R/zchunk_LA100.FAO_downscale_ctry.R
In rohmin9122/gcam-korea-release: Build the GCAM-Korea Input Datasets
Defines functions
module_aglu_LA100.FAO_downscale_ctry
Documented in
module_aglu_LA100.FAO_downscale_ctry
#' module_aglu_LA100.FAO_downscale_ctry
#'
#' Downscale FAO production and consumption agricultural data to AGLU countries.
#'
#' @param command API command to execute
#' @param ... other optional parameters, depending on command
#' @return Depends on \code{command}: either a vector of required inputs,
#' a vector of output names, or (if \code{command} is "MAKE") all
#' the generated outputs: \code{L100.FAO_ag_HA_ha}, \code{L100.FAO_ag_Prod_t}, \code{L100.FAO_ag_Exp_t}, \code{L100.FAO_ag_Feed_t}, \code{L100.FAO_ag_Food_t}, \code{L100.FAO_ag_Imp_t}, \code{L100.FAO_an_Exp_t}, \code{L100.FAO_an_Food_t}, \code{L100.FAO_an_Imp_t}, \code{L100.FAO_an_Prod_t}, \code{L100.FAO_CL_kha}, \code{L100.FAO_fallowland_kha}, \code{L100.FAO_harv_CL_kha}, \code{L100.FAO_Fert_Cons_tN}, \code{L100.FAO_Fert_Prod_tN}, \code{L100.FAO_For_Exp_m3}, \code{L100.FAO_For_Imp_m3}, \code{L100.FAO_For_Prod_m3}. The corresponding file in the
#' original data system was \code{LA100.FAO_downscale_ctry.R} (aglu level1).
#' @details Extrapolate each FAO dataset to 2011; match with country names; extrapolate to countries that
#' split or combined at some point (e.g. Czechoslovakia needs to be split into Czech Republic and
#' Slovakia); and calculate rolling five-year averages.
#' @importFrom assertthat assert_that
#' @importFrom tibble tibble
#' @importFrom stats aggregate
#' @import dplyr
#' @importFrom tidyr gather spread
#' @author BBL
module_aglu_LA100.FAO_downscale_ctry <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "aglu/AGLU_ctry",
FILE = "aglu/FAO/FAO_ag_HA_ha_PRODSTAT",
FILE = "aglu/FAO/FAO_ag_Prod_t_PRODSTAT",
FILE = "aglu/FAO/FAO_ag_Exp_t_SUA",
FILE = "aglu/FAO/FAO_ag_Feed_t_SUA",
FILE = "aglu/FAO/FAO_ag_Food_t_SUA",
FILE = "aglu/FAO/FAO_ag_Imp_t_SUA",
FILE = "aglu/FAO/FAO_an_Exp_t_SUA",
FILE = "aglu/FAO/FAO_an_Food_t_SUA",
FILE = "aglu/FAO/FAO_an_Imp_t_SUA",
FILE = "aglu/FAO/FAO_an_Prod_t_SUA",
FILE = "aglu/FAO/FAO_an_Stocks",
FILE = "aglu/FAO/FAO_an_Dairy_Stocks",
FILE = "aglu/FAO/FAO_CL_kha_RESOURCESTAT",
FILE = "aglu/FAO/FAO_fallowland_kha_RESOURCESTAT",
FILE = "aglu/FAO/FAO_harv_CL_kha_RESOURCESTAT",
FILE = "aglu/FAO/FAO_Fert_Cons_tN_RESOURCESTAT_archv",
FILE = "aglu/FAO/FAO_Fert_Cons_tN_RESOURCESTAT",
FILE = "aglu/FAO/FAO_Fert_Prod_tN_RESOURCESTAT_archv",
FILE = "aglu/FAO/FAO_Fert_Prod_tN_RESOURCESTAT",
FILE = "aglu/FAO/FAO_For_Exp_m3_FORESTAT",
FILE = "aglu/FAO/FAO_For_Imp_m3_FORESTAT",
FILE = "aglu/FAO/FAO_For_Prod_m3_FORESTAT"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L100.FAO_ag_HA_ha",
"L100.FAO_ag_Prod_t",
"L100.FAO_ag_Exp_t",
"L100.FAO_ag_Feed_t",
"L100.FAO_ag_Food_t",
"L100.FAO_ag_Imp_t",
"L100.FAO_an_Exp_t",
"L100.FAO_an_Food_t",
"L100.FAO_an_Imp_t",
"L100.FAO_an_Prod_t",
"L100.FAO_an_Stocks",
"L100.FAO_an_Dairy_Stocks",
"L100.FAO_CL_kha",
"L100.FAO_fallowland_kha",
"L100.FAO_harv_CL_kha",
"L100.FAO_Fert_Cons_tN",
"L100.FAO_Fert_Prod_tN",
"L100.FAO_For_Exp_m3",
"L100.FAO_For_Imp_m3",
"L100.FAO_For_Prod_m3"))
} else if(command == driver.MAKE) {
iso <- FAO_country <- `country codes` <- `element codes` <- `item codes` <-
year <- value <- countries <- country.codes <- item <- item.codes <-
element <- element.codes <- NULL # silence package chck.
