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lb-model-final-code.R
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lb-model-final-code.R
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#######################################
# LATE BLIGHT FORECASTING MODEL FOR PERÚ
# International Potato Center ; Lima, Perú
# Authors: Quispe, M., Juarez, H.
#######################################
# Install and call packages
pkgs = c("maptools", "data.table", "tidyr", "dplyr")
# install.packages(pkgs)
lapply(pkgs, library, character.only = TRUE)
######################################
# function for ranges
in_range <- function(val,min,max) {
return(val >= min & val <= max);
}
in_range2 <- function(val,min,max) {
return(val > min & val <= max);
}
# function for calculate blight units
# INPUTS: hrhours>90 , avg temp, variety type (resistance)
calc_bu <- function(hhr,htavg,vt) {
if(htavg > 27 & hhr == 24) {
return(0);
} else if(in_range2(htavg,22.5,27)) {
if (vt == "s") {
if(hhr == 6) {
return(0);
}
else if(in_range(hhr,7.0,9.0)) {
return(1);
}
else if(in_range(hhr,10.0,12.0)) {
return(2);
}
else if(in_range(hhr,13.0,15.0)) {
return(3);
}
else if(in_range(hhr,16.0,18.0)) {
return(4);
}
else if(in_range(hhr,19.0,24.0)) {
return(5);
}else {
return(0);
}
}
else if (vt == "ms") {
if(hhr == 9) {
return(0);
}
else if(in_range(hhr,10.0,18.0)) {
return(1);
}
else if(in_range(hhr,19.0,24.0)) {
return(2);
} else{
return(0);
}
}
else if (vt == "mr" | vt == "r" | vt == "hr") {
if(hhr == 15) {
return(0);
}
else if(in_range(hhr,16.0,24.0)) {
return(1);
} else{
return(0);
}
}
else {
return(0)
}
}
else if(in_range2(htavg,12.5,22.5)) {
if(vt == "s") {
if(hhr == 6) {
return(0);
}
else if(in_range(hhr,7.0,9.0)) {
return(5);
}
else if(in_range(hhr,10.0,12.0)) {
return(6);
}
else if(in_range(hhr,13.0,24.0)) {
return(7);
}
else{
return(0);
}
}
else if(vt == "ms") {
if(hhr == 6) {
return(0);
}
else if(hhr == 7) {
return(1);
}
else if(hhr == 8) {
return(2);
}
else if(hhr == 9) {
return(3);
}
else if(hhr == 10) {
return(4);
}
else if(in_range(hhr,11.0,12.0)) {
return(5);
}
else if(in_range(hhr,13.0,24.0)) {
return(6);
}
else{
return(0)
}
}
else if(vt == "mr" | vt == "r" | vt == "hr") {
if(hhr == 6) {
return(0);
}
else if(hhr == 7) {
return(1);
}
else if(hhr == 8) {
return(2);
}
else if(hhr == 9) {
return(3);
}
else if(in_range(hhr,10.0,12.0)) {
return(4);
}
else if(in_range(hhr,13.0,24.0)) {
return(5);
}
else {
return(0);
}
}
else{
return(0)
}
}
else if(in_range2(htavg,7.5,12.5)) {
if(vt == "s") {
if(hhr == 6) {
return(0);
}
else if(hhr == 7) {
return(1);
}
else if(in_range(hhr,8.0,9.0)) {
return(2);
}
else if(hhr == 10) {
return(3);
}
else if(in_range(hhr,11.0,12.0)) {
return(4);
}
else if(in_range(hhr,13.0,15.0)) {
return(5);
}
