Clustering Environments to Minimize Change in Rank of Cultivars

Abstract

Crossover interactions occur in evaluation trials when ranks of cultivars change across environments. Determining groups of environments within which crossover interactions are minimized may facilitate making cultivar recommendations. The goal of this research was to test a new approach for determining these environmental groups in which crossover interaction between a pair of cultivars was defined across all environments. The number of groups was based both on reduction in crossover interaction and repeatability of cultivar means within groups. The validity of this procedure was tested on three simulated data sets with known crossover interactions. For each data set, the approach divided the environments into the two groups that minimized crossover interactions. The approach also was applied to yield data from a maize (Zea mays L.) trial in which 59 environments previously had been clustered by a different measure of crossover interaction. Three groups of 12 environments and one group of 23 environments were defined. The previous clustering had identified six clusters. The amount of crossover interaction within the four environmental groups was reduced by 53% from the total crossover interaction in all 59 environments. The results of the clustering depended on whether all pairwise comparisons among cultivars or only the significant crossover interactions among the higher yielding cultivars were used. The latter method was deemed more appropriate when the goal is to recommend specific cultivars for specific groups of environments. Regardless of the approach used, clustering based on crossover interactions only has practical significance if these interactions are repeatable.