R/lateblight.R
In myaseen208/agricolae: Statistical Procedures for Agricultural Research
Defines functions
lateblight
Documented in
lateblight
#' LATEBLIGHT - Simulator for potato late blight Version LB2004
#'
#' LATEBLIGHT is a mathematical model that simulates the effect of weather,
#' host growth and resistance, and fungicide use on asexual development and
#' growth of Phytophthora infestans on potato foliage.
#'
#' LATEBLIGHT Version LB2004 was created in October 2004 (Andrade-Piedra et
#' al., 2005a, b and c), based on the C-version written by B.E. Ticknor ('BET
#' 21191 modification of cbm8d29.c'), reported by Doster et al. (1990) and
#' described in detail by Fry et al. (1991) (This version is referred as LB1990
#' by Andrade-Piedra et al. [2005a]). The first version of LATEBLIGHT was
#' developed by Bruhn and Fry (1981) and described in detail by Bruhn et al.
#' (1980).
#'
#' @param WS object weather-severity
#' @param Cultivar chr
#' @param ApplSys chr
#' @param InocDate days
#' @param LGR num, see example
#' @param IniSpor num
#' @param SR num, see example
#' @param IE num, Initialization infection
#' @param LP num, latent period
#' @param InMicCol num
#' @param MatTime chr
#' @param \dots plot graphics parameters
#' @return \item{Ofile}{"Date","nday","MicCol","SimSeverity",...}
#' \item{Gfile}{"dates","nday","MeanSeverity","StDevSeverity"}
#' @note All format data for date is yyyy-mm,dd, for example "2000-04-22".
#' change with function as.Date()
#' @author Jorge L. Andrade-Piedra (1) (j.andrade@@cgar.org), Gregory A. Forbes
#' (1) (g.forbes@@cgiar.org), Robert J. Hijmans (2) (rhijmans@@ucdavis.edu),
#' William E. Fry (3) (wef1@@cornell.edu) Translation from C language into SAS
#' language: G.A. Forbes Modifications: J.L. Andrade-Piedra and R.J. Hijmans
#' Translation from SAS into R: Felipe de Mendiburu (1) (1) International
#' Potato Center, P.O. Box 1558, Lima 12, Peru (2) University of California,
#' One Shields Avenue, Davis, California 95616, USA (3) Cornell University, 351
#' Plant Science, Ithaca, NY 14853, USA
#' @seealso \code{\link{weatherSeverity}}
#' @references Andrade-Piedra, J. L., Hijmans, R. J., Forbes, G. A., Fry, W.
#' E., and Nelson, R. J. 2005a. Simulation of potato late blight in the Andes.
#' I: Modification and parameterization of the LATEBLIGHT model. Phytopathology
#' 95:1191-1199.\cr Andrade-Piedra, J. L., Hijmans, R. J., Juarez, H. S.,
#' Forbes, G. A., Shtienberg, D., and Fry, W. E. 2005b. Simulation of potato
#' late blight in the Andes. II: Validation of the LATEBLIGHT model.
#' Phytopathology 95:1200-1208.\cr Andrade-Piedra, J. L., Forbes, G. A.,
#' Shtienberg, D., Grunwald, N. J., Chacon, M. G., Taipe, M. V., Hijmans, R.
#' J., and Fry, W. E. 2005c. Qualification of a plant disease simulation model:
#' Performance of the LATEBLIGHT model across a broad range of environments.
#' Phytopathology 95:1412-1422.\cr Bruhn, J.A., Bruck, R.I., Fry, W.E.,
#' Arneson, P.A., and Keokosky, E.V. 1980. User's manual for LATEBLIGHT: a
#' plant disease management game. Cornell University, Department of Plant
#' Pathology, Ithaca, NY, USA. Mimeo 80-1.\cr Bruhn, J.A., and Fry, W.E. 1981.
#' Analysis of potato late blight epidemiology by simulation modeling.
#' Phytopathology 71:612-616.\cr Doster, M. A., Milgroom, M. G., and Fry, W. E.
#' 1990. Quantification of factors influencing potato late blight suppression
#' and selection for metalaxyl resistance in Phytophthora infestans - A
#' simulation approach. Phytopathology 80:1190-1198.\cr Fry, W.E., Milgroom,
#' M.G., Doster, M.A., Bruhn, J.A., and Bruck, R.I. 1991. LATEBLIGHT: a plant
#' disease management game - User Manual. Version 3.1. Microsoft Windows
#' Adaptation by B. E. Ticknor, and P. A. Arneson. Ithaca, Cornell University,
#' Department of Plant Pathology, Ithaca, NY, USA.
