R/convertFAO_fishery.R
In pik-piam/moinput: Model Operations Input Data Library
Defines functions
convertFAO_fishery
#' convertFAO_fishery
#'
#' @description Converts readFAO_fishery output to complete MAgPIE object containing fishery data on country level (in tonnes)
#' @param x magpie object with uncoverted source data
#' @param subtype "capture" takes all fishdata into account that has been declared as capture fishery
#' "aquaculture" takes all fishdata into account that has been listed as aquaculture fishery
#' @return Fishery data as complete MAgPIE object on country level
#' @author Jasmin Wehner,Benjamin Leon Bodirsky
#' @seealso \code{\link{readSource}}
#' @importFrom madrat toolAggregate toolISOhistorical toolCountryFill readSource calcOutput
#' @importFrom magclass mbind collapseNames add_columns getNames getRegions
#' @export
convertFAO_fishery <- function(x, subtype){
if (subtype=="capture"){
#x <-readSource("FAO_fishery", subtype="capture", convert=FALSE)
x_capture=x
x_capture[is.na(x_capture)] <- 0
#move Other nei countries to ATA
getRegions(x_capture) <- gsub("XON","ATA",getRegions(x_capture))
#rename fish category species to make them compartible with fish demand categories
#unclear for freshwater fish. is diadromous fish now included or not in FBC
#Demersal Marine Fish
getNames(x_capture) <- gsub("Demersal Marine Fish","Demersal Fish",getNames(x_capture))
#Pelagic Marine FIsh
getNames(x_capture) <- gsub("Pelagic Marine Fish","Pelagic Fish",getNames(x_capture))
#Marine Fish NEI
getNames(x_capture) <- gsub("Marine Fish NEI","Marine Fish, Other",getNames(x_capture))
#"Molluscs excl Cephalopods"
getNames(x_capture) <- gsub("Molluscs excl Cephalopods","Molluscs, Other",getNames(x_capture))
#Freshwater and Diadromous Fish
getNames(x_capture) <- gsub("Freshwater and Diadromous Fish","Freshwater Fish",getNames(x_capture))
#Aquatic Animal NEI unclear whether to Meat, Aquatic Mammals or Aquatic Animals, Others
getNames(x_capture) <- gsub("Aquatic Animals NEI","Aquatic Animals, Others",getNames(x_capture))
#move ABW to ANT to make it align with toolISOhistorical
x_ABW <- x_capture["ABW",c("1950":"1984"),]
getRegions(x_ABW) <- "GLO"
x_capture["ANT",c("1950":"1984"),] <- x_capture["ANT",c("1950":"1984"),] + x_ABW
x_capture["ABW",c("1950":"1984"),] <- 0
# Adding country Zanzibar to Tanzania
x_capture_Zanzibar <- x_capture["XXX",,]
getRegions(x_capture_Zanzibar) <- "GLO"
x_capture["TZA",,] <- x_capture["TZA",,] + x_capture_Zanzibar
x_capture <- x_capture["XXX",,,invert = TRUE] # removing Zanzibar
# split Channel Island (XIS) to GGY and JEY and Sudan (former) (XSF) to SSD and SDN
x_captureSUD <- x_capture[c("SSD","SDN")]
x_capture <- x_capture[c("SSD","SDN"),,invert=TRUE]
m <- matrix(c(c("XIS","XIS","XSF","XSF"),c("GGY","JEY","SSD","SDN")),4)
w <- calcOutput("Population",aggregate=FALSE)[c("GGY","JEY","SSD","SDN"),2010,"pop_SSP2"]
x_split <- toolAggregate(x_capture[c("XIS","XSF"),,],m,weight=w)
#delete XSF and XIS from x
x_capture <- x_capture[c("XIS","XSF"),,invert=TRUE]
x_capture <- mbind(x_capture,x_split)
#combine it with data after 2011
x_capture[c("SSD","SDN"),c(2012:2017),] <- x_captureSUD[c("SSD","SDN"),c(2012:2017),]
#splitting up Former Soviet Area Inland waters
#definitions
formerSUAsiacountries <- c("AZE","ARM","GEO","KAZ","KGZ","TJK","TKM","UZB")
formerSUEuropecountries <- c("BLR","EST","LVA","LTU","MDA","RUS","UKR")
f <- c("BLR","EST","LVA","LTU","MDA","RUS","UKR","AZE","ARM","GEO","KAZ","KGZ","TJK","TKM","UZB")
x_Former_USSR <- x_capture["SUN",,"Former USSR area Inland waters"] #100 percent
getRegions(x_Former_USSR) <- "GLO"
categories <- c("Freshwater Fish","Crustaceans","Demersal Fish")
