R/zchunk_L2012.ag_For_Past_bio_input_irr_mgmt.R
In rohmin9122/gcam-korea-release: Build the GCAM-Korea Input Datasets
Defines functions
module_aglu_L2012.ag_For_Past_bio_input_irr_mgmt
Documented in
module_aglu_L2012.ag_For_Past_bio_input_irr_mgmt
#' module_aglu_L2012.ag_For_Past_bio_input_irr_mgmt
#'
#' Build agriculture, forest, pasture and biomass production inputs for all technologies.
#'
#' @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{L2012.AgSupplySector}, \code{L2012.AgSupplySubsector}, \code{L2012.AgProduction_ag_irr_mgmt}, \code{L2012.AgProduction_For}, \code{L2012.AgProduction_Past}, \code{L2012.AgHAtoCL_irr_mgmt}, \code{L2012.AgYield_bio_ref}. The corresponding file in the
#' original data system was \code{L2012.ag_For_Past_bio_input_irr_mgmt.R} (aglu level2).
#' @details This chunk specifies the input tables for agriculture, forest, pasture and biomass supply sectors and subsectors,
#' agricultural commodity production and harvest area to cropland by technologies, forest and pasture production,
#' and biomass grass and tree crops yield by technologies.
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select
#' @importFrom tidyr gather spread
#' @author RC August 2017
module_aglu_L2012.ag_For_Past_bio_input_irr_mgmt <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/GCAM_region_names",
FILE = "water/basin_to_country_mapping",
FILE = "aglu/A_agSupplySector",
FILE = "aglu/A_agSupplySubsector",
"L101.ag_Prod_Mt_R_C_Y_GLU",
"L113.ag_bioYield_GJm2_R_GLU",
"L122.ag_HA_to_CropLand_R_Y_GLU",
"L123.ag_Prod_Mt_R_Past_Y_GLU",
"L123.For_Prod_bm3_R_Y_GLU",
"L132.ag_an_For_Prices",
"L161.ag_irrProd_Mt_R_C_Y_GLU",
"L161.ag_rfdProd_Mt_R_C_Y_GLU",
"L163.ag_irrBioYield_GJm2_R_GLU",
"L163.ag_rfdBioYield_GJm2_R_GLU",
"L181.ag_Prod_Mt_R_C_Y_GLU_irr_level",
"L181.YieldMult_R_bio_GLU_irr"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L2012.AgSupplySector",
"L2012.AgSupplySubsector",
"L2012.AgProduction_ag_irr_mgmt",
"L2012.AgProduction_For",
"L2012.AgProduction_Past",
"L2012.AgHAtoCL_irr_mgmt",
"L2012.AgYield_bio_ref",
"L201.AgYield_bio_grass",
"L201.AgYield_bio_tree"))
} else if(command == driver.MAKE) {
GCAM_commodity <- GCAM_region_ID <- region <- value <- year <- GLU <- GLU_name <- GLU_code <-
AgSupplySector <- AgSupplySubsector <- AgProductionTechnology <- share.weight.year <- subs.share.weight <-
tech.share.weight <- logit.year.fillout <- logit.exponent <- calPrice <- calOutputValue <-
market <- IRR_RFD <- Irr_Rfd <- MGMT <- level <- yield <- generic.yield <- yield_irr <-
yieldmult <- Yield_GJm2 <- NULL # silence package check notes
all_data <- list(...)[[1]]
# Load required inputs
GCAM_region_names <- get_data(all_data, "common/GCAM_region_names")
basin_to_country_mapping <- get_data(all_data, "water/basin_to_country_mapping")
A_AgSupplySector <- get_data(all_data, "aglu/A_agSupplySector")
A_AgSupplySubsector <- get_data(all_data, "aglu/A_agSupplySubsector")
L101.ag_Prod_Mt_R_C_Y_GLU <- get_data(all_data, "L101.ag_Prod_Mt_R_C_Y_GLU")
L113.ag_bioYield_GJm2_R_GLU <- get_data(all_data, "L113.ag_bioYield_GJm2_R_GLU")
L122.ag_HA_to_CropLand_R_Y_GLU <- get_data(all_data, "L122.ag_HA_to_CropLand_R_Y_GLU")
L123.ag_Prod_Mt_R_Past_Y_GLU <- get_data(all_data, "L123.ag_Prod_Mt_R_Past_Y_GLU")
L123.For_Prod_bm3_R_Y_GLU <- get_data(all_data, "L123.For_Prod_bm3_R_Y_GLU")
L132.ag_an_For_Prices <- get_data(all_data, "L132.ag_an_For_Prices")
L161.ag_irrProd_Mt_R_C_Y_GLU <- get_data(all_data, "L161.ag_irrProd_Mt_R_C_Y_GLU")
L161.ag_rfdProd_Mt_R_C_Y_GLU <- get_data(all_data, "L161.ag_rfdProd_Mt_R_C_Y_GLU")
L163.ag_irrBioYield_GJm2_R_GLU <- get_data(all_data, "L163.ag_irrBioYield_GJm2_R_GLU")
L163.ag_rfdBioYield_GJm2_R_GLU <- get_data(all_data, "L163.ag_rfdBioYield_GJm2_R_GLU")
L181.ag_Prod_Mt_R_C_Y_GLU_irr_level <- get_data(all_data, "L181.ag_Prod_Mt_R_C_Y_GLU_irr_level")
L181.YieldMult_R_bio_GLU_irr <- get_data(all_data, "L181.YieldMult_R_bio_GLU_irr")
# L2012.AgSupplySector: Generic AgSupplySector characteristics (units, calprice, market, logit)
A_AgSupplySector %>%
# At the supplysector (market) level, all regions get all supplysectors
write_to_all_regions(c(LEVEL2_DATA_NAMES[["AgSupplySector"]], LOGIT_TYPE_COLNAME),
GCAM_region_names = GCAM_region_names) %>%
select(-calPrice) %>%
# Join calibration price data, there are missing value for biomass, use left_join instead
left_join(L132.ag_an_For_Prices, by = c("AgSupplySector" = "GCAM_commodity")) %>%
mutate(calPrice = replace(calPrice, AgSupplySector == "biomass", 1), # value irrelevant
# For regional commodities, specify market names with region names
market = replace(market, market == "regional", region[market == "regional"])) %>%
select(LEVEL2_DATA_NAMES[["AgSupplySector"]], LOGIT_TYPE_COLNAME) %>%
# Remove any regions for which agriculture and land use are not modeled
filter(!region %in% aglu.NO_AGLU_REGIONS) ->
L2012.AgSupplySector
# L2012.AgSupplySubsector: Generic AgSupplySubsector characteristics (none specified as competition is in the land allocator)
