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7_food_security_implications.R
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7_food_security_implications.R
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# csv containing calories and nutritional detail about each crop in the mapspam db
crop_parser <- read.csv(paste0(input_folder, "4-Methodology-Crops-of-SPAM-2005-2015-02-26.csv"), stringsAsFactors = F)
calories <- read.csv(paste0(input_folder, "calories.csv"), stringsAsFactors = F)
crop_parser$parsed <- NA
for (i in 1:length(crop_parser$FAONAMES)){
a <- agrep(crop_parser$FAONAMES[i], calories$ITEM, value=T, max.distance = 1, ignore.case = T)[which.min(nchar(agrep(crop_parser$FAONAMES[i], calories$ITEM, value=T, max.distance = 1, ignore.case = T)))]
crop_parser$parsed[i] <-ifelse(identical(a, character(0)), NA, a)
}
crop_parser$FAONAMES[is.na(crop_parser$parsed)]
crop_parser$parsed[is.na(crop_parser$parsed)] <- c("CEREALS NES", "YAUTIA (COCOYAM)", "BROAD BEANS,GREEN", "GROUNDNUTS IN SHELL", "OLIVES", "COTTONSEED", NA, NA, "ORANGES", "APPLES", "CABBAGES", "NA")
crop_parser$parsed[2] <- "RICE PADDY"
calories <- merge(calories, crop_parser, by.x="ITEM", by.y="parsed") %>% dplyr::select(1:5, 11)
calories[,2:4] <- calories[,2:4]*10 # convert to kg
#source("http://michael.hahsler.net/SMU/EMIS7332/R/copytable.R")
#copytable(calories)
###
# apply factor to convert kg of yield to kg of food
# calculate nutrition generation potential in each cell
clusters <- clusters %>%
mutate_at(vars(contains('yg_')), funs(ifelse(clusters$profit_yearly>0, ., 0)))
aa <- clusters
aa= st_set_geometry(aa, NULL)
#
#
c <- head(colnames(clusters)[grep("yg_", colnames(clusters))], -1)
for (i in c){
clusters[paste0("calories_gain_" , as.character(i))] = calories$kcal[calories$SPAM.short.name==substr(i, 4, 7)] * aa[,i]
clusters[paste0("proteins_gain_" , as.character(i))] = calories$PROTEIN_g[calories$SPAM.short.name==substr(i, 4, 7)] * aa[,i]
clusters[paste0("fats_gain_" , as.character(i))] = calories$FAT_g[calories$SPAM.short.name==substr(i, 4, 7)] * aa[,i]
}
aa <- clusters
aa <- aa %>% dplyr::select(starts_with("calories_gain_")) %>% st_set_geometry(NULL) %>% mutate(calories_gain_total = rowSums(., na.rm=T))
clusters$calories_gain_total <- as.numeric(aa$calories_gain_total)
aa <- clusters
aa <- aa %>% dplyr::select(starts_with("proteins_gain_")) %>% st_set_geometry(NULL) %>% mutate(proteins_gain_total = rowSums(., na.rm=T))
clusters$proteins_gain_total <- as.numeric(aa$proteins_gain_total)
aa <- clusters
aa <- aa %>% dplyr::select(starts_with("fats_gain_")) %>% st_set_geometry(NULL) %>% mutate(fats_gain_total = rowSums(., na.rm=T))
clusters$fats_gain_total <- as.numeric(aa$fats_gain_total)
# calculate potential people fed thanks to nutrition generation potential
calories_yearly_need <- 2000 * 365
proteinsg_yearly_need <- 50 * 365
fatsg_yearly_need <- 60 * 365
# national summary
nat_sum <- clusters
nat_sum = st_set_geometry(nat_sum, NULL)
nat_sum <- filter(nat_sum, !is.na(profit_yearly)) %>% group_by(sov_a3) %>% dplyr::summarise(calories_gain_total=sum(calories_gain_total, na.rm=T), proteins_gain_total=sum(proteins_gain_total, na.rm=T), fats_gain_total=sum(fats_gain_total, na.rm=T))
#
clusters$calories_newly_fed_people <- clusters$calories_gain_total / calories_yearly_need
clusters$proteins_newly_fed_people <- clusters$proteins_gain_total / proteinsg_yearly_need
clusters$fats_newly_fed_people <- clusters$fats_gain_total / fatsg_yearly_need
clusters$calories_gain_capita_day <- clusters$calories_gain_total / ifelse(clusters$pop==0 | clusters$calories_gain_total==0, NA, clusters$pop) / 365
clusters$proteins_gain_capita_day <- clusters$proteins_gain_total / ifelse(clusters$pop==0 | clusters$proteins_gain_total==0, NA, clusters$pop) / 365
clusters$fats_gain_capita_day <- clusters$fats_gain_total / ifelse(clusters$pop==0| clusters$fats_gain_total==0, NA, clusters$pop) / 365
# take national food insecurity estimates
food_insecurity <- read.csv("caloric_gap.csv")
food_insecurity$iso3c <- countrycode(food_insecurity$Country, 'country.name', 'iso3c')
pop <- wbstats::wb(indicator ="SP.POP.TOTL", mrv=1)
merger <- merge(food_insecurity, pop, by="iso3c")
merger <- dplyr::select(merger, value, Calories_gap, insec, iso3c)
#
clusters <- merge(clusters, merger, all.x=T, by.x="sov_a3", by.y="iso3c")
nat_sum <- merge(nat_sum, merger, by.x="sov_a3", by.y="iso3c", all.x=T)
#
write_rds(clusters, paste0("clusters_with_data_7_", paste(scenarios[scenario,], collapse="_"), ".Rds"))
write_rds(nat_sum, paste0("food_sum_", paste(scenarios[scenario,], collapse="_"), ".Rds"))
median(clusters$calories_gain_capita_day[!is.na(clusters$profit_yearly)], na.rm=T)
median(clusters$proteins_gain_capita_day[!is.na(clusters$profit_yearly)], na.rm=T)
median(clusters$fats_gain_capita_day[!is.na(clusters$profit_yearly)], na.rm=T)
sum(nat_sum$calories_gain_total, na.rm=T) / 365 / sum(nat_sum$value, na.rm=T)
sum(nat_sum$proteins_gain_total, na.rm=T) / 365 / sum(nat_sum$value, na.rm=T)
sum(nat_sum$fats_gain_total, na.rm=T) / 365 / sum(nat_sum$value, na.rm=T)