#' @keywords models
#' @importFrom graphics plot
#' @export
#' @examples
#'
#' library(agricolae)
#' f <- system.file("external/weather.csv", package="agricolae")
#' weather <- read.csv(f,header=FALSE)
#' f <- system.file("external/severity.csv", package="agricolae")
#' severity <- read.csv(f)
#' weather[,1]<-as.Date(weather[,1],format = "%m/%d/%Y")
#' # Parameters dates
#' dates<-c("2000-03-25","2000-04-09","2000-04-12","2000-04-16","2000-04-22")
#' dates<-as.Date(dates)
#' EmergDate <- as.Date('2000/01/19')
#' EndEpidDate <- as.Date("2000-04-22")
#' dates<-as.Date(dates)
#' NoReadingsH<- 1
#' RHthreshold <- 90
#' WS<-weatherSeverity(weather,severity,dates,EmergDate,EndEpidDate,
#' NoReadingsH,RHthreshold)
#' # Parameters Lateblight
#' InocDate<-"2000-03-18"
#' LGR <- 0.00410
#' IniSpor <- 0
#' SR <- 292000000
#' IE <- 1.0
#' LP <- 2.82
#' InMicCol <- 9
#' Cultivar <- 'NICOLA'
#' ApplSys <- "NOFUNGICIDE"
#' main<-"Cultivar: NICOLA"
#' #--------------------------
#' model<-lateblight(WS, Cultivar,ApplSys, InocDate, LGR,IniSpor,SR,IE, LP,
#' MatTime='LATESEASON',InMicCol,main=main,type="l",xlim=c(65,95),lwd=1.5,
#' xlab="Time (days after emergence)", ylab="Severity (Percentage)")
#' # reproduce graph
#' x<- model$Ofile$nday
#' y<- model$Ofile$SimSeverity
#' w<- model$Gfile$nday
#' z<- model$Gfile$MeanSeverity
#' Min<-model$Gfile$MinObs
#' Max<-model$Gfile$MaxObs
#' plot(x,y,type="l",xlim=c(65,95),lwd=1.5,xlab="Time (days after emergence)",
#' ylab="Severity (Percentage)")
#' points(w,z,col="blue",cex=1,pch=19)
#' npoints <- length(w)
#' for ( i in 1:npoints){
#' segments(w[i],Min[i],w[i],Max[i],lwd=1.5,col="blue")
#' }
#' legend("topleft",c("Disease progress curves","Weather-Severity"),
#' title="Description",lty=1,pch=c(3,19),col=c("black","blue"))
#'
#'
lateblight <-
function(WS, Cultivar,ApplSys,InocDate, LGR,IniSpor,SR,IE, LP, InMicCol,
MatTime=c('EARLYSEASON','MIDSEASON','LATESEASON'),...)
{
MatTime <- toupper(MatTime)
MatTime <- match.arg(MatTime)
Cultivar <- toupper(Cultivar)
ApplSys <- toupper(ApplSys)
MatTime <- toupper(MatTime)
Wfile<-WS$Wfile
Sfile<-WS$Sfile
EmergDate<-WS$EmergDate
EndEpidDate<-WS$EndEpidDate
# Start model lateblight
SeLenght <- nrow(Wfile)
# Obtaining nInocDay
nInocDayFile <-subset(Wfile, Wfile[,1] <= InocDate)
nInocDay <- nrow( nInocDayFile )
# End obtaining nInocDay
# Obtaining date after emergence
nday<-as.numeric(Wfile[,1]-EmergDate)
ModelFile<-data.frame(EmergDate,Date=Wfile[,1],nday,InocDate,EndEpidDate)
# End obtaining date after emergence
# Initialization and declaration of variables
ActAr <- 0
Age <- 0
Area <- 0
Catch <- 0
ColFct <- 0
ConstLfWet <- 3
DenFct <- 0
DirGrRt <- 0
Divisor <- 0
ExpCol <- 0
fTRH <- 0
FunEff <- 1
GrmZoo <- 0
GrSprg <- 0
HoRHSpor <- 6
HotFct <- 0
HumFct <- 0
IncrFct <- 0
IndGrRt <- 0
IE <- IE
InfSpRt <- 0
InfZoRt <- 0
IniLesDia <- 0.002
lagBlPr <- 0
LeafFct <- 0
LfWetFct <- 0
LGR <- LGR
LGRRt <- 0
MaxDirGr <- 0.29
MaxIndGr <- 0.81
MaxInfSp <- 0.10
MaxInfZo <- 0.01
MaxZooGr <- 0.96
n5DayPer <- 0
nday <- 0
nDay2 <- 0
nDay3 <- 0
rLGR <- 0
rSR <- 0
SporFct <- 0
SSAfrac <- 0
Tdirect <- 0
Tindir <- 0
TmpFct <- 0
TotSpor <- 0
TZooGrm <- 0
Zoo <- 0
ZooGrRt <- 0
ZooSpr <- 6.567
LesAr<-rep(0,16)
Early <- c(0.006, 0.06, 0.19, 0.45, 0.81,1.30, 1.88, 2.20, 2.42, 2.50)
Mid <- c(0.006,0.06,0.19,0.45,0.81,1.30,1.88,2.56,2.99,3.28,3.47,3.53)
Late <- c(0.006,0.06,0.19,0.45,0.81,1.30,1.88,2.76,3.29,3.74,4.16,4.38,4.50)
NumCol <- rep(0,16)
LesDia <- rep(0,16)
ArLP <- rep(0,SeLenght)
SimSeverity <- rep(0,SeLenght)
BlPr <- rep(0,SeLenght)
AUDPC <- rep(0,SeLenght)
MicCol <- rep(0,SeLenght)
AttchSp <- rep(0,SeLenght)
LatPer <- rep(0,SeLenght)
LesExInc <- rep(0,SeLenght)
LAI <- rep(0.01,SeLenght)
LPAux <- 0
rLP <- rep(0,SeLenght)
InvrLP <- rep(0,SeLenght)
Defol <- rep(0,SeLenght)
InArea <- rep(0,SeLenght)
TotCol <- rep(0,SeLenght)
Sprg <- rep(0,SeLenght)
SSA <- rep(0,SeLenght)
# End initialization and declaration of variables
# Start simulation
Rainfall <- Wfile$Rainfall