# At the subsector (production) level, only region x GLU combinations that actually exist are created.
# So start with template production tables of available region x commodity x glu for all commodities.
# First, biograss: available anywhere that has any crop production at all
L101.ag_Prod_Mt_R_C_Y_GLU %>%
select(GCAM_region_ID, GLU) %>%
unique %>%
mutate(GCAM_commodity = "biomass_grass") ->
L201.R_C_GLU_biograss
# Second, biotree: available anywhere that has any forest production at all
L123.For_Prod_bm3_R_Y_GLU %>%
select(GCAM_region_ID, GLU) %>%
unique %>%
mutate(GCAM_commodity = "biomass_tree") ->
L201.R_C_GLU_biotree
# Third, bind Ag commodties, forest, pasture and biomass all together
L101.ag_Prod_Mt_R_C_Y_GLU %>%
bind_rows(L123.For_Prod_bm3_R_Y_GLU, L123.ag_Prod_Mt_R_Past_Y_GLU) %>%
select(GCAM_region_ID, GCAM_commodity, GLU) %>%
unique %>%
bind_rows(L201.R_C_GLU_biograss, L201.R_C_GLU_biotree) %>%
arrange(GCAM_region_ID, GLU, GCAM_commodity) %>%
left_join_error_no_match(A_AgSupplySubsector, by = c("GCAM_commodity" = "AgSupplySubsector")) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(select(basin_to_country_mapping, GLU_code, GLU_name), by = c("GLU" = "GLU_code")) %>%
# Subsector isn't just the supplysector & GLU for biomass crops, as this is where the grass/tree split is done
mutate(AgSupplySubsector = paste(GCAM_commodity, GLU_name, sep = "_"),
# We do not actually care about the logit here but we need a value to avoid errors
logit.year.fillout = min(MODEL_BASE_YEARS),
logit.exponent = -3,
logit.type = NA) %>%
select(LEVEL2_DATA_NAMES[["AgSupplySubsector"]], LOGIT_TYPE_COLNAME) ->
L2012.AgSupplySubsector
# L2012.AgProduction_ag_irr_mgm: Agricultural commodities production by all technoligies
# Start with L2011.AgProduction_ag_irr to get subsector and technology shareweights
L161.ag_irrProd_Mt_R_C_Y_GLU %>%
mutate(IRR_RFD = "IRR") %>%
bind_rows(mutate(L161.ag_rfdProd_Mt_R_C_Y_GLU, IRR_RFD = "RFD")) %>%
filter(year %in% MODEL_BASE_YEARS) %>%
mutate(calOutputValue = round(value, digits = aglu.DIGITS_CALOUTPUT)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(select(basin_to_country_mapping, GLU_code, GLU_name), by = c("GLU" = "GLU_code")) %>%
# Set subsector year and technology shareweights, subsector shareweights are set at the aggregate level later
mutate(share.weight.year = year,
tech.share.weight = 0,
tech.share.weight = replace(tech.share.weight, calOutputValue > 0, 1)) ->
L2011.AgProduction_ag_irr
# Set subsector shareweights at the aggregate level across irrigated and rainfed production
L2011.AgProduction_ag_irr %>%
group_by(region, GCAM_commodity, GLU_name, year) %>%
summarise(calOutputValue = sum(calOutputValue)) %>%
ungroup %>%
mutate(subs.share.weight = 0,
subs.share.weight = replace(subs.share.weight, calOutputValue > 0, 1)) %>%
select(-calOutputValue) %>%
# Combine with subsector year and technology shareweights
right_join(L2011.AgProduction_ag_irr, by = c("region", "GCAM_commodity", "GLU_name", "year")) %>%
# Copy to high and low management levels
repeat_add_columns(tibble(MGMT = c("hi", "lo"))) %>%
# Add sector, subsector, technology names
mutate(AgSupplySector = GCAM_commodity,
AgSupplySubsector = paste(GCAM_commodity, GLU_name, sep = "_"),
AgProductionTechnology = paste(GCAM_commodity, GLU_name, IRR_RFD, MGMT, sep = "_")) %>%
select(LEVEL2_DATA_NAMES[["AgProduction"]]) ->
L2011.AgProduction_ag_irr
# For agricultural product calibrated output, use the specific management-partitioned data
L181.ag_Prod_Mt_R_C_Y_GLU_irr_level %>%
filter(year %in% MODEL_BASE_YEARS) %>%
mutate(calOutputValue = round(value, digits = aglu.DIGITS_CALOUTPUT)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(select(basin_to_country_mapping, GLU_code, GLU_name), by = c("GLU" = "GLU_code")) %>%
# Add sector, subsector, technology names
mutate(AgProductionTechnology = paste(GCAM_commodity, GLU_name, toupper(Irr_Rfd), level, sep = "_")) %>%
# Combine with subsector and technology shareweights
right_join(select(L2011.AgProduction_ag_irr, -calOutputValue),
by = c("region", "AgProductionTechnology", "year")) %>%
select(LEVEL2_DATA_NAMES[["AgProduction"]]) ->
L2012.AgProduction_ag_irr_mgmt
# L2012.AgProduction_For and L2012.AgProduction_Past: Forest and pasture product calibration (output)
L123.For_Prod_bm3_R_Y_GLU %>%
# Combine forest and pasture production by region x GLU
bind_rows(L123.ag_Prod_Mt_R_Past_Y_GLU) %>%
filter(year %in% MODEL_BASE_YEARS) %>%
mutate(calOutputValue = round(value, digits = aglu.DIGITS_CALOUTPUT)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(select(basin_to_country_mapping, GLU_code, GLU_name), by = c("GLU" = "GLU_code")) %>%
mutate(AgProductionTechnology = paste(GCAM_commodity, GLU_name, sep = "_")) ->
L201.For_Past_Prod_R_Y_GLU
# Subset only forest and pasture products from the main subsector table and paste in calibrated production, rounded
L2012.AgSupplySubsector %>%
# Use semi_join to filter region x GLU that have forest and pasture production
semi_join(L201.For_Past_Prod_R_Y_GLU, by = c("AgSupplySector" = "GCAM_commodity")) %>%
# No disaggregation of technologies
mutate(AgProductionTechnology = AgSupplySubsector) %>%
# Copy to all base years
repeat_add_columns(tibble(year = MODEL_BASE_YEARS)) %>%
left_join_error_no_match(L201.For_Past_Prod_R_Y_GLU, by = c("region", "AgProductionTechnology", "year")) %>%
# Subsector and technology shareweights (subsector requires the year as well)
mutate(share.weight.year = year,
subs.share.weight = 0,
subs.share.weight = replace(subs.share.weight, calOutputValue > 0, 1),
tech.share.weight = 0,
tech.share.weight = replace(tech.share.weight, calOutputValue > 0, 1)) %>%
select(LEVEL2_DATA_NAMES[["AgProduction"]]) ->
L2012.AgProduction_For_Past
# L2012.AgHAtoCL_irr_mgmt: Harvests area to cropland ratio per year, by technologies
# Start with the harvested-area-to-cropland table, extrapolate base year values to all model years
L122.ag_HA_to_CropLand_R_Y_GLU %>%
# Build a template table with all existing region x GLU combinations and by all model years
select(GCAM_region_ID, GLU) %>%
unique %>%
# Add all model years
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
# Full_join the original data to keep all model years for extrapolation
full_join(L122.ag_HA_to_CropLand_R_Y_GLU, by = c("GCAM_region_ID", "GLU", "year")) %>%
mutate(year = as.numeric(year), value = as.numeric(value)) %>%
group_by(GCAM_region_ID, GLU) %>%
# Copy the last base year value to all model years
mutate(value = approx_fun(year, value, rule = 2)) %>%
ungroup %>%
# Drop other historical years that are not in model base years
filter(year %in% MODEL_YEARS) %>%
mutate(harvests.per.year = round(value, digits = aglu.DIGITS_CALOUTPUT)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(select(basin_to_country_mapping, GLU_code, GLU_name), by = c("GLU" = "GLU_code")) ->
L201.ag_HA_to_CropLand_R_Y_GLU
# Paste in harvested-area-to-cropland only for agriculture commodities, repeat by irr/rfd and mgmt techs
L2012.AgSupplySubsector %>%
semi_join(L101.ag_Prod_Mt_R_C_Y_GLU, by = c("AgSupplySector" = "GCAM_commodity")) %>%
# Copy to all model years
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
# Separate the AgSupplySubsector variable to get GLU names for matching in the harvest data
mutate(AgSupplySubsector = sub("Root_Tuber", "RootTuber", AgSupplySubsector)) %>%
separate(AgSupplySubsector, c("GCAM_commodity", "GLU_name"), sep = "_") %>%
left_join(L201.ag_HA_to_CropLand_R_Y_GLU, by = c("region", "GLU_name", "year")) %>%
# Copy to both irrigated and rainfed technologies
repeat_add_columns(tibble(IRR_RFD = c("IRR", "RFD"))) %>%
# Copy to high and low management levels
repeat_add_columns(tibble(MGMT = c("hi", "lo"))) %>%
# Add subsector and technology names
mutate(GCAM_commodity = sub("RootTuber", "Root_Tuber", GCAM_commodity),
AgSupplySubsector = paste(GCAM_commodity, GLU_name, sep = "_"),
AgProductionTechnology = paste(GCAM_commodity, GLU_name, IRR_RFD, MGMT, sep = "_")) %>%
