lin.superiority {agridat} | R Documentation

## 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. http://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. http://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. http://doi.org/10.2134/agronj2000.923454x

### Examples

    ## Not run:

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

    require(latticeExtra)
    require(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
    # require(gge)
    # m1 <- gge(yield ~ gen*loc, env.group=region, data=dat,
    #           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)

[Package agridat version 1.16 Index]