all_data <- list(...)[[1]]
# Load required inputs
get_data(all_data, "aglu/AGLU_ctry") %>%
select(iso, FAO_country) %>%
distinct ->
AGLU_ctry
FAO_ag_HA_ha_PRODSTAT <- get_data(all_data, "aglu/FAO/FAO_ag_HA_ha_PRODSTAT")
FAO_ag_Prod_t_PRODSTAT <- get_data(all_data, "aglu/FAO/FAO_ag_Prod_t_PRODSTAT")
FAO_ag_Exp_t_SUA <- get_data(all_data, "aglu/FAO/FAO_ag_Exp_t_SUA")
FAO_ag_Feed_t_SUA <- get_data(all_data, "aglu/FAO/FAO_ag_Feed_t_SUA")
FAO_ag_Food_t_SUA <- get_data(all_data, "aglu/FAO/FAO_ag_Food_t_SUA")
FAO_ag_Imp_t_SUA <- get_data(all_data, "aglu/FAO/FAO_ag_Imp_t_SUA")
FAO_an_Exp_t_SUA <- get_data(all_data, "aglu/FAO/FAO_an_Exp_t_SUA")
FAO_an_Food_t_SUA <- get_data(all_data, "aglu/FAO/FAO_an_Food_t_SUA")
FAO_an_Imp_t_SUA <- get_data(all_data, "aglu/FAO/FAO_an_Imp_t_SUA")
FAO_an_Prod_t_SUA <- get_data(all_data, "aglu/FAO/FAO_an_Prod_t_SUA")
FAO_an_Stocks <- get_data(all_data, "aglu/FAO/FAO_an_Stocks")
FAO_an_Dairy_Stocks <- get_data(all_data, "aglu/FAO/FAO_an_Dairy_Stocks")
FAO_CL_kha_RESOURCESTAT <- get_data(all_data, "aglu/FAO/FAO_CL_kha_RESOURCESTAT")
FAO_fallowland_kha_RESOURCESTAT <- get_data(all_data, "aglu/FAO/FAO_fallowland_kha_RESOURCESTAT")
FAO_harv_CL_kha_RESOURCESTAT <- get_data(all_data, "aglu/FAO/FAO_harv_CL_kha_RESOURCESTAT")
FAO_Fert_Cons_tN_RESOURCESTAT_archv <- get_data(all_data, "aglu/FAO/FAO_Fert_Cons_tN_RESOURCESTAT_archv")
FAO_Fert_Cons_tN_RESOURCESTAT <- get_data(all_data, "aglu/FAO/FAO_Fert_Cons_tN_RESOURCESTAT")
FAO_Fert_Prod_tN_RESOURCESTAT_archv <- get_data(all_data, "aglu/FAO/FAO_Fert_Prod_tN_RESOURCESTAT_archv")
FAO_Fert_Prod_tN_RESOURCESTAT<- get_data(all_data, "aglu/FAO/FAO_Fert_Prod_tN_RESOURCESTAT")
FAO_For_Exp_m3_FORESTAT <- get_data(all_data, "aglu/FAO/FAO_For_Exp_m3_FORESTAT")
FAO_For_Imp_m3_FORESTAT <- get_data(all_data, "aglu/FAO/FAO_For_Imp_m3_FORESTAT")
FAO_For_Prod_m3_FORESTAT <- get_data(all_data, "aglu/FAO/FAO_For_Prod_m3_FORESTAT")
itel_colnames <- c("item", "item codes", "element", "element codes")
coitel_colnames <- c("countries", "country codes", itel_colnames)
FAO_histyear_cols <- as.character(aglu.FAO_HISTORICAL_YEARS)
# Replace the item and element code names with what is used in the more recent datasets
FAO_Fert_Cons_tN_RESOURCESTAT_archv[itel_colnames] <- FAO_Fert_Cons_tN_RESOURCESTAT[1, itel_colnames]
FAO_Fert_Prod_tN_RESOURCESTAT_archv[itel_colnames] <- FAO_Fert_Prod_tN_RESOURCESTAT[1, itel_colnames]
# Merge resourcestat fertilizer databases with 'archive' years (1961-2002) and more recent
# years (2002-2010). FAOSTAT notes that the methods changed between the two datasets; we
# ignore this discrepancy but use the 2002 data from the more recent dataset
FAO_Fert_Cons_tN_RESOURCESTAT_archv$`2002` <- NULL
FAO_Fert_Prod_tN_RESOURCESTAT_archv$`2002` <- NULL
# Interesting: dplyr can't go as fast as the approach taken in the original data system
# A number of dplyr operations are *considerably* slower with this big dataset, and take more lines
# So most of this function, the slowest in the entire data system, retains the original
# code (though cleaned up considerably) and logic
cons <- full_join(FAO_Fert_Cons_tN_RESOURCESTAT_archv,
FAO_Fert_Cons_tN_RESOURCESTAT, by = c("countries", "country codes", "item", "item codes", "element", "element codes"))
prod <- full_join(FAO_Fert_Prod_tN_RESOURCESTAT_archv,
FAO_Fert_Prod_tN_RESOURCESTAT, by = c("countries", "country codes", "item", "item codes", "element", "element codes"))
# Aggregate to complete the merge of the two datasets
FAO_Fert_Cons_tN_RESOURCESTAT <- aggregate(cons[names(cons) %in% FAO_histyear_cols],
by = as.list(cons[coitel_colnames]),
sum, na.rm = TRUE)
FAO_Fert_Prod_tN_RESOURCESTAT <- aggregate(prod[names(prod) %in% FAO_histyear_cols],
by = as.list(prod[coitel_colnames]),
sum, na.rm = TRUE)
# Some data in an_Stocks are in 1000s of heads instead of just heads; convert them
# Also remove the units column to be consistent with the other FAO tables.
fhyc <- names(FAO_an_Stocks) %in% aglu.FAO_HISTORICAL_YEARS
thr <- FAO_an_Stocks$units == "1000 Head"
FAO_an_Stocks[thr, fhyc] <- FAO_an_Stocks[thr, fhyc] * 1000
FAO_an_Stocks$units <- FAO_an_Dairy_Stocks$units <- NULL
# Not all databases go to 2011. Extrapolate each dataset to 2011, repeating
# the data for 2009/10. Where missing 1961, substitute 1962
list("FAO_ag_Exp_t_SUA" = FAO_ag_Exp_t_SUA,
"FAO_ag_Feed_t_SUA" = FAO_ag_Feed_t_SUA,
"FAO_ag_Food_t_SUA" = FAO_ag_Food_t_SUA,
"FAO_ag_Imp_t_SUA" = FAO_ag_Imp_t_SUA,
"FAO_an_Exp_t_SUA" = FAO_an_Exp_t_SUA,
"FAO_an_Food_t_SUA" = FAO_an_Food_t_SUA,
"FAO_an_Imp_t_SUA" = FAO_an_Imp_t_SUA,
"FAO_an_Prod_t_SUA" = FAO_an_Prod_t_SUA,
"FAO_an_Stocks" = FAO_an_Stocks,
"FAO_an_Dairy_Stocks" = FAO_an_Dairy_Stocks,
"FAO_Fert_Cons_tN_RESOURCESTAT" = FAO_Fert_Cons_tN_RESOURCESTAT,
"FAO_Fert_Prod_tN_RESOURCESTAT" = FAO_Fert_Prod_tN_RESOURCESTAT,
"FAO_ag_HA_ha_PRODSTAT" = FAO_ag_HA_ha_PRODSTAT,
"FAO_ag_Prod_t_PRODSTAT" = FAO_ag_Prod_t_PRODSTAT,
"FAO_CL_kha_RESOURCESTAT" = FAO_CL_kha_RESOURCESTAT,
"FAO_fallowland_kha_RESOURCESTAT" = FAO_fallowland_kha_RESOURCESTAT,
"FAO_harv_CL_kha_RESOURCESTAT" = FAO_harv_CL_kha_RESOURCESTAT,
"FAO_For_Exp_m3_FORESTAT" = FAO_For_Exp_m3_FORESTAT,
"FAO_For_Imp_m3_FORESTAT" = FAO_For_Imp_m3_FORESTAT,
"FAO_For_Prod_m3_FORESTAT" = FAO_For_Prod_m3_FORESTAT) %>%
# apply the following function over all list elements
lapply(FUN = function(df) {
if(!"1961" %in% colnames(df)) df$`1961` <- df$`1962`
if(!"2010" %in% colnames(df)) df$`2010` <- df$`2009`
if(!"2011" %in% colnames(df)) df$`2011` <- df$`2009`
df$element <- NULL
df
}) %>%
# combine everything together
bind_rows(.id = "element") ->
FAO_data_ALL
# Replace all missing values with 0
repl <- as.list(rep(0, ncol(FAO_data_ALL)))
names(repl) <- names(FAO_data_ALL)
FAO_data_ALL <- replace_na(FAO_data_ALL, repl)
# Match the iso names
FAO_data_ALL %>%
left_join(distinct(AGLU_ctry, FAO_country, .keep_all = TRUE), by = c("countries" = "FAO_country")) ->
