Multi-environment trial of wheat with Augmented design

Multi-environment trial of wheat in Nebraska with Augmented design

Usage

data("belamkar.augmented")

Format

A data frame with 2700 observations on the following 9 variables.

loc: location
rep: replicate
iblock: incomplete block
gen_new: new genotype (1=yes, 0=no)
gen_check: check genotype (0=no)
gen: genotype name
col: column ordinate
row: row ordinate
yield: yield, bu/ac

Details

The experiment had 8 locations with 270 new, experimental lines (genotypes) and 3 check lines. There were 10 incomplete blocks at each location. There were 2 replicate blocks at Alliance and 1 block at all other locations. Each plot was 3 m long by 1.2 m wide.

The electronic data were found in supplement S4 downloaded from https://doi.org/10.25387/g3.6249410 The license for the data is CC-BY 4.0.

Source

Vikas Belamkar, Mary J. Guttieri, Waseem Hussain, Diego Jarquín, Ibrahim El-basyoni, Jesse Poland, Aaron J. Lorenz, P. Stephen Baenziger (2018). Genomic Selection in Preliminary Yield Trials in a Winter Wheat Breeding Program. G3 Genes|Genomes|Genetics, 8, Pages 2735–2747. https://doi.org/10.1534/g3.118.200415

References

Same data appear in ASRtriala package: https://vsni.co.uk/free-software/asrtriala

Examples

if (FALSE) { # \dontrun{
  library(agridat)
  data(belamkar.augmented)
  dat <- belamkar.augmented

  libs(desplot)
  desplot(dat, yield ~ col*row|loc, out1=rep, out2=iblock)
  # Experiment design showing check placement
  dat$gen_check <- factor(dat$gen_check)
  desplot(dat, gen_check ~ col*row|loc, out1=rep, out2=iblock,
          main="belamkar.augmented")

  # Belamkar supplement S3 has R code for analysis
  if(require("asreml", quietly=TRUE)){
    library(asreml)

    # AR1xAR1 model to calculate BLUEs for a single loc
    d1 <- droplevels(subset(dat, loc=="Lincoln"))
    d1$colf <- factor(d1$col)
    d1$rowf <- factor(d1$row)
    d1$gen <- factor(d1$gen)
    d1$gen_check <- factor(d1$gen_check)
    d1 <- d1[order(d1$col),]
    d1 <- as.data.frame(d1)
    m1 <- asreml(fixed=yield ~ gen_check, data=d1,
                 random = ~ gen_new:gen,
                 residual = ~ar1(colf):ar1v(rowf) )
    p1 <- predict(m1, classify="gen")
    head(p1$pvals)
  }
} # }