ABSTRACT The aim of the study was to evaluate the genetic diversity, agronomic traits and promising combinations of cowpea genotypes under conditions of low technological level in the region south of Piaui. The experiment was conducted in rainfed condition in the experimental area of the Federal Institute of Piauí, Uruçuí - PI, in DBC with four replications. We used 10 genotypes of commercial cultivars. The following variables were evaluated: Cycle; Number of pods /plants; Number of grains/pods; Weight of 100 grains and yield. The genetic divergence was obtained by Mahalanobis distance and clustering by UPGMA method. The genotypes BRS Tumucumaque, BRS Pajeú and BRS Guariba are promising for cultivation in the first crop under conditions of low technological level in the Cerrado region of Piauí. The combinations of genotypes BRS Tumucumaque and BRS Imponente, BRS Tumucumaque and BRS Aracê, BRS Guariba and BRS Imponente, BRS Guariba and BRS Aracê and BRS Pajeú and BRS Imponente have great potential for use in breeding programs to obtain promising segregating populations for grain yield and other attributes. The traits weight of 100 grains, cycle and number of grains per pod have the greatest contribution to the genetic diversity in the studied population. Piaui Piauí PI replications 1 cultivars evaluated Cycle /plants plants grains/pods grainspods method attributes population

RESUMO A seleção de genótipos superiores de soja é um processo complexo; dessa forma, técnicas exploratórias multivariadas podem ser aplicadas para selecionar genótipos, analisando simultaneamente todos os caracteres agronômicos em estudo, aumentando a chance de êxito em um programa de melhoramento. O objetivo do presente estudo consistiu em selecionar genótipos de soja portadores do gene RR com bons caracteres agronômicos por meio de análise multivariada e índice de seleção, e identificar quais caracteres mais influenciam, além de verificar a concordância das técnicas multivariadas e índice de seleção no processo seletivo. O experimento foi conduzido no delineamento experimental de blocos aumentados, sendo avaliados 227 genótipos de soja de geração F5, sendo 85 destes, detectados pela técnica de PCR, como resistentes ao Roundup. Os caracteres avaliados foram: número de dias para maturidade, altura da planta na maturidade, acamamento, valor agronômico, número de ramos por planta, número de vagens por planta, peso de cem sementes e produtividade de grãos. A análise de componentes principais auxiliou na seleção de dezesseis genótipos, com maior produtividade de grãos. Os caracteres relacionados aos componentes de produção exerceram grande influência sobre a produtividade de grãos. Os resultados das análises de agrupamento pelo método de K-means e pelo o método de Ward quando comparados foram semelhantes, pois agruparam os genótipos específicos para os caracteres selecionados na análise de componentes principais, em um mesmo grupo. Houve concordância dos resultados das análises multivariadas com o índice de seleção de Mulamba e Mock quanto aos genótipos selecionados. As metodologias aplicadas são eficientes no processo de seleção de genótipos.

ABSTRACT 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.