Wheat yields in 7 years with genetic and environment covariates
Yield of Durum wheat, 7 genotypes, 6 years, with 16 genotypic variates and 16 environment variates.
data("vargas.wheat1.covs")
data("vargas.wheat1.traits")The vargas.wheat1.covs dataframe has 6 observations on the following 17 variables.
yearyear, 1990-1995
MTDMean daily max temperature December, deg C
MTJJanuary
MTFFebruary
MTMMarch
mTDMean daily minimum temperature December, deg C
mTJJanuary
mTFFebruary
mTMMarch
PRDMonthly precipitation in December, mm
PRJJanuary
PRFFebruary
PRMMarch
SHDa numeric vector
SHJJanuary
SHFFebruary
SHMMarch
The vargas.wheat1.traits dataframe has 126 observations on the following 19 variables.
yearyear, 1990-1995
repreplicate, 3 levels
gengenotype, 7 levels
yieldyield, kg/ha
ANTanthesis, days after emergence
MATmaturity, days after emergence
GFIgrainfill, MAT-ANT
PLHplant height, cm
BIObiomass above ground, kg/ha
HIDharvest index
STWstraw yield, kg/ha
NSMspikes / m^2
NGMgrains / m^2
NGSgrains per spike
TKWthousand kernel weight, g
WTIweight per tiller, g
SGWspike grain weight, g
VGRvegetative growth rate, kg/ha/day, STW/ANT
KGRkernel growth rate, mg/kernel/day
Conducted in Ciudad Obregon, Mexico.
Mateo Vargas and Jose Crossa and Ken Sayre and Matthew Renolds and Martha E Ramirez and Mike Talbot, 1998. Interpreting Genotype x Environment Interaction in Wheat by Partial Least Squares Regression, Crop Science, 38, 679–689. https://doi.org/10.2135/cropsci1998.0011183X003800030010x
Data provided by Jose Crossa.
library(agridat)
## Not run:
data(vargas.wheat1.covs)
data(vargas.wheat1.traits)
libs(pls)
libs(reshape2)
# Yield as a function of non-yield traits
Y0 <- vargas.wheat1.traits[,c('gen','rep','year','yield')]
Y0 <- acast(Y0, gen ~ year, value.var='yield', fun=mean)
Y0 <- sweep(Y0, 1, rowMeans(Y0))
Y0 <- sweep(Y0, 2, colMeans(Y0)) # GxE residuals
Y1 <- scale(Y0) # scaled columns
X1 <- vargas.wheat1.traits[, -4] # omit yield
X1 <- aggregate(cbind(ANT,MAT,GFI,PLH,BIO,HID,STW,NSM,NGM,
NGS,TKW,WTI,SGW,VGR,KGR) ~ gen, data=X1, FUN=mean)
rownames(X1) <- X1$gen
X1$gen <- NULL
X1 <- scale(X1) # scaled columns
m1 <- plsr(Y1~X1)
loadings(m1)[,1,drop=FALSE] # X loadings in Table 1 of Vargas
biplot(m1, cex=.5, which="x", var.axes=TRUE,
main="vargas.wheat1 - gen ~ trait") # Vargas figure 2a
# Yield as a function of environment covariates
Y2 <- t(Y0)
X2 <- vargas.wheat1.covs
rownames(X2) <- X2$year
X2$year <- NULL
Y2 <- scale(Y2)
X2 <- scale(X2)
m2 <- plsr(Y2~X2)
loadings(m2)[,1,drop=FALSE] # X loadings in Table 2 of Vargas
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