Giles, João Antonio D.Partelli, Fábio L.Ferreira, AdésioRodrigues, Joice P.Oliosi, GleisonSilva, Fernando H. Lima e2022-03-192022-03-192018GILES, J. A. D. et al. Genetic diversity of promising ‘conilon’ coffee clones based on morpho-agronomic variables. Anais da Academia Brasileira de Ciências, Rio de Janeiro, v. 90, n. 2 suppl. 1, p. 2437-2446, 2018.1678-2690http://dx.doi.org/10.1590/0001-3765201820170523http://www.sbicafe.ufv.br/handle/123456789/13359Knowledge of the genetic variability of a population is essential to guide its preservation and maintenance in addition to increasing the efficiency of genetic breeding programs. On this basis, this study was conducted to evaluate the genetic diversity of Coffea canephora genotypes using multivariate statistical procedures applied to a set of morpho-agronomic variables. The materials employed in this study constitute a crop located in Vila Valério - ES, Brazil, where the genotypes are arranged in a randomized-blocks experimental design with four replicates. Significant differences were detected by the F test at the 1% or 5% probability levels among the genotypes for all evaluated traits, demonstrating heterogeneity of genetic constitution in the studied population, which is favorable to breeding, as it indicates the possibility to identify superior and divergent individuals. Based on the generalized Mahalanobis distance, the most divergente combinations were obtained between genotypes 23 and 10 (256.43) and 23 and 17 (250.09). The clusters formed by Tocher’s optimization and the UPGMA hierarchical method agreed, both similarly grouping the genotypes into three clusters. Of the analyzed traits, mature fruit weight (19.08%), yield (15.50%), plant diameter (12.42%), and orthotropic-shoot internode length (10.94%) were the most efficient to explain the dissimilarity among the genotypes.pdfenOpen AccessCoffea canephoraDissimilarityMultivariate analysisPlant breedingCafeicultura::Genética e melhoramentoGenetic diversity of promising ‘conilon’ coffee clones based on morpho-agronomic variablesArtigo