x_Europe_Inland_waters <- x_capture[formerSUEuropecountries,"y1988","Europe Inland waters"]
getYears(x_Europe_Inland_waters) <- NULL
x_Asia_Inland_waters <- x_capture[formerSUAsiacountries,"y1988","Asia Inland waters"]
getYears(x_Asia_Inland_waters) <- NULL
#Asia Inland waters
share_Asia <- (dimSums(x_Asia_Inland_waters, dim=c(1,3))*100)/(dimSums(x_Asia_Inland_waters, dim=c(1,3))+dimSums(x_Europe_Inland_waters, dim=c(1,3)))
tmp_Soviet_Asia <- x_Former_USSR * (share_Asia/100) #100 percent for all Europe countries
x_Asia_country_shares <- (x_Asia_Inland_waters*100)/dimSums(x_Asia_Inland_waters, dim=1)
#select relevant categories
x_Asia_country_shares <- x_Asia_country_shares[,,"Asia Inland waters"][,,categories]
x_capture_final_shares_Asia <- collapseNames(tmp_Soviet_Asia) * (collapseNames(x_Asia_country_shares)/100)
#insert data into original magpie object
x_capture[formerSUAsiacountries,c("1950":"1987"),"Asia Inland waters"][,,categories] <- x_capture_final_shares_Asia[formerSUAsiacountries,c("1950":"1987"),categories]
#Europe Inland waters
share_Europe <- (dimSums(x_Europe_Inland_waters, dim=c(1,3))*100)/(dimSums(x_Asia_Inland_waters, dim=c(1,3))+dimSums(x_Europe_Inland_waters, dim=c(1,3)))
tmp_Soviet_Europe <- x_Former_USSR * (share_Europe/100) #100 percent for all Europe countries
x_Europe_country_shares <- (x_Europe_Inland_waters*100)/dimSums(x_Europe_Inland_waters, dim=1)
#select relevant categories
x_Europe_country_shares <- x_Europe_country_shares[,,"Europe Inland waters"][,,categories]
x_capture_final_shares_Europe <- collapseNames(tmp_Soviet_Europe) * (collapseNames(x_Europe_country_shares)/100)
#insert data into original magpie object
x_capture[formerSUEuropecountries,c("1950":"1987"),"Europe Inland waters"][,,categories] <- x_capture_final_shares_Europe[formerSUEuropecountries,c("1950":"1987"),][,,categories]
#delete all data from "Former USSR area Inland waters"
x_capture <- x_capture[,,"Former USSR area Inland waters", invert =TRUE]
#execute toolISOhistorical with dynamic transition years because transition years
#vary from category to category
SUN <- x_capture[c(f,"SUN"),,]
x_incl_transition<-NULL
for (name_x in getNames(SUN)) {
firstyear= min(getYears(SUN,as.integer = T)[colSums(SUN[f,,name_x])>0])
lastyear_sun = max(getYears(SUN,as.integer = T)[colSums(SUN["SUN",,name_x])>0])
if(firstyear-lastyear_sun>1&lastyear_sun>0){firstyear=lastyear_sun+1}
if(firstyear!=1950 & firstyear!=Inf) {
add_mapp=data.frame(fromISO=rep("SUN",length(f)),toISO=f,lastYear=paste0("y",firstyear -1),stringsAsFactors = FALSE)
single_indicator <- toolISOhistorical(SUN[,,name_x]+10^-10,mapping = add_mapp,overwrite = TRUE)
} else {
single_indicator <- SUN[f,,name_x]
}
x_incl_transition <- mbind(x_incl_transition, single_indicator)
}
#execute toolISOhistorical for the rest of historical countries
x_capture <- toolISOhistorical(x_capture, overwrite = T)
x_capture[f,,] <- x_incl_transition[f,,]
#because of overwrite feature I have to add the data from Former Soviet Area Inland Waters ex post
x_capture[formerSUEuropecountries,c("1950":"1987"),"Europe Inland waters"][,,categories] <- x_capture_final_shares_Europe[formerSUEuropecountries,c("1950":"1987"),][,,categories]
x_capture[formerSUAsiacountries,c("1950":"1987"),"Asia Inland waters"][,,categories] <- x_capture_final_shares_Asia[formerSUAsiacountries,c("1950":"1987"),categories]
x_capture["ABW",c("2011":"2017"),] <- x_capture["ABW",c("2011":"2017"),]
#for Cheung 2016
#move Atlantic Antarctic and Indian Ocean Antarctic to Pacific Antarctic to synchronize conversion table with magpie object
#define common fish categories
#x_capture_extra <- x_capture[,,c("Atlantic Antarctic","Indian Ocean Antarctic")]
#fish_category_common <- c("Aquatic Animals, Others","Cephalopods",
