# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' module_aglu_LB163.bio_Yield_R_GLU_irr
#'
#' Compute base year generic, rainfed, and irrigated bioenergy crop yields for each GCAM region and GLU.
#'
#' @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{L163.ag_irrBioYield_GJm2_R_GLU}, \code{L163.ag_rfdBioYield_GJm2_R_GLU}. The corresponding file in the
#' original data system was \code{LB163.bio_Yield_R_GLU_irr.R} (aglu level1).
#' @details A global average yield is calculated for each GTAP crop. This is then used to calculate a yield Ratio for each
#' iso-GLU-irrigation for each GTAP crop. This ratio and harvested area are then summed across all GTAP crops to the GCAM
#' region-GLU-irrigation level and are used to calculate a YieldIndex for each region-GLU-irrigation. This YieldIndex is
#' then multiplied by a base yield (calculated from USA yields) to get bioenergy yields for each region-GLU-irrigation.
#' @references Wullschleger, S.D., E.B. Davis, M.E. Borsuk, C.A. Gunderson, and L.R. Lynd. 2010.
#' Biomass production in switchgrass across the United States: database description and determinants
#' of yield. Agronomy Journal 102: 1158-1168. doi:10.2134/agronj2010.0087.
#' @importFrom assertthat assert_that
#' @importFrom dplyr bind_rows filter group_by left_join mutate pull select summarise
#' @author ACS June 2017
module_aglu_LB163.bio_Yield_R_GLU_irr <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/iso_GCAM_regID",
"L100.LDS_ag_HA_ha",
"L100.LDS_ag_prod_t",
"L101.ag_HA_bm2_R_C_Y_GLU",
"L151.ag_irrHA_ha_ctry_crop",
"L151.ag_irrProd_t_ctry_crop",
"L151.ag_rfdHA_ha_ctry_crop",
"L151.ag_rfdProd_t_ctry_crop"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L163.ag_irrBioYield_GJm2_R_GLU",
"L163.ag_rfdBioYield_GJm2_R_GLU",
"L113.ag_bioYield_GJm2_R_GLU"))
} else if(command == driver.MAKE) {
all_data <- list(...)[[1]]
GTAP_crop <- value <- Prod <- HA <- irrHA <- irrProd <- rfdHA <- rfdProd <- Yield_avg <-
Yield <- Ratio <- iso <- GCAM_region_ID <- GLU <- Irr_Rfd <- Ratio_weight <- . <-
YieldIndex <- NULL # silence package check notes
# Load required inputs
iso_GCAM_regID <- get_data(all_data, "common/iso_GCAM_regID")
L100.LDS_ag_HA_ha <- get_data(all_data, "L100.LDS_ag_HA_ha")
L100.LDS_ag_prod_t <- get_data(all_data, "L100.LDS_ag_prod_t")
L101.ag_HA_bm2_R_C_Y_GLU <- get_data(all_data, "L101.ag_HA_bm2_R_C_Y_GLU")
L151.ag_irrHA_ha_ctry_crop <- get_data(all_data, "L151.ag_irrHA_ha_ctry_crop")
L151.ag_irrProd_t_ctry_crop <- get_data(all_data, "L151.ag_irrProd_t_ctry_crop")
L151.ag_rfdHA_ha_ctry_crop <- get_data(all_data, "L151.ag_rfdHA_ha_ctry_crop")
L151.ag_rfdProd_t_ctry_crop <- get_data(all_data, "L151.ag_rfdProd_t_ctry_crop")
