James D.A. Millington Nov 2019
This document present code and analyses of pasture areas, meat production and pasture yield for all states 2001-2018, for three different classifications of the MapBiomas data both with and without disaggregation using planted area data.
See accompanying Excel file for the classifications. Input land cover
data, including the classification and disaggregation processes, are
created using the ClassifyDisaggregateMap.r script.
Show/Hide Code
rm(list=ls())
packages <- c(
"tidyverse",
"raster",
"readxl", #for reading Excel sheets
"scales", #useful for ggplotting
"knitr",
"rasterVis", #more useful raster plotting
"cowplot" #useful for ggplotting
)
#use lapply to suppress all wanings: https://stackoverflow.com/a/46685042
invisible(lapply(packages, function(xxx) suppressMessages(require(xxx, character.only = TRUE,quietly=TRUE,warn.conflicts = FALSE))))#raster to xyz (with help from https://stackoverflow.com/a/19847419)
#sepcify input raster, whether nodata cells should be output, whether a unique cell ID should be added
#return is a matrix. note format is row (Y) then col (X)
extractXYZ <- function(raster, nodata = FALSE, addCellID = TRUE){
vals <- raster::extract(raster, 1:ncell(raster)) #specify raster otherwise dplyr used
xys <- rowColFromCell(raster,1:ncell(raster))
combine <- cbind(xys,vals)
if(addCellID){
combine <- cbind(1:length(combine[,1]), combine)
}
if(!nodata){
combine <- combine[!rowSums(!is.finite(combine)),] #from https://stackoverflow.com/a/15773560
}
return(combine)
}
getLCs <- function(data)
{
#calculates proportion of each LC in the muni (ignoring NAs, help from https://stackoverflow.com/a/44290753)
data %>%
group_by(muniID) %>%
dplyr::summarise(LC1 = round(sum(map == 1, na.rm = T) / sum(!is.na(map)), 3),
LC2 = round(sum(map == 2, na.rm = T) / sum(!is.na(map)), 3),
LC3 = round(sum(map == 3, na.rm = T) / sum(!is.na(map)), 3),
LC4 = round(sum(map == 4, na.rm = T) / sum(!is.na(map)), 3),
LC5 = round(sum(map == 5, na.rm = T) / sum(!is.na(map)), 3),
NonNAs = sum(!is.na(map)),
NAs = sum(is.na(map))
) -> LCs
return(LCs)
}#unzip(zipfile="Data/sim10_BRmunis_latlon_5km_2018-04-27.zip",files="sim10_BRmunis_latlon_5km_2018-04-27.asc",exdir="ASCII") # unzip file
munis.r <- raster("Data/BaseMaps/sim10_BRmunis_latlon_5km.asc")
#extract cell values to table format
munis.t <- extractXYZ(munis.r, addCellID = F)
munis.t <- as.data.frame(munis.t)
munis.t <- plyr::rename(munis.t, c("vals" = "muniID"))#Specify classifications and years to examine. Classifications should be the names of Sheets in the Classifications Excel file. Years should be between 2001 and 2018
#classifications to loop through
cls <- c("PastureA","PastureB","PastureC")
yrls <- seq(2001,2018,1)#lists to hold data tables
CData_ls <- vector('list', length(cls))
CDataW_ls <- vector('list', length(cls))
SDataW_ls <- vector('list', length(cls))
Stotals_ls <- vector('list', length(cls))
SDataW_Adj_ls <- vector('list', length(cls))
mapStack_ls <- vector('list', length(cls))
names(CData_ls) <- cls
names(CDataW_ls) <- cls
names(SDataW_ls) <- cls
names(Stotals_ls) <- cls
names(SDataW_Adj_ls) <- cls
names(mapStack_ls) <- cls
#i <- 1
#j <- 1
#loop over classifications
for(i in seq_along(cls)){