else if(in_range(hhr,16.0,24.0)) {
return(6);
}
else{
return(0)
}
}
else if(vt == "ms") {
if(hhr == 6) {
return(0);
}
else if(in_range(hhr,7.0,9.0)) {
return(1);
}
else if(in_range(hhr,10.0,12.0)) {
return(2);
}
else if(in_range(hhr,13.0,15.0)) {
return(3);
}
else if(in_range(hhr,16.0,18.0)) {
return(4);
}
else if(in_range(hhr,19.0,24.0)) {
return(5);
}
else{
return(0);
}
}
else if(vt == "mr" | vt == "r" | vt == "hr") {
if(hhr == 9) {
return(0);
}
else if(in_range(hhr,10.0,12.0)) {
return(1);
}
else if(in_range(hhr,13.0,15.0)) {
return(2);
}
else if(in_range(hhr,16.0,24.0)) {
return(3);
}
else {
return(0);
}
}
else {
return(0);
}
}
else if(in_range(htavg,3,7.5)) {
if(vt == "s") {
if(hhr == 9) {
return(0);
}
else if(in_range(hhr,10.0,12.0)) {
return(1);
}
else if(in_range(hhr,13.0,15.0)) {
return(2);
}
else if(in_range(hhr,16.0,18.0)) {
return(3);
}
else if(in_range(hhr,19.0,24.0)) {
return(4);
}
else{
return(0);
}
}
else if(vt == "ms") {
if(hhr == 12) {
return(0);
}
else if(in_range(hhr,13.0,24.0)) {
return(1);
}
else{
return(0);
}
}
else if(vt == "mr" | vt == "r" | vt == "hr") {
if(hhr == 18) {
return(0);
}
else if(in_range(hhr,19.0,24.0)) {
return(1);
}
else{
return(0)
}
}
else{
return(0)
}
}
else if(htavg < 3 & hhr == 24) {
return(0)
}
else {
return(0)
}
}
# function for calculate fungicide units
# INPUTS: rain mm, dsa - days since fungicide aplication
calc_fu <- function(rain,dsa) {
if(rain < 1 & rain > 0) {
return(1);
} else {
if(dsa == 1) {
if(in_range(rain,1.0,1.45)) {
return(4);
}
else if(in_range2(rain,1.45,3.45)) {
return(5);
}
else if(in_range2(rain,3.45,6.0)) {
return(6);
}
else if(rain > 6) {
return(7);
}
else{
return(0);
}
}
else if(dsa == 2) {
if(in_range(rain,1.0,1.45)) {
return(3);
}
else if(in_range2(rain,1.45,4.45)) {
return(4);
}
else if(in_range2(rain,4.45,8.0)) {
return(5);
}
else if(rain > 8) {
return(6);
}
else{
return(0);
}
}
else if(dsa == 3) {
if(in_range(rain,1.0,2.45)) {
return(3);
}
else if(in_range2(rain,2.45,5.0)) {
return(4);
}
else if(rain > 5) {
return(5);
}
else{
return(0);
}
}
else if(in_range(dsa,4.0,5.0)) {
if(in_range(rain,1.0,2.45)) {
return(3);
}
else if(in_range2(rain,2.45,8)) {
return(4);
}
else if(rain > 8) {
return(5);
}
else{
return(0);
}
}
else if(in_range(dsa,6.0,9.0)) {
if(in_range(rain,1.0,4.0)) {
return(3);
}
else if(rain > 4) {
return(4);
}
else{
return(0);
}
}
else if (in_range(dsa,10.0,14.0)) {
if(in_range(rain,1.0,1.45)) {
return(2);
}
else if(in_range2(rain,1.45,8.0)) {
return(3);
}
else if(rain > 8) {
return(4);
}
else{
return(0);
}
}
else if (dsa > 14) {
if(in_range(rain,1.0,8.0)) {
return(2);
}
else if(rain > 8) {
return(3);
}
else{
return(0);
}
}
else {
return(0);
}
}
}
# function for decision rules
# INPUTS: abu, afu
check_bu_cutoff <- function(abu,vt) {
