R/zchunk_LB1321.regional_ag_prices.R
In bpbond/gcamdata: Build the GCAM Input Datasets
Defines functions
module_aglu_LB1321.regional_ag_prices
Documented in
module_aglu_LB1321.regional_ag_prices
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' module_aglu_LB1321.regional_ag_prices
#'
#' Calculate the calibration prices for all GCAM AGLU commodities.
#'
#' @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{L1321.ag_prP_R_C_75USDkg},
#' \code{L1321.an_prP_R_C_75USDkg}, \code{L1321.expP_R_F_75USDm3}
#' @details This chunk calculates average prices over calibration years by GCAM commodity and region. Averages across
#' years are unweighted; averages over FAO item are weighted by production.
#' @importFrom assertthat assert_that
#' @importFrom dplyr bind_rows filter if_else inner_join left_join mutate rename select
#' @importFrom tidyr complete drop_na gather nesting spread replace_na
#' @importFrom tibble tibble
#' @author GPK/RC/STW February 2019
module_aglu_LB1321.regional_ag_prices <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/iso_GCAM_regID",
FILE = "aglu/AGLU_ctry",
FILE = "aglu/FAO/FAO_ag_items_TRADE",
FILE = "aglu/FAO/FAO_ag_an_ProducerPrice",
FILE = "aglu/FAO/FAO_ag_Prod_t_PRODSTAT",
FILE = "common/FAO_GDP_Deflators",
FILE = "aglu/FAO/FAO_an_Prod_t_PRODSTAT",
FILE = "aglu/FAO/FAO_For_Exp_m3_USD_FORESTAT",
"L132.ag_an_For_Prices"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L1321.ag_prP_R_C_75USDkg",
"L1321.an_prP_R_C_75USDkg",
"L1321.expP_R_F_75USDm3"))
} else if(command == driver.MAKE) {
year <- value <- Year <- Value <- FAO_country <- iso <- deflator <-
countries <- currentUSD_per_baseyearUSD <- item <- pp_commod <- Cottonseed <-
`Cotton lint` <- item.codes <- production <- prod_commod <- item.code <-
GCAM_commodity <- GCAM_region_ID <- revenue <- avg_prP_C <- prP <- prPmult <-
production_wt_prPmult <- prPmult_R <- countries <- `item codes` <- calPrice <-
`element codes` <- element <- ExpV_kUSD <- Exp_m3 <- avg_expP_F <-
Price_USDm3 <- NULL # silence package check.
all_data <- list(...)[[1]]
# Load required inputs
iso_GCAM_regID <- get_data(all_data, "common/iso_GCAM_regID")
AGLU_ctry <- get_data(all_data, "aglu/AGLU_ctry")
FAO_ag_items_TRADE <- get_data(all_data, "aglu/FAO/FAO_ag_items_TRADE")
FAO_ag_an_ProducerPrice <- get_data(all_data, "aglu/FAO/FAO_ag_an_ProducerPrice")
FAO_ag_Prod_t_PRODSTAT <- get_data(all_data, "aglu/FAO/FAO_ag_Prod_t_PRODSTAT")
FAO_GDP_Deflators <- get_data(all_data, "common/FAO_GDP_Deflators")
FAO_an_Prod_t_PRODSTAT <- get_data(all_data, "aglu/FAO/FAO_an_Prod_t_PRODSTAT")
L132.ag_an_For_Prices <- get_data(all_data,"L132.ag_an_For_Prices")
# kbn 2019/09/23 added AGLU_Ctry_Unique since using AGLU_Ctry was causing extra rows to be added.
AGLU_Ctry_Unique<-distinct(AGLU_ctry,FAO_country,.keep_all = TRUE)
# xz 2021/4/14 added regional forest export prices
FAO_For_Exp_m3_USD_FORESTAT <- get_data(all_data, "aglu/FAO/FAO_For_Exp_m3_USD_FORESTAT")
# 1. Producer prices
# 1.1 GDP deflators (to 2005) by country and analysis year
# GDP deflators are used to convert each reported year- and country- values to a common unit of measure
# This step filters years, sets iso codes to countries, and converts all deflators from an index-100 with a base
# year of 2015 to a multiplier with an exogenous base year. The deflator base year is the year in which relative
# regional nominal prices are preserved in the constant dollar (i.e., 1975$ in this code) prices. For example, with
# deflator base year set to 2015, prices are in 2015 Constant USD but expressed in terms of 1975 USD.
# Sudan (former) is re-set to Sudan for building the full time series
# South Sudan is dropped as only a few data years are available and it isn't in the price data
L1321.GDPdefl_ctry <- FAO_GDP_Deflators %>%
gather_years() %>%
select(countries, year, value) %>%
mutate(countries= if_else(countries== "Sudan (former)", "Sudan", countries)) %>%
filter(countries!= "South Sudan") %>%
group_by(countries) %>%
mutate(currentUSD_per_baseyearUSD = (value / value[year == aglu.DEFLATOR_BASE_YEAR])) %>%
ungroup() %>%
filter(year %in% aglu.TRADE_CAL_YEARS) %>%
#kbn 2019/09/23 added AGLU_Ctry_Unique since using AGLU_Ctry was causing extra rows to be added.
