -} diff --git a/man/toolForestRelocate.Rd b/man/toolForestRelocate.Rd deleted file mode 100644 index a66e19aa..00000000 --- a/man/toolForestRelocate.Rd +++ /dev/null @@ -1,27 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/toolForestRelocate.R -\name{toolForestRelocate} -\alias{toolForestRelocate} -\title{toolForestRelocate} -\usage{ -toolForestRelocate(lu, luCountry, natTarget, vegC) -} -\arguments{ -\item{lu}{uncorrected landuse initialisation data set (cell level)} - -\item{luCountry}{uncorrected landuse initialisation on country level} - -\item{natTarget}{target natural land allocation on country level} - -\item{vegC}{vegetation carbon data used as reallocation weight} -} -\value{ -List of magpie object with results on cellular level -} -\description{ -Reallocates cellular forest information from LUH2 -to better match FAO forest information -} -\author{ -Kristine Karstens, Jan Philipp Dietrich, Felicitas Beier, Patrick v. Jeetze -} diff --git a/man/toolFreezeEffect.Rd b/man/toolFreezeEffect.Rd deleted file mode 100644 index d56082ee..00000000 --- a/man/toolFreezeEffect.Rd +++ /dev/null @@ -1,25 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/toolFreezeEffect.R -\name{toolFreezeEffect} -\alias{toolFreezeEffect} -\title{toolFreezeEffect} -\usage{ -toolFreezeEffect(x, year, constrain = FALSE) -} -\arguments{ -\item{x}{data set to freeze} - -\item{year}{year to hold constant (onwards)} - -\item{constrain}{if FALSE, no constrain. Other options: 'first_use' (freeze from 'first use' ( <=> !=0 ))} -} -\value{ -magpie object with global parameters -} -\description{ -This function freeze values given a specific year and optionally additionally at the first -non-zero value -} -\author{ -Kristine Karstens -} diff --git a/man/toolGetMappingCoord2Country.Rd b/man/toolGetMappingCoord2Country.Rd deleted file mode 100644 index 7e730f26..00000000 --- a/man/toolGetMappingCoord2Country.Rd +++ /dev/null @@ -1,24 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/toolGetMappingCoord2Country.R -\name{toolGetMappingCoord2Country} -\alias{toolGetMappingCoord2Country} -\title{\if{html}{\out{
}}\preformatted{ toolGetMappingCoord2Country -}\if{html}{\out{
}}} -\usage{ -toolGetMappingCoord2Country(pretty = FALSE, extended = FALSE) -} -\arguments{ -\item{pretty}{If TRUE, coordinate data is returned as numeric 'lon' and 'lat' columns} - -\item{extended}{If TRUE, additional cells missing in the original 67420 data set will be -returned as well.} -} -\value{ -data frame of mapping -} -\description{ -loads mapping of cellular coordinate data (67420 halfdegree cells) to country iso codes -} -\author{ -Felicitas Beier, Kristine Karstens -} diff --git a/man/toolHarmonize2Baseline.Rd b/man/toolHarmonize2Baseline.Rd deleted file mode 100644 index af2b74af..00000000 --- a/man/toolHarmonize2Baseline.Rd +++ /dev/null @@ -1,38 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/toolHarmonize2Baseline.R -\name{toolHarmonize2Baseline} -\alias{toolHarmonize2Baseline} -\title{toolHarmonize2Baseline} -\usage{ -toolHarmonize2Baseline( - x, - base, - ref_year = "y2015", - method = "limited", - hard_cut = FALSE -) -} -\arguments{ -\item{x}{magclass object that should be set on baseline} - -\item{base}{magclass object for baseline} - -\item{ref_year}{Reference year} - -\item{method}{additive: x is harmonized to base by additive factor -multiplicative: x is harmonized to base by multiplicative factor -limited: multiplicative harmonization, -but for an underestimated baseline the signal is -limited to the additive term rather than the multiplicative factor} - -\item{hard_cut}{Switch to TRUE for data that can not be harmonized, but have to be glued together} -} -\value{ -the averaged data in magclass format -} -\description{ -toolHarmonize2Baseline -} -\author{ -Kristine Karstens, Felicitas Beier -} diff --git a/man/toolHoldConstantBeyondEnd.Rd b/man/toolHoldConstantBeyondEnd.Rd deleted file mode 100644 index 61e1d733..00000000 --- a/man/toolHoldConstantBeyondEnd.Rd +++ /dev/null @@ -1,20 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/toolHoldConstantBeyondEnd.R -\name{toolHoldConstantBeyondEnd} -\alias{toolHoldConstantBeyondEnd} -\title{toolHoldConstantBeyondEnd} -\usage{ -toolHoldConstantBeyondEnd(x) -} -\arguments{ -\item{x}{MAgPIE object to be continued.} -} -\value{ -MAgPIE object with completed time dimensionality. -} -\description{ -Holds a historical dataset constant for the entire simulation period "time". -} -\author{ -Benjamin Leon Bodirsky -} diff --git a/man/toolIso2CellCountries.Rd b/man/toolIso2CellCountries.Rd deleted file mode 100644 index 7f1916b4..00000000 --- a/man/toolIso2CellCountries.Rd +++ /dev/null @@ -1,26 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/toolIso2CellCountries.R -\name{toolIso2CellCountries} -\alias{toolIso2CellCountries} -\title{toolIso2CellCountries} -\usage{ -toolIso2CellCountries(x, cells = "magpiecell", absolute = NULL) -} -\arguments{ -\item{x}{magpie object on iso country level} - -\item{cells}{switch between 59199 ("magpiecell") and 67420 ("lpjcell") cells} - -\item{absolute}{switch declaring the values as absolute (TRUE) or relative (FALSE) -for additional (type-specific) diagnostic information. If not defined (NULL) additional diagnostics -will not be shown.} -} -\value{ -return selected input data -} -\description{ -Select country names of countries which are present on cellular level -} -\author{ -Kristine Karstens, Felicitas Beier, Jan Philipp Dietrich -} diff --git a/man/toolLPJmLVersion.Rd b/man/toolLPJmLVersion.Rd deleted file mode 100644 index a88cd48c..00000000 --- a/man/toolLPJmLVersion.Rd +++ /dev/null @@ -1,22 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/toolLPJmLVersion.R -\name{toolLPJmLVersion} -\alias{toolLPJmLVersion} -\title{toolLPJmLVersion} -\usage{ -toolLPJmLVersion(version, climatetype) -} -\arguments{ -\item{version}{Switch between LPJmL versions (including add-ons (+*) for further version specification)} - -\item{climatetype}{Switch between different climate scenarios} -} -\value{ -configuration as list -} -\description{ -Specify default settings for LPJmL version and baseline settings -} -\author{ -Kristine Karstens -} diff --git a/man/toolSmooth.Rd b/man/toolSmooth.Rd deleted file mode 100644 index f7c7c4a3..00000000 --- a/man/toolSmooth.Rd +++ /dev/null @@ -1,22 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/toolSmooth.R -\name{toolSmooth} -\alias{toolSmooth} -\title{toolSmooth} -\usage{ -toolSmooth(x, method = "spline") -} -\arguments{ -\item{x}{magclass object that should be smoothed} - -\item{method}{spline, average or more (See default argument for current default setting)} -} -\value{ -smoothed data in magclass format -} -\description{ -Smooth a time series using a given method and its default settings -} -\author{ -Kristine Karstens -} diff --git a/man/toolSum2Country.Rd b/man/toolSum2Country.Rd deleted file mode 100644 index 37142648..00000000 --- a/man/toolSum2Country.Rd +++ /dev/null @@ -1,21 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/toolSum2Country.R -\name{toolSum2Country} -\alias{toolSum2Country} -\title{toolSum2Country} -\usage{ -toolSum2Country(x) -} -\arguments{ -\item{x}{magpie object on cellular level with countries in dim 1.1} -} -\value{ -return selected input data on ISO country level -} -\description{ -Efficient method to sum cellular data with country dimension as first -sub-dimension to country level -} -\author{ -Jan Philipp Dietrich -}