Artificial neural networks have been used for various purposes in plant breeding, including use in the investigation of genotype x environment interactions. The aim of this study was to use artificial neural networks in the selection of common bean genotypes with high phenotypic adaptability and stability, and to verify their consistency with the Eberhart and Russell method. Six trials were conducted using 13 genotypes of common bean between 2002 and 2006 in the municipalities of Aquidauana and Dourados. The experimental design was a randomized block with three replicates.
Phaseolus vulgaris L.
This study used Bayesian inference to investigate the genotype x environment interaction in common bean grown in Mato Grosso do Sul State, and it also evaluated the efficiency of using informative and minimally informative a priori distributions. Six trials were conducted in randomized blocks, and the grain yield of 13 common bean genotypes was assessed.
The objective of this study was to select genitors based on F1 and F2 generations, evaluated in different environments, to obtain segregating populations for the identification of strains showing improved earliness, yield, and carioca-type grains. Nine bean strains were crossed in a partial diallel scheme (4 x 5), in which group 1 included 4 strains with early cycles and group 2 included 5 elite strains. The F1 and F2 generations and the genitors were assessed in Coimbra and Viçosa in randomized blocks with 3 replications.
Cultivars of common bean with more erect plant architecture and greater tolerance to degree of lodging are required by producers. Thus, to evaluate the potential of hypocotyl diameter (HD) in family selection for plant architecture improvement of common bean, the HDs of 32 F2 plants were measured in 3 distinct populations, and the characteristics related to plant architecture were analyzed in their progenies.