#"Crustaceans" ,"Demersal Fish")
#x_capture_extra[,,"Indian Ocean Antarctic"][,,fish_category_common]<- x_capture_extra[,,"Indian Ocean Antarctic"][,,fish_category_common]+ x_capture_extra[,,"Atlantic Antarctic"][,,fish_category_common]
#x_capture_extra <- x_capture_extra[,,"Atlantic Antarctic", invert=T]
#removing fishing_area dimension
#x_capture_extra <- collapseNames(x_capture_extra, collapsedim=2)
#x_capture_extra <- add_dimension(x_capture_extra, dim=3.2, add="fishing_area",nm="Pacific Antarctic")
#extracting Molluscs,Other
#x_capture_extra_moll <- x_capture_extra[,,"Molluscs, Other"]
#x_capture_extra <- x_capture_extra[,,"Molluscs, Other", invert=T]
#x_capture[,,"Pacific Antarctic"] <- x_capture_extra[,,"Pacific Antarctic"]
#x_capture <- mbind(x_capture, x_capture_extra_moll)
#delete "Atlantic Antarctic","Indian Ocean Antarctic" entries
#x_capture <- x_capture[,,c("Atlantic Antarctic","Indian Ocean Antarctic"), invert=T]
#for Cheung 2018:
#remove Meditarrerranean Sea because they exlude semi enclosed fishing areas
#move Pacific Antarctic to Indian Ocean Antarctic (because fishing zone categories fit better)
#x_capture_PacAnt <- x_capture[,,"Pacific Antarctic"]
#x_capture_PacAnt <- dimReduce(x_capture_PacAnt, dim_exclude=c("iso","variable","fish_category"))
#i <- getNames(x_capture_PacAnt, dim=1)
#x_capture[,,"Indian Ocean Antarctic"][,,i] <- x_capture[,,"Indian Ocean Antarctic"][,,i] + x_capture_PacAnt[,,i]
#x_capture <- x_capture[,,c("Mediterranean and Black Sea","Pacific Antarctic"), invert=T]
x_capture <- toolCountryFill(x_capture, fill=0)
#unit conversion from t to Mio tonnes and dry matter conversion
wm<-calcOutput("Attributes",aggregate = F)[,,"fish"][,,"wm"]
x_capture <- x_capture/10^6/collapseNames(wm)
return(x_capture)
}
else if (subtype=="aquaculture"){
#x<-readSource("FAO_fishery", subtype="aquaculture", convert=FALSE)
x_aqua=x
#harmonization with x_capture
x_aqua <- add_columns(x_aqua, addnm = c("Arctic Sea","Atlantic Antarctic","Indian Ocean Antarctic","Pacific Antarctic"), dim = 3.2)
x_aqua[is.na(x_aqua)] <- 0
#rename fish category species to make them compartible with fish demand categories
#unclear for freshwater fish. is diadromous fish now included or not in FBC
#Demersal Marine Fish
getNames(x_aqua) <- gsub("Demersal Marine Fish","Demersal Fish",getNames(x_aqua))
#Pelagic Marine FIsh
getNames(x_aqua) <- gsub("Pelagic Marine Fish","Pelagic Fish",getNames(x_aqua))
#Marine Fish NEI
getNames(x_aqua) <- gsub("Marine Fish NEI","Marine Fish, Other",getNames(x_aqua))
#"Molluscs excl Cephalopods"
getNames(x_aqua) <- gsub("Molluscs excl Cephalopods","Molluscs, Other",getNames(x_aqua))
#Freshwater and Diadromous Fish
getNames(x_aqua) <- gsub("Freshwater and Diadromous Fish","Freshwater Fish",getNames(x_aqua))
#Aquatic Animal NEI unclear whether to Meat, Aquatic Mammals or Aquatic Animals, Others
getNames(x_aqua) <- gsub("Aquatic Animals NEI","Aquatic Animals, Others",getNames(x_aqua))
#split up Channel Islands into GGY and JEY
x_aquaSUD <- x_aqua[c("SSD","SDN")]
x_aqua <- x_aqua[c("SSD","SDN"),,invert=TRUE]
m <- matrix(c(c("XIS","XIS","XSF","XSF"),c("GGY","JEY","SSD","SDN")),4)
w <- calcOutput("Population",aggregate=FALSE)[c("GGY","JEY","SSD","SDN"),2010,"pop_SSP2"]
x_split <- toolAggregate(x_aqua[c("XIS","XSF"),,],m,weight=w)
#delete XIS from x
x_aqua <- x_aqua[c("XIS","XSF"),,invert=TRUE]
x_aqua <- mbind(x_aqua,x_split)
x_aqua[c("SSD","SDN"),c(2012:2017),] <- x_aquaSUD[c("SSD","SDN"),c(2012:2017),]
#add countries SXM and CUW and YUG as blank magpie objects to make toolisohistorical work
x_SXMCUWYUG <- new.magpie(cells_and_regions = c("SXM","CUW","YUG"), years = 1950:2017, getNames(x_aqua))
x_SXMCUWYUG[is.na(x_SXMCUWYUG)] <- 0