classification <- read_excel("Data/MapBiomas_CRAFTY_classifications_v4.xlsx", sheet = cls[i], range="B1:C28", col_names=T)
#reset mapStack for this Classification
mapStack <- stack()
#loop over years
for(j in seq_along(yrls)){
#read the classfied map to a raster
map <- raster(paste0("Data/Classified/LandCover",yrls[j],"_",cls[i],".asc"))
#add categories for later plotting etc. (see https://stackoverflow.com/a/37214431)
map <- ratify(map) #tell R that the map raster is categorical
rat <- levels(map)[[1]] #apply the levels (i.e. categories)
#not all classes may be present after classification, so conditionally construct labels
labs <- c()
if(1 %in% levels(map)[[1]]$ID) { labs <- c(labs, "Nature") }
if(2 %in% levels(map)[[1]]$ID) { labs <- c(labs, "OtherAgri") }
if(3 %in% levels(map)[[1]]$ID) { labs <- c(labs, "Agriculture") }
if(4 %in% levels(map)[[1]]$ID) { labs <- c(labs, "Other") }
if(5 %in% levels(map)[[1]]$ID) { labs <- c(labs, "Pasture") }
rat$landcover <- labs
levels(map) <- rat
#add to mapStack for later plotting
mapStack <- stack(map, mapStack)
#extract cell values to table format
map.t <- extractXYZ(map, addCellID = F)
map.t <- as.data.frame(map.t)
map.t <- plyr::rename(map.t, c("vals" = "map"))
#so need to join
map_munis <- left_join(as.data.frame(munis.t), as.data.frame(map.t), by = c("row" = "row", "col" = "col"))
#now summarise by muniID
lcs_map_munis <- getLCs(map_munis)
#convert cell counts to areas (km2) and add state id
map_areas_munis <- lcs_map_munis %>%
mutate(LC1area = round(LC1 * NonNAs) * 25) %>%
mutate(LC2area = round(LC2 * NonNAs) * 25) %>%
mutate(LC3area = round(LC3 * NonNAs) * 25) %>%
mutate(LC4area = round(LC4 * NonNAs) * 25) %>%
mutate(LC5area = round(LC5 * NonNAs) * 25) %>%
mutate(state = substr(muniID, 1, 2))
#drop original cell-count columns (work with area km2 from now on)
map_areas_munis <- map_areas_munis %>% dplyr::select(-LC1, -LC2, -LC3, -LC4, -LC5, -NonNAs, -NAs)
#summarise muni areas to state level
map_areas <- map_areas_munis %>%
group_by(state) %>%
dplyr::summarise_at(vars(LC1area:LC5area),sum, na.rm=T) %>% #use _at so state is not summarised
mutate_if(is.character, as.integer)
#gather to long format for union below
map_areas <- map_areas %>%
gather(key = LCa, value = area, -state)
#recode LCs for union below
map_areas <- map_areas %>%
mutate(LC = if_else(LCa == "LC1area", 1,
if_else(LCa == "LC2area", 2,
if_else(LCa == "LC3area", 3,
if_else(LCa == "LC4area", 4,
if_else(LCa == "LC5area", 5, 0)
)))))
#add source variable for plotting below (re-order to match map table for union below)
map_areas <- map_areas %>%
dplyr::select(-LCa) %>%
mutate(source = "Map") %>%
dplyr::select(state, LC, source, area)
#relabel states to characters
CData_yr <- map_areas %>%
mutate(state = if_else(state == 17, "TO",
if_else(state == 29, "BA",
if_else(state == 31, "MG",
if_else(state == 35, "SP",
if_else(state == 41, "PR",
if_else(state == 42, "SC",
if_else(state == 43, "RS",
if_else(state == 50, "MS",
if_else(state == 51, "MT",
if_else(state == 52, "GO", "NA"
))))))))))
)
#relabel LCs to characters
CData_yr <- CData_yr %>%
mutate(LC = if_else(LC == 1, "Nature",
if_else(LC == 2, "OtherAgri",
if_else(LC == 3, "Agri",
if_else(LC == 4, "Other",
if_else(LC == 5, "Pasture", "NA"
)))))
)
#add year column
CData_yr <- CData_yr %>%
mutate(year = yrls[j])
#union CData for years here.