FAO_data_ALL
# Downscale countries individually NOTE: This is complicated. The FAO data need to be downscaled
# to all FAO historical years (i.e. back to 1961 regardless of when we are starting our
# historical time series). Otherwise the early historical years will get averaged with zeroes.
# Czechoslovakia
FAO_data_ALL %>%
filter(iso %in% AGLU_ctry$iso[AGLU_ctry$FAO_country == "Czechoslovakia"]) %>%
downscale_FAO_country("Czechoslovakia", 1993L, years = aglu.FAO_HISTORICAL_YEARS) ->
FAO_data_ALL_cze
# USSR
FAO_data_ALL %>%
filter(iso %in% AGLU_ctry$iso[AGLU_ctry$FAO_country == "USSR"]) %>%
downscale_FAO_country("USSR", 1992L, years = aglu.FAO_HISTORICAL_YEARS) ->
FAO_data_ALL_ussr
# Yugoslavia
FAO_data_ALL %>%
filter(iso %in% AGLU_ctry$iso[AGLU_ctry$FAO_country == "Yugoslav SFR"]) %>%
downscale_FAO_country("Yugoslav SFR", 1992L, years = aglu.FAO_HISTORICAL_YEARS) ->
FAO_data_ALL_yug
# Drop these countries from the full database and combine
FAO_data_ALL %>%
filter(!iso %in% unique(c(FAO_data_ALL_cze$iso, FAO_data_ALL_ussr$iso, FAO_data_ALL_yug$iso))) %>%
# combine these downscaled databases
bind_rows(FAO_data_ALL_cze, FAO_data_ALL_ussr, FAO_data_ALL_yug) ->
FAO_data_ALL
# Make sure histyear_cols uses only names in our data set
FAO_histyear_cols <- intersect(FAO_histyear_cols, names(FAO_data_ALL))
# Drop observations where all years are zero
FAO_data_ALL <- FAO_data_ALL[rowSums(FAO_data_ALL[FAO_histyear_cols]) != 0, ]
# Calculate rolling five-year averages from available data
FAO_data_ALL_5yr <- FAO_data_ALL
# In the first and last two years, use the 3 and 4 available years
FAO_data_ALL_5yr[FAO_histyear_cols][1] <- rowMeans(FAO_data_ALL[FAO_histyear_cols][1:3])
FAO_data_ALL_5yr[FAO_histyear_cols][2] <- rowMeans(FAO_data_ALL[FAO_histyear_cols][1:4])
# Precalculate a few things for loop speed
lastcol <- ncol(FAO_data_ALL_5yr[FAO_histyear_cols]) - 2
x <- FAO_data_ALL[FAO_histyear_cols]
lenXFAO <- length(FAO_histyear_cols)
# Main calculation loop
for(i in 3:lastcol) {
FAO_data_ALL_5yr[FAO_histyear_cols][, i] <- rowMeans(x[i + -2:2])
}
FAO_data_ALL_5yr[FAO_histyear_cols][lenXFAO - 1] <-
rowMeans(FAO_data_ALL[FAO_histyear_cols][(lenXFAO - 3):lenXFAO])
FAO_data_ALL_5yr[FAO_histyear_cols][lenXFAO] <-
rowMeans(FAO_data_ALL[FAO_histyear_cols][(lenXFAO - 2):lenXFAO])
# From here on, only use the specified AGLU historical years
FAO_data_ALL_5yr <- FAO_data_ALL_5yr[c(coitel_colnames, "iso", as.character(aglu.AGLU_HISTORICAL_YEARS))]
# Rename columns to old names
FAO_data_ALL_5yr %>%
rename(country.codes = `country codes`,
element.codes = `element codes`,
item.codes = `item codes`) ->
FAO_data_ALL_5yr
# Change `element` columns to match old data and reshape
# FAO_data_ALL_5yr <- FAO_data_ALL_5yr[c(1:6,8:47,7)]
FAO_data_ALL_5yr$element <- gsub(pattern = "_[A-Z]*$", "", FAO_data_ALL_5yr$element)
FAO_data_ALL_5yr$element <- gsub(pattern = "^FAO_", "", FAO_data_ALL_5yr$element)
FAO_data_ALL_5yr <- gather_years(FAO_data_ALL_5yr)
# Re-split into separate tables for each element
L100.FAOlist <- split(seq(1, nrow(FAO_data_ALL_5yr)), FAO_data_ALL_5yr$element)
names(L100.FAOlist) <- lapply(names(L100.FAOlist), function(x) { paste0("L100.FAO_", x) })