Tmp <- Wfile$Tmp
HumidTmp <- Wfile$humidtmp
HumidHrs <- Wfile$HumidHrs
for( nday in 2:(SeLenght)) {
# set work.Wfile (Obs = &SeLenght);
if( !is.na( Rainfall[nday]) ){
Rainfall[nday] <- Rainfall[nday] / 10
# To go from rainfall in mm to rainfall in cm(for the fungicide sub-model)
}
# Temperature-dependent latent period;
LPAux <- LP
if( !is.na( Tmp[nday]) ){
rLP[nday] <- 4.571693 - 0.310652 * Tmp[nday]+ 0.006755 * Tmp[nday]^2
InvrLP[nday] <- 1 / rLP[nday]
}
for (nDay2 in 1:(nday - 1)) {
ArLP[nDay2] <- ArLP[nDay2] + InvrLP[nday];
}
nDay3 <- 0
for (nDay2 in 1:(nday - 1)) {
if (ArLP[nDay2] > (LPAux - 1)) nDay3 <- nDay2
}
if (nDay3 > 0) {
SSAfrac <- LPAux - ArLP[nDay3]
LatPer[nday] <- nday - nDay3
}
if (nDay3 <= 0) {
SSAfrac <- 1
if(InvrLP[nday]!=0)LatPer[nday] <- round(LPAux/InvrLP[nday],0)
}
if( !is.na( LatPer[nday]) ){
if (LatPer[nday] < 2) LatPer[nday] <- 2
if (LatPer[nday] > 8) LatPer[nday] <- 8
if (InvrLP[nday] < 0) {
SSAfrac <- 1
LatPer[nday] <- round(LPAux,0)
}
}
# End of the temperature-dependent latent period
# Environment sub-model
LfWetHrs <- HumidHrs[nday] + ConstLfWet
LfWetHrs <- max (0.0, LfWetHrs)
LfWetHrs <- min (24.0, LfWetHrs)
LfWetTmp <- (Tmp[nday] + HumidTmp[nday]) / 2.0
# End environment sub-model
# Pathogen sub-model
# Infection
Sprg[nday] <- Sprg[nday-1] + IniSpor
if (nday > nInocDay) IniSpor <- 0
# Direct germination of sporangia
if( !is.na( LfWetTmp ) ){
if ((LfWetTmp>=14.0) & (LfWetTmp <= 29.0)) {
TDirect <- 2.1 +LfWetTmp *(-.48 + LfWetTmp*(.031 +
LfWetTmp*(-4.5E-04 -4.4E-06*LfWetTmp)))
DirGrRt <- Tdirect * MaxDirGr
GrSprg <- Sprg[nday] * DirGrRt
}
else GrSprg <- 0.0
# Formation of zoospores
if ((LfWetTmp >= 0.0) & (LfWetTmp <= 24.0)) {
TIndir <- 1.314E-02 + LfWetTmp * (6.98E-02 + LfWetTmp*(1.815E-02 +
LfWetTmp * (-2.23E-03 + LfWetTmp * (7.48E-05 - 7.68E-07 * LfWetTmp))))
IndGrRt <- TIndir * MaxIndGr * ZooSpr
Zoo <- Sprg[nday] * IndGrRt
}
else Zoo <- 0.0
# Germination of zoospores
if (( LfWetTmp >= 0.0) & ( LfWetTmp <= 28)) {
TZooGrm <- 4.94E-03 + LfWetTmp*(.364 + LfWetTmp*(-5.813E-02 + LfWetTmp*
(4.52E-03 + LfWetTmp*(-1.617E-04 + 2.068E-06 * LfWetTmp))))
ZooGrRt <- TZooGrm * MaxZooGr
GrmZoo <- Zoo * ZooGrRt
}
else GrmZoo <- 0.0
}
# Calculation of LfWetFct
if( !is.na( HumidTmp[nday] ) ){
TmpFct <- min (1.0, 0.03 * HumidTmp[nday] + 0.4)
if ((LfWetHrs * TmpFct) > 8.0 ) {
LfWetFct <- min (0.8 + 0.0125 * (LfWetHrs * TmpFct - 8.0), 1.0)
}
else LfWetFct <- max (0.0, 0.16 * (LfWetHrs * TmpFct - 3.0))
# Infection;
if (( HumidTmp[nday] >= 1.5) & (HumidTmp[nday] <= 30.0) ) {
fTRH <- -.4846 + HumidTmp[nday] * (.3735 + HumidTmp[nday]*(-3.556E-02 +
HumidTmp[nday] * (1.509E-03 - 2.405E-05 * HumidTmp[nday])))
InfSpRt <- MaxInfSp * fTRH * LfWetFct * FunEff * IE
InfZoRt <- MaxInfZo * fTRH * LfWetFct * FunEff * IE
MicCol[nday] <- GrSprg * InfSpRt + GrmZoo * InfZoRt
}
else MicCol[nday] <- 0.0
}
# End of infection
if (nday == nInocDay) {
MicCol[nday] <- InMicCol
NumCol[1] <- MicCol[nday]
}
# Lesion expansion
if( !is.na( Tmp[nday] ) ){
if ( (Tmp[nday] >= 8) & (Tmp[nday] <= 32.94)) {
rLGR<-0.126636-0.038378*Tmp[nday]+0.008593*Tmp[nday]^2-0.000229*Tmp[nday]^3
}
if ((Tmp[nday] >= 4.09) & (Tmp[nday] < 8))rLGR<-0.064246*Tmp[nday]-0.262690
if (Tmp[nday] > 32.94) rLGR <- 0.0
if (Tmp[nday] < 4.09) rLGR <- 0.0
LesExInc[nday] <- rLGR * LGR
}
# Effect of disease density
if (nday > nInocDay ) {
for( Age in seq(16,2,-1)) NumCol[Age] <- NumCol[Age - 1]
}
if (nday > nInocDay) {
NumCol[1] <- MicCol[nday]
}
# New latent periods
if( !is.na( LatPer[nday] ) ){
if (LatPer[nday] == 2) ExpCol <- sum( NumCol[2:15] )
if (LatPer[nday] == 3) ExpCol <- sum( NumCol[3:15] )
if (LatPer[nday] == 4) ExpCol <- sum( NumCol[4:15] )
if (LatPer[nday] == 5) ExpCol <- sum( NumCol[5:15] )
if (LatPer[nday] == 6) ExpCol <- sum( NumCol[6:15] )
if (LatPer[nday] == 7) ExpCol <- sum( NumCol[7:15] )
if (LatPer[nday] == 8) ExpCol <- sum( NumCol[8:15] )
if(ExpCol>0.0)ColFct <-min(1.0, 2.7 - 0.32*log10(ExpCol)) else ColFct <- 0.0
}