# Perform computations
# old comment: This method follows the same method as LB113, with the exception that
# the yield indices are computed separately for rainfed/irrigated, but
# again against the global average for each crop, across both irrigated
# and rainfed.
#
# Step 1: Aggregate FAO harvested area and production for each GTAP_crop to get global
# yields in a base year.
# Harvested area:
L100.LDS_ag_HA_ha %>%
group_by(GTAP_crop) %>%
summarise(HA = sum(value)) %>%
ungroup() ->
L163.ag_HA_ha_glbl_crop
# Aggregate Production and join aggregated HA to calculate global average yield for each GTAP crop:
L100.LDS_ag_prod_t %>%
group_by(GTAP_crop) %>%
summarise(Prod = sum(value)) %>%
ungroup() %>%
left_join_error_no_match(L163.ag_HA_ha_glbl_crop, by = "GTAP_crop") %>%
mutate(Yield_avg = Prod / HA) ->
L163.ag_prod_t_glbl_crop
# Step 2: Calculate yield for each region-GLU-GTAPcrop-irrigation and compare to global
# average yield from Step 1.
#
# Process irrigated HA and production by iso-GLU-GTAPcrop for joining and calculating yield:
L151.ag_irrHA_ha_ctry_crop %>%
mutate(Irr_Rfd = "IRR") %>%
rename(HA = irrHA) ->
L151.ag_irrHA_ha_ctry_crop
L151.ag_irrProd_t_ctry_crop %>%
mutate(Irr_Rfd = "IRR") %>%
rename(Prod = irrProd) ->
L151.ag_irrProd_t_ctry_crop
# Process rainfed HA and production by iso-GLU-GTAPcrop for joining and calculating yield:
L151.ag_rfdHA_ha_ctry_crop %>%
mutate(Irr_Rfd = "RFD") %>%
rename(HA = rfdHA) ->
L151.ag_rfdHA_ha_ctry_crop
L151.ag_rfdProd_t_ctry_crop %>%
mutate(Irr_Rfd = "RFD") %>%
rename(Prod = rfdProd) ->
L151.ag_rfdProd_t_ctry_crop
# First calculated yields for generic crops and
# join in global average yield to compute a
# Ratio = Yield / Yield_avg and a
# Ratio_weight = Ratio * HA.
# HA and Ratio_weight can then be aggregated from iso to GCAM region
# and used to calculate YieldIndex = Ratio_weight/HA:
# GPK 1/3/2019 modification: the inner_join step below guarantees that bioenergy grass yields are only estimated in
# land use regions that have harvested area in FAOSTAT. There are some countries (e.g. San Marino) in Monfreda/LDS
# but not FAOSTAT, which can lead to inconsistency in whether bioenergy grass crops are available in a given land
# use region.
L100.LDS_ag_HA_ha %>%
rename(HA = value) %>%
left_join_error_no_match(L100.LDS_ag_prod_t, by = c("iso", "GLU", "GTAP_crop")) %>%
mutate(Yield = value / HA) %>%
# Drop the missing values, where the harvested area was above the min threshold but production was not
na.omit %>%
left_join_error_no_match(select(L163.ag_prod_t_glbl_crop, GTAP_crop, Yield_avg), by = "GTAP_crop") %>%
mutate(Ratio = Yield / Yield_avg,
Ratio_weight = Ratio * HA) %>%
left_join_error_no_match(select(iso_GCAM_regID, iso, GCAM_region_ID), by = "iso") %>%
group_by(GCAM_region_ID, GLU) %>%
summarise(HA = sum(HA), Ratio_weight = sum(Ratio_weight)) %>%
ungroup %>%
inner_join(distinct(select(L101.ag_HA_bm2_R_C_Y_GLU, GCAM_region_ID, GLU)),
by = c("GCAM_region_ID", "GLU")) %>%
mutate(YieldIndex = Ratio_weight / HA) ->
L113.YieldIndex_R_GLU
# Join all four processed L151 data frames and repeat above steps,
# but now for rainfed and irrigated, rather than generic, crops
L151.ag_irrHA_ha_ctry_crop %>%
bind_rows(L151.ag_rfdHA_ha_ctry_crop) %>%
left_join_error_no_match(bind_rows(L151.ag_irrProd_t_ctry_crop, L151.ag_rfdProd_t_ctry_crop),
