Agro-morphological characterization and diversity analysis of Coffea arabica germplasm collection from INIA, Peru
2023
Paredes Espinosa, Richard | Gutiérrez Reynoso, Dina Lida | Atoche Garay, Diego Fernando | Mansilla Córdova, Pedro Javier | Abad Romaní, Yudi Gertrudis | Girón Aguilar, Rita Carolina | Flores Torres, Itala | Montañez Artica, Ana Gabriela | Arbizu Berrocal, Carlos Irvin | Amasifuen Guerra, Carlos Alberto | Maicelo Quintana, Jorge Luis | Poemape Tuesta, Carlos Augusto | Guerrero Abad, Juan Carlos
Coffee (Coffea arabica L.) plays a major role in the economy of Peru and the world. The present study aims to elucidate the agro-morphological variability of coffee genotypes maintained in the INIA´s germplasm collection. Therefore, 20 vegetative, reproductive, and phytosanitary traits of 162 coffee accessions of INIA’s germplasm collection were evaluated and analyzed. Correlation results indicate that a simultaneous selection of characters, such as number of branches per plant, number of nodes per branch, leaf area and weight of a hundred fruits, can contribute to increase coffee yields. Additionally, coffee yield was negatively correlated with the incidence and severity of coffee leaf rust, and interestingly the occurrence of small and compact coffee plants with high resistance to the disease was also found. The analysis of Tocher and Mahalanobis D2 determined the formation of 10 groups of divergent coffee accessions; where clusters 1 (accession codes 20, 29, 38, 54, 67, 71, 117, 24, 26 and 27), 5 (accession codes 46 and 53), 9 (accession code 159), and 10 (accession code 203) group promising accessions that can be used in breeding programs. Principal component analysis (PCA) showed that at least five of its principal components managed to explain 70.01% of the total variation in the collection. Finally, the high coefficients obtained for the phenotypic, genotypic and heritability variation confirm the existence of additive genes in the evaluated population, that would ensure the success of coffee breeding programs based on the selection of traits of agronomic importance.
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