R/getGrowthPeriod.R
In khadijaaziz/icardaFIGSr: Subsetting using Focused Identification of the Germplasm Strategy (FIGS)
Defines functions
getGrowthPeriod
Documented in
getGrowthPeriod
#' @title Calculating Growing Degree Days and Lengths of Growth Stages for Various Crops Using Onset Data from ICARDA's Database
#' @description Calculates growing degree days (GDD) as well as cumulative GDD, and returns a list of various data frames based on specified arguments.
#' @param sitecode expression. Vector with names of sites from which to extract onset data.
#' @param crop character. Type of crop in ICARDA database. See section 'Details' for crops which have calculations available.
#' @param base integer. Minimum temperature constraint for the crop.
#' @param max integer. Maximum temperature constraint for the crop.
#' @param gdd boolean. If \code{TRUE}, returns a data frame containing calculated GDD and accumulated GDD together with climatic variables used for the calculations. Default: FALSE.
#' @return A list object with different data frames depending on specified option in \code{gdd}.
#' If \code{gdd = TRUE}, the object is a list containing three data frames: the first one with lengths of different growing stages, the second one with original onset data with phenological variables, and the third one with calculated GDD and accumulated GDD for the sites specified in \code{sitecode}.
#' If \code{gdd = FALSE}, the object is a list containing two data frames: the first one with lengths of different growing stages, and the second one with original onset data with phenological variables for the sites specified in \code{sitecode}.
#' @details Growing degree days for various crops are calculated using average daily minimum and maximum temperature values obtained from onset data. The temperature constraints specified in \code{base} and \code{max} are first applied before the calculations are done. These constraints ensure very low or high temperatures which prevent growth of a particular crop are not included.
#' Crops for which GDD calculations are available include: 'Durum wheat', 'Bread wheat', 'Barley', 'Chickpea', 'Lentil'. Each of these can be supplied as options for the argument \code{crop}.
#' Cumulative GDD values determine the length of different growing stages. Growing stages vary depending on the type of crop. Durum wheat, bread wheat and barley have five growth stages, i.e. beginning of heading, beginning and completion of flowering, and beginning and completion of grain filling. Chickpea and lentil have four growth stages, i.e. beginning of flowering, completion of 50% flowering, beginning of seed filling, and completion of 90% maturity (chickpea) or of full maturity (lentil).
#' The length of the full growth cycle of the crop for each site is also given in the output data frame.
#' @author Khadija Aouzal, Zakaria Kehel, Bancy Ngatia
#' @examples
#' if(interactive()){
#' # Calculate GDD for durum wheat
#' data(durumDaily)
#' growth <- getGrowthPeriod(sitecode = durumDaily$site_code,
#' crop = 'Durum wheat', base = 0,
#' max = 35, gdd = TRUE)
#'
#' # Get data frame with lengths of growth stages from list
#' # object returned
#' growth.lengths <- growth[[1]]
#'
#' # Get data frame with phenotypic variables from list
#' # object returned
#' growth.pheno <- growth[[2]]
#'
#' # Get data frame with GDD, cumulative GDD and climatic
