Description Usage Format Details Source Examples

Yield of Durum wheat, 7 genotypes, 6 years, with 16 genotypic variates and 16 environment variates.

1 2 |

The `vargas.wheat1.covs`

dataframe has 6 observations on the following 17 variables.

`year`

year, 1990-1995

`MTD`

Mean daily max temperature December, deg C

`MTJ`

January

`MTF`

February

`MTM`

March

`mTD`

Mean daily minimum temperature December, deg C

`mTJ`

January

`mTF`

February

`mTM`

March

`PRD`

Monthly precipitation in December, mm

`PRJ`

January

`PRF`

February

`PRM`

March

`SHD`

a numeric vector

`SHJ`

January

`SHF`

February

`SHM`

March

The `vargas.wheat1.traits`

dataframe has 126 observations on the following 19 variables.

`year`

year, 1990-1995

`rep`

replicate, 3 levels

`gen`

genotype, 7 levels

`yield`

yield, kg/ha

`ANT`

anthesis, days after emergence

`MAT`

maturity, days after emergence

`GFI`

grainfill, MAT-ANT

`PLH`

plant height, cm

`BIO`

biomass above ground, kg/ha

`HID`

harvest index

`STW`

straw yield, kg/ha

`NSM`

spikes / m^2

`NGM`

grains / m^2

`NGS`

grains per spike

`TKW`

thousand kernel weight, g

`WTI`

weight per tiller, g

`SGW`

spike grain weight, g

`VGR`

vegetative growth rate, kg/ha/day, STW/ANT

`KGR`

kernel growth rate, mg/kernel/day

Conducted in Ciudad Obregon, Mexico.

Mateo Vargas and Jose Crossa and Ken Sayre and Matthew Renolds and
Martha E Ramirez and Mike Talbot, 1998.
Interpreting Genotype x Environment Interaction in Wheat by
Partial Least Squares Regression, *Crop Science*, 38, 679–689.
http://doi.org/10.2135/cropsci1998.0011183X003800030010x

Data provided by Jose Crossa.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | ```
## Not run:
data(vargas.wheat1.covs)
data(vargas.wheat1.traits)
require(pls)
require(reshape2)
# Yield as a function of non-yield traits
Y0 <- vargas.wheat1.traits[,c('gen','rep','year','yield')]
Y0 <- acast(Y0, gen ~ year, value.var='yield', fun=mean)
Y0 <- sweep(Y0, 1, rowMeans(Y0))
Y0 <- sweep(Y0, 2, colMeans(Y0)) # GxE residuals
Y1 <- scale(Y0) # scaled columns
X1 <- vargas.wheat1.traits[, -4] # omit yield
X1 <- aggregate(cbind(ANT,MAT,GFI,PLH,BIO,HID,STW,NSM,NGM,
NGS,TKW,WTI,SGW,VGR,KGR) ~ gen, data=X1, FUN=mean)
rownames(X1) <- X1$gen
X1$gen <- NULL
X1 <- scale(X1) # scaled columns
m1 <- plsr(Y1~X1)
loadings(m1)[,1,drop=FALSE] # X loadings in Table 1 of Vargas
biplot(m1, cex=.5, which="x", var.axes=TRUE,
main="vargas.wheat1 - gen ~ trait") # Vargas figure 2a
# Yield as a function of environment covariates
Y2 <- t(Y0)
X2 <- vargas.wheat1.covs
rownames(X2) <- X2$year
X2$year <- NULL
Y2 <- scale(Y2)
X2 <- scale(X2)
m2 <- plsr(Y2~X2)
loadings(m2)[,1,drop=FALSE] # X loadings in Table 2 of Vargas
## End(Not run)
``` |

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