# change list names to match the legacy
# names
fixup <- function(irows, legacy.name) {
# If the name of the table being added is L100.FAO_ag_Food_t or L100.FAO_CL_kha it is
# self tested and does not need the test flags, see https://github.com/JGCRI/gcamdata/issues/918 for more
# details.
if(legacy.name %in% c("L100.FAO_ag_Food_t", "L100.FAO_CL_kha")) {
FAO_data_ALL_5yr[irows,] %>%
add_comments("Downscale countries; calculate 5-yr averages") %>%
add_legacy_name(legacy.name)
} else {
FAO_data_ALL_5yr[irows,] %>%
add_comments("Downscale countries; calculate 5-yr averages") %>%
add_legacy_name(legacy.name)
}
}
L100.FAOlist <- Map(fixup, L100.FAOlist, names(L100.FAOlist))
# Add description, units, process (done above), and precursor information
L100.FAOlist[["L100.FAO_ag_HA_ha"]] %>%
add_title("FAO agricultural harvested area by country, item, year") %>%
add_units("t") %>%
add_precursors("aglu/FAO/FAO_ag_HA_ha_PRODSTAT", "aglu/AGLU_ctry") ->
L100.FAO_ag_HA_ha
L100.FAOlist[["L100.FAO_ag_Prod_t"]] %>%
add_title("FAO agricultural production by country, item, year") %>%
add_units("t") %>%
add_precursors("aglu/FAO/FAO_ag_Prod_t_PRODSTAT", "aglu/AGLU_ctry") ->
L100.FAO_ag_Prod_t
L100.FAOlist[["L100.FAO_ag_Exp_t"]] %>%
add_title("FAO agricultural exports by country, item, year") %>%
add_units("t") %>%
add_precursors("aglu/FAO/FAO_ag_Exp_t_SUA", "aglu/AGLU_ctry") ->
L100.FAO_ag_Exp_t
L100.FAOlist[["L100.FAO_ag_Feed_t"]] %>%
add_title("FAO agricultural feed by country, item, year") %>%
add_units("t") %>%
add_precursors("aglu/FAO/FAO_ag_Feed_t_SUA", "aglu/AGLU_ctry") ->
L100.FAO_ag_Feed_t
L100.FAOlist[["L100.FAO_ag_Food_t"]] %>%
add_title("FAO agricultural food consumption by country, item, year") %>%
add_units("t") %>%
add_precursors("aglu/FAO/FAO_ag_Food_t_SUA", "aglu/AGLU_ctry") ->
L100.FAO_ag_Food_t
L100.FAOlist[["L100.FAO_ag_Imp_t"]] %>%
add_title("FAO agricultural imports by country, item, year") %>%
add_units("t") %>%
add_precursors("aglu/FAO/FAO_ag_Imp_t_SUA", "aglu/AGLU_ctry") ->
L100.FAO_ag_Imp_t
L100.FAOlist[["L100.FAO_an_Exp_t"]] %>%
add_title("FAO animal exports by country, item, year") %>%
add_units("t") %>%
add_precursors("aglu/FAO/FAO_an_Exp_t_SUA", "aglu/AGLU_ctry") ->
L100.FAO_an_Exp_t
L100.FAOlist[["L100.FAO_an_Food_t"]] %>%
add_title("FAO animal food consumption by country, item, year") %>%
add_units("t") %>%
add_precursors("aglu/FAO/FAO_an_Food_t_SUA", "aglu/AGLU_ctry") ->
L100.FAO_an_Food_t
L100.FAOlist[["L100.FAO_an_Imp_t"]] %>%
add_title("FAO animal imports by country, item, year") %>%
add_units("t") %>%
add_precursors("aglu/FAO/FAO_an_Imp_t_SUA", "aglu/AGLU_ctry") ->
L100.FAO_an_Imp_t
L100.FAOlist[["L100.FAO_an_Prod_t"]] %>%
add_title("FAO animal production by country, item, year") %>%
add_units("t") %>%
add_precursors("aglu/FAO/FAO_an_Prod_t_SUA", "aglu/AGLU_ctry") ->
L100.FAO_an_Prod_t
L100.FAOlist[["L100.FAO_an_Stocks"]] %>%
add_title("FAO animal stocks country, item, year") %>%
add_units("number") %>%
add_precursors("aglu/FAO/FAO_an_Stocks", "aglu/AGLU_ctry") ->
L100.FAO_an_Stocks
L100.FAOlist[["L100.FAO_an_Dairy_Stocks"]] %>%
add_title("FAO dairy producing animal stocks country, item, year") %>%
add_units("number") %>%
add_precursors("aglu/FAO/FAO_an_Dairy_Stocks", "aglu/AGLU_ctry") ->
L100.FAO_an_Dairy_Stocks
L100.FAOlist[["L100.FAO_CL_kha"]] %>%
add_title("FAO cropland area by country, year") %>%
add_units("kha") %>%
add_precursors("aglu/FAO/FAO_CL_kha_RESOURCESTAT", "aglu/AGLU_ctry") ->
L100.FAO_CL_kha
L100.FAOlist[["L100.FAO_fallowland_kha"]] %>%
add_title("FAO fallow land area by country, year") %>%
add_units("kha") %>%
add_precursors("aglu/FAO/FAO_fallowland_kha_RESOURCESTAT", "aglu/AGLU_ctry") ->
L100.FAO_fallowland_kha
L100.FAOlist[["L100.FAO_harv_CL_kha"]] %>%
add_title("FAO harvested cropland (temporary crops) area by country, year") %>%
add_units("kha") %>%
add_precursors("aglu/FAO/FAO_harv_CL_kha_RESOURCESTAT", "aglu/AGLU_ctry") ->
L100.FAO_harv_CL_kha
L100.FAOlist[["L100.FAO_Fert_Cons_tN"]] %>%
add_title("FAO fertilizer consumption by country, year") %>%
add_units("tonnes N") %>%
add_precursors("aglu/FAO/FAO_Fert_Cons_tN_RESOURCESTAT",
"aglu/FAO/FAO_Fert_Cons_tN_RESOURCESTAT_archv",
"aglu/AGLU_ctry") ->
L100.FAO_Fert_Cons_tN
L100.FAOlist[["L100.FAO_Fert_Prod_tN"]] %>%
add_title("FAO fertilizer production by country, year") %>%
add_units("tonnes N") %>%
add_precursors("aglu/FAO/FAO_Fert_Prod_tN_RESOURCESTAT",
"aglu/FAO/FAO_Fert_Prod_tN_RESOURCESTAT_archv",
"aglu/AGLU_ctry") ->
L100.FAO_Fert_Prod_tN
L100.FAOlist[["L100.FAO_For_Exp_m3"]] %>%
add_title("FAO forestry exports by country, year") %>%
add_units("m3") %>%
add_precursors("aglu/FAO/FAO_For_Exp_m3_FORESTAT", "aglu/AGLU_ctry") ->
L100.FAO_For_Exp_m3
L100.FAOlist[["L100.FAO_For_Imp_m3"]] %>%
add_title("FAO forestry imports by country, year") %>%
add_units("m3") %>%
add_precursors("aglu/FAO/FAO_For_Imp_m3_FORESTAT", "aglu/AGLU_ctry") ->
L100.FAO_For_Imp_m3
L100.FAOlist[["L100.FAO_For_Prod_m3"]] %>%
add_title("FAO forestry production by country, year") %>%
add_units("m3") %>%
add_precursors("aglu/FAO/FAO_For_Prod_m3_FORESTAT", "aglu/AGLU_ctry") ->
L100.FAO_For_Prod_m3
return_data(L100.FAO_ag_HA_ha,
L100.FAO_ag_Prod_t,
L100.FAO_ag_Exp_t,
L100.FAO_ag_Feed_t,
L100.FAO_ag_Food_t,
L100.FAO_ag_Imp_t,
L100.FAO_an_Exp_t,
L100.FAO_an_Food_t,
L100.FAO_an_Imp_t,
L100.FAO_an_Prod_t,
L100.FAO_an_Stocks,
L100.FAO_an_Dairy_Stocks,
L100.FAO_CL_kha,
L100.FAO_fallowland_kha,
L100.FAO_harv_CL_kha,
L100.FAO_Fert_Cons_tN,
L100.FAO_Fert_Prod_tN,
L100.FAO_For_Exp_m3,
L100.FAO_For_Imp_m3,
L100.FAO_For_Prod_m3)
} else {
stop("Unknown command")
}
}
rohmin9122/gcam-korea-release documentation built on Nov. 26, 2020, 8:11 a.m.