#' # variables from list object returned (when gdd = TRUE)
#' growth.gdd <- growth[[3]]
#' }
#' @rdname getGrowthPeriod
#' @importFrom dplyr group_by slice
#' @importFrom magrittr "%>%"
#' @importFrom plyr ddply join
#' @importFrom reshape2 melt
#' @export
getGrowthPeriod <- function(sitecode, crop, base, max, gdd = FALSE) {
if(crop == "Durum wheat") onsetcrop = "ICDW"
else if(crop == "Bread wheat") onsetcrop = "ICBW"
else if(crop == "Barley") onsetcrop = "ICB"
else if(crop == "Chickpea") onsetcrop = "ILC"
else if(crop == "Lentil") onsetcrop = "ILL"
onsetdata = getOnset(sites = levels(as.factor(sitecode)), var = c('tmin', 'tmax'), crop = onsetcrop)
tempdata = onsetdata[[1]]
phenodata = onsetdata[[2]]
names(tempdata)[names(tempdata) == grep("site", colnames(tempdata), value = TRUE)] <- "sitecode"
names(phenodata)[names(phenodata) == grep("site", colnames(phenodata), value = TRUE)] <- "sitecode"
names(phenodata)[names(phenodata) == grep("onset", colnames(phenodata), value = TRUE)] <- "onset"
tminvars = grep("tmin", colnames(tempdata), perl = TRUE, value = TRUE)
tmaxvars = grep("tmax", colnames(tempdata), perl = TRUE, value = TRUE)
data2 = reshape2::melt(tempdata, id = "sitecode")
data2 = data2[order(data2[ , "sitecode"]), ]
data2[3] = lapply(data2[3], function(x) replace(x, x < base, base))
gddtmin = data2[data2$variable %in% tminvars, "value"]
gddtmax = data2[data2$variable %in% tmaxvars, "value"]
gddvar = (gddtmin + gddtmax) / 2
data4 = data.frame(sitecode = data2[data2$variable %in% tminvars, "sitecode"], tmin = gddtmin, tmax = gddtmax, gdd = gddvar)
data4 = plyr::ddply(data4, plyr::.(sitecode), transform, cumgdd = cumsum(gdd))
data4 = plyr::ddply(data4, plyr::.(sitecode), transform, DAP = seq(1, 365, 1))
data5 = suppressMessages(plyr::join(data4, phenodata[ , c("sitecode", "onset")]))
if(crop %in% c("Durum wheat", "Bread wheat")) {
data5 = within(data5, tmax[tmax > 21 & cumgdd <= 395] <- 21)
data5 = within(data5, tmax[tmax > max & cumgdd > 395] <- max)
HB.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1567)))
HB.df$HB = ifelse((HB.df$DAP + HB.df$onset) > 365, (HB.df$DAP + HB.df$onset) - 365, HB.df$DAP + HB.df$onset)
HB.df2 <- HB.df[ , c("sitecode", "onset", "HB")]
FB.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1739)))
FB.df$FB = ifelse((FB.df$DAP + FB.df$onset) > 365, (FB.df$DAP + FB.df$onset) - 365, FB.df$DAP + FB.df$onset)
FB.df2 <- FB.df[ , c("sitecode", "onset", "FB")]
FC.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1768)))
FC.df$FC = ifelse((FC.df$DAP + FC.df$onset) > 365, (FC.df$DAP + FC.df$onset) - 365, FC.df$DAP + FC.df$onset)
FC.df2 <- FC.df[ , c("sitecode", "onset", "FC")]
GFB.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1825)))
GFB.df$GFB = ifelse((GFB.df$DAP + GFB.df$onset) > 365, (GFB.df$DAP + GFB.df$onset) - 365, GFB.df$DAP + GFB.df$onset)
GFB.df2 <- GFB.df[ , c("sitecode", "onset", "GFB")]
GFC.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 2170)))
GFC.df$GFC = ifelse((GFC.df$DAP + GFC.df$onset) > 365, (GFC.df$DAP + GFC.df$onset) - 365, GFC.df$DAP + GFC.df$onset)
GFC.df2 <- GFC.df[ , c("sitecode", "onset", "GFC")]
}
else if(crop == "Barley") {
data5 = within(data5, tmax[tmax > 21 & cumgdd <= 395] <- 21)
data5 = within(data5, tmax[tmax > max & cumgdd > 395] <- max)
HB.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1357)))
HB.df$HB = ifelse((HB.df$DAP + HB.df$onset) > 365, (HB.df$DAP + HB.df$onset) - 365, HB.df$DAP + HB.df$onset)