left_join_error_no_match(select(AGLU_Ctry_Unique, FAO_country, iso),
by = c(countries= "FAO_country")) %>%
select(iso, countries, year, currentUSD_per_baseyearUSD)
# 1.2. Producer prices by country, analysis year, and crop
# filter analysis years, re-name items for merging with other datasets w/different item lists
# inner_join producer prices and deflators in order to drop any countries and years w/o available price or deflator data
# calculate the revised prices as the reported prices divided by deflators
# Here "Sudan (former)" has data in most years, but "Sudan" is all missing values
L1321.prP_ctry_item_75USDkg <- FAO_ag_an_ProducerPrice %>%
filter(!countries %in% c("Sudan")) %>%
mutate(countries = if_else(countries == "Sudan (former)", "Sudan", countries)) %>%
gather_years() %>%
filter(year %in% aglu.TRADE_CAL_YEARS) %>%
inner_join(L1321.GDPdefl_ctry,
by = c("countries", "year")) %>%
mutate(value = as.numeric(value),
value = ((value / currentUSD_per_baseyearUSD) * gdp_deflator(1975, aglu.DEFLATOR_BASE_YEAR) / CONV_T_KG)) %>%
select(iso, pp_commod = item, year, value) %>%
drop_na(value)
# Fill out data for missing years
# Complete only the years (i.e. for each country and commodity, write out all possible years)
# replace_na() the missing values generated in the prior step with averages from available years
L1321.prP_ctry_item_75USDkg <- L1321.prP_ctry_item_75USDkg %>%
complete(nesting(iso, pp_commod), year) %>%
group_by(iso, pp_commod) %>%
mutate(value = replace_na(value, mean(value, na.rm=TRUE))) %>%
ungroup()
# 1.2.1 Revise cotton prices to cover for gaps in the data
# The "items" of cotton lint and cottonseed vary in price significantly (~7x), and where countries only report one
# or the other, the producer price of "seed cotton" (i.e., the total, averaged) is biased accordingly. This is
# particularly problematic for countries that only have cottonseed prices reported, as very low prices combined
# with nonLandVariableCosts of the total product return negative profit in calibration years.
# Because cottonseed is a by-product (from USDA stats, over 90% of the revenue of producing cotton is from the
# lint), and also because its prices depend on the presence of large industrial consumers, the cotton
# seed prices are not a good proxy for estimating the cotton lint prices in countries where the former are available
# and the latter are not. Instead, countries without cotton lint prices are dropped at this stage (to be filled in
# subsequent steps). Cotton seed prices where missing are assigned the cotton lint price divided by USA lint:seed
# price ratio.
P_Ratio_Cttn_Lint_Seed <-
sum(L1321.prP_ctry_item_75USDkg$value[
L1321.prP_ctry_item_75USDkg$pp_commod == "Cotton lint" & L1321.prP_ctry_item_75USDkg$iso == "usa"]) /
sum(L1321.prP_ctry_item_75USDkg$value[
L1321.prP_ctry_item_75USDkg$pp_commod == "Cottonseed" & L1321.prP_ctry_item_75USDkg$iso == "usa"])
L1321.prP_ctry_Cttn_75USDkg <- filter(L1321.prP_ctry_item_75USDkg,
pp_commod %in% c("Cotton lint", "Cottonseed")) %>%
spread(key = pp_commod, value = value) %>%
mutate(Cottonseed = if_else(is.na(Cottonseed), `Cotton lint` / P_Ratio_Cttn_Lint_Seed, Cottonseed)) %>%
drop_na() %>%
gather(key = pp_commod, value = value, -iso, -year)
L1321.prP_ctry_item_75USDkg <- bind_rows(subset(L1321.prP_ctry_item_75USDkg,
!pp_commod %in% c("Cotton lint", "Cottonseed")),
L1321.prP_ctry_Cttn_75USDkg)
# 1.3. Country- and FAO commodity-level production weights
# Average producer prices, aggregated from countries and FAO items by regions and commodities using production as
# the weighting factor. The pipeline below is mostly just filtering and cleaning data, and preparing a table to be
# joined in to the table of producer prices from above.
L1321.ag_prod_kt_ctry_item <- FAO_ag_Prod_t_PRODSTAT %>%
gather_years() %>%
rename(production = value) %>%
filter(year %in% aglu.TRADE_CAL_YEARS) %>%
left_join_keep_first_only(select(AGLU_ctry, FAO_country, iso),
by = c(countries = "FAO_country")) %>%
select(iso, item, item.code = item.codes, year, production)
# 1.3.1 Revising cotton production weights to cover for data gaps
# The cotton commodity in the PRODSTAT database (Seed cotton) does not match the two in the price database
# (Cotton lint and Cottonseed). The former is broken into the latter using default ratios.
L1321.prod_kt_ctry_CttnLnt <- filter(L1321.ag_prod_kt_ctry_item, item == "Seed cotton") %>%
mutate(item = "Cotton lint",
item.code = (FAO_ag_items_TRADE %>% filter(pp_commod == 'Cotton lint'))$item.code,
production = production * aglu.WEIGHT_COTTON_LINT)
L1321.prod_kt_ctry_CttnSd <- filter(L1321.ag_prod_kt_ctry_item, item == "Seed cotton") %>%
mutate(item = "Cottonseed",
item.code = (FAO_ag_items_TRADE %>% filter(pp_commod == 'Cottonseed'))$item.code,
production = production * (1 - aglu.WEIGHT_COTTON_LINT))
L1321.ag_prod_kt_ctry_item <- bind_rows(filter(L1321.ag_prod_kt_ctry_item, item != "Seed cotton"),
L1321.prod_kt_ctry_CttnLnt,
L1321.prod_kt_ctry_CttnSd)
L1321.an_prod_kt_ctry_item <- FAO_an_Prod_t_PRODSTAT %>%
gather_years() %>%
rename(production = value) %>%
filter(year %in% aglu.TRADE_CAL_YEARS) %>%
left_join_keep_first_only(select(AGLU_ctry, FAO_country, iso),
by = c(countries = "FAO_country")) %>%
select(iso, item, item.code = `item codes`, year, production)