by = c("iso", "GLU", "GTAP_crop", "Irr_Rfd")) %>%
mutate(Yield = Prod / HA) %>%
# drop NA's - values where HA = 0
na.omit() %>%
# join global average yield for each GTAP crop, and calculate Ratio and Ratio_weight
# for aggregation from iso to GCAM region
left_join_error_no_match(select(L163.ag_prod_t_glbl_crop, GTAP_crop, Yield_avg),
by = "GTAP_crop") %>%
mutate(Ratio = Yield / Yield_avg,
Ratio_weight = Ratio * HA) %>%
# add GCAM region info and aggregate HA and Ratio_weight
left_join_error_no_match(select(iso_GCAM_regID, iso, GCAM_region_ID),
by = "iso") %>%
group_by(GCAM_region_ID, GLU, Irr_Rfd) %>%
summarise(HA = sum(HA),
Ratio_weight = sum(Ratio_weight)) %>%
ungroup() %>%
mutate(YieldIndex = Ratio_weight / HA) ->
L163.YieldIndex_R_GLU_irr
# Step 3: Bioenergy yields are equal to the region-glu-irrigation index,
# calculated in Step 2 - L163.YieldIndex_R_GLU_irr, multiplied by a base yield.
# The base yield is taken to be the maximum of the yields in the USA
# region, or the region containing the USA because the Wullschleger paper
# from which the yield estimate was derived was for the USA.
# USA region ID:
iso_GCAM_regID %>%
filter(iso == "usa") %>%
pull(GCAM_region_ID) ->
USAreg
# Calculate the base yield, a scaler value:
L113.base_bio_yield_tha <- aglu.MAX_BIO_YIELD_THA / max(L113.YieldIndex_R_GLU$YieldIndex[L113.YieldIndex_R_GLU$GCAM_region_ID == USAreg])
L113.base_bio_yield_GJm2 <- L113.base_bio_yield_tha * aglu.BIO_ENERGY_CONTENT_GJT / CONV_HA_M2
L163.base_bio_yield_tha <- aglu.MAX_BIO_YIELD_THA / max(L163.YieldIndex_R_GLU_irr$YieldIndex[L163.YieldIndex_R_GLU_irr$GCAM_region_ID == USAreg])
L163.base_bio_yield_GJm2 <- L163.base_bio_yield_tha * aglu.BIO_ENERGY_CONTENT_GJT / CONV_HA_M2
# Finally, calculate bioenergy yields in each region-glu-irrigation combo:
L113.YieldIndex_R_GLU %>%
mutate(Yield_GJm2 = YieldIndex * L113.base_bio_yield_GJm2) %>%
select(-HA, -Ratio_weight, -YieldIndex) ->
L113.ag_bioYield_GJm2_R_GLU
L163.YieldIndex_R_GLU_irr %>%
mutate(Yield_GJm2 = YieldIndex * L163.base_bio_yield_GJm2) %>%
select(-HA, -Ratio_weight, -YieldIndex) ->
L163.ag_bioYield_GJm2_R_GLU_irr
# Step 4: Split rainfed and irrigated into separate tables for the write-out
# (to be consistent with other files)
L163.ag_bioYield_GJm2_R_GLU_irr %>%
filter(Irr_Rfd == "IRR") %>%
select(-Irr_Rfd) ->
L163.ag_irrBioYield_GJm2_R_GLU
L163.ag_bioYield_GJm2_R_GLU_irr %>%
filter(Irr_Rfd == "RFD") %>%
select(-Irr_Rfd) ->
L163.ag_rfdBioYield_GJm2_R_GLU
# Produce outputs
L113.ag_bioYield_GJm2_R_GLU %>%
add_title("Base year bioenergy yields by GCAM region and GLU") %>%
add_units(" GJ/m2") %>%
add_comments("Calculate global average yields for each FAO crop in the base year;") %>%
add_comments("calculate each region / zone / crop's comparative yield; compute bioenergy yields") %>%
add_comments("as this region/zone-specific index multiplied by a base yield") %>%
add_legacy_name("L113.ag_bioYield_GJm2_R_GLU") %>%
add_precursors("common/iso_GCAM_regID",
"L100.LDS_ag_HA_ha",
"L100.LDS_ag_prod_t",
"L101.ag_HA_bm2_R_C_Y_GLU") ->
L113.ag_bioYield_GJm2_R_GLU
L163.ag_irrBioYield_GJm2_R_GLU %>%
add_title("Reference base year bioenergy yields for irrigated crops by GCAM region / GLU") %>%
add_units("Gigajoule per square meter (GJ/m2)") %>%
add_comments("A global average yield is calculated for each GTAP_crop and is used to calculate") %>%
add_comments("aggregate irrigated harvested areas to the GCAM region level (summing over all GTAPcrops), ") %>%
add_comments("and then to calculate a Yield Index for each irrigated region-GLU. The region-GLU ") %>%
add_comments("specific index is then multiplied by a base yield to give irrigated bioenergy yields.") %>%
add_legacy_name("L163.ag_irrBioYield_GJm2_R_GLU") %>%
add_precursors("common/iso_GCAM_regID",
"L100.LDS_ag_HA_ha",
"L100.LDS_ag_prod_t",
"L151.ag_irrHA_ha_ctry_crop",
"L151.ag_irrProd_t_ctry_crop") ->
L163.ag_irrBioYield_GJm2_R_GLU
L163.ag_rfdBioYield_GJm2_R_GLU %>%
add_title("Reference base year bioenergy yields for rainfed crops by GCAM region / GLU") %>%
add_units("Gigajoule per square meter (GJ/m2)") %>%
add_comments("A global average yield is calculated for each GTAP_crop and is used to calculate") %>%
add_comments("aggregate rainfed harvested areas to the GCAM region level (summing over all GTAPcrops), ") %>%
add_comments("and then to calculate a Yield Index for each rainfed region-GLU. The region-GLU ") %>%
add_comments("specific index is then multiplied by a base yield to give rainfed bioenergy yields.") %>%
add_legacy_name("L163.ag_rfdBioYield_GJm2_R_GLU") %>%
add_precursors("common/iso_GCAM_regID",
"L100.LDS_ag_HA_ha",
"L100.LDS_ag_prod_t",
"L151.ag_rfdHA_ha_ctry_crop",
"L151.ag_rfdProd_t_ctry_crop") ->
L163.ag_rfdBioYield_GJm2_R_GLU
return_data(L113.ag_bioYield_GJm2_R_GLU, L163.ag_irrBioYield_GJm2_R_GLU, L163.ag_rfdBioYield_GJm2_R_GLU)
} else {
stop("Unknown command")
}
}
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