if(vt == "s" & abu >= 30) {
return(TRUE);
} else if(vt == "ms" & abu >= 35) {
return(TRUE);
} else if(vt == "mr" & abu >= 40) {
return(TRUE);
} else if(vt == "r" & abu >= 45) {
return(TRUE);
} else if(vt == "hr" & abu >= 50){
return(TRUE);
} else {
return(FALSE);
}
}
check_fu_cutoff <- function(afu,vt) {
if(vt == "s" & afu >= 15) {
return(TRUE);
} else if(vt == "ms" & afu >= 20) {
return(TRUE);
} else if(vt == "mr" & afu >= 25) {
return(TRUE);
} else if(vt == "r" & afu >= 30) {
return(TRUE);
} else if(vt == "hr" & afu >= 35){
return(TRUE);
} else {
return(FALSE);
}
}
# function for calculate humidity relative hourly
# INPUTS: meterological data, coordinates (lat y long)
calculate_hhr <- function(climdata, lon, lat) {
#########################
date_df <- as.Date(climdata[[1]], format = "%Y-%m-%d")
tn <- as.numeric(climdata[[4]])
tx <- as.numeric(climdata[[3]])
tavg <- (tn+tx)/2
hrn <- as.numeric(climdata[[8]])
hrx <- as.numeric(climdata[[7]])
hravg <- (hrn+hrx)/2
tdew <- tavg - ( (100 - hravg) / 5)
rain <- as.numeric(climdata[[5]])
# Calculate hourly for sunrise and sunset
crds <- matrix(c(lon[[1]], lat[[1]]), ncol = 2)
sunrise <- sunriset(crds, as.POSIXct(date_df),
proj4string=CRS("+proj=longlat +datum=WGS84"),
direction=c("sunrise"), POSIXct.out= F)
sunset <- sunriset(crds, as.POSIXct(date_df),
proj4string=CRS("+proj=longlat +datum=WGS84"),
direction=c("sunset"), POSIXct.out= F)
df_format_hhr <- data.frame(date_df[-c(length(date_df),1)], tn[-c(length(tn),1)], tn[-c(1:2)],
tx[-c(length(tx),1)], tx[-c(length(tx), length(tx) - 1)],
tdew[-c(length(tdew),1)],
sunrise[-c(length(sunrise),1)], sunset[-c(length(sunset),1)],
sunset[-c(length(sunset),length(sunset) - 1)])
names(df_format_hhr) <- c("date", "tmin", "tminnext",
"tmax", "tmaxold", "tdew",
"sunrise", "sunset", "sunset old")
#########################
# Tn temp min, Tx temp max, To sunset , Tp temp min next
# Hn sunrise, Hx h temp max, Ho h sunset , Hp h temp min next
# date <- df_in_hhr[[1]]
#####
model_temp_hr <- function(df_in_hhr){
Tn <- as.numeric(df_in_hhr[[2]])
Tp <- as.numeric(df_in_hhr[[3]])
Tx <- as.numeric(df_in_hhr[[4]])
Hn <- as.numeric(df_in_hhr[[7]]) * 24
Ho <- as.numeric(df_in_hhr[[8]]) * 24
tdew <- as.numeric(df_in_hhr[[6]])
#####
Tp_old <- Tn
Hp_old <- Hn + 24
Tx_old <- as.numeric(df_in_hhr[[5]])
Ho_old <- as.numeric(df_in_hhr[[9]]) * 24
To_old <- Tx_old - 0.39*(Tx_old - Tp_old)
# Parameters for model
To <- Tx - 0.39*(Tx - Tp)
Hp <- Hn + 24
Hx <- Ho - 4
alpha <- Tx - Tn
r <- Tx - To
beta1 <- (Tp - To) / sqrt(Hp - Ho)
beta2 <- (Tp_old - To_old) / sqrt(Hp_old - Ho_old)
t <- 1:24
T_model <- 0
for(i in 1:24) {
if(t[i] > Hn & t[i] <= Hx){
T_model[i] <- Tn + alpha * ( ((t[i]-Hn) / (Hx-Hn)) * (pi/2) )
}
else if(t[i] > Hx & t[i] <= Ho){