# 1.4. Join production weights into the producer price data
# Subset only the items that map to GCAM commodities that are modeled as traded,
# and only the ones that FAO includes in the producer prices database.
FAO_ag_items_TRADE_pp <- filter(FAO_ag_items_TRADE, !is.na(pp_commod)) %>%
select(pp_commod, prod_commod, item.code, GCAM_commodity)
L1321.ag_prP_ctry_item_75USDkg <- L1321.prP_ctry_item_75USDkg %>%
left_join(FAO_ag_items_TRADE_pp, by = "pp_commod") %>%
drop_na(GCAM_commodity) %>%
left_join(L1321.ag_prod_kt_ctry_item,
by = c("iso", "item.code", "prod_commod" = "item", "year")) %>%
# some of the country/crop combinations have no production weights. Drop em.
drop_na(production)
L1321.an_prP_ctry_item_75USDkg <- L1321.prP_ctry_item_75USDkg %>%
left_join(FAO_ag_items_TRADE_pp, by = "pp_commod") %>%
drop_na(GCAM_commodity) %>%
left_join(L1321.an_prod_kt_ctry_item,
by = c("iso", "item.code", "prod_commod" = "item", "year")) %>%
# some of the country/crop combinations have no production weights. Drop em.
drop_na(production)
# 1.5. Calculate producer prices by GCAM region and GCAM commodity
# Weighted average producer prices are calculated as total revenue (price times quantity, aggregated) divided by
# production weight volumes. Note that at this stage, years are included in the average prices. Averages between
# years are calculated later on an un-weighted basis in order to remove any bias from inter-annual fluctuation in
# yield (i.e., more productive years will have higher production weights and lower prices)
L1321.prP_R_C_Y_75USDkg <- L1321.ag_prP_ctry_item_75USDkg %>%
bind_rows(L1321.an_prP_ctry_item_75USDkg) %>%
mutate(revenue = value * production) %>%
left_join_error_no_match(select(iso_GCAM_regID, iso, GCAM_region_ID),
by = "iso") %>%
group_by(GCAM_region_ID, GCAM_commodity, year) %>%
summarise(revenue = sum(revenue),
production = sum(production)) %>%
ungroup() %>%
mutate(value = revenue / production)
# Default prices by GCAM commodity and regional price multipliers, used for filling missing values
# Missing GCAM region and commodity observations are replaced with the average producer price of the given crop
# times the regional average producer price multiplier.
# 1.5.1 Calculate default global average producer prices by crop (weighted by production)
L1321.prP_C_75USDkg <- L1321.prP_R_C_Y_75USDkg %>%
group_by(GCAM_commodity) %>%
summarise(revenue = sum(revenue),
production = sum(production)) %>%
ungroup() %>%
mutate(avg_prP_C = revenue / production) %>%
select(GCAM_commodity, avg_prP_C)
# 1.5.2 Calculate default regional price multipliers
# Regional default price multipliers are calculated as the sum of production-weighted price multipliers for each
# crop (production_wt_prPmult), divided by the sum of production.
# production_wt_prPmult = ratio between observed regional price and global average price of the given commodity
L1321.prPmult_R <- L1321.prP_R_C_Y_75USDkg %>%
rename(prP = value) %>%
left_join_error_no_match( L1321.prP_C_75USDkg, by = "GCAM_commodity") %>%
mutate(prPmult = prP / avg_prP_C,
production_wt_prPmult = prPmult * production) %>%
group_by(GCAM_region_ID) %>%
summarise(production_wt_prPmult = sum(production_wt_prPmult, na.rm=T),
production = sum(production, na.rm=T)) %>%
mutate(prPmult_R = production_wt_prPmult / production) %>%
select(GCAM_region_ID, prPmult_R)
# 1.5.3. Using defaults, fill out missing values for all necessary regions and commodities and years
# complete() the table to fill out missing values for all years
# Need this annual data to calculate regional pasture prices
# To determine the producer price by region and commodity, average across the years (unweighted) later
# (note that individual years are kept here, so that this dataset can be used to estimate grass prices below)
L1321.prP_R_C_Y_75USDkg <- L1321.prP_R_C_Y_75USDkg %>%
complete(GCAM_region_ID, GCAM_commodity, year) %>%
left_join_error_no_match(L1321.prP_C_75USDkg,
by = "GCAM_commodity") %>%
left_join_error_no_match(L1321.prPmult_R,
by = "GCAM_region_ID") %>%
mutate(value = if_else(is.na(value), avg_prP_C * prPmult_R, value)) %>%
select(GCAM_region_ID, GCAM_commodity, year, value)
# Take the unweighted average among years considered
L1321.prP_R_C_75USDkg <- L1321.prP_R_C_Y_75USDkg %>%
group_by(GCAM_region_ID, GCAM_commodity) %>%
summarise(value = mean(value)) %>%
ungroup %>%
arrange(GCAM_region_ID, GCAM_commodity)
L1321.ag_prP_R_C_75USDkg <- L1321.prP_R_C_75USDkg %>%
filter(GCAM_commodity %in% aglu.TRADED_CROPS)
L1321.an_prP_R_C_75USDkg <- L1321.prP_R_C_75USDkg %>%
filter(GCAM_commodity %in% unique(L1321.an_prP_ctry_item_75USDkg$GCAM_commodity))
# Addendum - to improve feed prices and meat price calibration, compute and include regionally adjusted foddergrass and pasture prices
L1321.ag_prP_R_Grass_75USDkg <- L1321.prPmult_R %>%