x_aqua <- mbind(x_aqua, x_SXMCUWYUG)
#delete entries for Former USSR area - Inland waters
formerSUAsiacountries <- c("AZE","ARM","GEO","KAZ","KGZ","TJK","TKM","UZB")
formerSUEuropecountries <- c("BLR","EST","LVA","LTU","MDA","RUS","UKR")
x_Former_USSR <- x_aqua["SUN",,"Former USSR area Inland waters"] #100 percent
f <- c("BLR","EST","LVA","LTU","MDA","RUS","UKR","AZE","ARM","GEO","KAZ","KGZ","TJK","TKM","UZB")
getRegions(x_Former_USSR) <- "GLO"
x_Asia_Inland_waters <- x_aqua[formerSUAsiacountries,"y1988","Asia Inland waters"]
getYears(x_Asia_Inland_waters) <- NULL
x_Europe_Inland_waters <- x_aqua[formerSUEuropecountries,"y1988","Europe Inland waters"]
getYears(x_Europe_Inland_waters) <- NULL
#Asia Inland waters
share_Asia <- (dimSums(x_Asia_Inland_waters, dim=c(1,3))*100)/(dimSums(x_Asia_Inland_waters, dim=c(1,3))+dimSums(x_Europe_Inland_waters, dim=c(1,3)))
tmp_Soviet_Asia <- x_Former_USSR * (share_Asia/100) #100 percent for all Europe countries
getRegions(tmp_Soviet_Asia) <- "GLO"
x_Asia_country_shares <- (x_Asia_Inland_waters*100)/dimSums(x_Asia_Inland_waters, dim=1)
#select relevant category
x_Asia_country_shares <- x_Asia_country_shares[,,"Freshwater Fish.Asia Inland waters.Freshwater"]
x_aqua_final_shares_Asia <- collapseNames(tmp_Soviet_Asia) * collapseNames((x_Asia_country_shares/100))
x_aqua[formerSUAsiacountries,c("1950":"1987"),"Freshwater Fish.Asia Inland waters.Freshwater"] <- x_aqua_final_shares_Asia[,c("1950":"1987"),]
#Europe Inland waters
share_Europe <- (dimSums(x_Europe_Inland_waters, dim=c(1,3))*100)/(dimSums(x_Asia_Inland_waters, dim=c(1,3))+dimSums(x_Europe_Inland_waters, dim=c(1,3)))
tmp_Soviet_Europe <- x_Former_USSR * (share_Europe/100) #100 percent for all Europe countries
getRegions(tmp_Soviet_Europe) <- "GLO"
x_Europe_country_shares <- (x_Europe_Inland_waters*100)/dimSums(x_Europe_Inland_waters, dim=1)
x_Europe_country_shares <- x_Europe_country_shares[,,"Freshwater Fish.Europe Inland waters.Freshwater"]
x_aqua_final_shares_Europe <- collapseNames(tmp_Soviet_Europe) * collapseNames((x_Europe_country_shares/100))
x_aqua[formerSUEuropecountries,c("1950":"1987"),"Freshwater Fish.Europe Inland waters.Freshwater"] <- x_aqua_final_shares_Europe[,c("1950":"1987"),]
x_aqua <- x_aqua[,,"Former USSR area Inland waters", invert =TRUE]
#execute toolISOhistorical with dynamic transition years
SUN <- x_aqua[c(f,"SUN"),,]
x_incl_transition<-NULL
for (name_x in getNames(SUN)) {
firstyear= min(getYears(SUN,as.integer = T)[colSums(SUN[f,,name_x])>0])
lastyear_sun = max(getYears(SUN,as.integer = T)[colSums(SUN["SUN",,name_x])>0])
if(firstyear-lastyear_sun>1&lastyear_sun>0){firstyear=lastyear_sun+1}
if(firstyear!=1950 & firstyear!=Inf) {
add_mapp=data.frame(fromISO=rep("SUN",length(f)),toISO=f,lastYear=paste0("y",firstyear -1),stringsAsFactors = FALSE)
single_indicator <- toolISOhistorical(SUN[,,name_x]+10^-10,mapping = add_mapp,overwrite = TRUE)
} else {
single_indicator <- SUN[f,,name_x]
}
x_incl_transition <- mbind(x_incl_transition, single_indicator)
}
#execute toolISohistorical for countries besides SUN
x_aqua <- toolISOhistorical(x_aqua, overwrite = T)
x_aqua[f,,] <- x_incl_transition[f,,]
#for Cheung 2018:
#remove Mediterrerranean Sea because they exlude semi enclosed fishing areas
#x_aqua <- x_aqua[,,"Mediterranean and Black Sea", invert=T]
#x_aqua <- dimReduce(x_aqua, dim_exclude = c("iso","variable","fish_category","fishing_area"))
x_aqua <- toolCountryFill(x_aqua, fill=0)
#unit conversion from t to Mio tonnes and dry matter conversion
wm<-calcOutput("Attributes",aggregate = F)[,,"fish"][,,"wm"]
x_aqua <- x_aqua/10^6/collapseNames(wm)
return(x_aqua)
}
}
pik-piam/moinput documentation built on June 9, 2020, 12:23 p.m.
