agridat (version 1.17)

fisher.barley: Multi-environment trial of 5 barley varieties, 6 locations, 2 years

Description

Multi-environment trial of 5 barley varieties, 6 locations, 2 years

Usage

data("fisher.barley")

Format

A data frame with 60 observations on the following 4 variables.

yield

yield, bu/ac

gen

genotype/variety, 5 levels

env

environment/location, 2 levels

year

year, 1931/1932

Details

Trials of 5 varieties of barley were conducted at 6 stations in Minnesota during the years 1931-1932.

This is a subset of Immer's barley data. The yield values here are totals of 3 reps (Immer gave the average yield of 3 reps).

References

George Fernandez (1991). Analysis of Genotype x Environment Interaction by Stability Estimates. Hort Science, 26, 947-950.

F. Yates & W. G. Cochran (1938). The Analysis of Groups of Experiments. Journal of Agricultural Science, 28, 556-580, table 1. http://doi.org/10.1017/S0021859600050978

G. K. Shukla, 1972. Some statistical aspects of partitioning of genotype-environmental components of variability. Heredity, 29, 237-245. Table 1. http://doi.org/10.1038/hdy.1972.87

Examples

# NOT RUN {
  library(agridat)
  data(fisher.barley)
  dat <- fisher.barley

  libs(dplyr,lattice)
  # Yates 1938 figure 1. Regression on env mean
  # Sum years within loc
  dat2 <- aggregate(yield ~ gen + env, data=dat, FUN=sum)
  # Avg within env
  emn <- aggregate(yield ~ env, data=dat2, FUN=mean)
  dat2$envmn <- emn$yield[match(dat2$env, emn$env)]
  xyplot(yield ~ envmn, dat2, group=gen, type=c('p','r'),
         main="fisher.barley - stability regression",
         xlab="Environment total", ylab="Variety mean",
         auto.key=list(columns=3))


  if(0){
    # calculate stability according to the sum-of-squares approach used by
    # Shukla (1972), eqn 11. match to Shukla, Table 4, M.S. column
    # also matches fernandez, table 3, stabvar column
    libs(dplyr)
    dat2 <- dat
    dat2 <- group_by(dat2, gen,env)
    dat2 <- summarize(dat2, yield=sum(yield)) # means across years
    dat2 <- group_by(dat2, env)
    dat2 <- mutate(dat2, envmn=mean(yield)) # env means
    dat2 <- group_by(dat2, gen)
    dat2 <- mutate(dat2, genmn=mean(yield)) # gen means
    dat2 <- ungroup(dat2)
    dat2 <- mutate(dat2, grandmn=mean(yield)) # grand mean
    # correction factor overall
    dat2 <- mutate(dat2, cf = sum((yield - genmn - envmn + grandmn)^2))
    t=5; s=6 # t genotypes, s environments
    dat2 <- group_by(dat2, gen)
    dat2 <- mutate(dat2, ss=sum((yield-genmn-envmn+grandmn)^2))
    # divide by 6 to scale down to plot-level
    dat2 <- mutate(dat2, sig2i = 1/((s-1)*(t-1)*(t-2)) * (t*(t-1)*ss-cf)/6)
    dat2[!duplicated(dat2$gen),c('gen','sig2i')]    
    ##       <chr>     <dbl>
    ## 1 Manchuria  25.87912
    ## 2  Peatland  75.68001
    ## 3  Svansota  19.59984
    ## 4     Trebi 225.52866
    ## 5    Velvet  22.73051
  }


  libs(asreml,lucid)
  if( utils::packageVersion("asreml") < "4") {
    # asreml3
    # mixed model approach gives similar results (but not identical)

    dat2 <- dat
    dat2 <- dplyr::group_by(dat2, gen,env)
    dat2 <- dplyr::summarize(dat2, yield=sum(yield)) # means across years
    dat2 <- dat2[order(dat2$gen),]

  # G-side
  m1g <- asreml(yield ~ gen, data=dat2,
                  random = ~ env + at(gen):units,
                  family=asreml.gaussian(dispersion=1.0))
  m1g <- update(m1g)
  summary(m1g)$varcomp[-1,1:2]/6
  ##                                          gamma   component
  ## at(gen, Manchuria):units!units.var  33.8318944  33.8318944
  ## at(gen, Peatland):units!units.var   70.4838297  70.4838297
  ## at(gen, Svansota):units!units.var   25.2558315  25.2558315
  ## at(gen, Trebi):units!units.var     231.6923935 231.6923935
  ## at(gen, Velvet):units!units.var     13.9189381  13.9189381
  ## R!variance                           0.1666667   0.1666667

  # R-side estimates = G-side estimate + 0.1666 (resid variance)
  m1r <- asreml(yield ~ gen, data=dat2,
                 random = ~ env,
                 rcov = ~ at(gen):units) # or diag(gen):units
  m1r <- update(m1r)
  summary(m1r)$varcomp[-1,1:2]/6
  ##                            gamma component
  ## gen_Manchuria!variance  34.03643  34.03643
  ## gen_Peatland!variance   70.72723  70.72723
  ## gen_Svansota!variance   25.38494  25.38494
  ## gen_Trebi!variance     231.84662 231.84662
  ## gen_Velvet!variance     14.05591  14.05591
  
}

# }