HB.df2 <- HB.df[ , c("sitecode", "onset", "HB")]
FB.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1426)))
FB.df$FB = ifelse((FB.df$DAP + FB.df$onset) > 365, (FB.df$DAP + FB.df$onset) - 365, FB.df$DAP + FB.df$onset)
FB.df2 <- FB.df[ , c("sitecode", "onset", "FB")]
FC.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1496)))
FC.df$FC = ifelse((FC.df$DAP + FC.df$onset) > 365, (FC.df$DAP + FC.df$onset) - 365, FC.df$DAP + FC.df$onset)
FC.df2 <- FC.df[ , c("sitecode", "onset", "FC")]
GFB.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1635)))
GFB.df$GFB = ifelse((GFB.df$DAP + GFB.df$onset) > 365, (GFB.df$DAP + GFB.df$onset) - 365, GFB.df$DAP + GFB.df$onset)
GFB.df2 <- GFB.df[ , c("sitecode", "onset", "GFB")]
GFC.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1960)))
GFC.df$GFC = ifelse((GFC.df$DAP + GFC.df$onset) > 365, (GFC.df$DAP + GFC.df$onset) - 365, GFC.df$DAP + GFC.df$onset)
GFC.df2 <- GFC.df[ , c("sitecode", "onset", "GFC")]
}
else if(crop == "Chickpea") {
data5 = within(data5, tmax[tmax > max] <- max)
FB.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 680)))
FB.df$FB = ifelse((FB.df$DAP + FB.df$onset) > 365, (FB.df$DAP + FB.df$onset) - 365, FB.df$DAP + FB.df$onset)
FB.df2 <- FB.df[ , c("sitecode", "onset", "FB")]
FC.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 870)))
FC.df$FC = ifelse((FC.df$DAP + FC.df$onset) > 365, (FC.df$DAP + FC.df$onset) - 365, FC.df$DAP + FC.df$onset)
FC.df2 <- FC.df[ , c("sitecode", "onset", "FC")]
GFB.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1075)))
GFB.df$GFB = ifelse((GFB.df$DAP + GFB.df$onset) > 365, (GFB.df$DAP + GFB.df$onset) - 365, GFB.df$DAP + GFB.df$onset)
GFB.df2 <- GFB.df[ , c("sitecode", "onset", "GFB")]
GFC.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1740)))
GFC.df$GFC = ifelse((GFC.df$DAP + GFC.df$onset) > 365, (GFC.df$DAP + GFC.df$onset) - 365, GFC.df$DAP + GFC.df$onset)
GFC.df2 <- GFC.df[ , c("sitecode", "onset", "GFC")]
}
else if(crop == "Lentil") {
data5 = within(data5, tmax[tmax > max] <- max)
FB.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 807)))
FB.df$FB = ifelse((FB.df$DAP + FB.df$onset) > 365, (FB.df$DAP + FB.df$onset) - 365, FB.df$DAP + FB.df$onset)
FB.df2 <- FB.df[ , c("sitecode", "onset", "FB")]
FC.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 980)))
FC.df$FC50 = ifelse((FC.df$DAP + FC.df$onset) > 365, (FC.df$DAP + FC.df$onset) - 365, FC.df$DAP + FC.df$onset)
FC.df2 <- FC.df[ , c("sitecode", "onset", "FC50")]
SF.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1187)))
SF.df$SF = ifelse((SF.df$DAP + SF.df$onset) > 365, (SF.df$DAP + SF.df$onset) - 365, SF.df$DAP + SF.df$onset)
SF.df2 <- SF.df[ , c("sitecode", "onset", "SF")]
M.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1808)))
M.df$M = ifelse((M.df$DAP + M.df$onset) > 365, (M.df$DAP + M.df$onset) - 365, M.df$DAP + M.df$onset)
M.df2 <- M.df[ , c("sitecode", "onset", "M")]
}
if(crop %in% c("Durum wheat", "Bread wheat", "Barley")) {
pheno.df = Reduce(function(...) merge(..., all = TRUE), list(HB.df2, FB.df2, FC.df2, GFB.df2, GFC.df2))
pheno.df$LHB = ifelse((pheno.df$HB - pheno.df$onset) < 0, (pheno.df$HB - pheno.df$onset) + 365, pheno.df$HB - pheno.df$onset)
pheno.df$LFB = ifelse((pheno.df$FB - pheno.df$HB) < 0, (pheno.df$FB - pheno.df$HB) + 365, pheno.df$FB - pheno.df$HB)
pheno.df$LFC = ifelse((pheno.df$FC - pheno.df$FB) < 0, (pheno.df$FC - pheno.df$FB) + 365, pheno.df$FC - pheno.df$FB)
