vargas.wheat1 {agridat} | R Documentation

## Wheat yields in 7 years with genetic and environment covariates

### Description

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`

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

Data provided by Jose Crossa.

### Examples

    ## Not run:

      data(vargas.wheat1.covs)
      data(vargas.wheat1.traits)

      require(pls)
      require(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

    ## End(Not run)

[Package agridat version 1.16 Index]