Abelardo J. de la Vega

Genotype by environment interaction and indirect selection for yield in sunflower: I. Two-mode pattern analysis of oil and biomass yield across environments in Argentina

A reference set of 10 sunflower hybrids was evaluated in 21 subtropical (northern), temperate (central), and managed environments of Argentina, to identify patterns of genotype-by-environment interaction and opportunities for indirect selection. Pattern analyses showed that the average genotypic discrimination effects of the central and northern regions for oil yield are strongly orthogonal. Photoperiod and minimum temperature would be partially underlying the observed interactions. These patterns are repeatable over seasons, which suggests that central and northern regions are different mega-environments and that selection for specific adaptation to each region would result in a faster genetic progress than selecting for wide adaptation to both regions. Cluster analysis revealed three genotypic groups: northern, central and broadly adapted. All central environments discriminated among genotypes in a similar fashion; discrimination in northern environments was more divergent across years and locations. Late planting dates in a central location associated positively with the northern environments. This represents an opportunity for indirect selection for the northern region from the breeding program headquarters in central Argentina. When photoperiod was extended to 15.5 h in these trials, genotypes exhibited responses similar to those of normal planting dates in central environments, indicating that photoperiod could be a central factor underlying this association. Analysis of specific genotype responses to photoperiod in terms of oil yield showed that these involved traits or processes distinct from time to flowering. Pattern analyses of physiological determinants and components of yield revealed the existence of different specific genotype responses to specific environmental challenges within the same genotype group.

Genotype by environment interaction and indirect selection for yield in sunflower: II. Three-mode principal component analysis of oil and biomass yield across environments in Argentina

Abstract The genotype by environment (G×E) interactions observed for sunflower oil yield in different regions of Argentina can be analyzed in terms of differences among genotypes in individual environments for its components grain number, grain weight, and oil content (yield analysis). Similarly, G×E interactions observed for oil-corrected grain yield can be analyzed in terms of its determinants total biomass and harvest index (physiological analysis). Three-mode (genotypes×environments×attributes) principal component analysis was applied to 10×21×4 and 10×11×3 matrices, for each of the first and the second analyses, respectively, to collectively interpret the changes in these attributes in a sunflower genotype–environment system, and to assess the relative importance of each trait as underlying determinant of the observed G×E interaction for oil yield. The 3×2×3 and 4×4×2 (genotypes×environments×attributes) principal component models explained about 65% of the variation computed for first and second approaches, respectively. For the yield analysis, the first environment component (54% of the variation) explained the common pattern of oil yield over environments and showed that oil content was highly positively correlated to oil yield, while grain number and grain weight showed lack of association with oil yield and were negatively correlated. The second environment component (11% of the variation) contrasted northern and central environments and showed that grain number is the main underlying determinant of the observed G×E interactions between these two mega-environments for oil yield. In the physiological analysis, the first environment component (29% of the variation) explained the common pattern of oil-corrected grain yield over environments and showed that harvest index was more strongly positively correlated to oil-corrected grain yield, but not to total oil-corrected biomass. The second environmental component (19% of the variation) contrasted northern and central environments and showed that oil-corrected biomass is the physiological attribute that is largely responsible for the G×E interactions for oil-corrected grain yield.

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.