Abstract:
A strategy for genetic improvement of coffee Coffea canephora plants is to aggregate through artificial crossings the characteristics of the Conilon botanical variety, such as shorter height and drought resistance, with the higher average grain size and resistance to pests and diseases of the Robusta variety. Efficiently separating the clones into these two groups with the aid of appropriate analytical procedures makes field tasks easier for professionals and, thus, allows the systematic production of intervarietal hybrids. This study verifies if the non-parametric discriminant analyzes of the k-nearest neighbors (k-NN) and k-average neighbors (k-AN) would be able to correctly classify 130 coffee clones in their botanical varieties previously designated as Conilon, Robusta and Intervarietal Hybrids populations from ten quantitative agronomic characteristics, including the processed coffee beans yield, considering the existing population genetic divergence. These characteristics were found to be good discriminatory variables and the discriminant analyzes k-NN and k-AN, based on the principle of similarity by neighborhood, classified the clones with high hit rates. The k-AN discriminant analysis was able to better discriminate intervarietal hybrids from the group clones Conilon. The results correctly reflected the genetic diversity between the botanical varieties and intervarietal hybrids of Coffea canephora, allowing us to conclude that these classification methods can assist breeders in the main task of discriminating Conilon from Robusta clones.