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Defines functions convertFAO_fishery
#' convertFAO_fishery
#'
#' @description Converts readFAO_fishery output to complete MAgPIE object containing fishery data on country level (in tonnes)
#' @param x magpie object with uncoverted source data
#' @param subtype "capture" takes all fishdata into account that has been declared as capture fishery
#' "aquaculture" takes all fishdata into account that has been listed as aquaculture fishery
#' @return Fishery data as complete MAgPIE object on country level
#' @author Jasmin Wehner,Benjamin Leon Bodirsky
#' @seealso \code{\link{readSource}}
#' @importFrom madrat toolAggregate toolISOhistorical toolCountryFill readSource calcOutput
#' @importFrom magclass mbind collapseNames add_columns getNames getRegions
#' @export
convertFAO_fishery <- function(x, subtype){
if (subtype=="capture"){
#x <-readSource("FAO_fishery", subtype="capture", convert=FALSE)
x_capture=x
x_capture[is.na(x_capture)] <- 0
#move Other nei countries to ATA
getRegions(x_capture) <- gsub("XON","ATA",getRegions(x_capture))
#rename fish category species to make them compartible with fish demand categories
#unclear for freshwater fish. is diadromous fish now included or not in FBC
#Demersal Marine Fish
getNames(x_capture) <- gsub("Demersal Marine Fish","Demersal Fish",getNames(x_capture))
#Pelagic Marine FIsh
getNames(x_capture) <- gsub("Pelagic Marine Fish","Pelagic Fish",getNames(x_capture))
#Marine Fish NEI
getNames(x_capture) <- gsub("Marine Fish NEI","Marine Fish, Other",getNames(x_capture))
#"Molluscs excl Cephalopods"
getNames(x_capture) <- gsub("Molluscs excl Cephalopods","Molluscs, Other",getNames(x_capture))
#Freshwater and Diadromous Fish
getNames(x_capture) <- gsub("Freshwater and Diadromous Fish","Freshwater Fish",getNames(x_capture))
#Aquatic Animal NEI unclear whether to Meat, Aquatic Mammals or Aquatic Animals, Others
getNames(x_capture) <- gsub("Aquatic Animals NEI","Aquatic Animals, Others",getNames(x_capture))
#move ABW to ANT to make it align with toolISOhistorical
x_ABW <- x_capture["ABW",c("1950":"1984"),]
getRegions(x_ABW) <- "GLO"
x_capture["ANT",c("1950":"1984"),] <- x_capture["ANT",c("1950":"1984"),] + x_ABW
x_capture["ABW",c("1950":"1984"),] <- 0
# Adding country Zanzibar to Tanzania
x_capture_Zanzibar <- x_capture["XXX",,]
getRegions(x_capture_Zanzibar) <- "GLO"
x_capture["TZA",,] <- x_capture["TZA",,] + x_capture_Zanzibar
x_capture <- x_capture["XXX",,,invert = TRUE] # removing Zanzibar
# split Channel Island (XIS) to GGY and JEY and Sudan (former) (XSF) to SSD and SDN
x_captureSUD <- x_capture[c("SSD","SDN")]
x_capture <- x_capture[c("SSD","SDN"),,invert=TRUE]
m <- matrix(c(c("XIS","XIS","XSF","XSF"),c("GGY","JEY","SSD","SDN")),4)
w <- calcOutput("Population",aggregate=FALSE)[c("GGY","JEY","SSD","SDN"),2010,"pop_SSP2"]
x_split <- toolAggregate(x_capture[c("XIS","XSF"),,],m,weight=w)
#delete XSF and XIS from x
x_capture <- x_capture[c("XIS","XSF"),,invert=TRUE]
x_capture <- mbind(x_capture,x_split)
#combine it with data after 2011
x_capture[c("SSD","SDN"),c(2012:2017),] <- x_captureSUD[c("SSD","SDN"),c(2012:2017),]
#splitting up Former Soviet Area Inland waters
#definitions
formerSUAsiacountries <- c("AZE","ARM","GEO","KAZ","KGZ","TJK","TKM","UZB")
formerSUEuropecountries <- c("BLR","EST","LVA","LTU","MDA","RUS","UKR")
f <- c("BLR","EST","LVA","LTU","MDA","RUS","UKR","AZE","ARM","GEO","KAZ","KGZ","TJK","TKM","UZB")
x_Former_USSR <- x_capture["SUN",,"Former USSR area Inland waters"] #100 percent
getRegions(x_Former_USSR) <- "GLO"
categories <- c("Freshwater Fish","Crustaceans","Demersal Fish")
x_Europe_Inland_waters <- x_capture[formerSUEuropecountries,"y1988","Europe Inland waters"]