repeat_add_columns(tibble(GCAM_commodity = c("Pasture", "FodderGrass"))) %>%
left_join_error_no_match(L132.ag_an_For_Prices, by = "GCAM_commodity") %>%
mutate(value = calPrice * prPmult_R) %>%
select(GCAM_region_ID, GCAM_commodity, value)
L1321.ag_prP_R_C_75USDkg <- bind_rows(L1321.ag_prP_R_C_75USDkg, L1321.ag_prP_R_Grass_75USDkg)
# Forest export price by country, analysis year, and crop
L1321.expP_ctry_item_75kUSDm3 <- FAO_For_Exp_m3_USD_FORESTAT %>%
select(-`element codes`) %>%
filter(countries != "Sudan") %>%
mutate(countries = if_else(countries == "Sudan (former)", "Sudan", countries)) %>%
gather_years() %>%
filter(year %in% aglu.TRADE_CAL_YEARS) %>%
mutate(element = if_else(element == "Export Quantity (m3)", "Exp_m3", "ExpV_kUSD"),
GCAM_commodity = "Forest") %>%
spread(element, value) %>%
inner_join(L1321.GDPdefl_ctry,
by = c("countries", "year")) %>%
mutate(ExpV_kUSD = as.numeric(ExpV_kUSD),
ExpV_kUSD = ((ExpV_kUSD / currentUSD_per_baseyearUSD) * gdp_deflator(1975, aglu.DEFLATOR_BASE_YEAR))) %>%
select(iso, GCAM_commodity, year, ExpV_kUSD, Exp_m3) %>%
drop_na(ExpV_kUSD, Exp_m3)
# Fill out data for missing years
# Complete only the years (i.e. for each country and commodity, write out all possible years)
# replace_na() the missing values generated in the prior step with averages from available years
L1321.expP_R_F_Y_75USDm3 <- L1321.expP_ctry_item_75kUSDm3 %>%
complete(nesting(iso, GCAM_commodity), year) %>%
group_by(iso, GCAM_commodity) %>%
mutate(ExpV_kUSD = replace_na(ExpV_kUSD, mean(ExpV_kUSD, na.rm=TRUE)),
Exp_m3 = replace_na(Exp_m3, mean(Exp_m3, na.rm=TRUE))) %>%
ungroup()%>%
left_join_error_no_match(select(iso_GCAM_regID, iso, GCAM_region_ID),
by = "iso") %>%
group_by(GCAM_region_ID, GCAM_commodity, year) %>%
summarise(ExpV_kUSD = sum(ExpV_kUSD),
Exp_m3 = sum(Exp_m3)) %>%
ungroup() %>%
# Calculate forest price as export value (in thous USD) divided by export quantity
mutate(Price_USDm3 = ExpV_kUSD * 1000 / Exp_m3)
# Calculate default global average export prices (weighted by volume)
L1321.expP_F_75USDm3 <- L1321.expP_R_F_Y_75USDm3 %>%
group_by(GCAM_commodity) %>%
summarise(ExpV_kUSD = sum(ExpV_kUSD),
Exp_m3 = sum(Exp_m3)) %>%
ungroup() %>%
mutate(avg_expP_F = ExpV_kUSD * 1000 / Exp_m3) %>%
select(GCAM_commodity, avg_expP_F)
# Using global values fill out missing values for all necessary regions and commodities
L1321.expP_R_F_Y_75USDm3 <- L1321.expP_R_F_Y_75USDm3 %>%
filter(GCAM_commodity %in% aglu.TRADED_FORESTS) %>%
complete(GCAM_region_ID, GCAM_commodity, year) %>%
left_join_error_no_match(L1321.expP_F_75USDm3,
by = "GCAM_commodity") %>%
mutate(Price_USDm3 = if_else(is.na(Price_USDm3), avg_expP_F , Price_USDm3)) %>%
select(GCAM_region_ID, GCAM_commodity, year, Price_USDm3)
# Final step - filter only traded crops and take the mean among years considered
L1321.expP_R_F_75USDm3 <- L1321.expP_R_F_Y_75USDm3 %>%
filter(GCAM_commodity %in% aglu.TRADED_FORESTS) %>%
group_by(GCAM_region_ID, GCAM_commodity) %>%
summarise(value = mean(Price_USDm3)) %>%
ungroup %>%
arrange(GCAM_region_ID, GCAM_commodity)
# Produce outputs
L1321.ag_prP_R_C_75USDkg %>%
add_title("Regional agricultural commodity prices for all traded primary GCAM AGLU commodities") %>%
add_units("1975$/kg") %>%
add_comments("Region-specific calibration prices by GCAM commodity and region") %>%
add_precursors("common/iso_GCAM_regID",
"aglu/AGLU_ctry",
"aglu/FAO/FAO_ag_items_TRADE",
"aglu/FAO/FAO_ag_an_ProducerPrice",
"aglu/FAO/FAO_ag_Prod_t_PRODSTAT",
"common/FAO_GDP_Deflators",
"L132.ag_an_For_Prices") ->
L1321.ag_prP_R_C_75USDkg
L1321.an_prP_R_C_75USDkg %>%
add_title("Regional animal commodity prices") %>%
add_units("1975$/kg") %>%
add_comments("Region-specific prices by GCAM commodity and region") %>%
same_precursors_as(L1321.ag_prP_R_C_75USDkg) %>%
add_precursors("aglu/FAO/FAO_an_Prod_t_PRODSTAT") ->
L1321.an_prP_R_C_75USDkg
L1321.expP_R_F_75USDm3 %>%
add_title("Regional prices for GCAM forest commodities") %>%
add_units("1975$/M3") %>%
add_comments("Region-specific calibration prices by GCAM commodity and region") %>%
add_precursors("common/iso_GCAM_regID",
"aglu/AGLU_ctry",
"aglu/FAO/FAO_ag_items_TRADE",
"aglu/FAO/FAO_For_Exp_m3_USD_FORESTAT",
"common/FAO_GDP_Deflators") ->
L1321.expP_R_F_75USDm3
return_data(L1321.ag_prP_R_C_75USDkg,
L1321.an_prP_R_C_75USDkg,
L1321.expP_R_F_75USDm3)
} else {
stop("Unknown command")
}
}
bpbond/gcamdata documentation built on March 22, 2023, 4:52 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions module_aglu_LB1321.regional_ag_prices
Documented in module_aglu_LB1321.regional_ag_prices
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' module_aglu_LB1321.regional_ag_prices
#'
#' Calculate the calibration prices for all GCAM AGLU commodities.
