vargas.wheat1 {agridat}

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

Data provided by Jose Crossa.

Examples

## Not run: 

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

## End(Not run)