getYears(x_Europe_Inland_waters) <- NULL
x_Asia_Inland_waters <- x_capture[formerSUAsiacountries,"y1988","Asia Inland waters"]
getYears(x_Asia_Inland_waters) <- NULL
#Asia Inland waters
share_Asia <- (dimSums(x_Asia_Inland_waters, dim=c(1,3))*100)/(dimSums(x_Asia_Inland_waters, dim=c(1,3))+dimSums(x_Europe_Inland_waters, dim=c(1,3)))
tmp_Soviet_Asia <- x_Former_USSR * (share_Asia/100) #100 percent for all Europe countries
x_Asia_country_shares <- (x_Asia_Inland_waters*100)/dimSums(x_Asia_Inland_waters, dim=1)
#select relevant categories
x_Asia_country_shares <- x_Asia_country_shares[,,"Asia Inland waters"][,,categories]
x_capture_final_shares_Asia <- collapseNames(tmp_Soviet_Asia) * (collapseNames(x_Asia_country_shares)/100)
#insert data into original magpie object
x_capture[formerSUAsiacountries,c("1950":"1987"),"Asia Inland waters"][,,categories] <- x_capture_final_shares_Asia[formerSUAsiacountries,c("1950":"1987"),categories]
#Europe Inland waters
share_Europe <- (dimSums(x_Europe_Inland_waters, dim=c(1,3))*100)/(dimSums(x_Asia_Inland_waters, dim=c(1,3))+dimSums(x_Europe_Inland_waters, dim=c(1,3)))
tmp_Soviet_Europe <- x_Former_USSR * (share_Europe/100) #100 percent for all Europe countries
x_Europe_country_shares <- (x_Europe_Inland_waters*100)/dimSums(x_Europe_Inland_waters, dim=1)
#select relevant categories
x_Europe_country_shares <- x_Europe_country_shares[,,"Europe Inland waters"][,,categories]
x_capture_final_shares_Europe <- collapseNames(tmp_Soviet_Europe) * (collapseNames(x_Europe_country_shares)/100)
#insert data into original magpie object
x_capture[formerSUEuropecountries,c("1950":"1987"),"Europe Inland waters"][,,categories] <- x_capture_final_shares_Europe[formerSUEuropecountries,c("1950":"1987"),][,,categories]
#delete all data from "Former USSR area Inland waters"
x_capture <- x_capture[,,"Former USSR area Inland waters", invert =TRUE]
#execute toolISOhistorical with dynamic transition years because transition years
#vary from category to category
SUN <- x_capture[c(f,"SUN"),,]
x_incl_transition<-NULL
for (name_x in getNames(SUN)) {
firstyear= min(getYears(SUN,as.integer = T)[colSums(SUN[f,,name_x])>0])
lastyear_sun = max(getYears(SUN,as.integer = T)[colSums(SUN["SUN",,name_x])>0])
if(firstyear-lastyear_sun>1&lastyear_sun>0){firstyear=lastyear_sun+1}
if(firstyear!=1950 & firstyear!=Inf) {
add_mapp=data.frame(fromISO=rep("SUN",length(f)),toISO=f,lastYear=paste0("y",firstyear -1),stringsAsFactors = FALSE)
single_indicator <- toolISOhistorical(SUN[,,name_x]+10^-10,mapping = add_mapp,overwrite = TRUE)
} else {
single_indicator <- SUN[f,,name_x]
}
x_incl_transition <- mbind(x_incl_transition, single_indicator)
}
#execute toolISOhistorical for the rest of historical countries
x_capture <- toolISOhistorical(x_capture, overwrite = T)
x_capture[f,,] <- x_incl_transition[f,,]
#because of overwrite feature I have to add the data from Former Soviet Area Inland Waters ex post
x_capture[formerSUEuropecountries,c("1950":"1987"),"Europe Inland waters"][,,categories] <- x_capture_final_shares_Europe[formerSUEuropecountries,c("1950":"1987"),][,,categories]
x_capture[formerSUAsiacountries,c("1950":"1987"),"Asia Inland waters"][,,categories] <- x_capture_final_shares_Asia[formerSUAsiacountries,c("1950":"1987"),categories]
x_capture["ABW",c("2011":"2017"),] <- x_capture["ABW",c("2011":"2017"),]
#for Cheung 2016
#move Atlantic Antarctic and Indian Ocean Antarctic to Pacific Antarctic to synchronize conversion table with magpie object
#define common fish categories
#x_capture_extra <- x_capture[,,c("Atlantic Antarctic","Indian Ocean Antarctic")]
#fish_category_common <- c("Aquatic Animals, Others","Cephalopods",
