Using artificial neural networks to select upright cowpea (Vigna unguiculata) genotypes with high productivity and phenotypic stability.
Cowpea (Vigna unguiculata) is grown in three Brazilian regions: the Midwest, North, and Northeast, and is consumed by people on low incomes. It is important to investigate the genotype x environment (GE) interaction to provide accurate recommendations for farmers. The aim of this study was to identify cowpea genotypes with high adaptability and phenotypic stability for growing in the Brazilian Cerrado, and to compare the use of artificial neural networks with the Eberhart and Russell (1966) method. Six trials with upright cowpea genotypes were conducted in 2005 and 2006 in the States of Mato Grosso do Sul and Mato Grosso. The data were subjected to adaptability and stability analysis by the Eberhart and Russell (1966) method and artificial neural networks. The genotypes MNC99-537F-4 and EVX91-2E-2 provided grain yields above the overall environment means, and exhibited high stability according to both methods. Genotype IT93K-93-10 was the most suitable for unfavorable environments. There was a high correlation between the results of both methods in terms of classifying the genotypes by their adaptability and stability. Therefore, this new approach would be effective in quantifying the GE interaction in upright cowpea breeding programs.
Department of Statistics, Federal University of Viçosa, Viçosa, MG, Brasil.
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LM
Barroso
Department of General Biology, Federal University of Viçosa, Viçosa, MG, Brasil eduteodoro@hotmail.com.
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PE
Teodoro
Department of Statistics, Federal University of Viçosa, Viçosa, MG, Brasil.
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Nascimento
Department of Crop Science, State University of Mato Grosso do Sul, Aquidauana, MS, Brasil.
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FE
Torres
Department of Statistics, Federal University of Viçosa, Viçosa, MG, Brasil.
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AC
Nascimento
Department of Statistics, Federal University of Viçosa, Viçosa, MG, Brasil.
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CF
Azevedo
Department of Statistics, Federal University of Viçosa, Viçosa, MG, Brasil.
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FR
Teixeira
D000704:Analysis of Variance; D001938:Brazil; D004777:Environment; D005838:Genotype; D016571:Neural Networks, Computer; D010641:Phenotype; D012639:Seeds / Q000235:genetics / Q000254:growth & development; D000070658:Vigna / Q000235:genetics* / Q000254:growth & development*