pheno.df$LGFB = ifelse((pheno.df$GFB - pheno.df$FC) < 0, (pheno.df$GFB - pheno.df$FC) + 365, pheno.df$GFB - pheno.df$FC)
pheno.df$LGFC = ifelse((pheno.df$GFC - pheno.df$GFB) < 0, (pheno.df$GFC - pheno.df$GFB) + 365, pheno.df$GFC - pheno.df$GFB)
data6 = pheno.df[ , c("sitecode", "onset", "LHB", "LFB", "LFC", "LGFB", "LGFC")]
data6$Cycle = rowSums(data6[ , -c(1:2)])
data5 = suppressMessages(plyr::join(data5, data6[ , c("sitecode", "Cycle")]))
dataCycle = data5 %>% dplyr::filter(data5$DAP<=data5$Cycle)
data7 = dataCycle[ , c("sitecode", "tmin", "tmax", "gdd", "cumgdd", "DAP")]
list.df1 = list(Growth_Period = data6, Onset_Data = phenodata)
list.df2 = list(Growth_Period = data6, Onset_Data = phenodata, Growing_Degree_Days = data7)
}
else if(crop == "Chickpea"){
pheno.df = Reduce(function(...) merge(..., all = TRUE), list(FB.df2, FC.df2, GFB.df2, GFC.df2))
pheno.df$LFB = ifelse((pheno.df$FB - pheno.df$onset) < 0, (pheno.df$FB - pheno.df$onset) + 365, pheno.df$FB - pheno.df$onset)
pheno.df$LFC = ifelse((pheno.df$FC - pheno.df$FB) < 0, (pheno.df$FC - pheno.df$FB) + 365, pheno.df$FC - pheno.df$FB)
pheno.df$LGFB = ifelse((pheno.df$GFB - pheno.df$FC) < 0, (pheno.df$GFB - pheno.df$FC) + 365, pheno.df$GFB - pheno.df$FC)
pheno.df$LGFC = ifelse((pheno.df$GFC - pheno.df$GFB) < 0, (pheno.df$GFC - pheno.df$GFB) + 365, pheno.df$GFC - pheno.df$GFB)
data6 = pheno.df[ , c("sitecode", "onset", "LFB", "LFC", "LGFB", "LGFC")]
data6$Cycle = rowSums(data6[ , -c(1:2)])
data5 = suppressMessages(plyr::join(data5, data6[ , c("sitecode", "Cycle")]))
dataCycle = data5 %>% dplyr::filter(data5$DAP<=data5$Cycle)
data7 = dataCycle[ , c("sitecode", "tmin", "tmax", "gdd", "cumgdd", "DAP")]
list.df1 = list(Growth_Period = data6, Onset_Data = phenodata)
list.df2 = list(Growth_Period = data6, Onset_Data = phenodata, Growing_Degree_Days = data7)
}
else if(crop == "Lentil"){
pheno.df = Reduce(function(...) merge(..., all = TRUE), list(FB.df2, FC.df2, SF.df2, M.df2))
pheno.df$LFB = ifelse((pheno.df$FB - pheno.df$onset) < 0, (pheno.df$FB - pheno.df$onset) + 365, pheno.df$FB - pheno.df$onset)
pheno.df$LFC = ifelse((pheno.df$FC50 - pheno.df$FB) < 0, (pheno.df$FC50 - pheno.df$FB) + 365, pheno.df$FC50 - pheno.df$FB)
pheno.df$LSF = ifelse((pheno.df$SF - pheno.df$FC) < 0, (pheno.df$SF - pheno.df$FC) + 365, pheno.df$SF - pheno.df$FC)
pheno.df$LM = ifelse((pheno.df$M - pheno.df$SF) < 0, (pheno.df$M - pheno.df$SF) + 365, pheno.df$M - pheno.df$SF)
data6 = pheno.df[ , c("sitecode", "onset", "LFB", "LFC", "LSF", "LM")]
data6$Cycle = rowSums(data6[ , -c(1:2)])
data5 = suppressMessages(plyr::join(data5, data6[ , c("sitecode", "Cycle")]))
dataCycle = data5 %>% dplyr::filter(data5$DAP<=data5$Cycle)
data7 = dataCycle[ , c("sitecode", "tmin", "tmax", "gdd", "cumgdd", "DAP")]
list.df1 = list(Growth_Period = data6, Onset_Data = phenodata)
list.df2 = list(Growth_Period = data6, Onset_Data = phenodata, Growing_Degree_Days = data7)
}
if(gdd) return(list.df2)
else if(!gdd) return(list.df1)
}
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Defines functions getGrowthPeriod
Documented in getGrowthPeriod
#' @title Calculating Growing Degree Days and Lengths of Growth Stages for Various Crops Using Onset Data from ICARDA's Database
#' @description Calculates growing degree days (GDD) as well as cumulative GDD, and returns a list of various data frames based on specified arguments.
