GGE Biplot for integrating agronomic and sensory attributes in coffee cultivar selection

Abstract

Coffee plant breeding has developed cultivars with disease resistance, high yield, and excellent sensory quality. However, recommending cultivars for different regions requires evaluations that account for genotype × environment (G × E) interactions. This study evaluated the agronomic performance of 30 Coffea arabica cultivars in six locations in southern Minas Gerais, focusing on adaptability and phenotypic stability for yield and sensory quality using the GGE Biplot method. Traits assessed included yield, the proportion of high-sieve and mocha beans, bean density, and sensory quality. Statistical analyses involved ANOVA, the Scott-Knott test, and GGE Biplot. Two mega-environments were identified for yield and three for sensory quality. IPR 100, IAC Obatã 4739, Arara, and Catucaí 2SL demonstrated high yield, while MGS Paraíso 2 excelled in cup quality. GGE Biplot proved effective in identifying stable and adapted cultivars, reinforcing the need to integrate sensory attributes in breeding and recommendation strategies for specialty coffee production.