lin.unbalanced {agridat}

Multi-environment trial of 33 barley genotypes in 18 locations

Description

Multi-environment trial of 33 barley genotypes in 18 locations

Usage

data("lin.unbalanced")

Format

A data frame with 405 observations on the following 4 variables.

gen

genotype/cultivar

loc

location

yield

yield (kg/ha)

region

region

Details

Yield of six-row barley from the 1986 Eastern Cooperative trial

The named cultivars Bruce, Laurier, Leger are checks, while the other cultivars were tests. Cultivar names use the following codes. "A" is for Atlantic-Quebec. "O" is for "Ontario". "S" is second-year. "T" is third-year.

Source

C. S. Lin, M. R. Binns (1988). A Method for Assessing Regional Trial Data When The Test Cultivars Are Unbalanced With Respect to Locations. Canadian Journal of Plant Science, 68(4): 1103-1110. https://doi.org/10.4141/cjps88-130

References

None

Examples

## Not run: 

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

# location maximum, Lin & Binns table 1
# aggregate(yield ~ loc, data=dat, FUN=max)

# location mean/index, Lin & Binns, table 1
dat2 <- subset(dat, is.element(dat$gen,
  c('Bruce','Laurier','Leger','S1','S2',
    'S3','S4','S5','S6','S7','T1','T2')))
aggregate(yield ~ loc, data=dat2, FUN=mean)

libs(reshape2)
dat3 <- acast(dat, gen ~ loc, value.var="yield")
libs(lattice)
lattice::levelplot(t(scale(dat3)), main="lin.unbalanced", xlab="loc", ylab="genotype")

# calculate the superiority measure of Lin & Binns 1988.
# lower is better
locmax <- apply(dat3, 2, max, na.rm=TRUE)
P <- apply(dat3, 1, function(x) {
  sum((x-locmax)^2, na.rm=TRUE)/(2*length(na.omit(x)))
})/1000
P <- sort(P)
round(P) # match Lin & Binns 1988 table 2, column P

## End(Not run)