Multi-environment trial of rapeseed in U.S. — shafii.rapeseed • agridat

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).

The experiment design was RCB with 4 replications per trial.

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

Reyhaneh Bijari, Hanisha Vemireddy and Sigurdur Olafsson (2025). A method for approximate rank confidence intervals in plant breeding experiments. Heliyon, 11. https://doi.org/10.1016/j.heliyon.2025.e42973

Examples

if (FALSE) { # \dontrun{

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")

} # }