# Calibrating the effect of BlPr on lesion growth
LeafFct <- ifelse(BlPr[nday] < 0.82, exp (-2.1 * BlPr[nday-1]),
exp (-2.1 * 0.9 * BlPr[nday-1]))
DenFct <- max (0.0, ColFct * LeafFct)
# Update inactive lesion area by adding in class 15
# Calculate lesion diameter
if( !is.na( LatPer[nday] ) ){
for( Age in seq(16,LatPer[nday],-1)) {
IncrFct <- max (0.0, min (1.0, 1.72 - (0.12 * (Age - 1))))
LGRRt <- LesExInc[nday] * IncrFct * DenFct
LesDia[Age] <- LesDia[Age-1] + LGRRt
}
LesDia[LatPer[nday]] <- IniLesDia * DenFct
# Calculate lesion area;
for( Age in LatPer[nday]:16) {
LesAr[Age] <- NumCol[Age] * ((LesDia[Age]^2) * 0.785398163397448)
}
ActAr <- sum(LesAr[1:15])
InArea[nday] <-InArea[nday-1] + LesAr[16]
Defol[nday] <- ActAr + InArea[nday]
}
# End lesion expansion
# Sporulation
if( !is.na( HumidHrs[nday] ) & !is.na(HumidTmp[nday] )){
HumFct <- min(max (((HumidHrs[nday] - HoRHSpor) / (24 - HoRHSpor)), 0), 1)
SSA[nday] <- SSAfrac * LesAr[LatPer[nday]]
for (Age in (LatPer[nday] + 1):15) {
SSA[nday]<-SSA[nday] + LesAr[Age]
}
if ((HumidTmp[nday] >= 7.26) & (HumidTmp[nday] <= 25.29))rSR <- -4.796667 +
1.021578*HumidTmp[nday]-0.056437*HumidTmp[nday]^2+0.000931*HumidTmp[nday]^3
if (HumidTmp[nday] > 25.29) rSR <- 0.0
if (HumidTmp[nday] < 7.26) rSR <- 0.0
if (rSR > 0) {
SporFct <- max(0, (.36 * SR * rSR * HumFct))
AttchSp[nday] <- SporFct * SSA[nday]
}
if (rSR <= 0) {
SporFct <- 0
AttchSp[nday] <- 0
}
TotSpor <- TotSpor + AttchSp[nday]
}
# End sporulation
# Dispersal
if( !is.na( Tmp[nday] ) ){
if (Tmp[nday] > 21) HotFct <- exp (6 * (- 0.0256 * Tmp[nday] + .537))
else HotFct <- 1
Catch <- .025 * (1 - BlPr[nday]) * HotFct
Sprg[nday] <- Catch * AttchSp[nday]
}
# End dispersal
# End pathogen sub-model
# Disease sub-model
BlPr[nday] <- ifelse(BlPr[nday]>0.975, 1,max((Defol[nday]/LAI[nday-1]),0))
if (BlPr[nday] > 1 ) BlPr[nday] <- 1
SimSeverity[nday] <- BlPr[nday] * 100
lagBlPr <- BlPr[nday-1]
Area <- ((BlPr[nday] + lagBlPr) / 2)
AUDPC[nday] <- AUDPC[nday-1] + Area
TotCol[nday] <- TotCol[nday-1] + MicCol[nday]
# End disease sub-model
# Host growth sub-model
n5DayPer <- nday %/% 5 + 1
Divisor <- n5DayPer - 1
if (MatTime=="EARLYSEASON")LAI[nday]<-Early[n5DayPer]+(((Early[n5DayPer + 1]
- Early[n5DayPer]) / 5) * (nday-5*Divisor))
if ( (MatTime == "EARLYSEASON")&(nday > 44))LAI[nday] <- 2.5
if ( MatTime=="MIDSEASON" )LAI[nday]<- Mid[n5DayPer] + (((Mid[n5DayPer + 1]
- Mid[n5DayPer]) / 5) * (nday - 5 * Divisor))
if ( (MatTime == "MIDSEASON") &(nday > 52)) LAI[nday] <- 3.5
if (MatTime=="LATESEASON" )LAI[nday]<-Late[n5DayPer]+ (((Late[n5DayPer + 1]
- Late[n5DayPer]) / 5) * (nday - 5 * Divisor))
if ( (MatTime == "LATESEASON") & (nday > 59) ) LAI[nday] <- 4.5
# End host growth sub-model
}
# End simulation
Ofile <- data.frame(Date=ModelFile$Date,nday=ModelFile$nday,
MicCol,SimSeverity,LAI, LatPer,LesExInc,AttchSp , AUDPC = AUDPC * 100,
rLP,InvrLP,BlPr,Defol)
# graphing disease progress curves
Gfile <-subset(Ofile,((Ofile$ApplSys == ApplSys )& ( Ofile$Cultivar == Cultivar)))
MinObs <- Sfile$MeanSeverity - Sfile$StDevSeverity
MaxObs <- Sfile$MeanSeverity + Sfile$StDevSeverity
Gfile <- data.frame(Sfile[,c(-1,-2)],MinObs,MaxObs)
plot(Ofile$nday,Ofile$SimSeverity,...)
points(Gfile$nday,Gfile$MeanSeverity,col="blue",cex=1,pch=19)
npoints <- nrow( Gfile )
for ( i in 1:npoints){
segments(Gfile[i,2],Gfile[i,5],Gfile[i,2],Gfile[i,6],lwd=1.5,col="blue")
}
legend("topleft",c("Disease progress curves","Weather-Severity"),
title=MatTime,lty=1,pch=c(3,19),col=c("black","blue"),cex=1)
# End graphing disease progress curves
return( list(Ofile = Ofile, Gfile=Gfile ))
}
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Defines functions lateblight
Documented in lateblight
#' LATEBLIGHT - Simulator for potato late blight Version LB2004
#'
#' LATEBLIGHT is a mathematical model that simulates the effect of weather,
#' host growth and resistance, and fungicide use on asexual development and
#' growth of Phytophthora infestans on potato foliage.
