Multi-environment trial of wheat cultivars introduced 1860-1982.

Yields of wheat cultivars introduced 1860-1982. Grown in 20 environments.

Usage

data("perry.springwheat")

Format

A data frame with 560 observations on the following 6 variables.

yield

yield, kg/ha

gen

genotype/cultivar factor, 28 levels

env

environment factor, 20 levels

site

site factor

year

year, 1979-1982

yor

year of release, 1860-1982

Details

Twenty-eight of the most significant wheat cultivars of the past century in Western Australia, were grown in 20 field trials over 4 years in the Central and Eastern wheat-belt of Australia.

At the Wongan Hills site there were separate early and late sown trials in 1979 and 1980. Later sowing dates generally have lower yields.

Note: Although not indicated by the original paper, it may be that the Merredin site in 1979 also had early/late sowing dates.

Used with permission of Mario D'Antuono and CSIRO Publishing.

Source

MW Perry and MF D'Antuono. (1989). Yield improvement and associated characteristics of some Australian spring wheat cultivars introduced between 1860 and 1982. Australian Journal of Agricultural Research, 40(3), 457–472. https://www.publish.csiro.au/nid/43/issue/1237.htm

Examples

if (FALSE) { # \dontrun{
  
  library(agridat)
  data(perry.springwheat)
  dat <- perry.springwheat

  libs(lattice)
  xyplot(yield~yor|env, dat, type=c('p','r'), xlab="year of release",
         main="perry.springwheat")
  
  # Show the genetic trend for each testing location * year.
  # libs(latticeExtra)
  # useOuterStrips(xyplot(yield~yor|site*factor(year), dat,
  #                       type=c('p','r')))


  # Perry reports a rate of gain of 5.8 kg/ha/year.  No model is given.
  # We fit a model with separate intercept/slope for each env
  m1 <- lm(yield ~ env + yor + env:yor, data=dat)
  # Average slope across environments
  mean(c(coef(m1)[21], coef(m1)[21]+coef(m1)[22:40]))
  ## [1] 5.496781


  # ----------

  # Now a mixed-effects model.  Fixed overall int/slope.  Random env int/slope.

  # First, re-scale response so we don't have huge variances
  dat$y <- dat$yield / 100

  libs(lme4)
  # Use || for uncorrelated int/slope.  Bad model.  See below.
  # m2 <- lmer(y ~ 1 + yor + (1+yor||env), data=dat)
  ## Warning messages:
  ## 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
  ##   Model failed to converge with max|grad| = 0.55842 (tol = 0.002, component 1)
  ## 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
  ##   Model is nearly unidentifiable: very large eigenvalue
  ##  - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
  ##  - Rescale variables?

  # Looks like lme4 is having trouble with variance of intercepts
  # There is nothing special about 1800 years, so change the
  # intercept -- 'correct' yor by subtracting 1800 and try again.
  dat$yorc <- dat$yor - 1800
  m3 <- lmer(y ~ 1 + yorc + (1+yorc||env), data=dat)
  
  # Now lme4 succeeds.  Rate of gain is 100*0.0549 = 5.49
  fixef(m3)
  ## (Intercept)        yorc
  ##  5.87492444  0.05494464

  if(require("asreml", quietly=TRUE)){
    libs(asreml,lucid)
    m3a <- asreml(y ~ 1 + yorc, data=dat, random = ~ env + env:yorc)
    lucid::vc(m3)
    ##      grp        var1 var2     vcov   sdcor
    ##      env (Intercept) <NA> 11.61    3.407
    ##    env.1        yorc <NA>  0.00063 0.02511
    ## Residual        <NA> <NA>  3.551   1.884
    
    lucid::vc(m3a)
    ##           effect component std.error z.ratio      con
    ##      env!env.var  11.61     4.385        2.6 Positive
    ## env:yorc!env.var   0.00063  0.000236     2.7 Positive
    ##       R!variance   3.551    0.2231      16   Positive
  }
  
} # }