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- Title
Reaction norms‐based approach applied to optimizing recommendations of cotton genotypes.
- Authors
Peixoto, Marco Antônio; Coelho, Igor Ferreira; Evangelista, Jeniffer Santana Pinto Coelho; Alves, Rodrigo Silva; Rocha, João Romero do Amaral Santos de Carvalho; Farias, Francisco José Correa; Carvalho, Luiz Paulo; Teodoro, Paulo Eduardo; Bhering, Leonardo Lopes
- Abstract
Reaction norms fitted through random regression models (RRM) have been widely used in animal and plant breeding for analyses of genotype × environment (G × E) interaction. However, in annual crops, they remain unexplored. Thus, this study aimed to evaluate the applicability and efficiency of RRM fitted through Legendre polynomials as a tool to recommend cotton (Gossypium hirsutum L.) genotypes. To this end, a data set with 12 genotypes of cotton evaluated in 10 environments for fiber length (FL) and fiber fineness was used. The restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) procedure was used to estimate the variance components and to predict the genetic values. Results showed that there was genetic variability among cotton genotypes and that the reaction norms over the environmental gradient illustrated the G × E interaction. Very high selective accuracies (rĝg>0.90) were found for both traits in all environments, which indicates high reliability in the genotype's recommendation. The areas under the reaction norms were calculated for the recommendation of genotypes for unfavorable, favorable, and overall environments. Regarding genotypes recommendation, areas under reaction norms allow recommending genotypes for unfavorable and favorable environments, as well as for overall recommendation, for both traits. This study is the first considering reaction norms fitted through RRM for the recommendation of cotton genotypes and demonstrated the potential of this technique in cotton breeding, besides its great potential to deal with G × E interactions.
- Subjects
GENOTYPES; COTTON; PLANT breeding; ANIMAL breeding; REGRESSION analysis; FORECASTING
- Publication
Agronomy Journal, 2020, Vol 112, Issue 6, p4613
- ISSN
0002-1962
- Publication type
Article
- DOI
10.1002/agj2.20433