#if first iteration of classification loop (re)create the tibble
if(j == 1){
CData <- CData_yr
} else {
#else join data to tibble (by creating another tibble, then join (ensure rows are not lost)
CData <- union_all(CData, CData_yr)
}
}
CData_ls[[i]] <- CData
names(mapStack) <- yrls
mapStack_ls[[i]] <- mapStack
}First, lets look at maps for the different classifications (for the final year) for quick visual comparison.
for(i in seq_along(mapStack_ls)){
clabs <- c()
if(1 %in% levels(mapStack_ls[[i]])[[1]]$ID) { clabs <- c(clabs, 'forestgreen') }
if(2 %in% levels(mapStack_ls[[i]])[[1]]$ID) { clabs <- c(clabs, 'darkolivegreen') }
if(3 %in% levels(mapStack_ls[[i]])[[1]]$ID) { clabs <- c(clabs, 'wheat1') }
if(4 %in% levels(mapStack_ls[[i]])[[1]]$ID) { clabs <- c(clabs, 'gray') }
if(5 %in% levels(mapStack_ls[[i]])[[1]]$ID) { clabs <- c(clabs, 'orange2') }
#print(cls[i])
#length(yrls) is the index of the final year
#tail(yrls, n=1) is the 'label' for the final year
print(rasterVis::levelplot(mapStack_ls[[i]][[length(yrls)]], pretty=T,att = 'landcover', col.regions=clabs, main=paste(cls[[i]], tail(yrls, n=1))))
}
From these maps, we can immediately see that PastureA has much less Pasture cover than the other two classifications.In particular, in the southern state of Rio Grande do Sul (RS) PastureA has much less Pasture areas as much of the state is classified as Grassland. The Grassland class is significant in Rio Grande do Sul because it is within the Pampa biome and has seen a long history of intense efforts by public and private groups to increase the (pasture) productivity of the grasslands.
As we will see below, this difference in classified land area leads to great differences in calculate pasture yields in RS. Other study area states have relatively small areas of grassland so are less affected by the inclusion, although Santa Catarina (a small state on the edge of the Pampa biome) is next most influenced.
Let’s examine these differences between the classifications further quantitatively, looking at both total area and the yield implied when compared to production data.
The figure below shows again that generally the PastureA classification produces much less Pasture land area.
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#add classification lable to the CData tables
for(i in seq_along(cls)){
CData_ls[[i]] <- CData_ls[[i]] %>%
mutate(classification = cls[i])
}
#union the CData tables for the different classifications
for(i in seq_along(cls)){
if(i == 1) { CDataU <- CData_ls[[i]] }
else { CDataU <- dplyr::union(CDataU, CData_ls[[i]]) }
}
#filter to pasture only
CData_Pas <- CDataU %>%
dplyr::filter(LC == "Pasture")CData_Pas %>%
ggplot(aes(x=classification, y=area, fill=classification)) +
geom_bar(stat="identity", colour="white", position = "dodge") +
scale_y_continuous(name=expression(Area~km^{2}), labels = scales::comma) +
facet_grid(year~state) +
xlab("") +
theme(axis.text.x = element_blank()) +
ggtitle("Pasture Area by Classification and State")
This first plot below shows that PastureA for Rio Grande do Sul has vastly greater pasture yield than all other classification-state combinations.
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#Load Production Data
meat_prod_Astates <- read_excel("Data/Cattle_meat_production_Kg_2001_2018_all_states.xlsx", sheet = "Plan1", skip = 1) #data for all states Astates#join to pasture areas
meat_areas <- inner_join(CData_Pas, meat_prod_Fstates_long, by = c("year", "state"))
#calculate intensities (yields)
meat_areas <- meat_areas %>%
mutate(intensity = kg / area)meat_areas %>%
ggplot(aes(x=classification, y=intensity, fill=classification)) +
geom_bar(stat="identity", colour="white", position = "dodge") +
scale_y_continuous(name=expression(Yield~kg~km^{2})) +
facet_grid(year~state) +
xlab("")+
theme(axis.text.x = element_blank()) +
ggtitle("Pasture Yield by Classification and State")
In fact, values are so large it is difficult to see variation for other states in this plot because of the y-axis scale, so let’s repeat this plot but limiting the upper boundary of the yield plotted (to 15,000 kg km-2).