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Defines functions module_aglu_LA100.FAO_downscale_ctry
Documented in module_aglu_LA100.FAO_downscale_ctry
#' module_aglu_LA100.FAO_downscale_ctry
#'
#' Downscale FAO production and consumption agricultural data to AGLU countries.
#'
#' @param command API command to execute
#' @param ... other optional parameters, depending on command
#' @return Depends on \code{command}: either a vector of required inputs,
#' a vector of output names, or (if \code{command} is "MAKE") all
#' the generated outputs: \code{L100.FAO_ag_HA_ha}, \code{L100.FAO_ag_Prod_t}, \code{L100.FAO_ag_Exp_t}, \code{L100.FAO_ag_Feed_t}, \code{L100.FAO_ag_Food_t}, \code{L100.FAO_ag_Imp_t}, \code{L100.FAO_an_Exp_t}, \code{L100.FAO_an_Food_t}, \code{L100.FAO_an_Imp_t}, \code{L100.FAO_an_Prod_t}, \code{L100.FAO_CL_kha}, \code{L100.FAO_fallowland_kha}, \code{L100.FAO_harv_CL_kha}, \code{L100.FAO_Fert_Cons_tN}, \code{L100.FAO_Fert_Prod_tN}, \code{L100.FAO_For_Exp_m3}, \code{L100.FAO_For_Imp_m3}, \code{L100.FAO_For_Prod_m3}. The corresponding file in the
#' original data system was \code{LA100.FAO_downscale_ctry.R} (aglu level1).
#' @details Extrapolate each FAO dataset to 2011; match with country names; extrapolate to countries that
#' split or combined at some point (e.g. Czechoslovakia needs to be split into Czech Republic and
#' Slovakia); and calculate rolling five-year averages.
#' @importFrom assertthat assert_that
#' @importFrom tibble tibble
#' @importFrom stats aggregate
#' @import dplyr
#' @importFrom tidyr gather spread
#' @author BBL
module_aglu_LA100.FAO_downscale_ctry <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "aglu/AGLU_ctry",
FILE = "aglu/FAO/FAO_ag_HA_ha_PRODSTAT",
FILE = "aglu/FAO/FAO_ag_Prod_t_PRODSTAT",
FILE = "aglu/FAO/FAO_ag_Exp_t_SUA",
FILE = "aglu/FAO/FAO_ag_Feed_t_SUA",
FILE = "aglu/FAO/FAO_ag_Food_t_SUA",
FILE = "aglu/FAO/FAO_ag_Imp_t_SUA",
FILE = "aglu/FAO/FAO_an_Exp_t_SUA",
FILE = "aglu/FAO/FAO_an_Food_t_SUA",
FILE = "aglu/FAO/FAO_an_Imp_t_SUA",
FILE = "aglu/FAO/FAO_an_Prod_t_SUA",
FILE = "aglu/FAO/FAO_an_Stocks",
FILE = "aglu/FAO/FAO_an_Dairy_Stocks",
FILE = "aglu/FAO/FAO_CL_kha_RESOURCESTAT",
FILE = "aglu/FAO/FAO_fallowland_kha_RESOURCESTAT",
FILE = "aglu/FAO/FAO_harv_CL_kha_RESOURCESTAT",
FILE = "aglu/FAO/FAO_Fert_Cons_tN_RESOURCESTAT_archv",
FILE = "aglu/FAO/FAO_Fert_Cons_tN_RESOURCESTAT",
FILE = "aglu/FAO/FAO_Fert_Prod_tN_RESOURCESTAT_archv",
FILE = "aglu/FAO/FAO_Fert_Prod_tN_RESOURCESTAT",
FILE = "aglu/FAO/FAO_For_Exp_m3_FORESTAT",
FILE = "aglu/FAO/FAO_For_Imp_m3_FORESTAT",
FILE = "aglu/FAO/FAO_For_Prod_m3_FORESTAT"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L100.FAO_ag_HA_ha",
"L100.FAO_ag_Prod_t",
"L100.FAO_ag_Exp_t",
"L100.FAO_ag_Feed_t",
"L100.FAO_ag_Food_t",
"L100.FAO_ag_Imp_t",
"L100.FAO_an_Exp_t",
"L100.FAO_an_Food_t",
"L100.FAO_an_Imp_t",
"L100.FAO_an_Prod_t",
"L100.FAO_an_Stocks",
"L100.FAO_an_Dairy_Stocks",
"L100.FAO_CL_kha",
"L100.FAO_fallowland_kha",
"L100.FAO_harv_CL_kha",
"L100.FAO_Fert_Cons_tN",
"L100.FAO_Fert_Prod_tN",
"L100.FAO_For_Exp_m3",
"L100.FAO_For_Imp_m3",
"L100.FAO_For_Prod_m3"))
} else if(command == driver.MAKE) {
iso <- FAO_country <- `country codes` <- `element codes` <- `item codes` <-
year <- value <- countries <- country.codes <- item <- item.codes <-
element <- element.codes <- NULL # silence package chck.