T_model[i] <- To + r * sin( (pi/2) + ( ((t[i]-Hx)/4) * (pi/2) ) )
}
else if( t[i] > Ho & t[i] <= 24 ){
T_model[i] <- To + beta1 * sqrt( (t[i] - Ho) )
}
else if (t[i] >= 1 & t[i] <= Hn){
T_model[i] <- To_old + beta2 * sqrt( (t[i]+24) - Ho_old)
}
else {
T_model[i] <- "Error"
}
}
# Buck formula for es and e (kPa)
es <- 0.61121 * exp( ( (18.678 - (T_model/234.5)) * T_model) / (257.14 + T_model))
e <- 0.61121 * exp( ( (18.678 - (tdew/234.5)) * tdew) / (257.14 + tdew))
# hr
hr <- (e/es) * 100
df <- data.frame(T_model, hr)
return(df)
}
df_temp_hr_hour <- apply(df_format_hhr, 1 , function(x) model_temp_hr(x))
df_temp_hr_hour_simcast <- list()
for (i in 1:(length(df_temp_hr_hour)-1)) {
df_temp_hr_hour_simcast[[i]] <- rbind(df_temp_hr_hour[[i]][13:24,],
df_temp_hr_hour[[i+1]][1:12,]);
}
hr_limit90_temp <- function(x){
temp_model_prom <- ifelse( nrow(x[ x[,2] > 90, ]) > 0,
mean(x[ x[,2] > 90, 1]),
mean(x[ , 1]))
hr_model_hours <- nrow(x[ x[,2] > 90, ])
df <- data.frame(temp_model_prom, hr_model_hours)
return(df)
}
list_hours <- lapply(df_temp_hr_hour_simcast, function(x) hr_limit90_temp(x))
df_hours <- rbindlist(list_hours)
df_output_hr <- data.frame(date_df[-c(length(date_df),1,2)], df_hours[, 2],
tavg[-c(length(date_df),1,2)], rain[-c(length(rain),1,2)])
names(df_output_hr) <- c("date", "hr90_hour", "tavg_C", "rain_mm")
print("Run temperature and humidity model...")
return(df_output_hr)
}
# function for SIMCAST model Fry et. al 1983
simcast_model <- function(df_in_simcast, vt){
vt <- as.character(vt[[1]])
lines <- list()
for(i in 1:nrow(df_in_simcast)){
lines[[i]] = df_in_simcast[i,]
}
#########
firstApplication <- TRUE
returnValue <- 1
bu <- 0
fu <- 0
bua <- 0
fua <- 0
afu <- 0
app <- 0
i <- 0
last_bua <- 0
last_fua <- 0
app_ctr <- 0
days_since_app <- 0
min_day <- 7
abu <- 0
tabu <- 0
tafu <- 0
#begin <- 0
end <- length(lines)
###############
bu_final <- 0
fu_final <- 0
bua_final <- 0
fua_final <- 0
abu_final <- 0
afu_final <- 0
app_final <- 0
for (k in 1:end){
line <- lines[[k]]
day <- line[[1]]
hhr <- as.numeric(line[[2]])
htavg <- as.numeric(line[[3]])
rain <- as.numeric(line[[4]])
bu <- calc_bu(hhr, htavg, vt)
last_bua <- bua
bua <- bu + bua
if(abu != 1 && afu != 1 && days_since_app <= 0){
days_since_app <- 0
} else {
days_since_app <- days_since_app + 1
fu = calc_fu(rain, days_since_app)
last_fua = fu
fua = fu + fua
}
# modify && by ll (and "!") if you need the first app according to the dss
if(days_since_app > min_day && !firstApplication){
if( check_bu_cutoff(bua, vt) ) {
bua <- 0
fua <- 0
}
if( check_fu_cutoff(fua, vt) ) {
bua <- 0
fua <- 0
}
}
if (bua >= last_bua || days_since_app <= min_day && !firstApplication) {
app <- FALSE
abu <- 0
} else {
abu <- 1
app <- TRUE
app_ctr <- app_ctr + 1
days_since_app <- 0