select(LEVEL2_DATA_NAMES[["AgHAtoCL"]]) ->
L2012.AgHAtoCL_irr_mgmt
# L2012.AgYield_bio_ref: bioenergy yields.
# For bio grass crops, start with data of irrigated and rainfed split;
# For bio tree crops, use the no-tech-split data of bio grass crops,
# whenever it is not available, use a minimum default yield;
# then split to irrigated and rainfed using irr:rfd yield ratios from grass crops;
# And last for both crops, apply different yield multipliers for high and low managements
# L2011.AgYield_bio_grass_irr: yields of bioenergy grass crops, with irrigated and rainfed split
L163.ag_irrBioYield_GJm2_R_GLU %>%
mutate(IRR_RFD = "IRR") %>%
# Combine irrigated and rainfet yield data
bind_rows(mutate(L163.ag_rfdBioYield_GJm2_R_GLU, IRR_RFD = "RFD")) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(select(basin_to_country_mapping, GLU_code, GLU_name), by = c("GLU" = "GLU_code")) %>%
mutate(AgSupplySubsector = paste("biomass_grass", GLU_name, sep = "_")) ->
L2011.AgYield_bio_grass_irr
# From the subsector generic table, filter bio grass crops
L2012.AgSupplySubsector %>%
filter(grepl("biomass_grass", AgSupplySubsector)) %>%
# Copy to all base years
repeat_add_columns(tibble(year = MODEL_BASE_YEARS)) %>%
# Copy to both irrigated and rainfed technologies
repeat_add_columns(tibble(IRR_RFD = c("IRR", "RFD"))) %>%
# Match in yield data, use left_join instead because of NAs
left_join(L2011.AgYield_bio_grass_irr, by = c("region", "AgSupplySubsector", "IRR_RFD")) %>%
# Round yield data
mutate(yield = round(Yield_GJm2, digits = aglu.DIGITS_CALOUTPUT)) ->
L2011.AgYield_bio_grass_irr
L2011.AgYield_bio_grass_irr %>%
# Places with no irrigated crop production will return missing values here.
# May as well set these yields to 0 to ensure that they get no irrigated bioenergy production
# in the future periods (most are tropical areas with no need for irrigation).
replace_na(list(yield = 0)) %>%
mutate(AgProductionTechnology = paste(AgSupplySubsector, IRR_RFD, sep = "_")) %>%
select(LEVEL2_DATA_NAMES[["AgYield"]]) ->
L2011.AgYield_bio_grass_irr
# L201.AgYield_bio_tree: base year biomass yields, tree bioenergy crops
# Start with the grass crop yields with no tech split where available
L113.ag_bioYield_GJm2_R_GLU %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(select(basin_to_country_mapping, GLU_code, GLU_name), by = c("GLU" = "GLU_code")) %>%
mutate(yield = round(Yield_GJm2, digits = aglu.DIGITS_CALOUTPUT)) %>%
select(-GCAM_region_ID, -GLU, -Yield_GJm2) ->
L201.AgYield_bio_grass
# From the subsector table, filter bio tree crops
L2012.AgSupplySubsector %>%
filter(grepl("biomass_tree", AgSupplySubsector)) %>%
# No tech split yet
mutate(AgProductionTechnology = AgSupplySubsector) %>%
# Copy to all base years
repeat_add_columns(tibble(year = MODEL_BASE_YEARS)) %>%
select(LEVEL2_DATA_NAMES[["AgTechYr"]]) %>%
mutate(GLU_name = AgSupplySubsector,
GLU_name = sub("biomass_tree_", "", GLU_name)) %>%
# Match in grass crop yields where available, use left_join instead because of NAs
left_join(L201.AgYield_bio_grass, by = c("region", "GLU_name")) %>%
# Where not available (i.e., where there is forest but not cropped land),
# use a default minimum as these are likely remote / unpopulated lands
replace_na(list(yield = min(L201.AgYield_bio_grass$yield))) %>%
select(LEVEL2_DATA_NAMES[["AgYield"]]) ->
L201.AgYield_bio_tree
# L2011.AgYield_bio_tree_irr: yield of bioenergy tree crops (use the irr:rfd yield ratios from grass crops)
# This method essentially copies prior versions of GCAM with irrigation, albeit in a different sequence.
# Before, we used the generic bioenergy crop yields by irr/rfd, multiplied by an assumed tree:grass yield conversion factor.
# Here, we start with the generic tree bioenergy crop yields in each land use region,
# and multiply by generic:irr and generic:rfd conversion factors.
# Compile the generic:irr and generic:rfd conversion factors
L201.AgYield_bio_grass %>%
mutate(AgSupplySubsector = paste("biomass_grass", GLU_name, sep = "_")) %>%
# Generic bio grass crop yield w/o tech split
rename(generic.yield = yield) %>%
# Match in irrigated and rainfed bio grass yields
right_join(L2011.AgYield_bio_grass_irr, by = c("region", "AgSupplySubsector")) %>%
# Compute conversion factor as irr/generic, and rfd/generic
mutate(factor = yield / generic.yield,
# Prepare for bio tree crops
AgSupplySubsector = sub("biomass_grass", "biomass_tree", AgSupplySubsector),
AgProductionTechnology = sub("biomass_grass", "biomass_tree", AgProductionTechnology)) %>%
select(-GLU_name, -yield, -generic.yield) ->
L2011.irr_rfd_factors
# Multiply generic tree crop yields by the conversion factors
# For regions that do not have grass crops but only tree crops (e.g., regions w forest but no ag production),
# simply use the generic defaults, which are likely minor ag regions anyway
L201.AgYield_bio_tree %>%
# Copy to both irrigated and rainfed technologies
repeat_add_columns(tibble(IRR_RFD = c("IRR", "RFD"))) %>%
mutate(AgProductionTechnology = paste(AgProductionTechnology, IRR_RFD, sep = "_")) %>%
# Match in conversion factors
left_join(L2011.irr_rfd_factors,
by = c("region", "AgSupplySector", "AgSupplySubsector", "AgProductionTechnology", "year")) %>%
# Calculate the irrigated and rainfed yields using the conversion factors from bio grass crops
mutate(yield_irr = yield * factor,
# When grass crops are not available, use the generic yields
yield = replace(yield, !is.na(yield_irr), yield_irr[!is.na(yield_irr)]),
yield = round(yield, digits = aglu.DIGITS_CALOUTPUT)) %>%
select(LEVEL2_DATA_NAMES[["AgYield"]]) ->
L2011.AgYield_bio_tree_irr
# Last step, apply different yield multipliers for high and low mgmt techs
# Prepare a yield multiplier table, with the mgmt level as a column ID
L181.YieldMult_R_bio_GLU_irr %>%
gather(level, yieldmult, -GCAM_region_ID, -GLU, -Irr_Rfd) %>%
mutate(level = sub("yieldmult_", "", level),
Irr_Rfd = toupper(Irr_Rfd)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(select(basin_to_country_mapping, GLU_code, GLU_name), by = c("GLU" = "GLU_code")) ->
L2012.YieldMult_R_bio_GLU_irr
# Bind the tree and grass tables, repeat by mgmt level, and match in the yield multipliers
L2011.AgYield_bio_grass_irr %>%
bind_rows(L2011.AgYield_bio_tree_irr) %>%