all_data <- list(...)[[1]]
# Load required inputs
get_data(all_data, "aglu/AGLU_ctry") %>%
select(iso, FAO_country) %>%
distinct ->
AGLU_ctry
FAO_ag_HA_ha_PRODSTAT <- get_data(all_data, "aglu/FAO/FAO_ag_HA_ha_PRODSTAT")
FAO_ag_Prod_t_PRODSTAT <- get_data(all_data, "aglu/FAO/FAO_ag_Prod_t_PRODSTAT")
FAO_ag_Exp_t_SUA <- get_data(all_data, "aglu/FAO/FAO_ag_Exp_t_SUA")
FAO_ag_Feed_t_SUA <- get_data(all_data, "aglu/FAO/FAO_ag_Feed_t_SUA")
FAO_ag_Food_t_SUA <- get_data(all_data, "aglu/FAO/FAO_ag_Food_t_SUA")
FAO_ag_Imp_t_SUA <- get_data(all_data, "aglu/FAO/FAO_ag_Imp_t_SUA")
FAO_an_Exp_t_SUA <- get_data(all_data, "aglu/FAO/FAO_an_Exp_t_SUA")
FAO_an_Food_t_SUA <- get_data(all_data, "aglu/FAO/FAO_an_Food_t_SUA")
FAO_an_Imp_t_SUA <- get_data(all_data, "aglu/FAO/FAO_an_Imp_t_SUA")
FAO_an_Prod_t_SUA <- get_data(all_data, "aglu/FAO/FAO_an_Prod_t_SUA")
FAO_an_Stocks <- get_data(all_data, "aglu/FAO/FAO_an_Stocks")
FAO_an_Dairy_Stocks <- get_data(all_data, "aglu/FAO/FAO_an_Dairy_Stocks")
FAO_CL_kha_RESOURCESTAT <- get_data(all_data, "aglu/FAO/FAO_CL_kha_RESOURCESTAT")
FAO_fallowland_kha_RESOURCESTAT <- get_data(all_data, "aglu/FAO/FAO_fallowland_kha_RESOURCESTAT")
FAO_harv_CL_kha_RESOURCESTAT <- get_data(all_data, "aglu/FAO/FAO_harv_CL_kha_RESOURCESTAT")
FAO_Fert_Cons_tN_RESOURCESTAT_archv <- get_data(all_data, "aglu/FAO/FAO_Fert_Cons_tN_RESOURCESTAT_archv")
FAO_Fert_Cons_tN_RESOURCESTAT <- get_data(all_data, "aglu/FAO/FAO_Fert_Cons_tN_RESOURCESTAT")
FAO_Fert_Prod_tN_RESOURCESTAT_archv <- get_data(all_data, "aglu/FAO/FAO_Fert_Prod_tN_RESOURCESTAT_archv")
FAO_Fert_Prod_tN_RESOURCESTAT<- get_data(all_data, "aglu/FAO/FAO_Fert_Prod_tN_RESOURCESTAT")
FAO_For_Exp_m3_FORESTAT <- get_data(all_data, "aglu/FAO/FAO_For_Exp_m3_FORESTAT")
FAO_For_Imp_m3_FORESTAT <- get_data(all_data, "aglu/FAO/FAO_For_Imp_m3_FORESTAT")
FAO_For_Prod_m3_FORESTAT <- get_data(all_data, "aglu/FAO/FAO_For_Prod_m3_FORESTAT")
itel_colnames <- c("item", "item codes", "element", "element codes")
coitel_colnames <- c("countries", "country codes", itel_colnames)
FAO_histyear_cols <- as.character(aglu.FAO_HISTORICAL_YEARS)
# Replace the item and element code names with what is used in the more recent datasets
FAO_Fert_Cons_tN_RESOURCESTAT_archv[itel_colnames] <- FAO_Fert_Cons_tN_RESOURCESTAT[1, itel_colnames]
FAO_Fert_Prod_tN_RESOURCESTAT_archv[itel_colnames] <- FAO_Fert_Prod_tN_RESOURCESTAT[1, itel_colnames]
# Merge resourcestat fertilizer databases with 'archive' years (1961-2002) and more recent
# years (2002-2010). FAOSTAT notes that the methods changed between the two datasets; we
# ignore this discrepancy but use the 2002 data from the more recent dataset
FAO_Fert_Cons_tN_RESOURCESTAT_archv$`2002` <- NULL
FAO_Fert_Prod_tN_RESOURCESTAT_archv$`2002` <- NULL
# Interesting: dplyr can't go as fast as the approach taken in the original data system
# A number of dplyr operations are *considerably* slower with this big dataset, and take more lines
# So most of this function, the slowest in the entire data system, retains the original
# code (though cleaned up considerably) and logic
cons <- full_join(FAO_Fert_Cons_tN_RESOURCESTAT_archv,
FAO_Fert_Cons_tN_RESOURCESTAT, by = c("countries", "country codes", "item", "item codes", "element", "element codes"))
prod <- full_join(FAO_Fert_Prod_tN_RESOURCESTAT_archv,
FAO_Fert_Prod_tN_RESOURCESTAT, by = c("countries", "country codes", "item", "item codes", "element", "element codes"))
# Aggregate to complete the merge of the two datasets
FAO_Fert_Cons_tN_RESOURCESTAT <- aggregate(cons[names(cons) %in% FAO_histyear_cols],
by = as.list(cons[coitel_colnames]),
sum, na.rm = TRUE)
FAO_Fert_Prod_tN_RESOURCESTAT <- aggregate(prod[names(prod) %in% FAO_histyear_cols],
by = as.list(prod[coitel_colnames]),
sum, na.rm = TRUE)
# Some data in an_Stocks are in 1000s of heads instead of just heads; convert them
# Also remove the units column to be consistent with the other FAO tables.
fhyc <- names(FAO_an_Stocks) %in% aglu.FAO_HISTORICAL_YEARS
thr <- FAO_an_Stocks$units == "1000 Head"
FAO_an_Stocks[thr, fhyc] <- FAO_an_Stocks[thr, fhyc] * 1000
FAO_an_Stocks$units <- FAO_an_Dairy_Stocks$units <- NULL
# Not all databases go to 2011. Extrapolate each dataset to 2011, repeating
# the data for 2009/10. Where missing 1961, substitute 1962
list("FAO_ag_Exp_t_SUA" = FAO_ag_Exp_t_SUA,
"FAO_ag_Feed_t_SUA" = FAO_ag_Feed_t_SUA,
"FAO_ag_Food_t_SUA" = FAO_ag_Food_t_SUA,
"FAO_ag_Imp_t_SUA" = FAO_ag_Imp_t_SUA,
"FAO_an_Exp_t_SUA" = FAO_an_Exp_t_SUA,
"FAO_an_Food_t_SUA" = FAO_an_Food_t_SUA,
"FAO_an_Imp_t_SUA" = FAO_an_Imp_t_SUA,
"FAO_an_Prod_t_SUA" = FAO_an_Prod_t_SUA,
"FAO_an_Stocks" = FAO_an_Stocks,
"FAO_an_Dairy_Stocks" = FAO_an_Dairy_Stocks,
"FAO_Fert_Cons_tN_RESOURCESTAT" = FAO_Fert_Cons_tN_RESOURCESTAT,
"FAO_Fert_Prod_tN_RESOURCESTAT" = FAO_Fert_Prod_tN_RESOURCESTAT,
"FAO_ag_HA_ha_PRODSTAT" = FAO_ag_HA_ha_PRODSTAT,
"FAO_ag_Prod_t_PRODSTAT" = FAO_ag_Prod_t_PRODSTAT,
"FAO_CL_kha_RESOURCESTAT" = FAO_CL_kha_RESOURCESTAT,
"FAO_fallowland_kha_RESOURCESTAT" = FAO_fallowland_kha_RESOURCESTAT,
"FAO_harv_CL_kha_RESOURCESTAT" = FAO_harv_CL_kha_RESOURCESTAT,
"FAO_For_Exp_m3_FORESTAT" = FAO_For_Exp_m3_FORESTAT,
"FAO_For_Imp_m3_FORESTAT" = FAO_For_Imp_m3_FORESTAT,
"FAO_For_Prod_m3_FORESTAT" = FAO_For_Prod_m3_FORESTAT) %>%
# apply the following function over all list elements
lapply(FUN = function(df) {
if(!"1961" %in% colnames(df)) df$`1961` <- df$`1962`
if(!"2010" %in% colnames(df)) df$`2010` <- df$`2009`
if(!"2011" %in% colnames(df)) df$`2011` <- df$`2009`
df$element <- NULL
df
}) %>%
# combine everything together
bind_rows(.id = "element") ->
FAO_data_ALL
# Replace all missing values with 0
repl <- as.list(rep(0, ncol(FAO_data_ALL)))
names(repl) <- names(FAO_data_ALL)
FAO_data_ALL <- replace_na(FAO_data_ALL, repl)
# Match the iso names
FAO_data_ALL %>%
left_join(distinct(AGLU_ctry, FAO_country, .keep_all = TRUE), by = c("countries" = "FAO_country")) ->
