Multi-environment trial of rapeseed in U.S.

Rapeseed yield multi-environment trial, 6 genotypes, 3 years, 14 loc, 3 rep

Format

A data frame with 648 observations on the following 5 variables.

year

year, numeric: 87, 88, 89

loc

location, 14 levels

rep

rep, 3 levels

gen

genotype, 6 levels

yield

yield, kg/ha

Details

The data are from the U.S. National Winter Rapeseed trials conducted in 1986, 1987, and 1988. Trial locations included Georgia (GGA, TGA), Idaho (ID), Kansas (KS), Mississippi (MS), Montana (MT), New York (NY), North Carolina (NC), Oregon (OR), South Carolina (SC), Tennessee (TN), Texas (TX), Virginia (VA), and Washington (WA).

SAS codes for the analysis can be found at https://webpages.uidaho.edu/cals-statprog/ammi/index.html

Electronic version from: https://www.uiweb.uidaho.edu/ag/statprog/ammi/yld.data

Used with permission of Bill Price.

Source

Bahman Shafii and William J Price, 1998. Analysis of Genotype-by-Environment Interaction Using the Additive Main Effects and Multiplicative Interaction Model and Stability Estimates. JABES, 3, 335–345. https://doi.org/10.2307/1400587

References

Matthew Kramer (2018). Using the Posterior Predictive Distribution as a Diagnostic Tool for Mixed Models. Joint Statistical Meetings 2018, Biometrics Section. https://www.ars.usda.gov/ARSUserFiles/3122/KramerProceedingsJSM2018.pdf

Reyhaneh Bijari and Sigurdur Olafsson (2022). Accounting for G×E interactions in plant breeding: a probabilistic approach https://doi.org/10.21203/rs.3.rs-2052233/v1

Examples

library(agridat)
data(shafii.rapeseed)
dat <- shafii.rapeseed

dat$gen <- with(dat, reorder(gen, yield, mean))
dat$loc <- with(dat, reorder(loc, yield, mean))
dat$yield <- dat$yield/1000

dat <- transform(dat, rep=factor(rep), year=as.factor(as.character(year)))
dat$locyr = paste(dat$loc, dat$year, sep="")

# The 'means' of reps
datm <- aggregate(yield~gen+year+loc+locyr, data=dat, FUN=mean)
datm <- datm[order(datm$gen),]
datm$gen <- as.character(datm$gen)
datm$gen <- factor(datm$gen,
                       levels=c("Bienvenu","Bridger","Cascade",
                         "Dwarf","Glacier","Jet"))
dat$locyr <- reorder(dat$locyr, dat$yield, mean)

libs(lattice)
# This picture tells most of the story
dotplot(loc~yield|gen,group=year,data=dat,
        auto.key=list(columns=3),
        par.settings=list(superpose.symbol=list(pch = c('7','8','9'))),
        main="shafii.rapeseed",ylab="Location")



# AMMI biplot.  Remove gen and locyr effects.
m1.lm <- lm(yield ~ gen + locyr, data=datm)
datm$res <- resid(m1.lm)
# Convert to a matrix
libs(reshape2)
dm <- melt(datm, measure.var='res', id.var=c('gen', 'locyr'))
dmat <- acast(dm, gen~locyr)
# AMMI biplot.  Figure 1 of Shafii (1998)
biplot(prcomp(dmat), main="shafii.rapeseed - AMMI biplot")