# Copy to high and low management levels
repeat_add_columns(tibble(MGMT = c("hi", "lo"))) %>%
# Separate technology to match in the multipliers
separate(AgProductionTechnology, c("biomass", "type", "GLU_name", "IRR_RFD")) %>%
# Match in multipliers, use left_join instead because of NAs
left_join(L2012.YieldMult_R_bio_GLU_irr, by = c("region", "GLU_name", "IRR_RFD" = "Irr_Rfd", "MGMT" = "level")) %>%
# For minor region/GLUs that are missing from the ag data, set the multipliers to 1 (effectively fixing yields in all periods)
replace_na(list(yieldmult = 1)) %>%
mutate(yield = yield * yieldmult,
# Add technology back
AgProductionTechnology = paste(AgSupplySubsector, IRR_RFD, MGMT, sep = "_")) %>%
select(LEVEL2_DATA_NAMES[["AgYield"]]) ->
L2012.AgYield_bio_ref
# Add sector, subsector, and technology information to L201.AgYield_bio_grass
L201.AgYield_bio_grass %>%
mutate(AgSupplySector = "biomass",
AgSupplySubsector = paste0("biomass_grass_", GLU_name),
AgProductionTechnology = AgSupplySubsector) %>%
repeat_add_columns((tibble(year = MODEL_BASE_YEARS))) %>%
select(-GLU_name) ->
L201.AgYield_bio_grass
# Produce outputs
L2012.AgSupplySector %>%
add_title("Generic information for agriculture supply sectors") %>%
add_units("Unitless") %>%
add_comments("Specify generic supply sector characteristics (units, calprice, market, logit)") %>%
add_comments("At the supplysector (market) level, all regions get all supplysectors") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L2012.AgSupplySector") %>%
add_precursors("common/GCAM_region_names",
"water/basin_to_country_mapping",
"aglu/A_agSupplySector",
"L132.ag_an_For_Prices") ->
L2012.AgSupplySector
L2012.AgSupplySubsector %>%
add_title("Generic information for agriculture supply subsectors") %>%
add_units("Unitless") %>%
add_comments("Specify generic supply subsector characteristics") %>%
add_comments("At the subsector (production) level, only region x GLU combinations that actually exist are created") %>%
add_legacy_name("L2012.AgSupplySubsector") %>%
add_precursors("common/GCAM_region_names",
"water/basin_to_country_mapping",
"aglu/A_agSupplySubsector",
"L101.ag_Prod_Mt_R_C_Y_GLU",
"L123.ag_Prod_Mt_R_Past_Y_GLU",
"L123.For_Prod_bm3_R_Y_GLU") ->
L2012.AgSupplySubsector
L2012.AgProduction_ag_irr_mgmt %>%
add_title("Input table for agriculture commodity production by all technologies") %>%
add_units("Mt") %>%
add_comments("Calibrated outputs are specified by each technology") %>%
add_comments("Subsector shareweights are set at the aggregated region x GLU level, same for all tech") %>%
add_comments("Technology shareweights are set by irrigated vs. rainfed, same for high and low mgmt") %>%
add_legacy_name("L2012.AgProduction_ag_irr_mgmt") %>%
add_precursors("common/GCAM_region_names",
"water/basin_to_country_mapping",
"L161.ag_irrProd_Mt_R_C_Y_GLU",
"L161.ag_rfdProd_Mt_R_C_Y_GLU",
"L181.ag_Prod_Mt_R_C_Y_GLU_irr_level") ->
L2012.AgProduction_ag_irr_mgmt
L2012.AgProduction_For_Past %>%
filter(AgSupplySector == "Forest") %>%
add_title("Input table for forest production") %>%
add_units("bm3") %>%
add_comments("Calibrated ouputs or shareweights are not specify by technology") %>%
add_legacy_name("L2012.AgProduction_For") %>%
same_precursors_as("L2012.AgSupplySubsector") ->
L2012.AgProduction_For
L2012.AgProduction_For_Past %>%
filter(AgSupplySector == "Pasture") %>%
add_title("Input table for pasture production") %>%
add_units("Mt") %>%
add_comments("Calibrated ouputs or shareweights are not specify by technology") %>%
add_legacy_name("L2012.AgProduction_Past") %>%
same_precursors_as("L2012.AgSupplySubsector") ->
L2012.AgProduction_Past
L2012.AgHAtoCL_irr_mgmt %>%
add_title("Harvest area to cropland value of agricultural commodities by year and technology") %>%
add_units("Uniteless") %>%
add_comments("Copy the same value to all technologies") %>%
add_comments("Exclude forest and pasture") %>%
add_legacy_name("L2012.AgHAtoCL_irr_mgmt") %>%
same_precursors_as("L2012.AgProduction_ag_irr_mgmt") %>%
add_precursors("L122.ag_HA_to_CropLand_R_Y_GLU") ->
L2012.AgHAtoCL_irr_mgmt
L2012.AgYield_bio_ref %>%
add_title("Bioenergy grass and tree crops yields by all technologies") %>%
add_units("GJ/m2") %>%
add_comments("Grass crops yields are used for tree crops where available") %>%
add_comments("Apply the irr:generic and rfd:generic conversion factors from grass crops") %>%
add_comments("Use default minimum value where grass crops are not available") %>%
add_comments("Apply different multipliers to high and low management for both grass and tree crops") %>%
add_legacy_name("L2012.AgYield_bio_ref") %>%
add_precursors("common/GCAM_region_names",
"water/basin_to_country_mapping",
"aglu/A_agSupplySector",
"aglu/A_agSupplySubsector",
"L113.ag_bioYield_GJm2_R_GLU",
"L163.ag_irrBioYield_GJm2_R_GLU",
"L163.ag_rfdBioYield_GJm2_R_GLU",
"L181.YieldMult_R_bio_GLU_irr") ->
L2012.AgYield_bio_ref
L201.AgYield_bio_grass %>%
add_title("Bioenergy grass crops yields for aggregate tech") %>%
add_units("GJ/m2") %>%
add_comments("Append region and basin names to L113.ag_bioYield_GJm2_R_GLU") %>%
add_legacy_name("L201.AgYield_bio_grass") %>%
add_precursors("common/GCAM_region_names",
"water/basin_to_country_mapping",
"L113.ag_bioYield_GJm2_R_GLU") ->
L201.AgYield_bio_grass
L201.AgYield_bio_tree %>%
add_title("Bioenergy tree crops yields for aggregate tech") %>%
add_units("GJ/m2") %>%
add_comments("Set bioenergy tree crop yield to the same as the bioenergy grass yield") %>%
add_comments("If data is missing for a region/basin, use the minimum yield") %>%
add_legacy_name("L201.AgYield_bio_tree") %>%
same_precursors_as("L201.AgYield_bio_grass") %>%
same_precursors_as("L2012.AgSupplySubsector") ->
L201.AgYield_bio_tree
return_data(L2012.AgSupplySector, L2012.AgSupplySubsector, L2012.AgProduction_ag_irr_mgmt, L2012.AgProduction_For, L2012.AgProduction_Past, L2012.AgHAtoCL_irr_mgmt, L2012.AgYield_bio_ref, L201.AgYield_bio_grass, L201.AgYield_bio_tree)
} 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_L2012.ag_For_Past_bio_input_irr_mgmt
Documented in module_aglu_L2012.ag_For_Past_bio_input_irr_mgmt
#' module_aglu_L2012.ag_For_Past_bio_input_irr_mgmt
#'
#' Build agriculture, forest, pasture and biomass production inputs for all technologies.
