agridat (version 1.23)

vargas.wheat2: Multi-environment trial of wheat with environmental covariates

Description

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

Examples

if (FALSE) {
  
  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)
  
}