meat_areas %>%
ggplot(aes(x=classification, y=intensity, fill=classification)) +
geom_bar(stat="identity", colour="white", position = "dodge") +
scale_y_continuous(name=expression(Yield~kg~km^{2}),limits = c(0, 15000)) +
facet_grid(year~state) +
xlab("")+
theme(axis.text.x = element_blank()) +
ggtitle("Pasture Yield by Classification and State (limited at 15,000 kg km-2")## Warning: Removed 25 rows containing missing values (geom_bar).
Where bars are missing in the plot above, this is because the value for that bar is greater than 15,000. Now we can see more clearly, that in other states PastureA is also generally has the highest yield compared to the other classifications.
Now let’s summarise the data over time and examine their means and medians (with variation - error bar is one SE) and maxima during 2001-2018.
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meat_summary %>%
ggplot(aes(x=classification, y=int_mn, fill=classification)) +
geom_errorbar(aes(ymin=int_mn-int_se, ymax=int_mn+int_se), width=.1) +
geom_bar(stat="identity", colour="white", position = "dodge") +
facet_grid(.~state) +
scale_y_continuous(name=expression(Yield~kg~km^{2}), limits = c(0, 10000), labels = scales::comma) +
xlab("")+
theme(axis.text.x = element_blank()) +
ggtitle("Pasture Yield, Mean")## Warning: Removed 2 rows containing missing values (geom_errorbar).
## Warning: Removed 2 rows containing missing values (geom_bar).
meat_summary %>%
ggplot(aes(x=classification, y=int_md, fill=classification)) +
geom_errorbar(aes(ymin=int_md-int_se, ymax=int_md+int_se), width=.1) +
geom_bar(stat="identity", colour="white", position = "dodge") +
facet_grid(.~state) +
scale_y_continuous(name=expression(Yield~kg~km^{2}), limits = c(0, 10000), labels = scales::comma) +
xlab("")+
theme(axis.text.x = element_blank()) +
ggtitle("Pasture Yield, Median")## Warning: Removed 2 rows containing missing values (geom_errorbar).
## Warning: Removed 2 rows containing missing values (geom_bar).
meat_summary %>%
ggplot(aes(x=classification, y=int_max, fill=classification)) +
geom_bar(stat="identity", colour="white", position = "dodge") +
facet_grid(.~state) +
scale_y_continuous(name=expression(Yield~kg~km^{2}), limits = c(0, 15000), labels = scales::comma) +
xlab("")+
theme(axis.text.x = element_blank()) +
ggtitle("Pasture Yield, Maxima")## Warning: Removed 2 rows containing missing values (geom_bar).
In these three plots, we again we see the same pattern; PastureA produces greater yields than PastureB and PastureC (except for Sao Paolo state).
But how do these yields compare to observed and projected yields? The table below presents yields by state and classification (sorted descending on the maximum).
