+library(ggplot2)
+library(dplyr)
+library(patchwork)
+library(RColorBrewer)
+
+devtools::load_all()
+"L100.FAO_SUA_APE_balance" %>% load_from_cache() %>% first -> L100.FAO_SUA_APE_balance
+"L100.FAO_PRODSTAT_TO_DOWNSCAL" %>% load_from_cache() %>% first -> L100.FAO_PRODSTAT_TO_DOWNSCAL
+"FAO_Food_Macronutrient_All_2010_2019" %>% load_from_cache() %>% first -> FAO_Food_Macronutrient_All_2010_2019
+
+c("Opening stocks", "Import", "Production") -> ElEMSupply
+c("Food", "Feed", "Processed", "Other uses", "Export", "Closing stocks", "Residuals") -> ELEMDemand
+c(ElEMSupply, ELEMDemand) -> ELEMAll
+
+# Define color pal.
+c(brewer.pal(n = length(ELEMAll), name = "Spectral")[1:length(ElEMSupply)],
+ brewer.pal(n = length(ELEMAll), name = "Spectral")[length(ElEMSupply)+1:length(ELEMDemand)]
+) -> ColUpdate
+
+GCAMCrops <-
+ c( "Corn", "Wheat", "Rice", "OtherGrain", "RootTuber", "Soybean" ,
+ "OilCrop", "OilPalm", "SugarCrop", "Vegetables", "Fruits",
+ "NutsSeeds", "Legumes", "FiberCrop", "MiscCrop")
+GCAMLivestock <-
+ c("Beef", "Dairy", "Pork", "Poultry", "SheepGoat", "OtherMeat_Fish")
+
+L100.FAO_SUA_APE_balance %>%
+ filter(!GCAM_commodity %in% GCAMLivestock) %>%
+ filter(year == 2015) %>%
+ mutate(region = if_else(GCAM_region_ID == 1, "USA", "ROW")) %>%
+ group_by(GCAM_commodity, region, element) %>%
+ summarize(value = sum(value)) %>% ungroup() %>%
+ filter(element != "Net_Export") %>%
+ mutate(value = if_else(element %in% c("Export", "Other uses", "Food", "Feed"), -value, value)) %>%
+ mutate(region = factor(region, levels = c("USA", "ROW"))) %>%
+ mutate(GCAM_commodity = factor(GCAM_commodity, levels = GCAMCrops)) %>%
+ mutate(element = factor(element, levels = c("Import", "Production", "Export", "Other uses", "Feed", "Food"))) %>%
+ ggplot() + facet_wrap(~region, scales = "free") +
+ geom_bar(aes(x = GCAM_commodity, y = value, fill = element),
+ stat = "identity", color = "black", position="stack") +
+ geom_hline(yintercept = 0) +
+ scale_fill_brewer(palette = "Spectral") +
+ labs(x = "GCAM Crop Commodities",
+ y = "Million tonnes", fill = "Element",
+ title = "(A) Supply utilization accounts in primary equivalent for GCAM crop commodities",
+ #subtitle = "Note: other uses include seed use, losses, storage variation, and industrial use",
+ #caption = "Data: FAOSTAT \n Package: JGCRI/gcamfaostat"
+ ) +
+ theme_bw() +
+ theme(text = element_text(size = 16),
+ axis.text = element_text(colour = "black", size = 16),
+ axis.text.x = element_text(angle = 35, hjust = 1, vjust = 1),
+ strip.background = element_rect(fill = "grey100"),
+ strip.text = element_text(size = 16),
+ panel.spacing.x = unit(0.8, "lines")) -> p1;p1
+
+
+L100.FAO_PRODSTAT_TO_DOWNSCAL %>%
+ filter(GCAM_commodity %in% GCAMCrops) %>%
+ mutate(region = if_else(GCAM_region_ID == 1, "USA", "ROW")) %>%
+ filter(year == 2015) %>%
+ group_by(GCAM_commodity, region) %>%
+ summarize(Prod_t = sum(Prod_t), Area_harvested_ha = sum(Area_harvested_ha)) %>% ungroup() %>%
+ mutate(value = Prod_t / Area_harvested_ha) %>%
+ mutate(region = factor(region, levels = c("USA", "ROW"))) %>%
+ mutate(GCAM_commodity = factor(GCAM_commodity, levels = GCAMCrops)) %>%
+ ggplot() +
+ geom_point(aes(x = GCAM_commodity, y = value, fill = region), size = 2.5, shape = 21, color = "black" ) +
+ geom_line(aes(x = GCAM_commodity, y = value, color = region, group = region), size = 1 ) +