#'
#' @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{L2012.AgSupplySector}, \code{L2012.AgSupplySubsector}, \code{L2012.AgProduction_ag_irr_mgmt}, \code{L2012.AgProduction_For}, \code{L2012.AgProduction_Past}, \code{L2012.AgHAtoCL_irr_mgmt}, \code{L2012.AgYield_bio_ref}. The corresponding file in the
#' original data system was \code{L2012.ag_For_Past_bio_input_irr_mgmt.R} (aglu level2).
#' @details This chunk specifies the input tables for agriculture, forest, pasture and biomass supply sectors and subsectors,
#' agricultural commodity production and harvest area to cropland by technologies, forest and pasture production,
#' and biomass grass and tree crops yield by technologies.
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select
#' @importFrom tidyr gather spread
#' @author RC August 2017
module_aglu_L2012.ag_For_Past_bio_input_irr_mgmt <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/GCAM_region_names",
FILE = "water/basin_to_country_mapping",
FILE = "aglu/A_agSupplySector",
FILE = "aglu/A_agSupplySubsector",
"L101.ag_Prod_Mt_R_C_Y_GLU",
"L113.ag_bioYield_GJm2_R_GLU",
"L122.ag_HA_to_CropLand_R_Y_GLU",
"L123.ag_Prod_Mt_R_Past_Y_GLU",
"L123.For_Prod_bm3_R_Y_GLU",
"L132.ag_an_For_Prices",
"L161.ag_irrProd_Mt_R_C_Y_GLU",
"L161.ag_rfdProd_Mt_R_C_Y_GLU",
"L163.ag_irrBioYield_GJm2_R_GLU",
"L163.ag_rfdBioYield_GJm2_R_GLU",
"L181.ag_Prod_Mt_R_C_Y_GLU_irr_level",
"L181.YieldMult_R_bio_GLU_irr"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L2012.AgSupplySector",
"L2012.AgSupplySubsector",
"L2012.AgProduction_ag_irr_mgmt",
"L2012.AgProduction_For",
"L2012.AgProduction_Past",
"L2012.AgHAtoCL_irr_mgmt",
"L2012.AgYield_bio_ref",
"L201.AgYield_bio_grass",
"L201.AgYield_bio_tree"))
} else if(command == driver.MAKE) {
GCAM_commodity <- GCAM_region_ID <- region <- value <- year <- GLU <- GLU_name <- GLU_code <-
AgSupplySector <- AgSupplySubsector <- AgProductionTechnology <- share.weight.year <- subs.share.weight <-
tech.share.weight <- logit.year.fillout <- logit.exponent <- calPrice <- calOutputValue <-
market <- IRR_RFD <- Irr_Rfd <- MGMT <- level <- yield <- generic.yield <- yield_irr <-
yieldmult <- Yield_GJm2 <- NULL # silence package check notes
all_data <- list(...)[[1]]
# Load required inputs
GCAM_region_names <- get_data(all_data, "common/GCAM_region_names")
basin_to_country_mapping <- get_data(all_data, "water/basin_to_country_mapping")
A_AgSupplySector <- get_data(all_data, "aglu/A_agSupplySector")
A_AgSupplySubsector <- get_data(all_data, "aglu/A_agSupplySubsector")
L101.ag_Prod_Mt_R_C_Y_GLU <- get_data(all_data, "L101.ag_Prod_Mt_R_C_Y_GLU")
L113.ag_bioYield_GJm2_R_GLU <- get_data(all_data, "L113.ag_bioYield_GJm2_R_GLU")
L122.ag_HA_to_CropLand_R_Y_GLU <- get_data(all_data, "L122.ag_HA_to_CropLand_R_Y_GLU")
L123.ag_Prod_Mt_R_Past_Y_GLU <- get_data(all_data, "L123.ag_Prod_Mt_R_Past_Y_GLU")
L123.For_Prod_bm3_R_Y_GLU <- get_data(all_data, "L123.For_Prod_bm3_R_Y_GLU")
L132.ag_an_For_Prices <- get_data(all_data, "L132.ag_an_For_Prices")
L161.ag_irrProd_Mt_R_C_Y_GLU <- get_data(all_data, "L161.ag_irrProd_Mt_R_C_Y_GLU")
L161.ag_rfdProd_Mt_R_C_Y_GLU <- get_data(all_data, "L161.ag_rfdProd_Mt_R_C_Y_GLU")
L163.ag_irrBioYield_GJm2_R_GLU <- get_data(all_data, "L163.ag_irrBioYield_GJm2_R_GLU")
L163.ag_rfdBioYield_GJm2_R_GLU <- get_data(all_data, "L163.ag_rfdBioYield_GJm2_R_GLU")
L181.ag_Prod_Mt_R_C_Y_GLU_irr_level <- get_data(all_data, "L181.ag_Prod_Mt_R_C_Y_GLU_irr_level")
L181.YieldMult_R_bio_GLU_irr <- get_data(all_data, "L181.YieldMult_R_bio_GLU_irr")
# L2012.AgSupplySector: Generic AgSupplySector characteristics (units, calprice, market, logit)
A_AgSupplySector %>%
# At the supplysector (market) level, all regions get all supplysectors
write_to_all_regions(c(LEVEL2_DATA_NAMES[["AgSupplySector"]], LOGIT_TYPE_COLNAME),
GCAM_region_names = GCAM_region_names) %>%
select(-calPrice) %>%
# Join calibration price data, there are missing value for biomass, use left_join instead
left_join(L132.ag_an_For_Prices, by = c("AgSupplySector" = "GCAM_commodity")) %>%
mutate(calPrice = replace(calPrice, AgSupplySector == "biomass", 1), # value irrelevant
# For regional commodities, specify market names with region names
market = replace(market, market == "regional", region[market == "regional"])) %>%
select(LEVEL2_DATA_NAMES[["AgSupplySector"]], LOGIT_TYPE_COLNAME) %>%
# Remove any regions for which agriculture and land use are not modeled
filter(!region %in% aglu.NO_AGLU_REGIONS) ->
L2012.AgSupplySector
# L2012.AgSupplySubsector: Generic AgSupplySubsector characteristics (none specified as competition is in the land allocator)