#'
#' @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{L1321.ag_prP_R_C_75USDkg},
#' \code{L1321.an_prP_R_C_75USDkg}, \code{L1321.expP_R_F_75USDm3}
#' @details This chunk calculates average prices over calibration years by GCAM commodity and region. Averages across
#' years are unweighted; averages over FAO item are weighted by production.
#' @importFrom assertthat assert_that
#' @importFrom dplyr bind_rows filter if_else inner_join left_join mutate rename select
#' @importFrom tidyr complete drop_na gather nesting spread replace_na
#' @importFrom tibble tibble
#' @author GPK/RC/STW February 2019
module_aglu_LB1321.regional_ag_prices <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/iso_GCAM_regID",
FILE = "aglu/AGLU_ctry",
FILE = "aglu/FAO/FAO_ag_items_TRADE",
FILE = "aglu/FAO/FAO_ag_an_ProducerPrice",
FILE = "aglu/FAO/FAO_ag_Prod_t_PRODSTAT",
FILE = "common/FAO_GDP_Deflators",
FILE = "aglu/FAO/FAO_an_Prod_t_PRODSTAT",
FILE = "aglu/FAO/FAO_For_Exp_m3_USD_FORESTAT",
"L132.ag_an_For_Prices"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L1321.ag_prP_R_C_75USDkg",
"L1321.an_prP_R_C_75USDkg",
"L1321.expP_R_F_75USDm3"))
} else if(command == driver.MAKE) {
year <- value <- Year <- Value <- FAO_country <- iso <- deflator <-
countries <- currentUSD_per_baseyearUSD <- item <- pp_commod <- Cottonseed <-
`Cotton lint` <- item.codes <- production <- prod_commod <- item.code <-
GCAM_commodity <- GCAM_region_ID <- revenue <- avg_prP_C <- prP <- prPmult <-
production_wt_prPmult <- prPmult_R <- countries <- `item codes` <- calPrice <-
`element codes` <- element <- ExpV_kUSD <- Exp_m3 <- avg_expP_F <-
Price_USDm3 <- NULL # silence package check.
all_data <- list(...)[[1]]
# Load required inputs
iso_GCAM_regID <- get_data(all_data, "common/iso_GCAM_regID")
AGLU_ctry <- get_data(all_data, "aglu/AGLU_ctry")
FAO_ag_items_TRADE <- get_data(all_data, "aglu/FAO/FAO_ag_items_TRADE")
FAO_ag_an_ProducerPrice <- get_data(all_data, "aglu/FAO/FAO_ag_an_ProducerPrice")
FAO_ag_Prod_t_PRODSTAT <- get_data(all_data, "aglu/FAO/FAO_ag_Prod_t_PRODSTAT")
FAO_GDP_Deflators <- get_data(all_data, "common/FAO_GDP_Deflators")
FAO_an_Prod_t_PRODSTAT <- get_data(all_data, "aglu/FAO/FAO_an_Prod_t_PRODSTAT")
L132.ag_an_For_Prices <- get_data(all_data,"L132.ag_an_For_Prices")
# kbn 2019/09/23 added AGLU_Ctry_Unique since using AGLU_Ctry was causing extra rows to be added.
AGLU_Ctry_Unique<-distinct(AGLU_ctry,FAO_country,.keep_all = TRUE)
# xz 2021/4/14 added regional forest export prices
FAO_For_Exp_m3_USD_FORESTAT <- get_data(all_data, "aglu/FAO/FAO_For_Exp_m3_USD_FORESTAT")
# 1. Producer prices
# 1.1 GDP deflators (to 2005) by country and analysis year
# GDP deflators are used to convert each reported year- and country- values to a common unit of measure
# This step filters years, sets iso codes to countries, and converts all deflators from an index-100 with a base
# year of 2015 to a multiplier with an exogenous base year. The deflator base year is the year in which relative
# regional nominal prices are preserved in the constant dollar (i.e., 1975$ in this code) prices. For example, with
# deflator base year set to 2015, prices are in 2015 Constant USD but expressed in terms of 1975 USD.
# Sudan (former) is re-set to Sudan for building the full time series
# South Sudan is dropped as only a few data years are available and it isn't in the price data
L1321.GDPdefl_ctry <- FAO_GDP_Deflators %>%
gather_years() %>%
select(countries, year, value) %>%
mutate(countries= if_else(countries== "Sudan (former)", "Sudan", countries)) %>%
filter(countries!= "South Sudan") %>%
group_by(countries) %>%
mutate(currentUSD_per_baseyearUSD = (value / value[year == aglu.DEFLATOR_BASE_YEAR])) %>%
ungroup() %>%
filter(year %in% aglu.TRADE_CAL_YEARS) %>%
#kbn 2019/09/23 added AGLU_Ctry_Unique since using AGLU_Ctry was causing extra rows to be added.