#'
#' LATEBLIGHT Version LB2004 was created in October 2004 (Andrade-Piedra et
#' al., 2005a, b and c), based on the C-version written by B.E. Ticknor ('BET
#' 21191 modification of cbm8d29.c'), reported by Doster et al. (1990) and
#' described in detail by Fry et al. (1991) (This version is referred as LB1990
#' by Andrade-Piedra et al. [2005a]). The first version of LATEBLIGHT was
#' developed by Bruhn and Fry (1981) and described in detail by Bruhn et al.
#' (1980).
#'
#' @param WS object weather-severity
#' @param Cultivar chr
#' @param ApplSys chr
#' @param InocDate days
#' @param LGR num, see example
#' @param IniSpor num
#' @param SR num, see example
#' @param IE num, Initialization infection
#' @param LP num, latent period
#' @param InMicCol num
#' @param MatTime chr
#' @param \dots plot graphics parameters
#' @return \item{Ofile}{"Date","nday","MicCol","SimSeverity",...}
#' \item{Gfile}{"dates","nday","MeanSeverity","StDevSeverity"}
#' @note All format data for date is yyyy-mm,dd, for example "2000-04-22".
#' change with function as.Date()
#' @author Jorge L. Andrade-Piedra (1) (j.andrade@@cgar.org), Gregory A. Forbes
#' (1) (g.forbes@@cgiar.org), Robert J. Hijmans (2) (rhijmans@@ucdavis.edu),
#' William E. Fry (3) (wef1@@cornell.edu) Translation from C language into SAS
#' language: G.A. Forbes Modifications: J.L. Andrade-Piedra and R.J. Hijmans
#' Translation from SAS into R: Felipe de Mendiburu (1) (1) International
#' Potato Center, P.O. Box 1558, Lima 12, Peru (2) University of California,
#' One Shields Avenue, Davis, California 95616, USA (3) Cornell University, 351
#' Plant Science, Ithaca, NY 14853, USA
#' @seealso \code{\link{weatherSeverity}}
#' @references Andrade-Piedra, J. L., Hijmans, R. J., Forbes, G. A., Fry, W.
#' E., and Nelson, R. J. 2005a. Simulation of potato late blight in the Andes.
#' I: Modification and parameterization of the LATEBLIGHT model. Phytopathology
#' 95:1191-1199.\cr Andrade-Piedra, J. L., Hijmans, R. J., Juarez, H. S.,
#' Forbes, G. A., Shtienberg, D., and Fry, W. E. 2005b. Simulation of potato
#' late blight in the Andes. II: Validation of the LATEBLIGHT model.
#' Phytopathology 95:1200-1208.\cr Andrade-Piedra, J. L., Forbes, G. A.,
#' Shtienberg, D., Grunwald, N. J., Chacon, M. G., Taipe, M. V., Hijmans, R.
#' J., and Fry, W. E. 2005c. Qualification of a plant disease simulation model:
#' Performance of the LATEBLIGHT model across a broad range of environments.
#' Phytopathology 95:1412-1422.\cr Bruhn, J.A., Bruck, R.I., Fry, W.E.,
#' Arneson, P.A., and Keokosky, E.V. 1980. User's manual for LATEBLIGHT: a
#' plant disease management game. Cornell University, Department of Plant
#' Pathology, Ithaca, NY, USA. Mimeo 80-1.\cr Bruhn, J.A., and Fry, W.E. 1981.
#' Analysis of potato late blight epidemiology by simulation modeling.
#' Phytopathology 71:612-616.\cr Doster, M. A., Milgroom, M. G., and Fry, W. E.
#' 1990. Quantification of factors influencing potato late blight suppression
#' and selection for metalaxyl resistance in Phytophthora infestans - A
#' simulation approach. Phytopathology 80:1190-1198.\cr Fry, W.E., Milgroom,
#' M.G., Doster, M.A., Bruhn, J.A., and Bruck, R.I. 1991. LATEBLIGHT: a plant
#' disease management game - User Manual. Version 3.1. Microsoft Windows
#' Adaptation by B. E. Ticknor, and P. A. Arneson. Ithaca, Cornell University,
#' Department of Plant Pathology, Ithaca, NY, USA.