# At the subsector (production) level, only region x GLU combinations that actually exist are created.
# So start with template production tables of available region x commodity x glu for all commodities.
# First, biograss: available anywhere that has any crop production at all
L101.ag_Prod_Mt_R_C_Y_GLU %>%
select(GCAM_region_ID, GLU) %>%
unique %>%
mutate(GCAM_commodity = "biomass_grass") ->
L201.R_C_GLU_biograss
# Second, biotree: available anywhere that has any forest production at all
L123.For_Prod_bm3_R_Y_GLU %>%
select(GCAM_region_ID, GLU) %>%
unique %>%
mutate(GCAM_commodity = "biomass_tree") ->
L201.R_C_GLU_biotree
# Third, bind Ag commodties, forest, pasture and biomass all together
L101.ag_Prod_Mt_R_C_Y_GLU %>%
bind_rows(L123.For_Prod_bm3_R_Y_GLU, L123.ag_Prod_Mt_R_Past_Y_GLU) %>%
select(GCAM_region_ID, GCAM_commodity, GLU) %>%
unique %>%
bind_rows(L201.R_C_GLU_biograss, L201.R_C_GLU_biotree) %>%
arrange(GCAM_region_ID, GLU, GCAM_commodity) %>%
left_join_error_no_match(A_AgSupplySubsector, by = c("GCAM_commodity" = "AgSupplySubsector")) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(select(basin_to_country_mapping, GLU_code, GLU_name), by = c("GLU" = "GLU_code")) %>%
# Subsector isn't just the supplysector & GLU for biomass crops, as this is where the grass/tree split is done
mutate(AgSupplySubsector = paste(GCAM_commodity, GLU_name, sep = "_"),
# We do not actually care about the logit here but we need a value to avoid errors
logit.year.fillout = min(MODEL_BASE_YEARS),
logit.exponent = -3,
logit.type = NA) %>%
select(LEVEL2_DATA_NAMES[["AgSupplySubsector"]], LOGIT_TYPE_COLNAME) ->
L2012.AgSupplySubsector
# L2012.AgProduction_ag_irr_mgm: Agricultural commodities production by all technoligies
# Start with L2011.AgProduction_ag_irr to get subsector and technology shareweights
L161.ag_irrProd_Mt_R_C_Y_GLU %>%
mutate(IRR_RFD = "IRR") %>%
bind_rows(mutate(L161.ag_rfdProd_Mt_R_C_Y_GLU, IRR_RFD = "RFD")) %>%
filter(year %in% MODEL_BASE_YEARS) %>%
mutate(calOutputValue = round(value, digits = aglu.DIGITS_CALOUTPUT)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(select(basin_to_country_mapping, GLU_code, GLU_name), by = c("GLU" = "GLU_code")) %>%
# Set subsector year and technology shareweights, subsector shareweights are set at the aggregate level later
mutate(share.weight.year = year,
tech.share.weight = 0,
tech.share.weight = replace(tech.share.weight, calOutputValue > 0, 1)) ->
L2011.AgProduction_ag_irr
# Set subsector shareweights at the aggregate level across irrigated and rainfed production
L2011.AgProduction_ag_irr %>%
group_by(region, GCAM_commodity, GLU_name, year) %>%
summarise(calOutputValue = sum(calOutputValue)) %>%
ungroup %>%
mutate(subs.share.weight = 0,
subs.share.weight = replace(subs.share.weight, calOutputValue > 0, 1)) %>%
select(-calOutputValue) %>%
# Combine with subsector year and technology shareweights
right_join(L2011.AgProduction_ag_irr, by = c("region", "GCAM_commodity", "GLU_name", "year")) %>%
# Copy to high and low management levels
repeat_add_columns(tibble(MGMT = c("hi", "lo"))) %>%
# Add sector, subsector, technology names
mutate(AgSupplySector = GCAM_commodity,
AgSupplySubsector = paste(GCAM_commodity, GLU_name, sep = "_"),
AgProductionTechnology = paste(GCAM_commodity, GLU_name, IRR_RFD, MGMT, sep = "_")) %>%
select(LEVEL2_DATA_NAMES[["AgProduction"]]) ->
L2011.AgProduction_ag_irr
# For agricultural product calibrated output, use the specific management-partitioned data
L181.ag_Prod_Mt_R_C_Y_GLU_irr_level %>%
filter(year %in% MODEL_BASE_YEARS) %>%
mutate(calOutputValue = round(value, digits = aglu.DIGITS_CALOUTPUT)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(select(basin_to_country_mapping, GLU_code, GLU_name), by = c("GLU" = "GLU_code")) %>%
# Add sector, subsector, technology names
mutate(AgProductionTechnology = paste(GCAM_commodity, GLU_name, toupper(Irr_Rfd), level, sep = "_")) %>%
# Combine with subsector and technology shareweights
right_join(select(L2011.AgProduction_ag_irr, -calOutputValue),
by = c("region", "AgProductionTechnology", "year")) %>%
select(LEVEL2_DATA_NAMES[["AgProduction"]]) ->
L2012.AgProduction_ag_irr_mgmt
# L2012.AgProduction_For and L2012.AgProduction_Past: Forest and pasture product calibration (output)
L123.For_Prod_bm3_R_Y_GLU %>%
# Combine forest and pasture production by region x GLU
bind_rows(L123.ag_Prod_Mt_R_Past_Y_GLU) %>%
filter(year %in% MODEL_BASE_YEARS) %>%
mutate(calOutputValue = round(value, digits = aglu.DIGITS_CALOUTPUT)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(select(basin_to_country_mapping, GLU_code, GLU_name), by = c("GLU" = "GLU_code")) %>%
mutate(AgProductionTechnology = paste(GCAM_commodity, GLU_name, sep = "_")) ->
L201.For_Past_Prod_R_Y_GLU
# Subset only forest and pasture products from the main subsector table and paste in calibrated production, rounded
L2012.AgSupplySubsector %>%
# Use semi_join to filter region x GLU that have forest and pasture production
semi_join(L201.For_Past_Prod_R_Y_GLU, by = c("AgSupplySector" = "GCAM_commodity")) %>%
# No disaggregation of technologies
mutate(AgProductionTechnology = AgSupplySubsector) %>%
# Copy to all base years
repeat_add_columns(tibble(year = MODEL_BASE_YEARS)) %>%
left_join_error_no_match(L201.For_Past_Prod_R_Y_GLU, by = c("region", "AgProductionTechnology", "year")) %>%
# Subsector and technology shareweights (subsector requires the year as well)
mutate(share.weight.year = year,
subs.share.weight = 0,
subs.share.weight = replace(subs.share.weight, calOutputValue > 0, 1),
tech.share.weight = 0,
tech.share.weight = replace(tech.share.weight, calOutputValue > 0, 1)) %>%
select(LEVEL2_DATA_NAMES[["AgProduction"]]) ->
L2012.AgProduction_For_Past
# L2012.AgHAtoCL_irr_mgmt: Harvests area to cropland ratio per year, by technologies
# Start with the harvested-area-to-cropland table, extrapolate base year values to all model years
L122.ag_HA_to_CropLand_R_Y_GLU %>%
# Build a template table with all existing region x GLU combinations and by all model years
select(GCAM_region_ID, GLU) %>%
unique %>%
# Add all model years
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
# Full_join the original data to keep all model years for extrapolation
full_join(L122.ag_HA_to_CropLand_R_Y_GLU, by = c("GCAM_region_ID", "GLU", "year")) %>%
mutate(year = as.numeric(year), value = as.numeric(value)) %>%
group_by(GCAM_region_ID, GLU) %>%
# Copy the last base year value to all model years
mutate(value = approx_fun(year, value, rule = 2)) %>%
ungroup %>%
# Drop other historical years that are not in model base years
filter(year %in% MODEL_YEARS) %>%
mutate(harvests.per.year = round(value, digits = aglu.DIGITS_CALOUTPUT)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(select(basin_to_country_mapping, GLU_code, GLU_name), by = c("GLU" = "GLU_code")) ->
L201.ag_HA_to_CropLand_R_Y_GLU
# Paste in harvested-area-to-cropland only for agriculture commodities, repeat by irr/rfd and mgmt techs
L2012.AgSupplySubsector %>%
semi_join(L101.ag_Prod_Mt_R_C_Y_GLU, by = c("AgSupplySector" = "GCAM_commodity")) %>%
# Copy to all model years
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
# Separate the AgSupplySubsector variable to get GLU names for matching in the harvest data
mutate(AgSupplySubsector = sub("Root_Tuber", "RootTuber", AgSupplySubsector)) %>%
separate(AgSupplySubsector, c("GCAM_commodity", "GLU_name"), sep = "_") %>%
left_join(L201.ag_HA_to_CropLand_R_Y_GLU, by = c("region", "GLU_name", "year")) %>%
# Copy to both irrigated and rainfed technologies
repeat_add_columns(tibble(IRR_RFD = c("IRR", "RFD"))) %>%
# Copy to high and low management levels
repeat_add_columns(tibble(MGMT = c("hi", "lo"))) %>%
# Add subsector and technology names
mutate(GCAM_commodity = sub("RootTuber", "Root_Tuber", GCAM_commodity),
AgSupplySubsector = paste(GCAM_commodity, GLU_name, sep = "_"),
AgProductionTechnology = paste(GCAM_commodity, GLU_name, IRR_RFD, MGMT, sep = "_")) %>%