#' @param sitecode expression. Vector with names of sites from which to extract onset data.
#' @param crop character. Type of crop in ICARDA database. See section 'Details' for crops which have calculations available.
#' @param base integer. Minimum temperature constraint for the crop.
#' @param max integer. Maximum temperature constraint for the crop.
#' @param gdd boolean. If \code{TRUE}, returns a data frame containing calculated GDD and accumulated GDD together with climatic variables used for the calculations. Default: FALSE.
#' @return A list object with different data frames depending on specified option in \code{gdd}.
#' If \code{gdd = TRUE}, the object is a list containing three data frames: the first one with lengths of different growing stages, the second one with original onset data with phenological variables, and the third one with calculated GDD and accumulated GDD for the sites specified in \code{sitecode}.
#' If \code{gdd = FALSE}, the object is a list containing two data frames: the first one with lengths of different growing stages, and the second one with original onset data with phenological variables for the sites specified in \code{sitecode}.
#' @details Growing degree days for various crops are calculated using average daily minimum and maximum temperature values obtained from onset data. The temperature constraints specified in \code{base} and \code{max} are first applied before the calculations are done. These constraints ensure very low or high temperatures which prevent growth of a particular crop are not included.
#' Crops for which GDD calculations are available include: 'Durum wheat', 'Bread wheat', 'Barley', 'Chickpea', 'Lentil'. Each of these can be supplied as options for the argument \code{crop}.
#' Cumulative GDD values determine the length of different growing stages. Growing stages vary depending on the type of crop. Durum wheat, bread wheat and barley have five growth stages, i.e. beginning of heading, beginning and completion of flowering, and beginning and completion of grain filling. Chickpea and lentil have four growth stages, i.e. beginning of flowering, completion of 50% flowering, beginning of seed filling, and completion of 90% maturity (chickpea) or of full maturity (lentil).
#' The length of the full growth cycle of the crop for each site is also given in the output data frame.
#' @author Khadija Aouzal, Zakaria Kehel, Bancy Ngatia
#' @examples
#' if(interactive()){
#' # Calculate GDD for durum wheat
#' data(durumDaily)
#' growth <- getGrowthPeriod(sitecode = durumDaily$site_code,
#' crop = 'Durum wheat', base = 0,
#' max = 35, gdd = TRUE)
#'
#' # Get data frame with lengths of growth stages from list
#' # object returned
#' growth.lengths <- growth[[1]]
#'
#' # Get data frame with phenotypic variables from list
#' # object returned
#' growth.pheno <- growth[[2]]
#'
#' # Get data frame with GDD, cumulative GDD and climatic
#' # variables from list object returned (when gdd = TRUE)
#' growth.gdd <- growth[[3]]
#' }
#' @rdname getGrowthPeriod
#' @importFrom dplyr group_by slice
#' @importFrom magrittr "%>%"
#' @importFrom plyr ddply join
#' @importFrom reshape2 melt
#' @export
getGrowthPeriod <- function(sitecode, crop, base, max, gdd = FALSE) {
