Multivariate analysis can help to select superior genotypes through the simultaneous analysis of original information containing the characters of interest. The present study objectives were to select soybean genotypes for good agronomic attributes with focus on yield from amonggenotypes carrying genes resistantto Asian soybean rustusing multivariate analyses and as well as to define the main plant characters which influence the selection decision. Ninety five soybean genotypes of the F6 endogamous generation were evaluated in a random block experiment with two replications for agronomic characters. The data were submitted to principal component and cluster analyses, besides the use of selection index. In the principal components analysis, four eigenvalues explained 71.6% of the variance in the original information, allowing the identification of seventeen superior genotypes, focusing on yield. In the dendrogram obtained from cluster analysis, the genotypes selected in the principal components were grouped in the same cluster, and a second selection was made in which seven genotypes were selected. Concerning the selection by index, most superior genotypes were coincident with the results of the multivariate analysis. In conclusion, multivariate analyses permitted the selection of superior genotypes for important agronomic characteristics in soybeans, principally for components linked to grain production.
Key words: Glycine max, yield, principal components, cluster analysis.
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