australia.soybean: Multi-environment trial of soybean in Australia

Description

Yield and other traits of 58 varieties of soybeans, grown in four locations across two years in Australia. This is four-way data of Year x Loc x Gen x Trait.

Arguments

Format

A data frame with 464 observations on the following 10 variables.

env: environment, 8 levels, first character of location and last two characters of year
loc: location
year: year
gen: genotype of soybeans, 1-58
yield: yield, metric tons / hectare
height: height (meters)
lodging: lodging
size: seed size, (millimeters)
protein: protein (percentage)
oil: oil (percentage)

Details

Measurement are available from four locations in Queensland, Australia in two consecutive years 1970, 1971.

The 58 different genotypes of soybeans consisted of 43 lines (40 local Australian selections from a cross, their two parents, and one other which was used a parent in earlier trials) and 15 other lines of which 12 were from the US.

Lines 1-40 were local Australian selections from Mamloxi (CPI 172) and Avoyelles (CPI 15939).

No.	Line
1-40	Local selections
41	Avoyelles (CPI 15939) Tanzania
42	Hernon 49 (CPI 15948) Tanzania
43	Mamloxi (CPI 172) Nigeria
44	Dorman USA
45	Hampton USA
46	Hill USA
47	Jackson USA
48	Leslie USA
49	Semstar Australia
50	Wills USA
51	C26673 Morocco
52	C26671 Morocco
53	Bragg USA
54	Delmar USA
55	Lee USA
56	Hood USA
57	Ogden USA
58	Wayne USA

Note on the data in Basford and Tukey book. The values for line 58 for Nambour 1970 and Redland Bay 1971 are incorrectly listed on page 477 as 20.490 and 15.070. They should be 17.350 and 13.000, respectively. In the data set made available here, these values have been corrected.

References

K E Basford. 1982. The Use of Multidimensional Scaling in Analysing Multi-Attribute Genotype Response Across Environments, Aust J Agric Res, 33, 473--480.

Kroonenberg, P. M., & Basford, K. E. B. (1989). An investigation of multi-attribute genotype response across environments using three-mode principal component analysis. Euphytica, 44, 109--123.

Marcin Kozak (2010). Use of parallel coordinate plots in multi-response selection of interesting genotypes. Communications in Biometry and Crop Science, 5, 83-95.

Examples

Run this code

# NOT RUN {
data(australia.soybean)
dat <- australia.soybean

if(require(reshape2)){
dm <- melt(dat, id.var=c('env', 'year','loc','gen'))

# Joint plot of genotypes & traits. Similar to Figure 1 of Kroonenberg 1989
dmat <- acast(dm, gen~variable, fun=mean)
dmat <- scale(dmat)
biplot(princomp(dmat), main="australia.soybean trait x gen biplot", cex=.75)
}

# Figure 1 of Kozak 2010, lines 44-58
# }
# NOT RUN {
  require(agridat); require(reshape2) ; require(lattice); require(latticeExtra)
  data(australia.soybean)
  dat <- australia.soybean
  dat <- melt(dat, id.var=c('env', 'year','loc','gen'))
  dat <- acast(dat, gen~variable, fun=mean)
  dat <- scale(dat)
  dat <- as.data.frame(dat)[,c(2:6,1)]
  dat$gen <- rownames(dat)
  # data for the graphic by Kozak
  dat2 <- dat[44:58,]
  dat3 <- subset(dat2, is.element(gen, c("G48","G49","G50","G51")))
  
  parallelplot( ~ dat3[,1:6]|dat3$gen, main="australia.soybean",
               as.table=TRUE, horiz=FALSE) +
    parallelplot( ~ dat2[,1:6], horiz=FALSE, col="gray80") +
    parallelplot( ~ dat3[,1:6]|dat3$gen,
                 as.table=TRUE, horiz=FALSE, lwd=2)
# }
# NOT RUN {
# }

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