FAO_data_ALL
# Downscale countries individually NOTE: This is complicated. The FAO data need to be downscaled
# to all FAO historical years (i.e. back to 1961 regardless of when we are starting our
# historical time series). Otherwise the early historical years will get averaged with zeroes.
# Czechoslovakia
FAO_data_ALL %>%
filter(iso %in% AGLU_ctry$iso[AGLU_ctry$FAO_country == "Czechoslovakia"]) %>%
downscale_FAO_country("Czechoslovakia", 1993L, years = aglu.FAO_HISTORICAL_YEARS) ->
FAO_data_ALL_cze
# USSR
FAO_data_ALL %>%
filter(iso %in% AGLU_ctry$iso[AGLU_ctry$FAO_country == "USSR"]) %>%
downscale_FAO_country("USSR", 1992L, years = aglu.FAO_HISTORICAL_YEARS) ->
FAO_data_ALL_ussr
# Yugoslavia
FAO_data_ALL %>%
filter(iso %in% AGLU_ctry$iso[AGLU_ctry$FAO_country == "Yugoslav SFR"]) %>%
downscale_FAO_country("Yugoslav SFR", 1992L, years = aglu.FAO_HISTORICAL_YEARS) ->
FAO_data_ALL_yug
# Drop these countries from the full database and combine
FAO_data_ALL %>%
filter(!iso %in% unique(c(FAO_data_ALL_cze$iso, FAO_data_ALL_ussr$iso, FAO_data_ALL_yug$iso))) %>%
# combine these downscaled databases
bind_rows(FAO_data_ALL_cze, FAO_data_ALL_ussr, FAO_data_ALL_yug) ->
FAO_data_ALL
# Make sure histyear_cols uses only names in our data set
FAO_histyear_cols <- intersect(FAO_histyear_cols, names(FAO_data_ALL))
# Drop observations where all years are zero
FAO_data_ALL <- FAO_data_ALL[rowSums(FAO_data_ALL[FAO_histyear_cols]) != 0, ]
# Calculate rolling five-year averages from available data
FAO_data_ALL_5yr <- FAO_data_ALL
# In the first and last two years, use the 3 and 4 available years
FAO_data_ALL_5yr[FAO_histyear_cols][1] <- rowMeans(FAO_data_ALL[FAO_histyear_cols][1:3])
FAO_data_ALL_5yr[FAO_histyear_cols][2] <- rowMeans(FAO_data_ALL[FAO_histyear_cols][1:4])
# Precalculate a few things for loop speed
lastcol <- ncol(FAO_data_ALL_5yr[FAO_histyear_cols]) - 2
x <- FAO_data_ALL[FAO_histyear_cols]
lenXFAO <- length(FAO_histyear_cols)
# Main calculation loop
for(i in 3:lastcol) {
FAO_data_ALL_5yr[FAO_histyear_cols][, i] <- rowMeans(x[i + -2:2])
}
FAO_data_ALL_5yr[FAO_histyear_cols][lenXFAO - 1] <-
rowMeans(FAO_data_ALL[FAO_histyear_cols][(lenXFAO - 3):lenXFAO])
FAO_data_ALL_5yr[FAO_histyear_cols][lenXFAO] <-
rowMeans(FAO_data_ALL[FAO_histyear_cols][(lenXFAO - 2):lenXFAO])
# From here on, only use the specified AGLU historical years
FAO_data_ALL_5yr <- FAO_data_ALL_5yr[c(coitel_colnames, "iso", as.character(aglu.AGLU_HISTORICAL_YEARS))]
# Rename columns to old names
FAO_data_ALL_5yr %>%
rename(country.codes = `country codes`,
element.codes = `element codes`,
item.codes = `item codes`) ->
FAO_data_ALL_5yr
# Change `element` columns to match old data and reshape
# FAO_data_ALL_5yr <- FAO_data_ALL_5yr[c(1:6,8:47,7)]
FAO_data_ALL_5yr$element <- gsub(pattern = "_[A-Z]*$", "", FAO_data_ALL_5yr$element)
FAO_data_ALL_5yr$element <- gsub(pattern = "^FAO_", "", FAO_data_ALL_5yr$element)
FAO_data_ALL_5yr <- gather_years(FAO_data_ALL_5yr)
# Re-split into separate tables for each element
L100.FAOlist <- split(seq(1, nrow(FAO_data_ALL_5yr)), FAO_data_ALL_5yr$element)
names(L100.FAOlist) <- lapply(names(L100.FAOlist), function(x) { paste0("L100.FAO_", x) })