#' @keywords models
#' @importFrom graphics plot
#' @export
#' @examples
#'
#' library(agricolae)
#' f <- system.file("external/weather.csv", package="agricolae")
#' weather <- read.csv(f,header=FALSE)
#' f <- system.file("external/severity.csv", package="agricolae")
#' severity <- read.csv(f)
#' weather[,1]<-as.Date(weather[,1],format = "%m/%d/%Y")
#' # Parameters dates
#' dates<-c("2000-03-25","2000-04-09","2000-04-12","2000-04-16","2000-04-22")
#' dates<-as.Date(dates)
#' EmergDate <- as.Date('2000/01/19')
#' EndEpidDate <- as.Date("2000-04-22")
#' dates<-as.Date(dates)
#' NoReadingsH<- 1
#' RHthreshold <- 90
#' WS<-weatherSeverity(weather,severity,dates,EmergDate,EndEpidDate,
#' NoReadingsH,RHthreshold)
#' # Parameters Lateblight
#' InocDate<-"2000-03-18"
#' LGR <- 0.00410
#' IniSpor <- 0
#' SR <- 292000000
#' IE <- 1.0
#' LP <- 2.82
#' InMicCol <- 9
#' Cultivar <- 'NICOLA'
#' ApplSys <- "NOFUNGICIDE"
#' main<-"Cultivar: NICOLA"
#' #--------------------------
#' model<-lateblight(WS, Cultivar,ApplSys, InocDate, LGR,IniSpor,SR,IE, LP,
#' MatTime='LATESEASON',InMicCol,main=main,type="l",xlim=c(65,95),lwd=1.5,
#' xlab="Time (days after emergence)", ylab="Severity (Percentage)")
#' # reproduce graph
#' x<- model$Ofile$nday
#' y<- model$Ofile$SimSeverity
#' w<- model$Gfile$nday
#' z<- model$Gfile$MeanSeverity
#' Min<-model$Gfile$MinObs
#' Max<-model$Gfile$MaxObs
#' plot(x,y,type="l",xlim=c(65,95),lwd=1.5,xlab="Time (days after emergence)",
#' ylab="Severity (Percentage)")
#' points(w,z,col="blue",cex=1,pch=19)
#' npoints <- length(w)
#' for ( i in 1:npoints){
#' segments(w[i],Min[i],w[i],Max[i],lwd=1.5,col="blue")
#' }
#' legend("topleft",c("Disease progress curves","Weather-Severity"),
#' title="Description",lty=1,pch=c(3,19),col=c("black","blue"))
#'
#'
lateblight <-
function(WS, Cultivar,ApplSys,InocDate, LGR,IniSpor,SR,IE, LP, InMicCol,
MatTime=c('EARLYSEASON','MIDSEASON','LATESEASON'),...)
{
MatTime <- toupper(MatTime)
MatTime <- match.arg(MatTime)
Cultivar <- toupper(Cultivar)
ApplSys <- toupper(ApplSys)
MatTime <- toupper(MatTime)
Wfile<-WS$Wfile
Sfile<-WS$Sfile
EmergDate<-WS$EmergDate
EndEpidDate<-WS$EndEpidDate
# Start model lateblight
SeLenght <- nrow(Wfile)
# Obtaining nInocDay
nInocDayFile <-subset(Wfile, Wfile[,1] <= InocDate)
nInocDay <- nrow( nInocDayFile )
# End obtaining nInocDay
# Obtaining date after emergence
nday<-as.numeric(Wfile[,1]-EmergDate)
ModelFile<-data.frame(EmergDate,Date=Wfile[,1],nday,InocDate,EndEpidDate)
# End obtaining date after emergence
# Initialization and declaration of variables
ActAr <- 0
Age <- 0
Area <- 0
Catch <- 0
ColFct <- 0
ConstLfWet <- 3
DenFct <- 0
DirGrRt <- 0
Divisor <- 0
ExpCol <- 0
fTRH <- 0
FunEff <- 1
GrmZoo <- 0
GrSprg <- 0
HoRHSpor <- 6
HotFct <- 0
HumFct <- 0
IncrFct <- 0
IndGrRt <- 0
IE <- IE
InfSpRt <- 0
InfZoRt <- 0
IniLesDia <- 0.002
lagBlPr <- 0
LeafFct <- 0
LfWetFct <- 0
LGR <- LGR
LGRRt <- 0
MaxDirGr <- 0.29
MaxIndGr <- 0.81
MaxInfSp <- 0.10
MaxInfZo <- 0.01
MaxZooGr <- 0.96
n5DayPer <- 0
nday <- 0
nDay2 <- 0
nDay3 <- 0
rLGR <- 0
rSR <- 0
SporFct <- 0
SSAfrac <- 0
Tdirect <- 0
Tindir <- 0
TmpFct <- 0
TotSpor <- 0
TZooGrm <- 0
Zoo <- 0
ZooGrRt <- 0
ZooSpr <- 6.567
LesAr<-rep(0,16)
Early <- c(0.006, 0.06, 0.19, 0.45, 0.81,1.30, 1.88, 2.20, 2.42, 2.50)
Mid <- c(0.006,0.06,0.19,0.45,0.81,1.30,1.88,2.56,2.99,3.28,3.47,3.53)
Late <- c(0.006,0.06,0.19,0.45,0.81,1.30,1.88,2.76,3.29,3.74,4.16,4.38,4.50)
NumCol <- rep(0,16)
LesDia <- rep(0,16)
ArLP <- rep(0,SeLenght)
SimSeverity <- rep(0,SeLenght)
BlPr <- rep(0,SeLenght)
AUDPC <- rep(0,SeLenght)
MicCol <- rep(0,SeLenght)
AttchSp <- rep(0,SeLenght)
LatPer <- rep(0,SeLenght)
LesExInc <- rep(0,SeLenght)
LAI <- rep(0.01,SeLenght)
LPAux <- 0
rLP <- rep(0,SeLenght)
InvrLP <- rep(0,SeLenght)
Defol <- rep(0,SeLenght)
InArea <- rep(0,SeLenght)
TotCol <- rep(0,SeLenght)
Sprg <- rep(0,SeLenght)