#"Crustaceans" ,"Demersal Fish")
#x_capture_extra[,,"Indian Ocean Antarctic"][,,fish_category_common]<- x_capture_extra[,,"Indian Ocean Antarctic"][,,fish_category_common]+ x_capture_extra[,,"Atlantic Antarctic"][,,fish_category_common]
#x_capture_extra <- x_capture_extra[,,"Atlantic Antarctic", invert=T]
#removing fishing_area dimension
#x_capture_extra <- collapseNames(x_capture_extra, collapsedim=2)
#x_capture_extra <- add_dimension(x_capture_extra, dim=3.2, add="fishing_area",nm="Pacific Antarctic")
#extracting Molluscs,Other
#x_capture_extra_moll <- x_capture_extra[,,"Molluscs, Other"]
#x_capture_extra <- x_capture_extra[,,"Molluscs, Other", invert=T]
#x_capture[,,"Pacific Antarctic"] <- x_capture_extra[,,"Pacific Antarctic"]
#x_capture <- mbind(x_capture, x_capture_extra_moll)
#delete "Atlantic Antarctic","Indian Ocean Antarctic" entries
#x_capture <- x_capture[,,c("Atlantic Antarctic","Indian Ocean Antarctic"), invert=T]
#for Cheung 2018:
#remove Meditarrerranean Sea because they exlude semi enclosed fishing areas
#move Pacific Antarctic to Indian Ocean Antarctic (because fishing zone categories fit better)
#x_capture_PacAnt <- x_capture[,,"Pacific Antarctic"]
#x_capture_PacAnt <- dimReduce(x_capture_PacAnt, dim_exclude=c("iso","variable","fish_category"))
#i <- getNames(x_capture_PacAnt, dim=1)
#x_capture[,,"Indian Ocean Antarctic"][,,i] <- x_capture[,,"Indian Ocean Antarctic"][,,i] + x_capture_PacAnt[,,i]
#x_capture <- x_capture[,,c("Mediterranean and Black Sea","Pacific Antarctic"), invert=T]
x_capture <- toolCountryFill(x_capture, fill=0)
#unit conversion from t to Mio tonnes and dry matter conversion
wm<-calcOutput("Attributes",aggregate = F)[,,"fish"][,,"wm"]
x_capture <- x_capture/10^6/collapseNames(wm)
return(x_capture)
}
else if (subtype=="aquaculture"){
#x<-readSource("FAO_fishery", subtype="aquaculture", convert=FALSE)
x_aqua=x
#harmonization with x_capture
x_aqua <- add_columns(x_aqua, addnm = c("Arctic Sea","Atlantic Antarctic","Indian Ocean Antarctic","Pacific Antarctic"), dim = 3.2)
x_aqua[is.na(x_aqua)] <- 0
#rename fish category species to make them compartible with fish demand categories
#unclear for freshwater fish. is diadromous fish now included or not in FBC
#Demersal Marine Fish
getNames(x_aqua) <- gsub("Demersal Marine Fish","Demersal Fish",getNames(x_aqua))
#Pelagic Marine FIsh
getNames(x_aqua) <- gsub("Pelagic Marine Fish","Pelagic Fish",getNames(x_aqua))
#Marine Fish NEI
getNames(x_aqua) <- gsub("Marine Fish NEI","Marine Fish, Other",getNames(x_aqua))
#"Molluscs excl Cephalopods"
getNames(x_aqua) <- gsub("Molluscs excl Cephalopods","Molluscs, Other",getNames(x_aqua))
#Freshwater and Diadromous Fish
getNames(x_aqua) <- gsub("Freshwater and Diadromous Fish","Freshwater Fish",getNames(x_aqua))
#Aquatic Animal NEI unclear whether to Meat, Aquatic Mammals or Aquatic Animals, Others
getNames(x_aqua) <- gsub("Aquatic Animals NEI","Aquatic Animals, Others",getNames(x_aqua))
#split up Channel Islands into GGY and JEY
x_aquaSUD <- x_aqua[c("SSD","SDN")]
x_aqua <- x_aqua[c("SSD","SDN"),,invert=TRUE]
m <- matrix(c(c("XIS","XIS","XSF","XSF"),c("GGY","JEY","SSD","SDN")),4)
w <- calcOutput("Population",aggregate=FALSE)[c("GGY","JEY","SSD","SDN"),2010,"pop_SSP2"]
x_split <- toolAggregate(x_aqua[c("XIS","XSF"),,],m,weight=w)
#delete XIS from x
x_aqua <- x_aqua[c("XIS","XSF"),,invert=TRUE]
x_aqua <- mbind(x_aqua,x_split)
x_aqua[c("SSD","SDN"),c(2012:2017),] <- x_aquaSUD[c("SSD","SDN"),c(2012:2017),]
#add countries SXM and CUW and YUG as blank magpie objects to make toolisohistorical work
x_SXMCUWYUG <- new.magpie(cells_and_regions = c("SXM","CUW","YUG"), years = 1950:2017, getNames(x_aqua))
x_SXMCUWYUG[is.na(x_SXMCUWYUG)] <- 0
