Multi-environment trial of wheat with environmental covariates

The yield of 8 wheat genotypes was measured in 21 low-humidity environments. Each environment had 13 covariates recorded.

Usage

data("vargas.wheat2.covs")
data("vargas.wheat2.yield")

Format

The 'vargas.wheat2.covs' data frame has 21 observations on the following 14 variables.

env: environment
CYC: length of growth cycle in days
mTC: mean daily minimum temperature in degrees Celsius
MTC: mean daily maximum temperature
SHC: sun hours per day
mTV: mean daily minimum temp during vegetative stage
MTV: mean daily maximum temp during vegetative stage
SHV: sun hours per day during vegetative stage
mTS: mean daily minimum temp during spike growth stage
MTS: mean daily maximum temp during spike growth stage
SHS: sun hours per day during spike growth stage
mTG: mean daily minimum temp during grainfill stage
MTG: mean daily maximum temp during grainfill stage
SHG: sun hours per day during grainfill stage

The 'vargas.wheat2.yield' data frame has 168 observations on the following 3 variables.

env: environment
gen: genotype
yield: yield (kg/ha)

Details

Grain yields (kg/ha) for 8 wheat genotypes at 21 low-humidity environments grown during 1990-1994. The data is environment-centered and genotype-centered. The rows and columns of the GxE matrix have mean zero. The locations of the experiments were:

OBD	Ciudad Obregon, Mexico, planted in December
SUD	Wad Medani, Sudan
TLD	Tlaltizapan, Mexico, planted in December
TLF	Tlaltizapan, Mexico, planted in February
IND	Dharwar, India
SYR	Aleppo, Syria
NIG	Kadawa, Nigeria

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)
  libs(pls,reshape2)
  
  data(vargas.wheat2.covs)
  datc <- vargas.wheat2.covs

  data(vargas.wheat2.yield)
  daty <- vargas.wheat2.yield

  # Cast to matrix
  daty <- acast(daty, env ~ gen, value.var='yield')
  rownames(datc) <- datc$env
  datc$env <- NULL
  
  # The pls package centers, but does not (by default) use scaled covariates
  # Vargas says you should
  # daty <- scale(daty)
  datc <- scale(datc)

  m2 <- plsr(daty ~ datc)

  # Plot predicted vs observed for each genotype using all components
  plot(m2)

  # Loadings
  # plot(m2, "loadings", xaxt='n')
  # axis(1, at=1:ncol(datc), labels=colnames(datc), las=2)

  # Biplots
  biplot(m2, cex=.5, which="y", var.axes=TRUE,
         main="vargas.wheat2 - daty ~ datc") # Vargas figure 2a
  biplot(m2, cex=.5, which="x", var.axes=TRUE) # Vectors form figure 2 b
  # biplot(m2, cex=.5, which="scores", var.axes=TRUE)
  # biplot(m2, cex=.5, which="loadings", var.axes=TRUE)
  
} # }