SSA <- rep(0,SeLenght)
# End initialization and declaration of variables
# Start simulation
Rainfall <- Wfile$Rainfall
Tmp <- Wfile$Tmp
HumidTmp <- Wfile$humidtmp
HumidHrs <- Wfile$HumidHrs
for( nday in 2:(SeLenght)) {
# set work.Wfile (Obs = &SeLenght);
if( !is.na( Rainfall[nday]) ){
Rainfall[nday] <- Rainfall[nday] / 10
# To go from rainfall in mm to rainfall in cm(for the fungicide sub-model)
}
# Temperature-dependent latent period;
LPAux <- LP
if( !is.na( Tmp[nday]) ){
rLP[nday] <- 4.571693 - 0.310652 * Tmp[nday]+ 0.006755 * Tmp[nday]^2
InvrLP[nday] <- 1 / rLP[nday]
}
for (nDay2 in 1:(nday - 1)) {
ArLP[nDay2] <- ArLP[nDay2] + InvrLP[nday];
}
nDay3 <- 0
for (nDay2 in 1:(nday - 1)) {
if (ArLP[nDay2] > (LPAux - 1)) nDay3 <- nDay2
}
if (nDay3 > 0) {
SSAfrac <- LPAux - ArLP[nDay3]
LatPer[nday] <- nday - nDay3
}
if (nDay3 <= 0) {
SSAfrac <- 1
if(InvrLP[nday]!=0)LatPer[nday] <- round(LPAux/InvrLP[nday],0)
}
if( !is.na( LatPer[nday]) ){
if (LatPer[nday] < 2) LatPer[nday] <- 2
if (LatPer[nday] > 8) LatPer[nday] <- 8
if (InvrLP[nday] < 0) {
SSAfrac <- 1
LatPer[nday] <- round(LPAux,0)
}
}
# End of the temperature-dependent latent period
# Environment sub-model
LfWetHrs <- HumidHrs[nday] + ConstLfWet
LfWetHrs <- max (0.0, LfWetHrs)
LfWetHrs <- min (24.0, LfWetHrs)
LfWetTmp <- (Tmp[nday] + HumidTmp[nday]) / 2.0
# End environment sub-model
# Pathogen sub-model
# Infection
Sprg[nday] <- Sprg[nday-1] + IniSpor
if (nday > nInocDay) IniSpor <- 0
# Direct germination of sporangia
if( !is.na( LfWetTmp ) ){
if ((LfWetTmp>=14.0) & (LfWetTmp <= 29.0)) {
TDirect <- 2.1 +LfWetTmp *(-.48 + LfWetTmp*(.031 +
LfWetTmp*(-4.5E-04 -4.4E-06*LfWetTmp)))
DirGrRt <- Tdirect * MaxDirGr
GrSprg <- Sprg[nday] * DirGrRt
}
else GrSprg <- 0.0
# Formation of zoospores
if ((LfWetTmp >= 0.0) & (LfWetTmp <= 24.0)) {
TIndir <- 1.314E-02 + LfWetTmp * (6.98E-02 + LfWetTmp*(1.815E-02 +
LfWetTmp * (-2.23E-03 + LfWetTmp * (7.48E-05 - 7.68E-07 * LfWetTmp))))
IndGrRt <- TIndir * MaxIndGr * ZooSpr
Zoo <- Sprg[nday] * IndGrRt
}
else Zoo <- 0.0
# Germination of zoospores
if (( LfWetTmp >= 0.0) & ( LfWetTmp <= 28)) {
TZooGrm <- 4.94E-03 + LfWetTmp*(.364 + LfWetTmp*(-5.813E-02 + LfWetTmp*
(4.52E-03 + LfWetTmp*(-1.617E-04 + 2.068E-06 * LfWetTmp))))
ZooGrRt <- TZooGrm * MaxZooGr
GrmZoo <- Zoo * ZooGrRt
}
else GrmZoo <- 0.0
}
# Calculation of LfWetFct
if( !is.na( HumidTmp[nday] ) ){
TmpFct <- min (1.0, 0.03 * HumidTmp[nday] + 0.4)
if ((LfWetHrs * TmpFct) > 8.0 ) {
LfWetFct <- min (0.8 + 0.0125 * (LfWetHrs * TmpFct - 8.0), 1.0)
}
else LfWetFct <- max (0.0, 0.16 * (LfWetHrs * TmpFct - 3.0))
# Infection;
if (( HumidTmp[nday] >= 1.5) & (HumidTmp[nday] <= 30.0) ) {
fTRH <- -.4846 + HumidTmp[nday] * (.3735 + HumidTmp[nday]*(-3.556E-02 +
HumidTmp[nday] * (1.509E-03 - 2.405E-05 * HumidTmp[nday])))
InfSpRt <- MaxInfSp * fTRH * LfWetFct * FunEff * IE
InfZoRt <- MaxInfZo * fTRH * LfWetFct * FunEff * IE
MicCol[nday] <- GrSprg * InfSpRt + GrmZoo * InfZoRt
}
else MicCol[nday] <- 0.0
}
# End of infection
if (nday == nInocDay) {
MicCol[nday] <- InMicCol
NumCol[1] <- MicCol[nday]
}
# Lesion expansion
if( !is.na( Tmp[nday] ) ){
if ( (Tmp[nday] >= 8) & (Tmp[nday] <= 32.94)) {
rLGR<-0.126636-0.038378*Tmp[nday]+0.008593*Tmp[nday]^2-0.000229*Tmp[nday]^3
}
if ((Tmp[nday] >= 4.09) & (Tmp[nday] < 8))rLGR<-0.064246*Tmp[nday]-0.262690
if (Tmp[nday] > 32.94) rLGR <- 0.0
if (Tmp[nday] < 4.09) rLGR <- 0.0
LesExInc[nday] <- rLGR * LGR
}
# Effect of disease density
if (nday > nInocDay ) {
for( Age in seq(16,2,-1)) NumCol[Age] <- NumCol[Age - 1]
}
if (nday > nInocDay) {
NumCol[1] <- MicCol[nday]
}
# New latent periods
if( !is.na( LatPer[nday] ) ){
if (LatPer[nday] == 2) ExpCol <- sum( NumCol[2:15] )
if (LatPer[nday] == 3) ExpCol <- sum( NumCol[3:15] )
if (LatPer[nday] == 4) ExpCol <- sum( NumCol[4:15] )
if (LatPer[nday] == 5) ExpCol <- sum( NumCol[5:15] )
