Investigating the physiological bases of predictable and unpredictable genotype by environment interactions using three-mode pattern analysis

Abstract

Understanding of the underlying physiology of the genotype-specific responses to predictable and unpredictable environmental variation would improve the efficiency of selection within a complex target population of environments. Three-mode principal component analysis (PCA) can be used for interpreting the complex three-way (genotypes, environments, attributes) trial datasets from which this understanding should emerge. The efficiency of this method largely depends on the right combination between the biological and statistical models used, especially on the attributes selected to describe the genotypic responses and the centring of the three-way input data. In this study, we assessed the scope of yield determination models and double-centring of input data for generating some physiological understanding of the genotype×environment (G×E) interactions observed in a sunflower genotype–environment system and for developing ideotype-based breeding strategies. Double-centring of the three-way arrays permitted the separation of predictable and unpredictable G×E interactions. This, in combination with the use of models that explain the physiological bases of yield variation among genotypes, has served to identify three relevant sources of genotypic variation for use in a breeding program, namely: (i) attributes that can be selected to achieve specific adaptation to the target environment by emphasising predictable interactions (e.g. duration of grain filling, a trait associated with canopy stay green); (ii) attributes that allow the unpredictable G×E interactions to be accommodated, improving the linkage between managed-environments and target production environments (e.g. grain set); and (iii) genotypes of similar response pattern for yield but contrasting relative behaviour for the primary and secondary yield determinants. Breeding projects involving crosses between these genotypes could generate better opportunities for yield improvements for individual mega-environments.