Título:	Evaluation of genotype x environment interactions in cotton using the method proposed by Eberhart and Russell and reaction norm models.
Autoria:	ALVES, R. S. TEODORO, P. E. FARIAS, F. C. FARIAS, F. J. C. CARVALHO, L. P. de RODRIGUES, J. I. S. BHERING, L. L. RESENDE, M. D. V. de
Afiliação:	R. S. Alves, UFV; P. E. Teodoro, UFV; F. C. Farias, Universidade Federal de Goiás; FRANCISCO JOSE CORREIA FARIAS, CNPA; LUIZ PAULO DE CARVALHO, CNPA; J. I. S. Rodrigues; L. L. Bhering, UFV; MARCOS DEON VILELA DE RESENDE, CNPF.
Ano de publicação:	2017
Referência:	Genetics and Molecular Research, v. 16, n. 3, gmr16039726, 2017.
Páginas:	12 p.
Conteúdo:	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.
Thesagro:	Gossypium hirsutum Seleção genética
NAL Thesaurus:	Natural selection
Palavras-chave:	Genetic selection Stability and adaptability Mixed model methodology Random regression
Digital Object Identifier:	10.4238/gmr16039726
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (CNPF)