if(crop == "Durum wheat") onsetcrop = "ICDW"
else if(crop == "Bread wheat") onsetcrop = "ICBW"
else if(crop == "Barley") onsetcrop = "ICB"
else if(crop == "Chickpea") onsetcrop = "ILC"
else if(crop == "Lentil") onsetcrop = "ILL"
onsetdata = getOnset(sites = levels(as.factor(sitecode)), var = c('tmin', 'tmax'), crop = onsetcrop)
tempdata = onsetdata[[1]]
phenodata = onsetdata[[2]]
names(tempdata)[names(tempdata) == grep("site", colnames(tempdata), value = TRUE)] <- "sitecode"
names(phenodata)[names(phenodata) == grep("site", colnames(phenodata), value = TRUE)] <- "sitecode"
names(phenodata)[names(phenodata) == grep("onset", colnames(phenodata), value = TRUE)] <- "onset"
tminvars = grep("tmin", colnames(tempdata), perl = TRUE, value = TRUE)
tmaxvars = grep("tmax", colnames(tempdata), perl = TRUE, value = TRUE)
data2 = reshape2::melt(tempdata, id = "sitecode")
data2 = data2[order(data2[ , "sitecode"]), ]
data2[3] = lapply(data2[3], function(x) replace(x, x < base, base))
gddtmin = data2[data2$variable %in% tminvars, "value"]
gddtmax = data2[data2$variable %in% tmaxvars, "value"]
gddvar = (gddtmin + gddtmax) / 2
data4 = data.frame(sitecode = data2[data2$variable %in% tminvars, "sitecode"], tmin = gddtmin, tmax = gddtmax, gdd = gddvar)
data4 = plyr::ddply(data4, plyr::.(sitecode), transform, cumgdd = cumsum(gdd))
data4 = plyr::ddply(data4, plyr::.(sitecode), transform, DAP = seq(1, 365, 1))
data5 = suppressMessages(plyr::join(data4, phenodata[ , c("sitecode", "onset")]))
if(crop %in% c("Durum wheat", "Bread wheat")) {
data5 = within(data5, tmax[tmax > 21 & cumgdd <= 395] <- 21)
data5 = within(data5, tmax[tmax > max & cumgdd > 395] <- max)
HB.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1567)))
HB.df$HB = ifelse((HB.df$DAP + HB.df$onset) > 365, (HB.df$DAP + HB.df$onset) - 365, HB.df$DAP + HB.df$onset)
HB.df2 <- HB.df[ , c("sitecode", "onset", "HB")]
FB.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1739)))
FB.df$FB = ifelse((FB.df$DAP + FB.df$onset) > 365, (FB.df$DAP + FB.df$onset) - 365, FB.df$DAP + FB.df$onset)
FB.df2 <- FB.df[ , c("sitecode", "onset", "FB")]
FC.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1768)))
FC.df$FC = ifelse((FC.df$DAP + FC.df$onset) > 365, (FC.df$DAP + FC.df$onset) - 365, FC.df$DAP + FC.df$onset)
FC.df2 <- FC.df[ , c("sitecode", "onset", "FC")]
GFB.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1825)))
GFB.df$GFB = ifelse((GFB.df$DAP + GFB.df$onset) > 365, (GFB.df$DAP + GFB.df$onset) - 365, GFB.df$DAP + GFB.df$onset)
GFB.df2 <- GFB.df[ , c("sitecode", "onset", "GFB")]
GFC.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 2170)))
GFC.df$GFC = ifelse((GFC.df$DAP + GFC.df$onset) > 365, (GFC.df$DAP + GFC.df$onset) - 365, GFC.df$DAP + GFC.df$onset)
GFC.df2 <- GFC.df[ , c("sitecode", "onset", "GFC")]
}
else if(crop == "Barley") {
data5 = within(data5, tmax[tmax > 21 & cumgdd <= 395] <- 21)
data5 = within(data5, tmax[tmax > max & cumgdd > 395] <- max)
HB.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1357)))
HB.df$HB = ifelse((HB.df$DAP + HB.df$onset) > 365, (HB.df$DAP + HB.df$onset) - 365, HB.df$DAP + HB.df$onset)
HB.df2 <- HB.df[ , c("sitecode", "onset", "HB")]
