lin.superiority: Multi-environment trial of 33 barley genotypes in 12...

In kwstat/agridat: Agricultural Datasets

Multi-environment trial of 33 barley genotypes in 12 locations

Description

Multi-environment trial of 33 barley genotypes in 12 locations

Usage

data("lin.superiority")

Format

A data frame with 396 observations on the following 4 variables.

gen

genotype/cultivar

region

region

loc

location

yield

yield (kg/ha)

Details

Yield of six-row barley from the 1983 annual report of Eastern Cooperative Test in Canada.

The named cultivars Bruce, Conquest, Laurier, Leger are checks, while the other cultivars were tests.

Source

C. S. Lin, M. R. Binns (1985). Procedural approach for assessing cultivar-location data: Pairwise genotype-environment interactions of test cultivars with checks Canadian Journal of Plant Science, 1985, 65(4): 1065-1071. Table 1. https://doi.org/10.4141/cjps85-136

References

C. S. Lin, M. R. Binns (1988). A Superiority Measure Of Cultivar Performance For Cultivar x Location Data. Canadian Journal of Plant Science, 68, 193-198. https://doi.org/10.4141/cjps88-018

Mohammed Ali Hussein, Asmund Bjornstad, and A. H. Aastveit (2000). SASG x ESTAB: A SAS Program for Computing Genotype x Environment Stability Statistics. Agronomy Journal, 92; 454-459. https://doi.org/10.2134/agronj2000.923454x

Examples

## Not run: 

library(agridat)
data(lin.superiority)
dat <- lin.superiority

libs(latticeExtra)
libs(reshape2)
# calculate the superiority measure of Lin & Binns 1988

dat2 <- acast(dat, gen ~ loc, value.var="yield")
locmean <- apply(dat2, 2, mean)
locmax <- apply(dat2, 2, max)
P <- apply(dat2, 1, function(x) {
  sum((x-locmax)^2)/(2*length(x))
})/1000
P <- sort(P)
round(P) # match Lin & Binns 1988 table 2, column Pi

# atlantic & quebec regions overlap
# libs(gge)
# m1 <- gge(dat, yield ~ gen*loc, env.group=region,
#           main="lin.superiority")
# biplot(m1)

# create a figure similar to Lin & Binns 1988

# add P, locmean, locmax back into the data
dat$locmean <- locmean[match(dat$loc, names(locmean))]
dat$locmax <- locmax[match(dat$loc, names(locmax))]
dat$P <- P[match(dat$gen, names(P))]
dat$gen <- reorder(dat$gen, dat$P)
xyplot(locmax ~ locmean|gen, data=dat,
       type=c('p','r'), as.table=TRUE, col="gray",
       main="lin.superiority - Superiority index",
       xlab="Location Mean",
       ylab="Yield of single cultivars (blue) & Maximum (gray)") +
  xyplot(yield ~ locmean|gen, data=dat,
         type=c('p','r'), as.table=TRUE, pch=19)


## End(Not run)

kwstat/agridat documentation built on Nov. 2, 2024, 6:19 a.m.