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")

Format

The vargas.wheat1.covs dataframe has 6 observations on the following 17 variables.

year: year, 1990-1995
MTD: Mean daily max temperature December, deg C
MTJ: January
MTF: February
MTM: March
mTD: Mean daily minimum temperature December, deg C
mTJ: January
mTF: February
mTM: March
PRD: Monthly precipitation in December, mm
PRJ: January
PRF: February
PRM: March
SHD: a numeric vector
SHJ: January
SHF: February
SHM: March

The vargas.wheat1.traits dataframe has 126 observations on the following 19 variables.

year: year, 1990-1995
rep: replicate, 3 levels
gen: genotype, 7 levels
yield: yield, kg/ha
ANT: anthesis, days after emergence
MAT: maturity, days after emergence
GFI: grainfill, MAT-ANT
PLH: plant height, cm
BIO: biomass above ground, kg/ha
HID: harvest index
STW: straw yield, kg/ha
NSM: spikes / m^2
NGM: grains / m^2
NGS: grains per spike
TKW: thousand kernel weight, g
WTI: weight per tiller, g
SGW: spike grain weight, g
VGR: vegetative growth rate, kg/ha/day, STW/ANT
KGR: kernel growth rate, mg/kernel/day

Details

Conducted in Ciudad Obregon, Mexico.

Source

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.

Examples


library(agridat)

# \dontrun{

  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
#>          Comp 1
#> ANT -0.21589859
#> MAT -0.15861226
#> GFI  0.14008021
#> PLH  0.31638335
#> BIO  0.29630833
#> HID  0.34930528
#> STW -0.03876374
#> NSM -0.03472883
#> NGM  0.34359779
#> NGS  0.35502558
#> TKW -0.19102141
#> WTI  0.20568622
#> SGW  0.34543954
#> VGR  0.21702042
#> KGR -0.32935119

  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
#>          Comp 1
#> MTD -0.29946437
#> MTJ  0.14665034
#> MTF  0.07040468
#> MTM  0.40236460
#> mTD  0.32859831
#> mTJ  0.18260903
#> mTF  0.25749215
#> mTM  0.14059032
#> PRD -0.08387786
#> PRJ -0.12277326
#> PRF -0.03457021
#> PRM -0.21542249
#> SHD -0.38252353
#> SHJ -0.05597998
#> SHF -0.43981570
#> SHM  0.37609945
# }