if (LatPer[nday] == 6) ExpCol <- sum( NumCol[6:15] )
if (LatPer[nday] == 7) ExpCol <- sum( NumCol[7:15] )
if (LatPer[nday] == 8) ExpCol <- sum( NumCol[8:15] )
if(ExpCol>0.0)ColFct <-min(1.0, 2.7 - 0.32*log10(ExpCol)) else ColFct <- 0.0
}
# Calibrating the effect of BlPr on lesion growth
LeafFct <- ifelse(BlPr[nday] < 0.82, exp (-2.1 * BlPr[nday-1]),
exp (-2.1 * 0.9 * BlPr[nday-1]))
DenFct <- max (0.0, ColFct * LeafFct)
# Update inactive lesion area by adding in class 15
# Calculate lesion diameter
if( !is.na( LatPer[nday] ) ){
for( Age in seq(16,LatPer[nday],-1)) {
IncrFct <- max (0.0, min (1.0, 1.72 - (0.12 * (Age - 1))))
LGRRt <- LesExInc[nday] * IncrFct * DenFct
LesDia[Age] <- LesDia[Age-1] + LGRRt
}
LesDia[LatPer[nday]] <- IniLesDia * DenFct
# Calculate lesion area;
for( Age in LatPer[nday]:16) {
LesAr[Age] <- NumCol[Age] * ((LesDia[Age]^2) * 0.785398163397448)
}
ActAr <- sum(LesAr[1:15])
InArea[nday] <-InArea[nday-1] + LesAr[16]
Defol[nday] <- ActAr + InArea[nday]
}
# End lesion expansion
# Sporulation
if( !is.na( HumidHrs[nday] ) & !is.na(HumidTmp[nday] )){
HumFct <- min(max (((HumidHrs[nday] - HoRHSpor) / (24 - HoRHSpor)), 0), 1)
SSA[nday] <- SSAfrac * LesAr[LatPer[nday]]
for (Age in (LatPer[nday] + 1):15) {
SSA[nday]<-SSA[nday] + LesAr[Age]
}
if ((HumidTmp[nday] >= 7.26) & (HumidTmp[nday] <= 25.29))rSR <- -4.796667 +
1.021578*HumidTmp[nday]-0.056437*HumidTmp[nday]^2+0.000931*HumidTmp[nday]^3
if (HumidTmp[nday] > 25.29) rSR <- 0.0
if (HumidTmp[nday] < 7.26) rSR <- 0.0
if (rSR > 0) {
SporFct <- max(0, (.36 * SR * rSR * HumFct))
AttchSp[nday] <- SporFct * SSA[nday]
}
if (rSR <= 0) {
SporFct <- 0
AttchSp[nday] <- 0
}
TotSpor <- TotSpor + AttchSp[nday]
}
# End sporulation
# Dispersal
if( !is.na( Tmp[nday] ) ){
if (Tmp[nday] > 21) HotFct <- exp (6 * (- 0.0256 * Tmp[nday] + .537))
else HotFct <- 1
Catch <- .025 * (1 - BlPr[nday]) * HotFct
Sprg[nday] <- Catch * AttchSp[nday]
}
# End dispersal
# End pathogen sub-model
# Disease sub-model
BlPr[nday] <- ifelse(BlPr[nday]>0.975, 1,max((Defol[nday]/LAI[nday-1]),0))
if (BlPr[nday] > 1 ) BlPr[nday] <- 1
SimSeverity[nday] <- BlPr[nday] * 100
lagBlPr <- BlPr[nday-1]
Area <- ((BlPr[nday] + lagBlPr) / 2)
AUDPC[nday] <- AUDPC[nday-1] + Area
TotCol[nday] <- TotCol[nday-1] + MicCol[nday]
# End disease sub-model
# Host growth sub-model
n5DayPer <- nday %/% 5 + 1
Divisor <- n5DayPer - 1
if (MatTime=="EARLYSEASON")LAI[nday]<-Early[n5DayPer]+(((Early[n5DayPer + 1]
- Early[n5DayPer]) / 5) * (nday-5*Divisor))
if ( (MatTime == "EARLYSEASON")&(nday > 44))LAI[nday] <- 2.5
if ( MatTime=="MIDSEASON" )LAI[nday]<- Mid[n5DayPer] + (((Mid[n5DayPer + 1]
- Mid[n5DayPer]) / 5) * (nday - 5 * Divisor))
if ( (MatTime == "MIDSEASON") &(nday > 52)) LAI[nday] <- 3.5
if (MatTime=="LATESEASON" )LAI[nday]<-Late[n5DayPer]+ (((Late[n5DayPer + 1]
- Late[n5DayPer]) / 5) * (nday - 5 * Divisor))
if ( (MatTime == "LATESEASON") & (nday > 59) ) LAI[nday] <- 4.5
# End host growth sub-model
}
# End simulation
Ofile <- data.frame(Date=ModelFile$Date,nday=ModelFile$nday,
MicCol,SimSeverity,LAI, LatPer,LesExInc,AttchSp , AUDPC = AUDPC * 100,
rLP,InvrLP,BlPr,Defol)
# graphing disease progress curves
Gfile <-subset(Ofile,((Ofile$ApplSys == ApplSys )& ( Ofile$Cultivar == Cultivar)))
MinObs <- Sfile$MeanSeverity - Sfile$StDevSeverity
MaxObs <- Sfile$MeanSeverity + Sfile$StDevSeverity
Gfile <- data.frame(Sfile[,c(-1,-2)],MinObs,MaxObs)
plot(Ofile$nday,Ofile$SimSeverity,...)
points(Gfile$nday,Gfile$MeanSeverity,col="blue",cex=1,pch=19)
npoints <- nrow( Gfile )
for ( i in 1:npoints){
segments(Gfile[i,2],Gfile[i,5],Gfile[i,2],Gfile[i,6],lwd=1.5,col="blue")
}
legend("topleft",c("Disease progress curves","Weather-Severity"),
title=MatTime,lty=1,pch=c(3,19),col=c("black","blue"),cex=1)
# End graphing disease progress curves
return( list(Ofile = Ofile, Gfile=Gfile ))
}
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