Multivariate exploratory approach and influence of six agronomic traits on soybean genotypes selection

Abstract

A comprehensive analysis for selection of superior genotypes can be useful and necessary. The aim of this study was to select soybean genotypes with superior agronomic traits through the use of multivariate exploratory analysis and to identify those traits that are most influent over grain productivity and selection. We evaluated 29 soybean genotypes in a randomized block design with three replications. Agronomic traits analyzed were: plant height at flowering (PHF), plant height at maturity (PHM), height at insertion of the first pod (HIP), grain productivity (GP), number of nodes (NN) and number of pods (NP). Data were submitted to principal component analysis. Two eigenvalues explained 68.17% of the variance contained in the original information, generating two components with relevant amount of information. These were characterized by the traits PHF, PHM, HIP, NP and GP which allowed to discriminate and select 6 genotypes with good agronomic traits with emphasis on grain productivity. Direct (positive) relationships were observed between GP and the traits PHF, PHM, HIP and NP, as these were observed to influence the distribution of genotypes with greater GP in the two-dimensional plane. The traits that were more related and favorably influencing GP of selected genotypes were plant height at maturity and number of pods.