agridat (version 1.5)

australia.soybean: Australia soybeans

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.

Usage

data(australia.soybean)

source

Basford, K. E., and Tukey, J. W. (1999). Graphical analysis of multiresponse data illustrated with a plant breeding trial. Chapman and Hall/CRC. Retrieved from: http://three-mode.leidenuniv.nl/data/soybeaninf.htm. Used with permission of Kaye Basford, Pieter Kroonenberg.

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). ll{ 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.

Examples

dat <- australia.soybean
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
dmat2 <- dmat[44:58,]
require("lattice")
parallelplot(dmat2[,c(2:6,1)], horiz=FALSE)