Multi-environment trial of potato in UK, yields and trait scores at 12 locations

Yield and 14 trait scores for each of 9 potato varieties at 12 locations in UK.

data("talbot.potato.traits")
data("talbot.potato.yield")

Format

The talbot.potato.yield dataframe has 126 observations on the following 3 variables.

gen

genotype/variety

trait

trait

score

trait score, 1-9

The talbot.potato.yield dataframe has 108 observations on the following 3 variables.

gen

genotype/variety

loc

location/center

yield

yield, t/ha

Details

The talbot.potato.yield dataframe contains mean tuber yields (t/ha) of 9 varieties of potato at 12 centers in the United Kingdom over five years 1983-1987. The following abbreviations are used for the centers.

BU	Bush
CA	Cambridge
CB	Conon Bridge
CC	Crossacreevy
CP	Cockle Park
CR	Craibstone
GR	Greenmount
HA	Harper Adams
MO	Morley
RO	Rosemaund
SB	Sutton Bonnington
TE	Terrington

Source

Mike Talbot and A V Wheelwright, 1989, The analysis of genotype x analysis interactions by partial least squares regression. Biuletyn Oceny Odmian, 21/22, 19--25.

Used with permission of Mike Talbot.

Examples

library(agridat)

libs(pls, reshape2)

data(talbot.potato.traits)
datt <- talbot.potato.traits
data(talbot.potato.yield)
daty <- talbot.potato.yield

datt <- acast(datt, gen ~ trait, value.var='score')
daty <- acast(daty, gen ~ loc, value.var='yield')

# Transform columns to zero mean and unit variance
datt <- scale(datt)
daty <- scale(daty)

m1 <- plsr(daty ~ datt, ncomp=3)
summary(m1)
#> Data: 	X dimension: 9 14 
#> 	Y dimension: 9 12
#> Fit method: kernelpls
#> Number of components considered: 3
#> TRAINING: % variance explained
#>      1 comps  2 comps  3 comps
#> X   34.75196   51.176    69.43
#> BU  23.45729   43.184    50.67
#> CA  17.43483   54.890    58.94
#> CB  43.53317   62.991    76.86
#> CC  68.01120   82.451    82.99
#> CP  48.47678   49.634    89.26
#> CR  67.06064   71.160    83.44
#> GR   0.03819   36.128    39.76
#> HA  61.84794   87.342    87.49
#> MO  61.46510   65.841    65.84
#> RO  20.82397   27.029    46.44
#> SB  16.47977   27.939    29.75
#> TE   6.32481    9.181    22.36

# Loadings factor 1
lo <- loadings(m1)[,1,drop=FALSE]
round(-1*lo[order(-1*lo),1,drop=FALSE],2)
#>               Comp 1
#> DryMatter      -0.38
#> CrispColor     -0.38
#> FoliageMatur   -0.33
#> TuberBlight    -0.27
#> ExtDamage      -0.21
#> Gangrene       -0.20
#> FoliageBlight  -0.01
#> VirusY          0.12
#> IntBruise       0.13
#> FoliageCover    0.17
#> CommonScab      0.25
#> Dormancy        0.30
#> LeafRoll        0.36
#> DroughtResis    0.41

biplot(m1, main="talbot.potato - biplot")