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.

Usage

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: Mean max in January
MTF: Mean max in February
MTM: Mean max in March
mTD: Mean daily minimum temperature December, deg C
mTJ: Mean min in January
mTF: Mean min in February
mTM: Mean min in March
PRD: Monthly precipitation in December, mm
PRJ: Precipitation in January
PRF: Precipitation in February
PRM: Precipitation in March
SHD: Sun hours in December
SHJ: Sun hours in January
SHF: Sun hours in February
SHM: Sun hours in 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

if (FALSE) { # \dontrun{

library(agridat)
  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
} # }