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Effectiveness of breeding selection for grain quality in common bean

Polianna Alves Silva Dias, Danilo Valente Almeida, P. G. S. Melo, H. S. Pereira, L. C. Melo

2020Crop Science23 citationsDOIOpen Access PDF

Abstract

Abstract The aims of this study were to investigate the genetic variability and the genotype × environment interaction for quality and yield traits in common bean ( Phaseolus vulgaris L.), to evaluate the degree of informativeness of the evaluations of grain quality in only one environment, to estimate genetic parameters for grain quality traits, and to select lines with superior grain quality. We evaluated 81 carioca common bean lines in preliminary line trials in several environments for nutritional, technological, and commercial quality and selected the 20 superior lines, which were evaluated in validation trials in nine environments. Individual and combined ANOVAs were performed for all the traits. Correlations were estimated between Fe and Zn concentrations and yield; adaptability and phenotypic stability were analyzed; and superior genotypes were selected based on the Mulamba & Mock index. It is possible to increase the Fe, Zn, and crude protein concentrations and reduce cooking time; however, increasing crude fiber is a challenge. Preliminary evaluation of the quality traits in only one environment was effective and sufficient for selection of genotypes superior in Fe concentration, crude fiber, crude protein, and cooking time; and genetic gains can be obtained from selection for these traits. Genetic and phenotypic correlations were observed between Fe and Zn concentrations. The lines CNFC 16627, CNFC 16518, CNFC 16602, CNFC 16615, and CNFC 16520 are superior based on the selection index and are recommended for breeding for grain quality in carioca common bean.

Topics & Concepts

PhaseolusBiologySelection (genetic algorithm)Gene–environment interactionAdaptabilityGrain yieldBiotechnologyInteractionGenotypeAgronomyIndex selectionGrain qualityGenetic variationGenetic variabilityYield (engineering)GeneticsEcologyGeneComputer scienceMaterials scienceMachine learningMetallurgyPlant pathogens and resistance mechanismsGenetics and Plant BreedingAgricultural pest management studies
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