#firstApplication <- FALSE
}
if(k == 25) {
abu <- 1
app <- TRUE
app_ctr <- app_ctr + 1
days_since_app <- 0
firstApplication <- FALSE
bua <- 0
fua <- 0
}
if (fua < last_fua && days_since_app > min_day) {
afu <- 1
app <- TRUE
app_ctr <- app_ctr + 1
days_since_app <- 0
} else {
app <- FALSE
afu <- 0
}
tabu <- abu + tabu
tafu <- afu + tafu
bu_final[k] <- bu
fu_final[k] <- fu
bua_final[k] <- bua
fua_final[k] <- fua
abu_final[k] <- abu
afu_final[k] <- afu
app_final[k] <- app_ctr
}
output_simcast <- data.frame(bu_final, fu_final, bua_final, fua_final,
abu_final, afu_final, app_final)
names(output_simcast) <- c("BU", "FU", "BUA", "FUA", "ABU", "AFU", "APP")
output_scmodel <- cbind(df_in_simcast, output_simcast)
print("Running simcast model ... Loading")
return(output_scmodel)
}
####################
# Import data and sampling
setwd("C:/Users/Asus/Desktop/")
df_clima <- fread("weather_data.csv",header=T)
#df_clima$DAT <- as.Date(df_clima$DAT, format = "%Y-%m-%d")
str(df_clima)
# -2 days initial / +1 day final for growing period
dias_cultivo1 <- seq(as.Date('2007-11-13'),as.Date('2008-04-16'),by = 1)
dias_cultivo2 <- seq(as.Date('2008-07-13'),as.Date('2008-12-16'),by = 1)
dias_cultivo3 <- seq(as.Date('2008-04-13'),as.Date('2008-07-31'),by = 1)
# select area of interest
df_zonas_papa <- read.csv("zonas-papa.csv", header = T,
stringsAsFactors = F, sep =",")
unique(df_zonas_papa$Type)
df_zona1 <- df_zonas_papa[df_zonas_papa$Type == "Sierra Alta [>3,000]", 2]
df_zona2 <- df_zonas_papa[df_zonas_papa$Type == "Sierra Media [2000-3000 masl]", 2]
df_zona3 <- df_zonas_papa[df_zonas_papa$Type == "Costa [0-500 msnm] Interandino [<2,000]", 2]
df_select1 <- df_clima[df_clima$DAT %in% dias_cultivo1 & df_clima$CELL5M %in% df_zona1 , ]
df_select2 <- df_clima[df_clima$DAT %in% dias_cultivo2 & df_clima$CELL5M %in% df_zona2 , ]
df_select3 <- df_clima[df_clima$DAT %in% dias_cultivo3 & df_clima$CELL5M %in% df_zona3 , ]
###############3
df_select1_2 <- df_select1 %>% nest(data = c(DAT, SRAD, TMAX, TMIN, RAIN, WIND, RH_MAX, RH_MIN))
df_select2_2 <- df_select2 %>% nest(data = c(DAT, SRAD, TMAX, TMIN, RAIN, WIND, RH_MAX, RH_MIN))
df_select3_2 <- df_select3 %>% nest(data = c(DAT, SRAD, TMAX, TMIN, RAIN, WIND, RH_MAX, RH_MIN))
rm(df_clima, df_select1, df_select2, df_select3)
head(df_select1_2)
df_coords <- read.csv("data_coords.csv", stringsAsFactors = F, sep = ",")
df_merg1 <- inner_join(df_select1_2, df_coords, by="CELL5M")
df_merg2 <- inner_join(df_select2_2, df_coords, by="CELL5M")
df_merg3 <- inner_join(df_select3_2, df_coords, by="CELL5M")
###################
# inputs for dataframe with date, hr>90, tavg and rain:
# climate data, lon, lat
input_runsimcast1 <- apply(df_merg1, 1, function(x) calculate_hhr(x[[2]], x[[10]], x[[9]]))