left_join_error_no_match(select(AGLU_Ctry_Unique, FAO_country, iso),
by = c(countries= "FAO_country")) %>%
select(iso, countries, year, currentUSD_per_baseyearUSD)
# 1.2. Producer prices by country, analysis year, and crop
# filter analysis years, re-name items for merging with other datasets w/different item lists
# inner_join producer prices and deflators in order to drop any countries and years w/o available price or deflator data
# calculate the revised prices as the reported prices divided by deflators
# Here "Sudan (former)" has data in most years, but "Sudan" is all missing values
L1321.prP_ctry_item_75USDkg <- FAO_ag_an_ProducerPrice %>%
filter(!countries %in% c("Sudan")) %>%
mutate(countries = if_else(countries == "Sudan (former)", "Sudan", countries)) %>%
gather_years() %>%
filter(year %in% aglu.TRADE_CAL_YEARS) %>%
inner_join(L1321.GDPdefl_ctry,
by = c("countries", "year")) %>%
mutate(value = as.numeric(value),
value = ((value / currentUSD_per_baseyearUSD) * gdp_deflator(1975, aglu.DEFLATOR_BASE_YEAR) / CONV_T_KG)) %>%
select(iso, pp_commod = item, year, value) %>%
drop_na(value)
# Fill out data for missing years
# Complete only the years (i.e. for each country and commodity, write out all possible years)
# replace_na() the missing values generated in the prior step with averages from available years
L1321.prP_ctry_item_75USDkg <- L1321.prP_ctry_item_75USDkg %>%
complete(nesting(iso, pp_commod), year) %>%
group_by(iso, pp_commod) %>%
mutate(value = replace_na(value, mean(value, na.rm=TRUE))) %>%
ungroup()
# 1.2.1 Revise cotton prices to cover for gaps in the data
# The "items" of cotton lint and cottonseed vary in price significantly (~7x), and where countries only report one
# or the other, the producer price of "seed cotton" (i.e., the total, averaged) is biased accordingly. This is
# particularly problematic for countries that only have cottonseed prices reported, as very low prices combined
# with nonLandVariableCosts of the total product return negative profit in calibration years.
# Because cottonseed is a by-product (from USDA stats, over 90% of the revenue of producing cotton is from the
# lint), and also because its prices depend on the presence of large industrial consumers, the cotton
# seed prices are not a good proxy for estimating the cotton lint prices in countries where the former are available
# and the latter are not. Instead, countries without cotton lint prices are dropped at this stage (to be filled in
# subsequent steps). Cotton seed prices where missing are assigned the cotton lint price divided by USA lint:seed
# price ratio.
P_Ratio_Cttn_Lint_Seed <-
sum(L1321.prP_ctry_item_75USDkg$value[
L1321.prP_ctry_item_75USDkg$pp_commod == "Cotton lint" & L1321.prP_ctry_item_75USDkg$iso == "usa"]) /
sum(L1321.prP_ctry_item_75USDkg$value[
L1321.prP_ctry_item_75USDkg$pp_commod == "Cottonseed" & L1321.prP_ctry_item_75USDkg$iso == "usa"])
L1321.prP_ctry_Cttn_75USDkg <- filter(L1321.prP_ctry_item_75USDkg,
pp_commod %in% c("Cotton lint", "Cottonseed")) %>%
spread(key = pp_commod, value = value) %>%
mutate(Cottonseed = if_else(is.na(Cottonseed), `Cotton lint` / P_Ratio_Cttn_Lint_Seed, Cottonseed)) %>%
drop_na() %>%
gather(key = pp_commod, value = value, -iso, -year)
L1321.prP_ctry_item_75USDkg <- bind_rows(subset(L1321.prP_ctry_item_75USDkg,
!pp_commod %in% c("Cotton lint", "Cottonseed")),
L1321.prP_ctry_Cttn_75USDkg)
# 1.3. Country- and FAO commodity-level production weights
# Average producer prices, aggregated from countries and FAO items by regions and commodities using production as
# the weighting factor. The pipeline below is mostly just filtering and cleaning data, and preparing a table to be
# joined in to the table of producer prices from above.
L1321.ag_prod_kt_ctry_item <- FAO_ag_Prod_t_PRODSTAT %>%
gather_years() %>%
rename(production = value) %>%
filter(year %in% aglu.TRADE_CAL_YEARS) %>%
left_join_keep_first_only(select(AGLU_ctry, FAO_country, iso),
by = c(countries = "FAO_country")) %>%
select(iso, item, item.code = item.codes, year, production)
# 1.3.1 Revising cotton production weights to cover for data gaps
# The cotton commodity in the PRODSTAT database (Seed cotton) does not match the two in the price database
# (Cotton lint and Cottonseed). The former is broken into the latter using default ratios.
L1321.prod_kt_ctry_CttnLnt <- filter(L1321.ag_prod_kt_ctry_item, item == "Seed cotton") %>%
mutate(item = "Cotton lint",
item.code = (FAO_ag_items_TRADE %>% filter(pp_commod == 'Cotton lint'))$item.code,
production = production * aglu.WEIGHT_COTTON_LINT)
L1321.prod_kt_ctry_CttnSd <- filter(L1321.ag_prod_kt_ctry_item, item == "Seed cotton") %>%
mutate(item = "Cottonseed",
item.code = (FAO_ag_items_TRADE %>% filter(pp_commod == 'Cottonseed'))$item.code,
production = production * (1 - aglu.WEIGHT_COTTON_LINT))
L1321.ag_prod_kt_ctry_item <- bind_rows(filter(L1321.ag_prod_kt_ctry_item, item != "Seed cotton"),
L1321.prod_kt_ctry_CttnLnt,
L1321.prod_kt_ctry_CttnSd)
L1321.an_prod_kt_ctry_item <- FAO_an_Prod_t_PRODSTAT %>%
gather_years() %>%
rename(production = value) %>%
filter(year %in% aglu.TRADE_CAL_YEARS) %>%
left_join_keep_first_only(select(AGLU_ctry, FAO_country, iso),
by = c(countries = "FAO_country")) %>%
select(iso, item, item.code = `item codes`, year, production)