FB.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1426)))
FB.df$FB = ifelse((FB.df$DAP + FB.df$onset) > 365, (FB.df$DAP + FB.df$onset) - 365, FB.df$DAP + FB.df$onset)
FB.df2 <- FB.df[ , c("sitecode", "onset", "FB")]
FC.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1496)))
FC.df$FC = ifelse((FC.df$DAP + FC.df$onset) > 365, (FC.df$DAP + FC.df$onset) - 365, FC.df$DAP + FC.df$onset)
FC.df2 <- FC.df[ , c("sitecode", "onset", "FC")]
GFB.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1635)))
GFB.df$GFB = ifelse((GFB.df$DAP + GFB.df$onset) > 365, (GFB.df$DAP + GFB.df$onset) - 365, GFB.df$DAP + GFB.df$onset)
GFB.df2 <- GFB.df[ , c("sitecode", "onset", "GFB")]
GFC.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1960)))
GFC.df$GFC = ifelse((GFC.df$DAP + GFC.df$onset) > 365, (GFC.df$DAP + GFC.df$onset) - 365, GFC.df$DAP + GFC.df$onset)
GFC.df2 <- GFC.df[ , c("sitecode", "onset", "GFC")]
}
else if(crop == "Chickpea") {
data5 = within(data5, tmax[tmax > max] <- max)
FB.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 680)))
FB.df$FB = ifelse((FB.df$DAP + FB.df$onset) > 365, (FB.df$DAP + FB.df$onset) - 365, FB.df$DAP + FB.df$onset)
FB.df2 <- FB.df[ , c("sitecode", "onset", "FB")]
FC.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 870)))
FC.df$FC = ifelse((FC.df$DAP + FC.df$onset) > 365, (FC.df$DAP + FC.df$onset) - 365, FC.df$DAP + FC.df$onset)
FC.df2 <- FC.df[ , c("sitecode", "onset", "FC")]
GFB.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1075)))
GFB.df$GFB = ifelse((GFB.df$DAP + GFB.df$onset) > 365, (GFB.df$DAP + GFB.df$onset) - 365, GFB.df$DAP + GFB.df$onset)
GFB.df2 <- GFB.df[ , c("sitecode", "onset", "GFB")]
GFC.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1740)))
GFC.df$GFC = ifelse((GFC.df$DAP + GFC.df$onset) > 365, (GFC.df$DAP + GFC.df$onset) - 365, GFC.df$DAP + GFC.df$onset)
GFC.df2 <- GFC.df[ , c("sitecode", "onset", "GFC")]
}
else if(crop == "Lentil") {
data5 = within(data5, tmax[tmax > max] <- max)
FB.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 807)))
FB.df$FB = ifelse((FB.df$DAP + FB.df$onset) > 365, (FB.df$DAP + FB.df$onset) - 365, FB.df$DAP + FB.df$onset)
FB.df2 <- FB.df[ , c("sitecode", "onset", "FB")]
FC.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 980)))
FC.df$FC50 = ifelse((FC.df$DAP + FC.df$onset) > 365, (FC.df$DAP + FC.df$onset) - 365, FC.df$DAP + FC.df$onset)
FC.df2 <- FC.df[ , c("sitecode", "onset", "FC50")]
SF.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1187)))
SF.df$SF = ifelse((SF.df$DAP + SF.df$onset) > 365, (SF.df$DAP + SF.df$onset) - 365, SF.df$DAP + SF.df$onset)
SF.df2 <- SF.df[ , c("sitecode", "onset", "SF")]
M.df <- data5 %>% dplyr::group_by(sitecode) %>% dplyr::slice(which.min(abs(cumgdd - 1808)))
M.df$M = ifelse((M.df$DAP + M.df$onset) > 365, (M.df$DAP + M.df$onset) - 365, M.df$DAP + M.df$onset)
M.df2 <- M.df[ , c("sitecode", "onset", "M")]
}
if(crop %in% c("Durum wheat", "Bread wheat", "Barley")) {
pheno.df = Reduce(function(...) merge(..., all = TRUE), list(HB.df2, FB.df2, FC.df2, GFB.df2, GFC.df2))
pheno.df$LHB = ifelse((pheno.df$HB - pheno.df$onset) < 0, (pheno.df$HB - pheno.df$onset) + 365, pheno.df$HB - pheno.df$onset)
