convert = list(
Corn = "CO", Rice = "RI", Soya = "SO", Cass = "CA", SugC = "SU",
BeaD = "BE", SwPo = "SP", Cott = "CT", Gnut = "GT", OPAL = "OP",
Srgh = "SR", Whea = "WH", Pota = "PO", Barl = "BA",
BaseArea = "BA", IR_basin = "IRB", IR_furrow = "IRF",
IR_drip = "IRD", IR_sprink = "IRS",
TEMP = "TM", OTHR = "OT", HUMI = "HU", ARID = "AR"
)
for(year in paste(seq(2000, 2050, 5)))
convert[[year]] = substr(year, 3, 4)
result <- colrow::processFile(
"BrazilCR.shp",
"CROP_DATA_COMPARE_5yr_FC_Had_rcp8p5.CSV",
colrow::attrs(COUNTRY, ID, ALTICLASS, SLPCLASS, SOILCLASS, XX1, USE, XX2, USE2, XXX3, SCENARIO, SCENARIO2, YEAR, VALUE)
)
data <- sf::as_Spatial(result)
biomes <- sf::read_sf(system.file("extdata/shape", "br_biomes.shp", package = "colrow"))
biomessp <- list("sp.polygons", biomes, fill = "transparent", col = "black", add = TRUE, first = FALSE)
ylGn <- RColorBrewer::brewer.pal(7, "RdPu")
cuts <-c (0, 1, 2, 3, 4, 5, 6, 7)
spplot(
data[,"ARIDBeaDHIBeaD2040"],
at = cuts,
col = "transparent", # remove this line to draw the border of each CR
col.regions = ylGn,
sp.layout = list(biomessp)
)
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