vargas.wheat2 {agridat} | R Documentation

## 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

### 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. http://doi.org/10.2135/cropsci1998.0011183X003800030010x

Data provided by Jose Crossa.

### Examples

    if(require(pls) & require(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)
    }

[Package agridat version 1.16 Index]