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#' Get market balances without intra trade from CAPMOD results
#'
#' @param datacube A dplyr table with the raw capmod results
#' @param region_list A character list of regions
#' @param product_list List of commodities
#' @param scenario Scenario for which you want to retrieve results
#'
#' @return A dply table containing the market balance
#'
get_market_balance <- function(datacube, region_list, product_list){
bal_item <- c("PROD", "HCON", "PROC", "BIOF", "FEED", "ISCH", "Imports_noIntra", "Exports_noIntra")
datacube <- datacube %>%
filter(i1 %in% region_list, i2 == "", i3 %in% bal_item, i4 %in% product_list, i5 != "BAS") %>%
select(i1, i3, i4, i5, value)
return(datacube)
}
#' Get product balances from CAPMOD results
#'
#'
get_product_balance <- function(datacube, region_list, product_list){
bal_item <- c("GROF", "LOSF", "SEDF", "INTF", "MAPR", "IMPT", "INTP", "EXPT", "NTRD", "DOMM", "FEDM", "SEDM", "PRCM", "BIOF", "INDM", "LOSM", "STCM")
datacube <- datacube %>%
filter(i1 %in% region_list, i2 == "", i3 %in% bal_item, i4 %in% product_list, i5 != "BAS") %>%
select(i1, i3, i4, i5, value)
return(datacube)
}
#' Get the table Farm|Supply details
#'
#'
get_supply_detail <- function(datacube, region_list, product_list){
bal_item <- c("MGVA", "YILD", "LEVL")
# note that supply must be calculated = YILD * LEVL
datacube <- datacube %>%
filter(i1 %in% region_list, i2 == "", i3 %in% product_list, i4 %in% bal_item, i5 != "BAS") %>%
select(i1, i3, i4, i5, value)
return(datacube)
}
#' Convert market balances into a pre-defined format (for reporting purposes)
#'
#' 1: calcualate nettrade
#' 2: append years (2010, 2013, 2020, 2025, 2030)
#'
#' @param region_list A character list of regions
#' @param product_list List of commodities
#' @param year_list List of years
#'
convert_balance_ntrd <- function(region_list, product_list, scenario_list, folder = "mydata"){
for(i in 1:length(scenario_list)) {load(file=paste(folder, "/", scenario_list[i], ".RData", sep=""))}
balance <- get_market_balance(get(scenario_list[1]), region_list, product_list)
if (length(scenario_list) > 1) {
for(i in 2:length(scenario_list)){
balance <- rbind(balance, get_market_balance(get(scenario_list[i]), region_list, product_list))
}
}
# calculate net trade (exports - imports)
balance <- spread(balance, i3, value)
# replace NAs with zeros
balance[is.na(balance)] <- 0
# add columns if missing (missing data)
if(!("PROD" %in% colnames(balance))) { balance <- mutate(balance, PROD = 0)}
if(!("HCON" %in% colnames(balance))) { balance <- mutate(balance, HCON = 0)}
if(!("PROC" %in% colnames(balance))) { balance <- mutate(balance, PROC = 0)}
if(!("BIOF" %in% colnames(balance))) { balance <- mutate(balance, BIOF = 0)}
if(!("FEED" %in% colnames(balance))) { balance <- mutate(balance, FEED = 0)}
if(!("ISCH" %in% colnames(balance))) { balance <- mutate(balance, ISCH = 0)}
if(!("Imports_noIntra" %in% colnames(balance))) { balance <- mutate(balance, Imports_noIntra = 0)}
if(!("Exports_noIntra" %in% colnames(balance))) { balance <- mutate(balance, Exports_noIntra = 0)}
# calculate net trade
balance <- balance %>% mutate(nettrade = Exports_noIntra - Imports_noIntra)
# calculate aggregate demand/supply (product specific demand positions)
meat_item <- c("PROD", "HCON", "ISCH", "Imports_noIntra", "Exports_noIntra")
dairy_item <- c("PROD", "HCON", "PROC", "BIOF", "FEED", "ISCH", "Imports_noIntra", "Exports_noIntra")
meat_product <- c("PORK", "POUM", "BEEF", "SGMT")
dairy_product <- c("MILK", "BUTT", "CREM", "FRMI", "CHES", "SMIP", "COCM", "WMIO", "CASE", "WHEP")
cereal_product <- c("CERE", "RYEM", "WHEA", "OATS", "BARL", "OCER", "MAIZ", "RICE")
oilseeds_product <- c("RAPE", "SOYA", "SUNF")
cakes_product <- c("RAPC", "SUNC", "SOYC", "CAKS")
oils_product <- c("RAPO", "SUNO", "SOYO", "OLIO", "PLMO")
meats <- balance %>% filter(i4 %in% meat_product)
dairy <- balance %>% filter(i4 %in% dairy_product)
cereals <- balance %>% filter(i4 %in% cereal_product)
oilseeds<- balance %>% filter(i4 %in% oilseeds_product)
cakes <- balance %>% filter(i4 %in% cakes_product)
oils <- balance %>% filter(i4 %in% oils_product)
rm(balance)
meats <- meats %>%
mutate(supply = PROD + ISCH) %>%
mutate(demand = HCON)
meats <- meats %>% select(i1, i4, i5, supply, demand, Exports_noIntra, Imports_noIntra, nettrade)
cereals <- cereals %>%
mutate(supply = PROD + ISCH) %>%
mutate(demand = HCON + PROC + BIOF + FEED)
cereals <- cereals %>% select(i1, i4, i5, supply, demand, Exports_noIntra, Imports_noIntra, nettrade)
dairy <- dairy %>%
mutate(supply = PROD) %>%
mutate(demand = HCON + PROC + FEED)
dairy <- dairy %>% select(i1, i4, i5, supply, demand, Exports_noIntra, Imports_noIntra, nettrade)
oilseeds <- oilseeds %>%
mutate(supply = PROD) %>%
mutate(demand = HCON + PROC + FEED)
oilseeds <- oilseeds %>% select(i1, i4, i5, supply, demand, Exports_noIntra, Imports_noIntra, nettrade)
cakes <- cakes %>%
mutate(supply = PROD) %>%
mutate(demand = HCON + FEED)
cakes <- cakes %>% select(i1, i4, i5, supply, demand, Exports_noIntra, Imports_noIntra, nettrade)
oils <- oils %>%
mutate(supply = PROD) %>%
mutate(demand = HCON + PROC + FEED + BIOF)
oils <- oils %>% select(i1, i4, i5, supply, demand, Exports_noIntra, Imports_noIntra, nettrade)
balance <- rbind(meats, dairy, cereals, oilseeds, cakes, oils)
# get rid of WHEA if not selected by the user
balance <- balance %>% filter(i4 %in% product_list)
balance <- balance %>%
arrange(i4, i1, i5) %>%
select(i4, i1, i5, supply, demand, Exports_noIntra, Imports_noIntra, nettrade)
return(balance)
}
#' Auxiliary function to load results (LEVL, YILD) related to dairying.