# change list names to match the legacy
# names
fixup <- function(irows, legacy.name) {
# If the name of the table being added is L100.FAO_ag_Food_t or L100.FAO_CL_kha it is
# self tested and does not need the test flags, see https://github.com/JGCRI/gcamdata/issues/918 for more
# details.
if(legacy.name %in% c("L100.FAO_ag_Food_t", "L100.FAO_CL_kha")) {
FAO_data_ALL_5yr[irows,] %>%
add_comments("Downscale countries; calculate 5-yr averages") %>%
add_legacy_name(legacy.name)
} else {
FAO_data_ALL_5yr[irows,] %>%
add_comments("Downscale countries; calculate 5-yr averages") %>%
add_legacy_name(legacy.name)
}
}
L100.FAOlist <- Map(fixup, L100.FAOlist, names(L100.FAOlist))
# Add description, units, process (done above), and precursor information
L100.FAOlist[["L100.FAO_ag_HA_ha"]] %>%
add_title("FAO agricultural harvested area by country, item, year") %>%
add_units("t") %>%
add_precursors("aglu/FAO/FAO_ag_HA_ha_PRODSTAT", "aglu/AGLU_ctry") ->
L100.FAO_ag_HA_ha
L100.FAOlist[["L100.FAO_ag_Prod_t"]] %>%
add_title("FAO agricultural production by country, item, year") %>%
add_units("t") %>%
add_precursors("aglu/FAO/FAO_ag_Prod_t_PRODSTAT", "aglu/AGLU_ctry") ->
L100.FAO_ag_Prod_t
L100.FAOlist[["L100.FAO_ag_Exp_t"]] %>%
add_title("FAO agricultural exports by country, item, year") %>%
add_units("t") %>%
add_precursors("aglu/FAO/FAO_ag_Exp_t_SUA", "aglu/AGLU_ctry") ->
L100.FAO_ag_Exp_t
L100.FAOlist[["L100.FAO_ag_Feed_t"]] %>%
add_title("FAO agricultural feed by country, item, year") %>%
add_units("t") %>%
add_precursors("aglu/FAO/FAO_ag_Feed_t_SUA", "aglu/AGLU_ctry") ->
L100.FAO_ag_Feed_t
L100.FAOlist[["L100.FAO_ag_Food_t"]] %>%
add_title("FAO agricultural food consumption by country, item, year") %>%
add_units("t") %>%
add_precursors("aglu/FAO/FAO_ag_Food_t_SUA", "aglu/AGLU_ctry") ->
L100.FAO_ag_Food_t
L100.FAOlist[["L100.FAO_ag_Imp_t"]] %>%
add_title("FAO agricultural imports by country, item, year") %>%
add_units("t") %>%
add_precursors("aglu/FAO/FAO_ag_Imp_t_SUA", "aglu/AGLU_ctry") ->
L100.FAO_ag_Imp_t
L100.FAOlist[["L100.FAO_an_Exp_t"]] %>%
add_title("FAO animal exports by country, item, year") %>%
add_units("t") %>%
add_precursors("aglu/FAO/FAO_an_Exp_t_SUA", "aglu/AGLU_ctry") ->
L100.FAO_an_Exp_t
L100.FAOlist[["L100.FAO_an_Food_t"]] %>%
add_title("FAO animal food consumption by country, item, year") %>%
add_units("t") %>%
add_precursors("aglu/FAO/FAO_an_Food_t_SUA", "aglu/AGLU_ctry") ->
L100.FAO_an_Food_t
L100.FAOlist[["L100.FAO_an_Imp_t"]] %>%
add_title("FAO animal imports by country, item, year") %>%
add_units("t") %>%
add_precursors("aglu/FAO/FAO_an_Imp_t_SUA", "aglu/AGLU_ctry") ->
L100.FAO_an_Imp_t
L100.FAOlist[["L100.FAO_an_Prod_t"]] %>%
add_title("FAO animal production by country, item, year") %>%
add_units("t") %>%
add_precursors("aglu/FAO/FAO_an_Prod_t_SUA", "aglu/AGLU_ctry") ->
L100.FAO_an_Prod_t
L100.FAOlist[["L100.FAO_an_Stocks"]] %>%
add_title("FAO animal stocks country, item, year") %>%
add_units("number") %>%
add_precursors("aglu/FAO/FAO_an_Stocks", "aglu/AGLU_ctry") ->
L100.FAO_an_Stocks
L100.FAOlist[["L100.FAO_an_Dairy_Stocks"]] %>%
add_title("FAO dairy producing animal stocks country, item, year") %>%
add_units("number") %>%
add_precursors("aglu/FAO/FAO_an_Dairy_Stocks", "aglu/AGLU_ctry") ->
L100.FAO_an_Dairy_Stocks
L100.FAOlist[["L100.FAO_CL_kha"]] %>%
add_title("FAO cropland area by country, year") %>%
add_units("kha") %>%
add_precursors("aglu/FAO/FAO_CL_kha_RESOURCESTAT", "aglu/AGLU_ctry") ->
L100.FAO_CL_kha
L100.FAOlist[["L100.FAO_fallowland_kha"]] %>%
add_title("FAO fallow land area by country, year") %>%
add_units("kha") %>%
add_precursors("aglu/FAO/FAO_fallowland_kha_RESOURCESTAT", "aglu/AGLU_ctry") ->
L100.FAO_fallowland_kha
L100.FAOlist[["L100.FAO_harv_CL_kha"]] %>%
add_title("FAO harvested cropland (temporary crops) area by country, year") %>%
add_units("kha") %>%
add_precursors("aglu/FAO/FAO_harv_CL_kha_RESOURCESTAT", "aglu/AGLU_ctry") ->
L100.FAO_harv_CL_kha
L100.FAOlist[["L100.FAO_Fert_Cons_tN"]] %>%
add_title("FAO fertilizer consumption by country, year") %>%
add_units("tonnes N") %>%
add_precursors("aglu/FAO/FAO_Fert_Cons_tN_RESOURCESTAT",
"aglu/FAO/FAO_Fert_Cons_tN_RESOURCESTAT_archv",
"aglu/AGLU_ctry") ->
L100.FAO_Fert_Cons_tN
L100.FAOlist[["L100.FAO_Fert_Prod_tN"]] %>%
add_title("FAO fertilizer production by country, year") %>%
add_units("tonnes N") %>%
add_precursors("aglu/FAO/FAO_Fert_Prod_tN_RESOURCESTAT",
"aglu/FAO/FAO_Fert_Prod_tN_RESOURCESTAT_archv",
"aglu/AGLU_ctry") ->
L100.FAO_Fert_Prod_tN
L100.FAOlist[["L100.FAO_For_Exp_m3"]] %>%
add_title("FAO forestry exports by country, year") %>%
add_units("m3") %>%
add_precursors("aglu/FAO/FAO_For_Exp_m3_FORESTAT", "aglu/AGLU_ctry") ->
L100.FAO_For_Exp_m3
L100.FAOlist[["L100.FAO_For_Imp_m3"]] %>%
add_title("FAO forestry imports by country, year") %>%
add_units("m3") %>%
add_precursors("aglu/FAO/FAO_For_Imp_m3_FORESTAT", "aglu/AGLU_ctry") ->
L100.FAO_For_Imp_m3
L100.FAOlist[["L100.FAO_For_Prod_m3"]] %>%
add_title("FAO forestry production by country, year") %>%
add_units("m3") %>%
add_precursors("aglu/FAO/FAO_For_Prod_m3_FORESTAT", "aglu/AGLU_ctry") ->
L100.FAO_For_Prod_m3
return_data(L100.FAO_ag_HA_ha,
L100.FAO_ag_Prod_t,
L100.FAO_ag_Exp_t,
L100.FAO_ag_Feed_t,
L100.FAO_ag_Food_t,
L100.FAO_ag_Imp_t,
L100.FAO_an_Exp_t,
L100.FAO_an_Food_t,
L100.FAO_an_Imp_t,
L100.FAO_an_Prod_t,
L100.FAO_an_Stocks,
L100.FAO_an_Dairy_Stocks,
L100.FAO_CL_kha,
L100.FAO_fallowland_kha,
L100.FAO_harv_CL_kha,
L100.FAO_Fert_Cons_tN,
L100.FAO_Fert_Prod_tN,
L100.FAO_For_Exp_m3,
L100.FAO_For_Imp_m3,
L100.FAO_For_Prod_m3)
} else {
stop("Unknown command")
}
}
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