# 1.4. Join production weights into the producer price data
# Subset only the items that map to GCAM commodities that are modeled as traded,
# and only the ones that FAO includes in the producer prices database.
FAO_ag_items_TRADE_pp <- filter(FAO_ag_items_TRADE, !is.na(pp_commod)) %>%
select(pp_commod, prod_commod, item.code, GCAM_commodity)
L1321.ag_prP_ctry_item_75USDkg <- L1321.prP_ctry_item_75USDkg %>%
left_join(FAO_ag_items_TRADE_pp, by = "pp_commod") %>%
drop_na(GCAM_commodity) %>%
left_join(L1321.ag_prod_kt_ctry_item,
by = c("iso", "item.code", "prod_commod" = "item", "year")) %>%
# some of the country/crop combinations have no production weights. Drop em.
drop_na(production)
L1321.an_prP_ctry_item_75USDkg <- L1321.prP_ctry_item_75USDkg %>%
left_join(FAO_ag_items_TRADE_pp, by = "pp_commod") %>%
drop_na(GCAM_commodity) %>%
left_join(L1321.an_prod_kt_ctry_item,
by = c("iso", "item.code", "prod_commod" = "item", "year")) %>%
# some of the country/crop combinations have no production weights. Drop em.
drop_na(production)
# 1.5. Calculate producer prices by GCAM region and GCAM commodity
# Weighted average producer prices are calculated as total revenue (price times quantity, aggregated) divided by
# production weight volumes. Note that at this stage, years are included in the average prices. Averages between
# years are calculated later on an un-weighted basis in order to remove any bias from inter-annual fluctuation in
# yield (i.e., more productive years will have higher production weights and lower prices)
L1321.prP_R_C_Y_75USDkg <- L1321.ag_prP_ctry_item_75USDkg %>%
bind_rows(L1321.an_prP_ctry_item_75USDkg) %>%
mutate(revenue = value * production) %>%
left_join_error_no_match(select(iso_GCAM_regID, iso, GCAM_region_ID),
by = "iso") %>%
group_by(GCAM_region_ID, GCAM_commodity, year) %>%
summarise(revenue = sum(revenue),
production = sum(production)) %>%
ungroup() %>%
mutate(value = revenue / production)
# Default prices by GCAM commodity and regional price multipliers, used for filling missing values
# Missing GCAM region and commodity observations are replaced with the average producer price of the given crop
# times the regional average producer price multiplier.
# 1.5.1 Calculate default global average producer prices by crop (weighted by production)
L1321.prP_C_75USDkg <- L1321.prP_R_C_Y_75USDkg %>%
group_by(GCAM_commodity) %>%
summarise(revenue = sum(revenue),
production = sum(production)) %>%
ungroup() %>%
mutate(avg_prP_C = revenue / production) %>%
select(GCAM_commodity, avg_prP_C)
# 1.5.2 Calculate default regional price multipliers
# Regional default price multipliers are calculated as the sum of production-weighted price multipliers for each
# crop (production_wt_prPmult), divided by the sum of production.
# production_wt_prPmult = ratio between observed regional price and global average price of the given commodity
L1321.prPmult_R <- L1321.prP_R_C_Y_75USDkg %>%
rename(prP = value) %>%
left_join_error_no_match( L1321.prP_C_75USDkg, by = "GCAM_commodity") %>%
mutate(prPmult = prP / avg_prP_C,
production_wt_prPmult = prPmult * production) %>%
group_by(GCAM_region_ID) %>%
summarise(production_wt_prPmult = sum(production_wt_prPmult, na.rm=T),
production = sum(production, na.rm=T)) %>%
mutate(prPmult_R = production_wt_prPmult / production) %>%
select(GCAM_region_ID, prPmult_R)
# 1.5.3. Using defaults, fill out missing values for all necessary regions and commodities and years
# complete() the table to fill out missing values for all years
# Need this annual data to calculate regional pasture prices
# To determine the producer price by region and commodity, average across the years (unweighted) later
# (note that individual years are kept here, so that this dataset can be used to estimate grass prices below)
L1321.prP_R_C_Y_75USDkg <- L1321.prP_R_C_Y_75USDkg %>%
complete(GCAM_region_ID, GCAM_commodity, year) %>%
left_join_error_no_match(L1321.prP_C_75USDkg,
by = "GCAM_commodity") %>%
left_join_error_no_match(L1321.prPmult_R,
by = "GCAM_region_ID") %>%
mutate(value = if_else(is.na(value), avg_prP_C * prPmult_R, value)) %>%
select(GCAM_region_ID, GCAM_commodity, year, value)
# Take the unweighted average among years considered
L1321.prP_R_C_75USDkg <- L1321.prP_R_C_Y_75USDkg %>%
group_by(GCAM_region_ID, GCAM_commodity) %>%
summarise(value = mean(value)) %>%
ungroup %>%
arrange(GCAM_region_ID, GCAM_commodity)
L1321.ag_prP_R_C_75USDkg <- L1321.prP_R_C_75USDkg %>%
filter(GCAM_commodity %in% aglu.TRADED_CROPS)
L1321.an_prP_R_C_75USDkg <- L1321.prP_R_C_75USDkg %>%
filter(GCAM_commodity %in% unique(L1321.an_prP_ctry_item_75USDkg$GCAM_commodity))
