Patterns recognition methods to study genotypic similarity in flood-irrigated rice

ABSTRACT Genetic diversity studies are performed based on information on a set of traits measured in a group of genotypes, considering one or more environments. The pattern recognition methods allow classifying genotypes from a set of important agronomic information. Thus, this study aimed to present and compare pattern recognition methods to inquire about the similarity of environments and genotypes in flood-irrigated rice for the recommendation of cultivars. The experim>ents were performed in the municipalities of Leopoldina, Lambari, and Janaúba, state of Minas Gerais, Brazil. To evaluate the pattern of similarity, 25 rice genotypes in three environments belonging to the flood-irrigated rice breeding program were used. Among these genotypes, five cultivars were used as an experimental control for the grain yield, the height of the plant, flowering, panicle length, grains filled by panicles, percentages of grains filled by panicles, in the 2012/2013 agricultural year. The methods us>ed were mixtures of multivariate normal distributions and density-based clustering algorithm. It was observed, therefore, that the genotypes are distributed in three distinct groups, in which there are intragroup homogeneity and intergroup heterogeneity for the agronomic traits of the flooded rice culture. The methods >used to assess the dissimilarity of environments using pattern recognition methods were efficient in classifying flooded rice irrigated environments.