select(LEVEL2_DATA_NAMES[["AgHAtoCL"]]) ->
L2012.AgHAtoCL_irr_mgmt
# L2012.AgYield_bio_ref: bioenergy yields.
# For bio grass crops, start with data of irrigated and rainfed split;
# For bio tree crops, use the no-tech-split data of bio grass crops,
# whenever it is not available, use a minimum default yield;
# then split to irrigated and rainfed using irr:rfd yield ratios from grass crops;
# And last for both crops, apply different yield multipliers for high and low managements
# L2011.AgYield_bio_grass_irr: yields of bioenergy grass crops, with irrigated and rainfed split
L163.ag_irrBioYield_GJm2_R_GLU %>%
mutate(IRR_RFD = "IRR") %>%
# Combine irrigated and rainfet yield data
bind_rows(mutate(L163.ag_rfdBioYield_GJm2_R_GLU, IRR_RFD = "RFD")) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(select(basin_to_country_mapping, GLU_code, GLU_name), by = c("GLU" = "GLU_code")) %>%
mutate(AgSupplySubsector = paste("biomass_grass", GLU_name, sep = "_")) ->
L2011.AgYield_bio_grass_irr
# From the subsector generic table, filter bio grass crops
L2012.AgSupplySubsector %>%
filter(grepl("biomass_grass", AgSupplySubsector)) %>%
# Copy to all base years
repeat_add_columns(tibble(year = MODEL_BASE_YEARS)) %>%
# Copy to both irrigated and rainfed technologies
repeat_add_columns(tibble(IRR_RFD = c("IRR", "RFD"))) %>%
# Match in yield data, use left_join instead because of NAs
left_join(L2011.AgYield_bio_grass_irr, by = c("region", "AgSupplySubsector", "IRR_RFD")) %>%
# Round yield data
mutate(yield = round(Yield_GJm2, digits = aglu.DIGITS_CALOUTPUT)) ->
L2011.AgYield_bio_grass_irr
L2011.AgYield_bio_grass_irr %>%
# Places with no irrigated crop production will return missing values here.
# May as well set these yields to 0 to ensure that they get no irrigated bioenergy production
# in the future periods (most are tropical areas with no need for irrigation).
replace_na(list(yield = 0)) %>%
mutate(AgProductionTechnology = paste(AgSupplySubsector, IRR_RFD, sep = "_")) %>%
select(LEVEL2_DATA_NAMES[["AgYield"]]) ->
L2011.AgYield_bio_grass_irr
# L201.AgYield_bio_tree: base year biomass yields, tree bioenergy crops
# Start with the grass crop yields with no tech split where available
L113.ag_bioYield_GJm2_R_GLU %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(select(basin_to_country_mapping, GLU_code, GLU_name), by = c("GLU" = "GLU_code")) %>%
mutate(yield = round(Yield_GJm2, digits = aglu.DIGITS_CALOUTPUT)) %>%
select(-GCAM_region_ID, -GLU, -Yield_GJm2) ->
L201.AgYield_bio_grass
# From the subsector table, filter bio tree crops
L2012.AgSupplySubsector %>%
filter(grepl("biomass_tree", AgSupplySubsector)) %>%
# No tech split yet
mutate(AgProductionTechnology = AgSupplySubsector) %>%
# Copy to all base years
repeat_add_columns(tibble(year = MODEL_BASE_YEARS)) %>%
select(LEVEL2_DATA_NAMES[["AgTechYr"]]) %>%
mutate(GLU_name = AgSupplySubsector,
GLU_name = sub("biomass_tree_", "", GLU_name)) %>%
# Match in grass crop yields where available, use left_join instead because of NAs
left_join(L201.AgYield_bio_grass, by = c("region", "GLU_name")) %>%
# Where not available (i.e., where there is forest but not cropped land),
# use a default minimum as these are likely remote / unpopulated lands
replace_na(list(yield = min(L201.AgYield_bio_grass$yield))) %>%
select(LEVEL2_DATA_NAMES[["AgYield"]]) ->
L201.AgYield_bio_tree
# L2011.AgYield_bio_tree_irr: yield of bioenergy tree crops (use the irr:rfd yield ratios from grass crops)
# This method essentially copies prior versions of GCAM with irrigation, albeit in a different sequence.
# Before, we used the generic bioenergy crop yields by irr/rfd, multiplied by an assumed tree:grass yield conversion factor.
# Here, we start with the generic tree bioenergy crop yields in each land use region,
# and multiply by generic:irr and generic:rfd conversion factors.
# Compile the generic:irr and generic:rfd conversion factors
L201.AgYield_bio_grass %>%
mutate(AgSupplySubsector = paste("biomass_grass", GLU_name, sep = "_")) %>%
# Generic bio grass crop yield w/o tech split
rename(generic.yield = yield) %>%
# Match in irrigated and rainfed bio grass yields
right_join(L2011.AgYield_bio_grass_irr, by = c("region", "AgSupplySubsector")) %>%
# Compute conversion factor as irr/generic, and rfd/generic
mutate(factor = yield / generic.yield,
# Prepare for bio tree crops
AgSupplySubsector = sub("biomass_grass", "biomass_tree", AgSupplySubsector),
AgProductionTechnology = sub("biomass_grass", "biomass_tree", AgProductionTechnology)) %>%
select(-GLU_name, -yield, -generic.yield) ->
L2011.irr_rfd_factors
# Multiply generic tree crop yields by the conversion factors
# For regions that do not have grass crops but only tree crops (e.g., regions w forest but no ag production),
# simply use the generic defaults, which are likely minor ag regions anyway
L201.AgYield_bio_tree %>%
# Copy to both irrigated and rainfed technologies
repeat_add_columns(tibble(IRR_RFD = c("IRR", "RFD"))) %>%
mutate(AgProductionTechnology = paste(AgProductionTechnology, IRR_RFD, sep = "_")) %>%
# Match in conversion factors
left_join(L2011.irr_rfd_factors,
by = c("region", "AgSupplySector", "AgSupplySubsector", "AgProductionTechnology", "year")) %>%
# Calculate the irrigated and rainfed yields using the conversion factors from bio grass crops
mutate(yield_irr = yield * factor,
# When grass crops are not available, use the generic yields
yield = replace(yield, !is.na(yield_irr), yield_irr[!is.na(yield_irr)]),
yield = round(yield, digits = aglu.DIGITS_CALOUTPUT)) %>%
select(LEVEL2_DATA_NAMES[["AgYield"]]) ->
L2011.AgYield_bio_tree_irr
# Last step, apply different yield multipliers for high and low mgmt techs
# Prepare a yield multiplier table, with the mgmt level as a column ID
L181.YieldMult_R_bio_GLU_irr %>%
gather(level, yieldmult, -GCAM_region_ID, -GLU, -Irr_Rfd) %>%
mutate(level = sub("yieldmult_", "", level),
Irr_Rfd = toupper(Irr_Rfd)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(select(basin_to_country_mapping, GLU_code, GLU_name), by = c("GLU" = "GLU_code")) ->
L2012.YieldMult_R_bio_GLU_irr
# Bind the tree and grass tables, repeat by mgmt level, and match in the yield multipliers
L2011.AgYield_bio_grass_irr %>%