int_data <- meat_summary %>%
dplyr::select(-starts_with("prod"), -starts_with("area"))
kable(arrange(int_data, desc(int_max), state, classification), caption="Intensities, sorted on max values descending")| state | classification | source | int_mn | int_md | int_max | int_sd | int_se |
|---|---|---|---|---|---|---|---|
| RS | PastureA | Map | 49527 | 52089 | 81027 | 17473 | 970.708 |
| SC | PastureA | Map | 12605 | 14628 | 20830 | 5139 | 285.482 |
| SP | PastureA | Map | 7437 | 7504 | 9028 | 1188 | 66.012 |
| SP | PastureB | Map | 7422 | 7491 | 9006 | 1184 | 65.800 |
| SC | PastureB | Map | 5780 | 6373 | 8967 | 2135 | 118.606 |
| SP | PastureC | Map | 7091 | 7093 | 8752 | 1172 | 65.094 |
| SC | PastureC | Map | 4225 | 4442 | 7337 | 1738 | 96.548 |
| PR | PastureA | Map | 4944 | 4741 | 7259 | 1259 | 69.949 |
| PR | PastureB | Map | 4909 | 4699 | 7233 | 1259 | 69.920 |
| PR | PastureC | Map | 4620 | 4443 | 6966 | 1234 | 68.554 |
| MT | PastureA | Map | 4700 | 4697 | 6582 | 1079 | 59.959 |
| RS | PastureB | Map | 3378 | 3701 | 4943 | 886 | 49.226 |
| MT | PastureB | Map | 3473 | 3550 | 4852 | 839 | 46.590 |
| MT | PastureC | Map | 3473 | 3550 | 4852 | 839 | 46.590 |
| RS | PastureC | Map | 3256 | 3548 | 4835 | 871 | 48.388 |
| MS | PastureA | Map | 3788 | 3671 | 4637 | 452 | 25.133 |
| GO | PastureA | Map | 2976 | 2914 | 3811 | 587 | 32.621 |
| MS | PastureB | Map | 3055 | 2984 | 3711 | 349 | 19.390 |
| MS | PastureC | Map | 3045 | 2974 | 3698 | 346 | 19.237 |
| TO | PastureA | Map | 2845 | 2949 | 3461 | 482 | 26.779 |
| GO | PastureB | Map | 2646 | 2536 | 3398 | 528 | 29.348 |
| GO | PastureC | Map | 2640 | 2529 | 3391 | 528 | 29.357 |
| MG | PastureA | Map | 1443 | 1510 | 1977 | 380 | 21.118 |
| TO | PastureB | Map | 1428 | 1526 | 1761 | 292 | 16.244 |
| TO | PastureC | Map | 1428 | 1526 | 1761 | 292 | 16.244 |
| MG | PastureB | Map | 1256 | 1312 | 1724 | 333 | 18.490 |
| MG | PastureC | Map | 1227 | 1283 | 1686 | 322 | 17.901 |
| BA | PastureA | Map | 1022 | 1156 | 1388 | 325 | 18.080 |
| BA | PastureB | Map | 842 | 955 | 1158 | 276 | 15.316 |
| BA | PastureC | Map | 837 | 948 | 1153 | 274 | 15.237 |
Intensities, sorted on max values descending
We can also calculate the mean state maximum across all years for each of the classifications:
meat_summary %>%
dplyr::select(-starts_with("prod"), -int_sd, -int_se) %>%
group_by(classification) %>%
summarise_at(vars(int_max), mean)## # A tibble: 3 x 2
## classification int_max
## <chr> <dbl>
## 1 PastureA 14000
## 2 PastureB 4675.
## 3 PastureC 4443.
Largest values for PastureB and PastureC seem reasonable and align well with expected future yields (important to have a max contemporary yield that is feasible in future, to be able to run the model into the future). For example, this report expects yields of 8,730kg/km2 in 2030 if the Brazilian sector continues to modernize (in “Produção de @/hectare” @ is equivalent to 15kg). This potential aligns well with the maximum previously observed intensity of 9,100kg/km2 (which is likely an outlier given remaining uncertainty in land cover classification). Ultimately, the PastureA classification produces yield values which are just not feasible relatively to observed values.
Now that we have discounted the PastureA classification, let’s examine
PastureB and PastureC in more detail after they have been
disaggregated using planted area data (see ClassifyDisaggregateMap.r).
We’ll repeat much of the above analysis, but using disaggregated maps as
input.
