Research Article Evaluation of genotype x environment interactions in cotton using the method proposed by Eberhart and Russell and reaction norm models.

Abstract

ABSTRACT. Cotton produces one of the most important textile fibers of the world and has great relevance in the world economy. It is an economically important crop in Brazil, which is the world's fifth largest producer. However, studies evaluating the genotype x environment (G x E) interactions in cotton are scarce in this country. Therefore, the goal of this study was to evaluate the G x E interactions in two important traits in cotton (fiber yield and fiber length) using the method proposed by Eberhart and Russell (simple linear regression) and reaction norm models (random regression). Eight trials with sixteen upland cotton genotypes, conducted in a randomized block design, were used. It was possible to identify a genotype with wide adaptability and stability for both traits. Reaction norm models have excellent theoretical and practical properties and led to more informative and accurate results than the method proposed by Eberhart and Russell and should, therefore, be preferred. Curves of genotypic values as a function of the environmental gradient, which predict the behavior of the genotypes along the environmental gradient, were generated. These curves make possible the recommendation to untested environmental levels.

Reaction norms‐based approach applied to optimizing recommendations of cotton genotypes

“…Also, RRM allows predicting genetic values over a continuous environment gradient, since this attribute is related to the RRM approach, once the genotypes are represented by a function that describes the deviation of genotypes along the environment gradient used in the analysis. These points mentioned above reinforce the capacity of the RRM to deal with G × E interaction and highlights the adequacy of RRM to overcomes some aspects of methods based on simple linear regression (Alves et al, 2017). Also, ANOVA-based methods such GGE biplot and AMMI should be avoided since they assume that the genotypic effect is fixed and does not deal with the heterogeneity of residual variance.…”