Multi-environment trial of sorghum at 3 locations across 5 years

Format

A data frame with 289 observations on the following 6 variables.

gen: genotype, 28 levels
trial: trial, 2 levels
env: environment, 13 levels
yield: yield kg/ha
year: year, 2001-2005
loc: location, 3 levels

Details

Sorghum yields at 3 locations across 5 years. The trials were carried out at three locations in dry, hot lowlands of Ethiopia:

Melkassa (39 deg 21 min E, 8 deg 24 min N)

Mieso (39 deg 22 min E, 8 deg 41 min N)

Kobo (39 deg 37 min E, 12 deg 09 min N)

Trial 1 was 14 hybrids and one open-pollinated variety.

Trial 2 was 12 experimental lines.

Used with permission of Asfaw Adugna.

Source

Asfaw Adugna (2008). Assessment of yield stability in sorghum using univariate and multivariate statistical approaches. Hereditas, 145, 28--37. https://doi.org/10.1111/j.0018-0661.2008.2023.x

Examples

# \dontrun{

library(agridat)
data(adugna.sorghum)
dat <- adugna.sorghum

libs(lattice)
redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
levelplot(yield ~ env*gen, data=dat, main="adugna.sorghum gxe heatmap",
          col.regions=redblue)

# Genotype means match Adugna
tapply(dat$yield, dat$gen, mean)
#>      G01      G02      G03      G04      G05      G06      G07      G08 
#> 5080.125 4672.875 5043.500 4842.000 4704.250 4769.500 4523.625 4142.625 
#>      G09      G10      G11      G12      G13      G14      G15      G16 
#> 4443.500 4377.250 4950.125 4306.625 4390.625 4635.625 4285.000 3396.692 
#>      G17      G18      G19      G20      G21      G22      G23      G24 
#> 3392.154 3279.231 3833.154 3569.769 3968.538 3556.308 3758.538 3421.077 
#>      G25      G26      G27      G28 
#> 3383.308 3323.077 3171.000 3312.154 

# CV for each genotype.  G1..G15 match, except for G2.
# The table in Adugna scrambles the means for G16..G28
libs(reshape2)
mat <- acast(dat, gen~env,  value.var='yield')
round(sqrt(apply(mat, 1, var, na.rm=TRUE)) / apply(mat, 1, mean, na.rm=TRUE) * 100,2)
#>   G01   G02   G03   G04   G05   G06   G07   G08   G09   G10   G11   G12   G13 
#> 26.45 24.89 18.68 36.45 22.18 30.47 43.32 21.42 26.02 21.38 13.36 15.44 23.54 
#>   G14   G15   G16   G17   G18   G19   G20   G21   G22   G23   G24   G25   G26 
#> 18.05 26.87 43.77 40.23 42.02 51.50 43.33 40.99 42.73 36.48 54.29 39.62 39.96 
#>   G27   G28 
#> 39.51 42.60 

# Shukla stability.  G1..G15 match Adugna.  Can't match G16..G28.
dat1 <- droplevels(subset(dat, trial=="T1"))
mat1 <- acast(dat1, gen~env,  value.var='yield')
w <- mat1; k=15; n=8  # k=p gen, n=q env
w <- sweep(w, 1, rowMeans(mat1, na.rm=TRUE))
w <- sweep(w, 2, colMeans(mat1, na.rm=TRUE))
w <- w + mean(mat1, na.rm=TRUE)
w <- rowSums(w^2, na.rm=TRUE)
sig2 <- k*w/((k-2)*(n-1)) - sum(w)/((k-1)*(k-2)*(n-1))
round(sig2/10000,1) # Genotypes in T1 are divided by 10000
#>   G01   G02   G03   G04   G05   G06   G07   G08   G09   G10   G11   G12   G13 
#>  15.6   7.0   9.2  74.2   3.6  28.3 133.5   9.6  14.2   9.7  44.9  67.9  23.0 
#>   G14   G15 
#>  28.3  20.2 
# }