Classifying genotypic data from plant breeding trials: a preliminary investigation using repeated checks

Several subjective choices must be made when classifying genotypes based on data from plant breeding trials. One choice involves the method used to weight the contribution each environment makes to the classification. A second involves the use of either genotype-means for each environment or genotypevalues for each block, i.e., considering each block to be a different environment. Another involves whether environments (or blocks) in which genotypes are nonsignificantly different should be included or excluded from such classifications. An alternative to the use of raw or standardized data, is proposed in which each environment is weighted by a discrimination index (DI) that is based on the concept of repeatability. In this study the effect of three weighting methods (raw, standardized and DI), the choice of using environments or blocks, and the choice of including or excluding environments or blocks in which genotypic effects were not significant, were considered in factorial combination to give 12 options. A data set comprised of five check cultivars each repeated six times in each of three blocks at six environments was used. The effect of these options on the ability of a hierarchical clustering technique to correctly classify the repeats into five groups, each consisting of all the six repeats of a particular check cultivar, was investigated. It was found that the DI weighting method generally led to better recovery of the known structure. Using block data rather than environmental data also improved structure recovery for each of the three weighting methods. The exclusive use of environments in which genotypic effects were significant decreased structure recovery while the contrary generally occurred for blocks. The best structure recovery was obtained from the DI weighting applied to blocks (whether genotypes were significant or not).