Show/Hide Code
#Specify classifications and years to examine. Classifications should be the names of Sheets in the Classifications Excel file. Years should be between 2001 and 2018
#classifications to loop through
cls <- c("PastureB","PastureC")
yrls <- seq(2001,2018,1)#lists to hold data tables
CData_ls <- vector('list', length(cls))
CDataW_ls <- vector('list', length(cls))
SDataW_ls <- vector('list', length(cls))
Stotals_ls <- vector('list', length(cls))
SDataW_Adj_ls <- vector('list', length(cls))
mapStack_ls <- vector('list', length(cls))
names(CData_ls) <- cls
names(CDataW_ls) <- cls
names(SDataW_ls) <- cls
names(Stotals_ls) <- cls
names(SDataW_Adj_ls) <- cls
names(mapStack_ls) <- cls
#i <- 1
#j <- 1
#loop over classifications
for(i in seq_along(cls)){
classification <- read_excel("Data/MapBiomas_CRAFTY_classifications_v4.xlsx", sheet = cls[i], range="B1:C28", col_names=T)
#reset mapStack for this Classification
mapStack <- stack()
#loop over years
for(j in seq_along(yrls)){
#read the classfied map to a raster
map <- raster(paste0("Data/Classified/LandCover",yrls[j],"_",cls[i],"_Disagg.asc"))
#add categories for later plotting etc. (see https://stackoverflow.com/a/37214431)
map <- ratify(map) #tell R that the map raster is categorical
rat <- levels(map)[[1]] #apply the levels (i.e. categories)
#not all classes may be present after classification, so conditionally construct labels
labs <- c()
if(1 %in% levels(map)[[1]]$ID) { labs <- c(labs, "Nature") }
if(2 %in% levels(map)[[1]]$ID) { labs <- c(labs, "OtherAgri") }
if(3 %in% levels(map)[[1]]$ID) { labs <- c(labs, "Agriculture") }
if(4 %in% levels(map)[[1]]$ID) { labs <- c(labs, "Other") }
if(5 %in% levels(map)[[1]]$ID) { labs <- c(labs, "Pasture") }
rat$landcover <- labs
levels(map) <- rat
#add to mapStack for later plotting
mapStack <- stack(map, mapStack)
#extract cell values to table format
map.t <- extractXYZ(map, addCellID = F)
map.t <- as.data.frame(map.t)
map.t <- plyr::rename(map.t, c("vals" = "map"))
#so need to join
map_munis <- left_join(as.data.frame(munis.t), as.data.frame(map.t), by = c("row" = "row", "col" = "col"))
#now summarise by muniID
lcs_map_munis <- getLCs(map_munis)
#convert cell counts to areas (km2) and add state id
map_areas_munis <- lcs_map_munis %>%
mutate(LC1area = round(LC1 * NonNAs) * 25) %>%
mutate(LC2area = round(LC2 * NonNAs) * 25) %>%
mutate(LC3area = round(LC3 * NonNAs) * 25) %>%
mutate(LC4area = round(LC4 * NonNAs) * 25) %>%
mutate(LC5area = round(LC5 * NonNAs) * 25) %>%
mutate(state = substr(muniID, 1, 2))
#drop original cell-count columns (work with area km2 from now on)
map_areas_munis <- map_areas_munis %>% dplyr::select(-LC1, -LC2, -LC3, -LC4, -LC5, -NonNAs, -NAs)
#summarise muni areas to state level
map_areas <- map_areas_munis %>%
group_by(state) %>%
dplyr::summarise_at(vars(LC1area:LC5area),sum, na.rm=T) %>% #use _at so state is not summarised
mutate_if(is.character, as.integer)
#gather to long format for union below
map_areas <- map_areas %>%
gather(key = LCa, value = area, -state)
#recode LCs for union below
map_areas <- map_areas %>%
mutate(LC = if_else(LCa == "LC1area", 1,
if_else(LCa == "LC2area", 2,
if_else(LCa == "LC3area", 3,
if_else(LCa == "LC4area", 4,
if_else(LCa == "LC5area", 5, 0)
)))))
#add source variable for plotting below (re-order to match map table for union below)
map_areas <- map_areas %>%
dplyr::select(-LCa) %>%
mutate(source = "Map") %>%
dplyr::select(state, LC, source, area)
#relabel states to characters
CData_yr <- map_areas %>%
mutate(state = if_else(state == 17, "TO",
if_else(state == 29, "BA",
if_else(state == 31, "MG",
if_else(state == 35, "SP",
if_else(state == 41, "PR",
if_else(state == 42, "SC",
if_else(state == 43, "RS",
if_else(state == 50, "MS",
if_else(state == 51, "MT",
if_else(state == 52, "GO", "NA"
))))))))))
)
#relabel LCs to characters
CData_yr <- CData_yr %>%
mutate(LC = if_else(LC == 1, "Nature",
if_else(LC == 2, "OtherAgri",
if_else(LC == 3, "Agri",
if_else(LC == 4, "Other",
if_else(LC == 5, "Pasture", "NA"
)))))
)
#add year column
CData_yr <- CData_yr %>%
mutate(year = yrls[j])
#union CData for years here.