+ labs(x = "GCAM Crop Commodities",
+ y = "Tonnes per hectare", fill = "Region", color = "Region",
+ title = "(B) Crop yield: production vs. harvested area"
+ ) +
+ scale_fill_brewer(palette = "Set1") +
+ scale_color_brewer(palette = "Set1") +
+ theme_bw() +
+ theme(text = element_text(size = 16),
+ axis.text = element_text(colour = "black", size = 16),
+ axis.text.x = element_text(angle = 35, hjust = 1, vjust = 1),
+ strip.background = element_rect(fill = "grey100"),
+ strip.text = element_text(size = 16),
+ panel.spacing.x = unit(0.8, "lines")) -> p2;p2
+
+
+
+FAO_Food_Macronutrient_All_2010_2019 %>%
+ filter(year %in% aglu.MODEL_MACRONUTRIENT_YEARS) %>%
+ # Aggregate to region and GCAM commodity
+ dplyr::group_by_at(vars(GCAM_region_ID, GCAM_commodity, year, macronutrient)) %>%
+ summarise(value = sum(value), .groups = "drop") %>%
+ # Mean over aglu.MODEL_MACRONUTRIENT_YEARS
+ dplyr::group_by_at(vars(GCAM_region_ID, GCAM_commodity, macronutrient)) %>%
+ summarise(value = mean(value), .groups = "drop") %>%
+ spread(macronutrient, value) ->
+ DF_Macronutrient_FoodItem1
+
+DF_Macronutrient_FoodItem1 %>%
+ # NEC is removed by joining
+ # though not all food items are consumed in all regions (deal with NA later)
+ right_join(
+ L100.FAO_SUA_APE_balance %>% # Unit is Mt
+ filter(element == "Food",
+ year == dplyr::last(MODEL_BASE_YEARS)),
+ by = c("GCAM_region_ID", "GCAM_commodity")
+ ) %>%
+ filter(GCAM_commodity %in% GCAMCrops) %>%
+ mutate(region = if_else(GCAM_region_ID == 1, "USA", "ROW")) %>%
+ filter(year == 2015) %>%
+ group_by(GCAM_commodity, region) %>%
+ summarize(value = sum(value), MKcal = sum(MKcal)) %>%
+ # Both data were average already
+ transmute(region, GCAM_commodity,
+ calperg = MKcal / value / 1000) ->
+ DF_Macronutrient_FoodItem2
+
+DF_Macronutrient_FoodItem2 %>%
+ mutate(region = factor(region, levels = c("USA", "ROW"))) %>%
+ mutate(GCAM_commodity = factor(GCAM_commodity, levels = GCAMCrops)) %>%
+ mutate(calperg = calperg / 1000) %>%
+ ggplot() +
+ geom_point(aes(x = GCAM_commodity, y = calperg, fill = region), size = 2.5, shape = 21, color = "black" ) +
+ geom_line(aes(x = GCAM_commodity, y = calperg, color = region, group = region), size = 1 ) +
+ labs(x = "GCAM Crop Commodities",
+ y = "kilocalories per gram", fill = "Region", color = "Region",
+ title = "(C) Calorie intensity: food calories vs. food availability"
+ ) +
+ scale_fill_brewer(palette = "Set1") +
+ scale_color_brewer(palette = "Set1") +
+ theme_bw() +
+ theme(text = element_text(size = 16),
+ axis.text = element_text(colour = "black", size = 16),
+ axis.text.x = element_text(angle = 35, hjust = 1, vjust = 1),
+ strip.background = element_rect(fill = "grey100"),
+ strip.text = element_text(size = 16),
+ panel.spacing.x = unit(0.8, "lines")) -> p3;p3
+
+(p1 + theme(axis.title.x = element_blank()) )/ (p2 + p3 + patchwork::plot_layout(guides = "collect")) +
+ plot_layout(heights = c(1, 0.8)) -> pp
+
+ggsave(file.path("../man/figures", "Fig_SUA_land_food_crops.png"), pp, width = 16, height = 15)
+
+
+#----
+L100.FAO_SUA_APE_balance %>%
+ filter(GCAM_commodity %in% GCAMLivestock) %>%
+ filter(year == 2015) %>%
+ mutate(region = if_else(GCAM_region_ID == 1, "USA", "ROW")) %>%
+ group_by(GCAM_commodity, region, element) %>%
+ summarize(value = sum(value)) %>% ungroup() %>%
