Multi-environment trial of wheat to illustrate stability statistics

Multi-environment trial to illustrate stability statistics

data("huehn.wheat")

Format

A data frame with 200 observations on the following 3 variables.

gen: genotype
env: environment
yield: yield dt/ha

Details

Yields for a winter-wheat trial of 20 genotypes at 10 environments.

Note: Huehn 1979 does not use genotype-centered data when calculating stability statistics.

Source

Manfred Huehn (1979). Beitrage zur Erfassung der phanotypischen Stabilitat I. Vorschlag einiger auf Ranginformationen beruhenden Stabilitatsparameter. EDV in Medizin und Biologie, 10 (4), 112-117. Table 1. https://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-145979

References

Nassar, R and Huehn, M. (1987). Studies on estimation of phenotypic stability: Tests of significance for nonparametric measures of phenotypic stability. Biometrics, 43, 45-53.

Examples

# \dontrun{

  library(agridat)
  data(huehn.wheat)
  dat <- huehn.wheat
  
  # Nassar & Huehn, p. 51 "there is no evidence for differences in stability
  # among the 20 varieties".
  libs(gge)
#> 
#> Attaching package: 'gge'
#> The following object is masked from 'package:desplot':
#> 
#>     RedGrayBlue
  m1 <- gge(dat, yield ~ gen*env)
  biplot(m1, main="huehn.wheat")
  
  libs(reshape2)
  datm <- acast(dat, gen~env, value.var='yield')

  apply(datm,1,mean) # Gen means match Huehn 1979 table 1
#>   Ack712  Beun781  Breu737  Brnd758  Brnd759   Caribo   Cbc710 Diplomat 
#>    65.11    74.87    63.29    67.29    67.55    69.04    71.03    67.10 
#>  Doer750  Firl777  Firl779  Firl780  Frah743  Jubilar  Loch744  Loch745 
#>    68.60    66.60    64.31    69.63    70.89    66.45    70.46    70.72 
#>     Pem2     Pem3  Ruem711  Stru721 
#>    71.72    72.15    69.06    71.13 
  apply(datm,2,mean) # Env means
#>    E01    E02    E03    E04    E05    E06    E07    E08    E09    E10 
#> 81.770 80.845 66.745 67.540 70.915 51.450 61.095 80.660 52.835 74.645 
  apply(datm, 2, rank) # Ranks match Huehn table 1
#>           E01  E02  E03  E04  E05  E06 E07  E08  E09 E10
#> Ack712    6.5  7.0  9.0  8.0 11.0  4.0  12  1.0  1.0   5
#> Beun781  19.0 20.0 17.0 11.5 20.0  7.0  20 18.0 12.5  20
#> Breu737   1.0  2.0  1.0  7.0  9.0  2.0   7  4.0  2.0   2
#> Brnd758   3.0  9.0  8.0  1.0 17.0 12.0   1 20.0 19.0   6
#> Brnd759  17.0  1.0  3.0  3.0 12.0  5.0   4  8.0 17.0  13
#> Caribo    6.5 16.0 11.0 18.0  2.0 13.5  11 17.0 10.0   7
#> Cbc710    9.5 18.0 15.0 16.0 19.0 15.0  19  3.0  4.0  18
#> Diplomat  8.0 14.0  4.5  2.0  4.0 16.0   5 12.0  7.0  11
#> Doer750  20.0 10.5  2.0 13.0  6.5  9.0  16  6.0  6.0   3
#> Firl777   5.0 10.5 10.0 10.0  1.0 11.0   3  7.0  3.0  12
#> Firl779   2.0  5.0  4.5  4.0  3.0 18.0   2 13.0  5.0   1
#> Firl780   9.5 13.0 14.0 17.0  8.0 20.0   9  9.5 12.5   9
#> Frah743  18.0  6.0  6.0 11.5 18.0  3.0  14 16.0 20.0  16
#> Jubilar   4.0  8.0  7.0  6.0  5.0 10.0  10  2.0 11.0   4
#> Loch744  13.0  4.0 19.0 15.0 15.5  1.0  17 15.0 16.0  10
#> Loch745  12.0 12.0 16.0  9.0  6.5  8.0  18 14.0 18.0  15
#> Pem2     14.0 15.0 18.0 14.0 10.0 17.0   8 19.0  8.0  19
#> Pem3     15.0 17.0 20.0 19.0 15.5 13.5  13 11.0  9.0  17
#> Ruem711  11.0  3.0 12.0  5.0 14.0 19.0   6  5.0 15.0  14
#> Stru721  16.0 19.0 13.0 20.0 13.0  6.0  15  9.5 14.0   8

  # Huehn 1979 did not use genotype-centered data, and his definition
  # of S2 is different from later papers.

  # I'm not sure where 'huehn' function is found
  # apply(huehn(datm, corrected=FALSE), 2, round,2) # S1 matches Huehn
  ##          MeanRank   S1
  ## Jubilar      6.70 3.62
  ## Diplomat     8.35 5.61
  ## Caribo      11.20 6.07
  ## Cbc710      13.65 6.70

  # Very close match to Nassar & Huehn 1987 table 4.
  # apply(huehn(datm, corrected=TRUE), 2, round,2)
  ##          MeanRank   S1   Z1    S2   Z2
  ## Jubilar      10.2 4.00 5.51 11.29 4.29
  ## Diplomat     11.0 6.31 0.09 27.78 0.27
  ## Caribo       10.6 6.98 0.08 34.49 0.01
  ## Cbc710       10.9 8.16 1.78 47.21 1.73

# }