#if first iteration of classification loop (re)create the tibble
if(j == 1){
CData <- CData_yr
} else {
#else join data to tibble (by creating another tibble, then join (ensure rows are not lost)
CData <- union_all(CData, CData_yr)
}
}
CData_ls[[i]] <- CData
names(mapStack) <- yrls
mapStack_ls[[i]] <- mapStack
}#add classification lable to the CData tables
for(i in seq_along(cls)){
CData_ls[[i]] <- CData_ls[[i]] %>%
mutate(classification = cls[i])
}
#union the CData tables for the different classifications
for(i in seq_along(cls)){
if(i == 1) { CDataU <- CData_ls[[i]] }
else { CDataU <- dplyr::union(CDataU, CData_ls[[i]]) }
}
#filter to pasture only
CData_Pas <- CDataU %>%
dplyr::filter(LC == "Pasture")#join to pasture areas
meat_areas <- inner_join(CData_Pas, meat_prod_Fstates_long, by = c("year", "state"))
#calculate intensities (yields)
meat_areas <- meat_areas %>%
mutate(intensity = kg / area)We see that in general there is little difference between the two classifications; Santa Catarina state has possibly the largest differences with PastureB resulting in greater Pasture yields.
meat_areas %>%
#filter(source == "Map") %>%
ggplot(aes(x=classification, y=intensity, fill=classification)) +
geom_bar(stat="identity", colour="white", position = "dodge") +
scale_y_continuous(name=expression(Yield~kg~km^{2})) +
facet_grid(year~state) +
xlab("")+
theme(axis.text.x = element_blank()) +
ggtitle("Pasture Yield by Classification and State")
Now let’s summarise the data over time again, and examine their means and medians (with variation - error bar is one SE) and maxima during 2001-2018.
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meat_summary %>%
#filter(source == "MB") %>%
ggplot(aes(x=classification, y=int_mn, fill=classification)) +
geom_errorbar(aes(ymin=int_mn-int_se, ymax=int_mn+int_se), width=.1) +
geom_bar(stat="identity", colour="white", position = "dodge") +
facet_grid(.~state) +
scale_y_continuous(name=expression(Yield~kg~km^{2}), limits = c(0, 10000), labels = scales::comma) +
xlab("")+
theme(axis.text.x = element_blank()) +
ggtitle("Pasture Yield, Mean")
meat_summary %>%
#filter(source == "Map") %>%
ggplot(aes(x=classification, y=int_md, fill=classification)) +
geom_errorbar(aes(ymin=int_md-int_se, ymax=int_md+int_se), width=.1) +
geom_bar(stat="identity", colour="white", position = "dodge") +
facet_grid(.~state) +
scale_y_continuous(name=expression(Yield~kg~km^{2}), limits = c(0, 10000), labels = scales::comma) +
xlab("")+
theme(axis.text.x = element_blank()) +
ggtitle("Pasture Yield, Median")
meat_summary %>%
#filter(source == "Map") %>%
ggplot(aes(x=classification, y=int_max, fill=classification)) +
#geom_errorbar(aes(ymin=int_md-int_se, ymax=int_md+int_se), width=.1) +
geom_bar(stat="identity", colour="white", position = "dodge") +
facet_grid(.~state) +
scale_y_continuous(name=expression(Yield~kg~km^{2}), limits = c(0, 10000), labels = scales::comma) +
xlab("")+
theme(axis.text.x = element_blank()) +
ggtitle("Pasture Yield, Maxima")
As above, when looking at summaries we see that the two classifications are generally similar, but with PastureB producing more land area, notably for Santa Catarina, Sao Paolo and Parana.
