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vol.48 número5SPEInteração genótipos x ambientes em feijão-caupi via modelos mistosSeleção de populações de feijão-caupi tolerantes ao déficit hídrico por índice de seleção índice de autoresíndice de assuntospesquisa de artigos
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Revista Ciência Agronômica

versão impressa ISSN 0045-6888versão On-line ISSN 1806-6690

Resumo

ROCHA, Maurisrael de Moura et al. Yield adaptability and stability of semi-prostrate cowpea genotypes in the Northeast region of Brazil by REML/BLUP. Rev. Ciênc. Agron. [online]. 2017, vol.48, n.5spe, pp.879-888. ISSN 0045-6888.  http://dx.doi.org/10.5935/1806-6690.20170104.

Cowpea is grown in different environmental conditions of the Northeast region of Brazil. Thus, selecting and developing cultivars with high yield, stability, and adaptability for this region is necessary due to the genotype × environment interaction. The objective of this work was to select cowpea lines of semi-prostrate plant simultaneously for high yield, adaptability, and genotypic stability in the Northeast region of Brazil by the REML/BLUP procedure. Twenty semi-prostrate genotypes-16 lines and four cultivars-were evaluated in 36 environments of the Northeast region from 2013-2015. The experiments were carried out under rainfed conditions in a completely randomized block design with four replications. The adaptability and genotypic stability were evaluated by the REML/BLUP procedure. The genotype × environment interaction was complex-type, with the grain yield ranging from 260 kg ha-1 (Campo Grande do Piauí PI, 2015) to 2,764 kg ha-1 (Araripina PE, 2015), with overall mean of 1,304 kg ha-1. According to the Harmonic Mean of Relative Performance of Genetic Values (HMRPGV) estimates, the cultivars BRS Marataoã and BRS Pajeú and the line MNC04-792F-129 had simultaneously high yield, adaptability, and genotypic stability, and can be recommended and grown with greater probability of success in all the environments of the Northeast region of Brazil.

Palavras-chave : Vigna unguiculata; Grain yield; Genotype × environment interaction; Mixed models.

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