The objectives of this study were to use mixed models to confirm the presence of genetic variability in 16 soybean genotypes, to compare clusters generated by artificial neural networks (ANNs) with those created by the Ward modified location model (MLM) technique, and to indicate parental combinations that hold promise for obtaining superior segregating populations of soybean. A field trial was conducted between November 2014 and February 2015 at Universidade Estadual de Mato Grosso do Sul, Aquidauana, MS.
This study aimed to evaluate the clustering pattern consistency of soybean (Glycine max) lines, using seven different clustering methods. Our aim was to evaluate the best method for the identification of promising genotypes to obtain segregating populations. We used 51 generations F5 and F6 soybean lines originating from different hybridizations and backcrosses obtained from the soybean breeding program of Universidade Federal de Uberlândia in addition to three controls (Emgopa 302, BRSGO Luziânia, and MG/BR46 Conquista).