input_runsimcast2 <- apply(df_merg2, 1, function(x) calculate_hhr(x[[2]], x[[10]], x[[9]]))
input_runsimcast3 <- apply(df_merg3, 1, function(x) calculate_hhr(x[[2]], x[[10]], x[[9]]))
# inputs for run simcast:
# input_runsimcast, variety type (resistence)
output_runsimcast1 <- lapply(input_runsimcast1,
function(x) simcast_model(x, "r"))
output_runsimcast2 <- lapply(input_runsimcast2,
function(x) simcast_model(x, "r"))
output_runsimcast3 <- lapply(input_runsimcast3,
function(x) simcast_model(x, "r"))
#############################################
# Create raster
library(raster)
library(ggplot2)
library(scales)
library(ggsn)
library(openxlsx)
runsimcast_app1 <- lapply(output_runsimcast1, function(x) x[[11]][length(x[[11]])])
runsimcast_app2 <- lapply(output_runsimcast2, function(x) x[[11]][length(x[[11]])])
runsimcast_app3 <- lapply(output_runsimcast3, function(x) x[[11]][length(x[[11]])])
runsimcast_bu_app1 <- lapply(output_runsimcast1, function(x) nrow(x[x$ABU == 1,]))
runsimcast_bu_app2 <- lapply(output_runsimcast2, function(x) nrow(x[x$ABU == 1,]))
runsimcast_bu_app3 <- lapply(output_runsimcast3, function(x) nrow(x[x$ABU == 1,]))
runsimcast_fu_app1 <- lapply(output_runsimcast1, function(x) nrow(x[x$AFU == 1,]))
runsimcast_fu_app2 <- lapply(output_runsimcast2, function(x) nrow(x[x$AFU == 1,]))
runsimcast_fu_app3 <- lapply(output_runsimcast3, function(x) nrow(x[x$AFU == 1,]))
df_app_coords1 <- cbind( df_merg1[,c(10,9)],
app_bu_total = as.numeric(cbind(runsimcast_bu_app1)),
app_fu_total = as.numeric(cbind(runsimcast_fu_app1 )),
app_total = as.numeric(cbind(runsimcast_app1)))
df_app_coords1$type <- "Sierra Alta [>3000 msnm]"
df_app_coords2 <- cbind( df_merg2[,c(10,9)],
app_bu_total = as.numeric(cbind(runsimcast_bu_app2)),
app_fu_total = as.numeric(cbind(runsimcast_fu_app2)),
app_total = as.numeric(cbind(runsimcast_app2)))
df_app_coords2$type <- "Sierra Media [2000-3000 msnm]"
df_app_coords3 <- cbind( df_merg3[,c(10,9)],
app_bu_total = as.numeric(cbind(runsimcast_bu_app3)),
app_fu_total = as.numeric(cbind(runsimcast_fu_app3)),
app_total = as.numeric(cbind(runsimcast_app3)))
df_app_coords3$type <- "Costa [0-500 msnm] - Interandino [<2000 msnm]"
df_app_coords <- rbind(df_app_coords1, df_app_coords2, df_app_coords3,
fill = T)
df_raster <- rasterFromXYZ(df_app_coords[,c(1,2,5)])
proj4string(df_raster)=CRS("+proj=longlat +datum=WGS84")
###########################
# Save data
setwd("C:/Users/Asus/Documents/R/simcast-lbf-cip/mapas")
writeRaster(df_raster, filename="R-08.asc", format='ascii', overwrite=TRUE)
write.xlsx(df_app_coords, "R-08.xlsx", row.names = F)
######
peru <- getData("GADM", country= "PER", level=1)
peru <- fortify(peru)
df_pol_peru <- data.frame(peru)
summary(df_app_coords$app_total)
#######
df_app_coords <- df_app_coords %>%