pheno.df$LFB = ifelse((pheno.df$FB - pheno.df$HB) < 0, (pheno.df$FB - pheno.df$HB) + 365, pheno.df$FB - pheno.df$HB)
pheno.df$LFC = ifelse((pheno.df$FC - pheno.df$FB) < 0, (pheno.df$FC - pheno.df$FB) + 365, pheno.df$FC - pheno.df$FB)
pheno.df$LGFB = ifelse((pheno.df$GFB - pheno.df$FC) < 0, (pheno.df$GFB - pheno.df$FC) + 365, pheno.df$GFB - pheno.df$FC)
pheno.df$LGFC = ifelse((pheno.df$GFC - pheno.df$GFB) < 0, (pheno.df$GFC - pheno.df$GFB) + 365, pheno.df$GFC - pheno.df$GFB)
data6 = pheno.df[ , c("sitecode", "onset", "LHB", "LFB", "LFC", "LGFB", "LGFC")]
data6$Cycle = rowSums(data6[ , -c(1:2)])
data5 = suppressMessages(plyr::join(data5, data6[ , c("sitecode", "Cycle")]))
dataCycle = data5 %>% dplyr::filter(data5$DAP<=data5$Cycle)
data7 = dataCycle[ , c("sitecode", "tmin", "tmax", "gdd", "cumgdd", "DAP")]
list.df1 = list(Growth_Period = data6, Onset_Data = phenodata)
list.df2 = list(Growth_Period = data6, Onset_Data = phenodata, Growing_Degree_Days = data7)
}
else if(crop == "Chickpea"){
pheno.df = Reduce(function(...) merge(..., all = TRUE), list(FB.df2, FC.df2, GFB.df2, GFC.df2))
pheno.df$LFB = ifelse((pheno.df$FB - pheno.df$onset) < 0, (pheno.df$FB - pheno.df$onset) + 365, pheno.df$FB - pheno.df$onset)
pheno.df$LFC = ifelse((pheno.df$FC - pheno.df$FB) < 0, (pheno.df$FC - pheno.df$FB) + 365, pheno.df$FC - pheno.df$FB)
pheno.df$LGFB = ifelse((pheno.df$GFB - pheno.df$FC) < 0, (pheno.df$GFB - pheno.df$FC) + 365, pheno.df$GFB - pheno.df$FC)
pheno.df$LGFC = ifelse((pheno.df$GFC - pheno.df$GFB) < 0, (pheno.df$GFC - pheno.df$GFB) + 365, pheno.df$GFC - pheno.df$GFB)
data6 = pheno.df[ , c("sitecode", "onset", "LFB", "LFC", "LGFB", "LGFC")]
data6$Cycle = rowSums(data6[ , -c(1:2)])
data5 = suppressMessages(plyr::join(data5, data6[ , c("sitecode", "Cycle")]))
dataCycle = data5 %>% dplyr::filter(data5$DAP<=data5$Cycle)
data7 = dataCycle[ , c("sitecode", "tmin", "tmax", "gdd", "cumgdd", "DAP")]
list.df1 = list(Growth_Period = data6, Onset_Data = phenodata)
list.df2 = list(Growth_Period = data6, Onset_Data = phenodata, Growing_Degree_Days = data7)
}
else if(crop == "Lentil"){
pheno.df = Reduce(function(...) merge(..., all = TRUE), list(FB.df2, FC.df2, SF.df2, M.df2))
pheno.df$LFB = ifelse((pheno.df$FB - pheno.df$onset) < 0, (pheno.df$FB - pheno.df$onset) + 365, pheno.df$FB - pheno.df$onset)
pheno.df$LFC = ifelse((pheno.df$FC50 - pheno.df$FB) < 0, (pheno.df$FC50 - pheno.df$FB) + 365, pheno.df$FC50 - pheno.df$FB)
pheno.df$LSF = ifelse((pheno.df$SF - pheno.df$FC) < 0, (pheno.df$SF - pheno.df$FC) + 365, pheno.df$SF - pheno.df$FC)
pheno.df$LM = ifelse((pheno.df$M - pheno.df$SF) < 0, (pheno.df$M - pheno.df$SF) + 365, pheno.df$M - pheno.df$SF)
data6 = pheno.df[ , c("sitecode", "onset", "LFB", "LFC", "LSF", "LM")]
data6$Cycle = rowSums(data6[ , -c(1:2)])
data5 = suppressMessages(plyr::join(data5, data6[ , c("sitecode", "Cycle")]))
dataCycle = data5 %>% dplyr::filter(data5$DAP<=data5$Cycle)
data7 = dataCycle[ , c("sitecode", "tmin", "tmax", "gdd", "cumgdd", "DAP")]
list.df1 = list(Growth_Period = data6, Onset_Data = phenodata)
list.df2 = list(Growth_Period = data6, Onset_Data = phenodata, Growing_Degree_Days = data7)
}
if(gdd) return(list.df2)
else if(!gdd) return(list.df1)
}
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