Identification of superior genotypes and soybean traits by multivariate analysis and selection index

• Autor(a) principal: Leite, Wallace de Sousa
• Data de Publicação: 2018
• Outros Autores: Unêda-Trevisoli, Sandra Helena [UNESP], da Silva, Fabiana Mota [UNESP], da Silva, Alysson Jalles [UNESP], Di Mauro, Antonio Orlando [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.5935/1806-6690.20180051; http://hdl.handle.net/11449/228574
• topic: Clustering analysis; Glycine max; Grain yield; Principal components; Selection gain

The selection of superior genotypes of soybean is a complex process, thus exploratory multivariate techniques can be applied to select genotypes analyzing the agronomic traits altogether, increasing the chance of success of a breeding program. Thus, the objective of this study was to select soybean genotypes carrying the RR gene with good agronomical performance through of multivariate analysis and selection index and identify those traits that influence, also verifying the agreement of multivariate techniques and selection index in the selection process. The experiment was conducted in an increased block experimental being evaluated 227 genotypes of F5 generation, which 85 of those were detected to be glyphosate-resistant by PCR. The following traits were evaluated: number of days to maturity, plant height at maturity, lodging, agronomic value, number of branches per plant, number of pods per plant, hundred seeds weight and grain yield. The principal components analysis resulted in the selection of sixteen genotypes with higher grain yield. The traits related to the production of components exerted great influence on grain yield. The clustering by K-means and Ward's methods were similar because they clustered the specific genotypes for the selected traits in the principal components analysis in the same group. There was an agreement on the results of the multivariate analysis in the selection index of Mulamba and Mock in relation to the selected genotypes. The methodologies applied are efficient for selecting genotypes.