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

Mean max in January

MTF

Mean max in February

MTM

Mean max in March

mTD

Mean daily minimum temperature December, deg C

mTJ

Mean min in January

mTF

Mean min in February

mTM

Mean min in March

PRD

Monthly precipitation in December, mm

PRJ

Precipitation in January

PRF

Precipitation in February

PRM

Precipitation in March

SHD

Sun hours in December

SHJ

Sun hours in January

SHF

Sun hours in February

SHM

Sun hours in 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)