#' Both low and high-intensity variants included
#'
#'
get_dairy_aux <- function(datacube, region_list, product_list = c("DCOH", "DCOL")){
# herd size and milk yield
bal_item <- c("LEVL", "YILD")
# select relevant slice of the whole capri datacube
datacube <- datacube %>%
filter(i1 %in% region_list, i2 == "", i4 %in% bal_item, i3 %in% product_list, i5 != "BAS") %>%
select(i1, i3, i4, i5, value)
# calculate milk supply from dairy yield and dairy cow herds
datacube <- spread(datacube, i4, value)
datacube <- datacube %>% mutate(supply = YILD * LEVL / 1000)
# aggregate high- and low-yield variants
datacube <- melt(datacube, id=c("i1","i3","i5"))
datacube <- datacube %>% group_by(i1, i5, variable) %>% summarise(value=sum(value))
# recalculate aggregate yields
datacube <- spread(datacube, variable, value)
datacube <- datacube %>% mutate(YILD = supply / LEVL)
return(datacube)
}
#' Auxiliary function: extract milk deliveries
#'
#'
get_milk_deliveries <- function(datacube, region_list, product_list = c("PRCC")){
bal_item <- c("COMI")
# select relevant slice of the whole capri datacube
datacube <- datacube %>%
filter(i1 %in% region_list, i2 == "", i4 %in% bal_item, i3 %in% product_list, i5 != "BAS") %>%
select(i1, i5, value)
return(datacube)
}
#' @param region_list List of regions
#' @param year_list List of simulation years
#' @param folder Folder where the baseline .RData files are stored. Default "mydata"
#'
#'
get_dairy <- function(region_list, product_list = c("DCOH", "DCOL"), scenario_list, folder = "mydata"){
# dairy cow prod. activity divided into low/high-yield variants
# load baselines for all year
for(i in 1:length(scenario_list)) {load(file=paste(folder, "/", scenario_list[i], ".RData", sep=""))}
# part 1) get herds/yields/supply
# start with the first year...
dairy_herd <- get_dairy_aux(get(scenario_list[1]), region_list, product_list)
# ...continue with the other years (if those exist)
if (length(scenario_list) > 1) {
for(i in 2:length(scenario_list)){
dairy_herd <- rbind(dairy_herd, get_dairy_aux(get(scenario_list[i]), region_list, product_list))
}
}
# part 2) get milk deliveries (not the same as supply...)
deliveries <- get_milk_deliveries(get(scenario_list[1]), region_list)
# ...continue with the other years (if those exist)
if (length(scenario_list) > 1) {
for(i in 2:length(scenario_list)){
deliveries <- rbind(deliveries, get_milk_deliveries(get(scenario_list[i]), region_list))
}
}
dairy_herd <- left_join(dairy_herd, deliveries, by = c("i1", "i5"))
return(dairy_herd)
}
get_cowmilk <- function(region_list, year_list){
attrib3_list <- c("COMI", "LEVL")
# load baselines for all year indicated in year_list
for(i in 1:length(year_list)) {load(file=paste("data/baseline",year_list[i], ".RData", sep=""))}
# start with the first year...
cowmilk <- get_cowmilk_aux(get(paste("baseline",year_list[1], sep="")), region_list, attrib3_list, year_list[1])
# ...continue with the others in a for loop
if (length(year_list) > 1) {
for(i in 2:length(year_list)){
cowmilk <- rbind(cowmilk, get_dairy_aux(get(paste("baseline",year_list[i], sep="")), region_list, attrib3_list, year_list[i]))
}
}
return(cowmilk)
}
get_cowmilk_aux <- function(datacube, region_list, attrib3_list = c("GROF", "PRCC", "DCOW"), year){
attrib4_list <- c("COMI", "LEVL")
datacube <- datacube %>%
filter(i1 %in% region_list, i2 == "", i4 %in% attrib4_list, i3 %in% attrib3_list, i5 == year) %>%
select(i1, i3, i4, i5, value)
return(datacube)
}