x_aqua <- mbind(x_aqua, x_SXMCUWYUG)
#delete entries for Former USSR area - Inland waters
formerSUAsiacountries <- c("AZE","ARM","GEO","KAZ","KGZ","TJK","TKM","UZB")
formerSUEuropecountries <- c("BLR","EST","LVA","LTU","MDA","RUS","UKR")
x_Former_USSR <- x_aqua["SUN",,"Former USSR area Inland waters"] #100 percent
f <- c("BLR","EST","LVA","LTU","MDA","RUS","UKR","AZE","ARM","GEO","KAZ","KGZ","TJK","TKM","UZB")
getRegions(x_Former_USSR) <- "GLO"
x_Asia_Inland_waters <- x_aqua[formerSUAsiacountries,"y1988","Asia Inland waters"]
getYears(x_Asia_Inland_waters) <- NULL
x_Europe_Inland_waters <- x_aqua[formerSUEuropecountries,"y1988","Europe Inland waters"]
getYears(x_Europe_Inland_waters) <- NULL
#Asia Inland waters
share_Asia <- (dimSums(x_Asia_Inland_waters, dim=c(1,3))*100)/(dimSums(x_Asia_Inland_waters, dim=c(1,3))+dimSums(x_Europe_Inland_waters, dim=c(1,3)))
tmp_Soviet_Asia <- x_Former_USSR * (share_Asia/100) #100 percent for all Europe countries
getRegions(tmp_Soviet_Asia) <- "GLO"
x_Asia_country_shares <- (x_Asia_Inland_waters*100)/dimSums(x_Asia_Inland_waters, dim=1)
#select relevant category
x_Asia_country_shares <- x_Asia_country_shares[,,"Freshwater Fish.Asia Inland waters.Freshwater"]
x_aqua_final_shares_Asia <- collapseNames(tmp_Soviet_Asia) * collapseNames((x_Asia_country_shares/100))
x_aqua[formerSUAsiacountries,c("1950":"1987"),"Freshwater Fish.Asia Inland waters.Freshwater"] <- x_aqua_final_shares_Asia[,c("1950":"1987"),]
#Europe Inland waters
share_Europe <- (dimSums(x_Europe_Inland_waters, dim=c(1,3))*100)/(dimSums(x_Asia_Inland_waters, dim=c(1,3))+dimSums(x_Europe_Inland_waters, dim=c(1,3)))
tmp_Soviet_Europe <- x_Former_USSR * (share_Europe/100) #100 percent for all Europe countries
getRegions(tmp_Soviet_Europe) <- "GLO"
x_Europe_country_shares <- (x_Europe_Inland_waters*100)/dimSums(x_Europe_Inland_waters, dim=1)
x_Europe_country_shares <- x_Europe_country_shares[,,"Freshwater Fish.Europe Inland waters.Freshwater"]
x_aqua_final_shares_Europe <- collapseNames(tmp_Soviet_Europe) * collapseNames((x_Europe_country_shares/100))
x_aqua[formerSUEuropecountries,c("1950":"1987"),"Freshwater Fish.Europe Inland waters.Freshwater"] <- x_aqua_final_shares_Europe[,c("1950":"1987"),]
x_aqua <- x_aqua[,,"Former USSR area Inland waters", invert =TRUE]
#execute toolISOhistorical with dynamic transition years
SUN <- x_aqua[c(f,"SUN"),,]
x_incl_transition<-NULL
for (name_x in getNames(SUN)) {
firstyear= min(getYears(SUN,as.integer = T)[colSums(SUN[f,,name_x])>0])
lastyear_sun = max(getYears(SUN,as.integer = T)[colSums(SUN["SUN",,name_x])>0])
if(firstyear-lastyear_sun>1&lastyear_sun>0){firstyear=lastyear_sun+1}
if(firstyear!=1950 & firstyear!=Inf) {
add_mapp=data.frame(fromISO=rep("SUN",length(f)),toISO=f,lastYear=paste0("y",firstyear -1),stringsAsFactors = FALSE)
single_indicator <- toolISOhistorical(SUN[,,name_x]+10^-10,mapping = add_mapp,overwrite = TRUE)
} else {
single_indicator <- SUN[f,,name_x]
}
x_incl_transition <- mbind(x_incl_transition, single_indicator)
}
#execute toolISohistorical for countries besides SUN
x_aqua <- toolISOhistorical(x_aqua, overwrite = T)
x_aqua[f,,] <- x_incl_transition[f,,]
#for Cheung 2018:
#remove Mediterrerranean Sea because they exlude semi enclosed fishing areas
#x_aqua <- x_aqua[,,"Mediterranean and Black Sea", invert=T]
#x_aqua <- dimReduce(x_aqua, dim_exclude = c("iso","variable","fish_category","fishing_area"))
x_aqua <- toolCountryFill(x_aqua, fill=0)
#unit conversion from t to Mio tonnes and dry matter conversion
wm<-calcOutput("Attributes",aggregate = F)[,,"fish"][,,"wm"]
x_aqua <- x_aqua/10^6/collapseNames(wm)
return(x_aqua)
}
}
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