# Addendum - to improve feed prices and meat price calibration, compute and include regionally adjusted foddergrass and pasture prices
L1321.ag_prP_R_Grass_75USDkg <- L1321.prPmult_R %>%
repeat_add_columns(tibble(GCAM_commodity = c("Pasture", "FodderGrass"))) %>%
left_join_error_no_match(L132.ag_an_For_Prices, by = "GCAM_commodity") %>%
mutate(value = calPrice * prPmult_R) %>%
select(GCAM_region_ID, GCAM_commodity, value)
L1321.ag_prP_R_C_75USDkg <- bind_rows(L1321.ag_prP_R_C_75USDkg, L1321.ag_prP_R_Grass_75USDkg)
# Forest export price by country, analysis year, and crop
L1321.expP_ctry_item_75kUSDm3 <- FAO_For_Exp_m3_USD_FORESTAT %>%
select(-`element codes`) %>%
filter(countries != "Sudan") %>%
mutate(countries = if_else(countries == "Sudan (former)", "Sudan", countries)) %>%
gather_years() %>%
filter(year %in% aglu.TRADE_CAL_YEARS) %>%
mutate(element = if_else(element == "Export Quantity (m3)", "Exp_m3", "ExpV_kUSD"),
GCAM_commodity = "Forest") %>%
spread(element, value) %>%
inner_join(L1321.GDPdefl_ctry,
by = c("countries", "year")) %>%
mutate(ExpV_kUSD = as.numeric(ExpV_kUSD),
ExpV_kUSD = ((ExpV_kUSD / currentUSD_per_baseyearUSD) * gdp_deflator(1975, aglu.DEFLATOR_BASE_YEAR))) %>%
select(iso, GCAM_commodity, year, ExpV_kUSD, Exp_m3) %>%
drop_na(ExpV_kUSD, Exp_m3)
# Fill out data for missing years
# Complete only the years (i.e. for each country and commodity, write out all possible years)
# replace_na() the missing values generated in the prior step with averages from available years
L1321.expP_R_F_Y_75USDm3 <- L1321.expP_ctry_item_75kUSDm3 %>%
complete(nesting(iso, GCAM_commodity), year) %>%
group_by(iso, GCAM_commodity) %>%
mutate(ExpV_kUSD = replace_na(ExpV_kUSD, mean(ExpV_kUSD, na.rm=TRUE)),
Exp_m3 = replace_na(Exp_m3, mean(Exp_m3, na.rm=TRUE))) %>%
ungroup()%>%
left_join_error_no_match(select(iso_GCAM_regID, iso, GCAM_region_ID),
by = "iso") %>%
group_by(GCAM_region_ID, GCAM_commodity, year) %>%
summarise(ExpV_kUSD = sum(ExpV_kUSD),
Exp_m3 = sum(Exp_m3)) %>%
ungroup() %>%
# Calculate forest price as export value (in thous USD) divided by export quantity
mutate(Price_USDm3 = ExpV_kUSD * 1000 / Exp_m3)
# Calculate default global average export prices (weighted by volume)
L1321.expP_F_75USDm3 <- L1321.expP_R_F_Y_75USDm3 %>%
group_by(GCAM_commodity) %>%
summarise(ExpV_kUSD = sum(ExpV_kUSD),
Exp_m3 = sum(Exp_m3)) %>%
ungroup() %>%
mutate(avg_expP_F = ExpV_kUSD * 1000 / Exp_m3) %>%
select(GCAM_commodity, avg_expP_F)
# Using global values fill out missing values for all necessary regions and commodities
L1321.expP_R_F_Y_75USDm3 <- L1321.expP_R_F_Y_75USDm3 %>%
filter(GCAM_commodity %in% aglu.TRADED_FORESTS) %>%
complete(GCAM_region_ID, GCAM_commodity, year) %>%
left_join_error_no_match(L1321.expP_F_75USDm3,
by = "GCAM_commodity") %>%
mutate(Price_USDm3 = if_else(is.na(Price_USDm3), avg_expP_F , Price_USDm3)) %>%
select(GCAM_region_ID, GCAM_commodity, year, Price_USDm3)
# Final step - filter only traded crops and take the mean among years considered
L1321.expP_R_F_75USDm3 <- L1321.expP_R_F_Y_75USDm3 %>%
filter(GCAM_commodity %in% aglu.TRADED_FORESTS) %>%
group_by(GCAM_region_ID, GCAM_commodity) %>%
summarise(value = mean(Price_USDm3)) %>%
ungroup %>%
arrange(GCAM_region_ID, GCAM_commodity)
# Produce outputs
L1321.ag_prP_R_C_75USDkg %>%
add_title("Regional agricultural commodity prices for all traded primary GCAM AGLU commodities") %>%
add_units("1975$/kg") %>%
add_comments("Region-specific calibration prices by GCAM commodity and region") %>%
add_precursors("common/iso_GCAM_regID",
"aglu/AGLU_ctry",
"aglu/FAO/FAO_ag_items_TRADE",
"aglu/FAO/FAO_ag_an_ProducerPrice",
"aglu/FAO/FAO_ag_Prod_t_PRODSTAT",
"common/FAO_GDP_Deflators",
"L132.ag_an_For_Prices") ->
L1321.ag_prP_R_C_75USDkg
L1321.an_prP_R_C_75USDkg %>%
add_title("Regional animal commodity prices") %>%
add_units("1975$/kg") %>%
add_comments("Region-specific prices by GCAM commodity and region") %>%
same_precursors_as(L1321.ag_prP_R_C_75USDkg) %>%
add_precursors("aglu/FAO/FAO_an_Prod_t_PRODSTAT") ->
L1321.an_prP_R_C_75USDkg
L1321.expP_R_F_75USDm3 %>%
add_title("Regional prices for GCAM forest commodities") %>%
add_units("1975$/M3") %>%
add_comments("Region-specific calibration prices by GCAM commodity and region") %>%
add_precursors("common/iso_GCAM_regID",
"aglu/AGLU_ctry",
"aglu/FAO/FAO_ag_items_TRADE",
"aglu/FAO/FAO_For_Exp_m3_USD_FORESTAT",
"common/FAO_GDP_Deflators") ->
L1321.expP_R_F_75USDm3
return_data(L1321.ag_prP_R_C_75USDkg,
L1321.an_prP_R_C_75USDkg,
L1321.expP_R_F_75USDm3)
} else {
stop("Unknown command")
}
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.