Let’s look again at yields by state and classification (sorted descending on the maximum).
int_data <- meat_summary %>%
#filter(source == "Map") %>%
dplyr::select(-starts_with("prod"), -starts_with("area")) #%>%
#filter(state != "SC")
kable(arrange(int_data, desc(int_max), state, classification), caption="Intensities, sorted on max values descending")| state | classification | source | int_mn | int_md | int_max | int_sd | int_se |
|---|---|---|---|---|---|---|---|
| SC | PastureB | Map | 5872 | 6485 | 9126 | 2177 | 120.966 |
| SP | PastureB | Map | 7462 | 7533 | 9063 | 1193 | 66.253 |
| SP | PastureC | Map | 7132 | 7133 | 8811 | 1181 | 65.594 |
| SC | PastureC | Map | 4286 | 4509 | 7449 | 1768 | 98.217 |
| PR | PastureB | Map | 4947 | 4739 | 7285 | 1267 | 70.412 |
| PR | PastureC | Map | 4661 | 4485 | 7022 | 1244 | 69.100 |
| MT | PastureB | Map | 3741 | 3811 | 5218 | 902 | 50.090 |
| MT | PastureC | Map | 3741 | 3811 | 5218 | 902 | 50.090 |
| RS | PastureB | Map | 3393 | 3718 | 4963 | 890 | 49.430 |
| RS | PastureC | Map | 3275 | 3569 | 4856 | 874 | 48.581 |
| MS | PastureB | Map | 3093 | 3024 | 3751 | 351 | 19.508 |
| MS | PastureC | Map | 3084 | 3015 | 3740 | 349 | 19.366 |
| GO | PastureB | Map | 2685 | 2572 | 3452 | 538 | 29.880 |
| GO | PastureC | Map | 2678 | 2566 | 3444 | 538 | 29.889 |
| TO | PastureB | Map | 1725 | 1836 | 2131 | 345 | 19.156 |
| TO | PastureC | Map | 1725 | 1836 | 2131 | 345 | 19.156 |
| MG | PastureB | Map | 1274 | 1330 | 1749 | 338 | 18.762 |
| MG | PastureC | Map | 1244 | 1301 | 1711 | 327 | 18.160 |
| BA | PastureB | Map | 860 | 975 | 1181 | 281 | 15.626 |
| BA | PastureC | Map | 855 | 968 | 1176 | 280 | 15.546 |
Intensities, sorted on max values descending
And the mean state maximum across all years for each of the classifications:
meat_summary %>%
#filter(source == "Map" & classification == "PastureB") %>%
dplyr::select(-starts_with("prod"), -int_sd, -int_se) %>%
#filter(state != "SC") %>%
group_by(classification) %>%
summarise_at(vars(int_max), mean)## # A tibble: 2 x 2
## classification int_max
## <chr> <dbl>
## 1 PastureB 4792.
## 2 PastureC 4556.
Yield values are generally quite similar to those calculated for non-disaggregated maps.
So, overall there is not much difference between the two classifications. We we will chose the PastureB classification as it seems to make more sense to include the MapBiomas Mosaic of Agriculture and Pasture class as Agriculture prior to disaggregation (given the disaggregation process used).
Having chosem the PastureB classification, we now need to identify the some indication of ‘perfect’ yield in our data to enable conversion of meat production (in kg) to CRAFTY service production units. We could use the maximum yield observed in our data (~9,100 kg/sq km) but to allow room for future scenarios of extraordinary continued yield improvement, we will use a higher value of 11,000 kg/sq km. In turn, 11,000 kg/sq km == 0.275 gg/25sq km We will use this value as a single unit of of ‘Pasture service’ from CRAFTY.