bind_rows(L2011.AgYield_bio_tree_irr) %>%
# Copy to high and low management levels
repeat_add_columns(tibble(MGMT = c("hi", "lo"))) %>%
# Separate technology to match in the multipliers
separate(AgProductionTechnology, c("biomass", "type", "GLU_name", "IRR_RFD")) %>%
# Match in multipliers, use left_join instead because of NAs
left_join(L2012.YieldMult_R_bio_GLU_irr, by = c("region", "GLU_name", "IRR_RFD" = "Irr_Rfd", "MGMT" = "level")) %>%
# For minor region/GLUs that are missing from the ag data, set the multipliers to 1 (effectively fixing yields in all periods)
replace_na(list(yieldmult = 1)) %>%
mutate(yield = yield * yieldmult,
# Add technology back
AgProductionTechnology = paste(AgSupplySubsector, IRR_RFD, MGMT, sep = "_")) %>%
select(LEVEL2_DATA_NAMES[["AgYield"]]) ->
L2012.AgYield_bio_ref
# Add sector, subsector, and technology information to L201.AgYield_bio_grass
L201.AgYield_bio_grass %>%
mutate(AgSupplySector = "biomass",
AgSupplySubsector = paste0("biomass_grass_", GLU_name),
AgProductionTechnology = AgSupplySubsector) %>%
repeat_add_columns((tibble(year = MODEL_BASE_YEARS))) %>%
select(-GLU_name) ->
L201.AgYield_bio_grass
# Produce outputs
L2012.AgSupplySector %>%
add_title("Generic information for agriculture supply sectors") %>%
add_units("Unitless") %>%
add_comments("Specify generic supply sector characteristics (units, calprice, market, logit)") %>%
add_comments("At the supplysector (market) level, all regions get all supplysectors") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L2012.AgSupplySector") %>%
add_precursors("common/GCAM_region_names",
"water/basin_to_country_mapping",
"aglu/A_agSupplySector",
"L132.ag_an_For_Prices") ->
L2012.AgSupplySector
L2012.AgSupplySubsector %>%
add_title("Generic information for agriculture supply subsectors") %>%
add_units("Unitless") %>%
add_comments("Specify generic supply subsector characteristics") %>%
add_comments("At the subsector (production) level, only region x GLU combinations that actually exist are created") %>%
add_legacy_name("L2012.AgSupplySubsector") %>%
add_precursors("common/GCAM_region_names",
"water/basin_to_country_mapping",
"aglu/A_agSupplySubsector",
"L101.ag_Prod_Mt_R_C_Y_GLU",
"L123.ag_Prod_Mt_R_Past_Y_GLU",
"L123.For_Prod_bm3_R_Y_GLU") ->
L2012.AgSupplySubsector
L2012.AgProduction_ag_irr_mgmt %>%
add_title("Input table for agriculture commodity production by all technologies") %>%
add_units("Mt") %>%
add_comments("Calibrated outputs are specified by each technology") %>%
add_comments("Subsector shareweights are set at the aggregated region x GLU level, same for all tech") %>%
add_comments("Technology shareweights are set by irrigated vs. rainfed, same for high and low mgmt") %>%
add_legacy_name("L2012.AgProduction_ag_irr_mgmt") %>%
add_precursors("common/GCAM_region_names",
"water/basin_to_country_mapping",
"L161.ag_irrProd_Mt_R_C_Y_GLU",
"L161.ag_rfdProd_Mt_R_C_Y_GLU",
"L181.ag_Prod_Mt_R_C_Y_GLU_irr_level") ->
L2012.AgProduction_ag_irr_mgmt
L2012.AgProduction_For_Past %>%
filter(AgSupplySector == "Forest") %>%
add_title("Input table for forest production") %>%
add_units("bm3") %>%
add_comments("Calibrated ouputs or shareweights are not specify by technology") %>%
add_legacy_name("L2012.AgProduction_For") %>%
same_precursors_as("L2012.AgSupplySubsector") ->
L2012.AgProduction_For
L2012.AgProduction_For_Past %>%
filter(AgSupplySector == "Pasture") %>%
add_title("Input table for pasture production") %>%
add_units("Mt") %>%
add_comments("Calibrated ouputs or shareweights are not specify by technology") %>%
add_legacy_name("L2012.AgProduction_Past") %>%
same_precursors_as("L2012.AgSupplySubsector") ->
L2012.AgProduction_Past
L2012.AgHAtoCL_irr_mgmt %>%
add_title("Harvest area to cropland value of agricultural commodities by year and technology") %>%
add_units("Uniteless") %>%
add_comments("Copy the same value to all technologies") %>%
add_comments("Exclude forest and pasture") %>%
add_legacy_name("L2012.AgHAtoCL_irr_mgmt") %>%
same_precursors_as("L2012.AgProduction_ag_irr_mgmt") %>%
add_precursors("L122.ag_HA_to_CropLand_R_Y_GLU") ->
L2012.AgHAtoCL_irr_mgmt
L2012.AgYield_bio_ref %>%
add_title("Bioenergy grass and tree crops yields by all technologies") %>%
add_units("GJ/m2") %>%
add_comments("Grass crops yields are used for tree crops where available") %>%
add_comments("Apply the irr:generic and rfd:generic conversion factors from grass crops") %>%
add_comments("Use default minimum value where grass crops are not available") %>%
add_comments("Apply different multipliers to high and low management for both grass and tree crops") %>%
add_legacy_name("L2012.AgYield_bio_ref") %>%
add_precursors("common/GCAM_region_names",
"water/basin_to_country_mapping",
"aglu/A_agSupplySector",
"aglu/A_agSupplySubsector",
"L113.ag_bioYield_GJm2_R_GLU",
"L163.ag_irrBioYield_GJm2_R_GLU",
"L163.ag_rfdBioYield_GJm2_R_GLU",
"L181.YieldMult_R_bio_GLU_irr") ->
L2012.AgYield_bio_ref
L201.AgYield_bio_grass %>%
add_title("Bioenergy grass crops yields for aggregate tech") %>%
add_units("GJ/m2") %>%
add_comments("Append region and basin names to L113.ag_bioYield_GJm2_R_GLU") %>%
add_legacy_name("L201.AgYield_bio_grass") %>%
add_precursors("common/GCAM_region_names",
"water/basin_to_country_mapping",
"L113.ag_bioYield_GJm2_R_GLU") ->
L201.AgYield_bio_grass
L201.AgYield_bio_tree %>%
add_title("Bioenergy tree crops yields for aggregate tech") %>%
add_units("GJ/m2") %>%
add_comments("Set bioenergy tree crop yield to the same as the bioenergy grass yield") %>%
add_comments("If data is missing for a region/basin, use the minimum yield") %>%
add_legacy_name("L201.AgYield_bio_tree") %>%
same_precursors_as("L201.AgYield_bio_grass") %>%
same_precursors_as("L2012.AgSupplySubsector") ->
L201.AgYield_bio_tree
return_data(L2012.AgSupplySector, L2012.AgSupplySubsector, L2012.AgProduction_ag_irr_mgmt, L2012.AgProduction_For, L2012.AgProduction_Past, L2012.AgHAtoCL_irr_mgmt, L2012.AgYield_bio_ref, L201.AgYield_bio_grass, L201.AgYield_bio_tree)
} else {
stop("Unknown command")
}
}
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