Interpretation of genotype × environment interaction for winter wheat yield in Ontario

An understanding of the causes of genotype × environment (GE) interaction can help identify traits that contribute to better cultivar performance and environments that facilitate cultivar evaluation. Through subjecting environment-centered yield of a multi-environment trial data to singular value decomposition, the portion of yield variation that is relevant to cultivar evaluation is partitioned into noncrossover and crossover GE interaction, quantified by the first two principal components (PC), respectively. Each PC is a set of genotypic scores multiplied by a set of environmental scores. By relating the PC scores to genotypic and environmental covariates, GE interaction represented by each PC can be interpreted in terms of trait × factor interactions. This strategy was employed in analysis of the 1992 to 1998 Ontario winter wheat (Triticum aestivum L.) performance trial data. Results indicated that plant height and maturity were the major genotypic causes of GE interaction, whereas cold temperature in the winter and hot temperature in the summer were the major environmental causes of GE interaction. Positive interactions were found between earlier maturity vs. warmer winters or hotter summers, and between shorter plant height vs. warmer winters or cooler summers. In addition, better resistance to septoria leaf blotch (caused by Septoria secalis Prill. & Delacr.) was frequently associated with overall performance. The results of this study should help in determining breeding objectives and for selecting test sites or environments for winter wheat breeding in Ontario.

[1]  Weikai Yan,et al.  Cultivar Evaluation and Mega‐Environment Investigation Based on the GGE Biplot , 2000 .

[2]  K. Sayre,et al.  Using Partial Least Squares Regression, Factorial Regression, and AMMI Models for Interpreting Genotype × Environment Interaction , 1999 .

[3]  Joe T. Ritchie,et al.  Low-Temperature Tolerance in Cereals: Model and Genetic Interpretation , 1999 .

[4]  K. Sayre,et al.  Interpreting Genotype ✕ Environment Interaction in Wheat by Partial Least Squares Regression , 1998 .

[5]  Bronwyn Harch,et al.  Wheat breeding nurseries, target environments, and indirect selection for grain yield , 1997 .

[6]  M. Kang USING GENOTYPE-BY-ENVIRONMENT INTERACTION FOR CROP CULTIVAR DEVELOPMENT , 1997 .

[7]  Graeme L. Hammer,et al.  The role of physiological understanding in plant breeding; From a breeding perspective , 1996 .

[8]  P. Bramel-Cox Breeding for Reliability of Performance Across Unpredictable Environments , 1996 .

[9]  Manjit S. Kang,et al.  Incorporating Additional Information on Genotypes and Environments in Models for Two-way Genotype by Environment Tables , 1996 .

[10]  Paolo Annicchiarico,et al.  Adaptation Patterns and Definition of Macro‐environments for Selection and Recommendation of Common‐wheat Genotypes in Italy , 1994 .

[11]  S. Ceccarelli,et al.  Genotype-by-Environment Interactions of Barley in the Mediterranean Region , 1993 .

[12]  G. B. Schaalje,et al.  Survival, height and genotype by environment interaction in winter wheat , 1993 .

[13]  Hugh G. Gauch,et al.  Statistical Analysis of a Yield Trial , 1988 .