+ filter(element != "Net_Export") %>%
+ mutate(value = if_else(element %in% c("Export", "Other uses", "Food", "Feed"), -value, value)) %>%
+ mutate(region = factor(region, levels = c("USA", "ROW"))) %>%
+ mutate(GCAM_commodity = factor(GCAM_commodity, levels = GCAMLivestock)) %>%
+ mutate(element = factor(element, levels = c("Import", "Production", "Export", "Other uses", "Feed", "Food"))) %>%
+ ggplot() + facet_wrap(~region, scales = "free") +
+ geom_bar(aes(x = GCAM_commodity, y = value, fill = element),
+ stat = "identity", color = "black", position="stack") +
+ geom_hline(yintercept = 0) +
+ scale_fill_brewer(palette = "Spectral") +
+ labs(x = "GCAM Livestock Commodities",
+ y = "Million tonnes", fill = "Element",
+ title = "(A) Supply utilization accounts in primary equivalent for GCAM livestock commodities",
+ #subtitle = "Note: other uses include seed use, losses, storage variation, and industrial use",
+ #caption = "Data: FAOSTAT \n Package: JGCRI/gcamfaostat"
+ ) +
+ theme_bw() +
+ theme(text = element_text(size = 16),
+ axis.text = element_text(colour = "black", size = 16),
+ axis.text.x = element_text(angle = 35, hjust = 1, vjust = 1),
+ strip.background = element_rect(fill = "grey100"),
+ strip.text = element_text(size = 16),
+ panel.spacing.x = unit(0.8, "lines")) -> p1;p1
+
+
+DF_Macronutrient_FoodItem1 %>%
+ # NEC is removed by joining
+ # though not all food items are consumed in all regions (deal with NA later)
+ right_join(
+ L100.FAO_SUA_APE_balance %>% # Unit is Mt
+ filter(element == "Food",
+ year == dplyr::last(MODEL_BASE_YEARS)),
+ by = c("GCAM_region_ID", "GCAM_commodity")
+ ) %>%
+ #filter(GCAM_commodity %in% GCAMCrops) %>%
+ filter(GCAM_commodity %in% c("Beef", "Dairy", "OtherMeat_Fish", "Pork", "Poultry", "SheepGoat")) %>%
+ mutate(region = if_else(GCAM_region_ID == 1, "USA", "ROW")) %>%
+ filter(year == 2015) %>%
+ group_by(GCAM_commodity, region) %>%
+ summarize(value = sum(value), MKcal = sum(MKcal)) %>%
+ # Both data were average already
+ transmute(region, GCAM_commodity,
+ calperg = MKcal / value / 1000) ->
+ DF_Macronutrient_FoodItem2
+
+DF_Macronutrient_FoodItem2 %>%
+ mutate(region = factor(region, levels = c("USA", "ROW"))) %>%
+ mutate(GCAM_commodity = factor(GCAM_commodity, levels = c("Beef", "Dairy", "Pork", "Poultry", "SheepGoat", "OtherMeat_Fish"))) %>%
+ mutate(calperg = calperg / 1000) %>%
+ ggplot() +
+ geom_point(aes(x = GCAM_commodity, y = calperg, fill = region), size = 2.5, shape = 21, color = "black" ) +
+ geom_line(aes(x = GCAM_commodity, y = calperg, color = region, group = region), size = 1 ) +
+ labs(x = "GCAM Livestock Commodities",
+ y = "kilocalories per gram", fill = "Region", color = "Region",
+ title = "(B) Calorie intensity: food calories vs. food availability"
+ ) +
+ scale_fill_brewer(palette = "Set1") +
+ scale_color_brewer(palette = "Set1") +
+ theme_bw() +
+ theme(text = element_text(size = 16),
+ axis.text = element_text(colour = "black", size = 16),
+ axis.text.x = element_text(angle = 35, hjust = 1, vjust = 1),
+ strip.background = element_rect(fill = "grey100"),
+ strip.text = element_text(size = 16),
+ panel.spacing.x = unit(0.8, "lines")) -> p3;p3
+
+
+p1/ (p3 + patchwork::plot_spacer()) +
+ plot_layout(heights = c(1, 1)) -> pp
+
+ggsave(file.path("../man/figures", "Fig_SUA_land_food_livestock.png"), pp, width = 15, height = 12)
