In marcusspiegel/agroEcoTradeoff: Landuse Tradeoff analysis model

Overview

Having examined the manner in which different weightings alter the impacts of different land use priorities, you can now choose a few of those weighting schemes and explore them more fully in relation to different crop production targets.

Set parameters

Give a reasoned value for the future target for each of the 9 crops in the prod_targ vector below. The values you give are a multiplier relative to current levels of production. Below that, give the values of the weights you want to apply to each of the four land use priorities, in the cbeta vector.

# production target list
prod_targ <- c("maize" = 4, 
               "cassava" = 2, 
               "ground" = 2, 
               "cotton" = 2, 
               "soy" = 2, 
               "pulse" = 2, 
               "sunflower" = 2, 
               "sugarcane" = 2, 
               "wheat" = 2)

# Weights vector
cbetas <- c("Ag" = 0.5, 
            "C" = 0.5, 
            "bd" = 0, 
            "cost" = 0)

Run the model

library(agroEcoTradeoff)
bcode <- run_code("ZA")
dnm <- paste0(full_path(set_base_path(), "external/output/batch/dt/"), bcode)
dir.create(dnm)

# Run model
ybetas <- list(1, 1)
tdt <- tradeoff_mod(prod_targ, ybetas, cbetas)  # 2.5 seconds

# Write out outputs
fnm <- full_path(set_base_path(), 
                 paste0("external/output/batch/dt/", bcode, "/", 
                        tdt$runcode, ".csv"))
write.table(tdt$conv, file = fnm, sep = ",", col.names = TRUE, 
            row.names = FALSE)
impacts <- tdt[[c("impacts")]]

fnm <- full_path(set_base_path(), 
                 paste0("external/output/batch/dt/", bcode, "/impacts.rda"))
save(impacts, file = fnm)
save(bcode, file = paste0("external/output/batch/dt/bcode.rda"))

Plot results

Where were the impacts?

library(agroEcoTradeoff)
setwd(set_base_path())
load(paste0("external/output/batch/dt/bcode.rda"))
CRSobj <- spatial_meta("ZA")$crs
dnm <- full_path("external/output/batch/dt", bcode)
dt <- fread(full_path(dnm, dir(dnm, pattern = ".csv")))

xy <- dt[, .(x, y)]
xy[, conv := dt[, rowSums(.SD), .SDcols = names(dt)[-c(1:2)]]]
dtr <- dt_to_raster(xy, CRSobj = CRSobj)

load(paste0(dnm, "/impacts.rda"))
load("external/data/parks_roads.rda")

v1 <- sum(impacts$conv_area) / 1000
v2 <- sum(impacts$pa_loss) / 1000
v3 <- mean(impacts[, 3], na.rm = TRUE)
v4 <- mean(impacts[, 5], na.rm = TRUE)
v5 <- round(mean(impacts$cost / 60 / 24 / 7, na.rm = TRUE))

par(mar = c(1, 1, 1, 1))
plot(dtr, axes = FALSE, box = FALSE, legend = FALSE)
plot(pas[pas@data$type == "gma", ], add = TRUE, col = rgb(1, 0, 0, alpha = 0.1),
     border = FALSE)
plot(pas[pas@data$type == "npark", ], add = TRUE, 
     col = rgb(0, 0, 1, alpha = 0.1), border = FALSE)
plot(roads, add = TRUE, lwd = 0.1, col = "grey")
mtext(paste(v1, "ha converted"), side = 3, adj = 0)
mtext(paste(v2, "PA ha converted"), side = 3, adj = 0, line = -1)
mtext(paste(round(v3, 1), "spp/tYield"), side = 3, adj = 0, line = -2)
mtext(paste(round(v4, 1), "tC/tYield"), side = 3, adj = 0, line = -3)
mtext(paste(round(v5, 1), "Cost"), side = 3, adj = 0, line = -4)