Use of the AMMI model for the analysis of the genotype-environment interaction in varieties of starchy maize from Tayacaja, Peru

• Autores: García Mendoza, Pedro José, Medina Castro, Darío Emiliano, Prieto Rosales, Gino Paul, Manayay Sánchez, Damián, Ortecho Llanos, Ronald
• Formato: artículo
• Fecha de Publicación: 2021
• Institución: Universidad Nacional Autónoma de Tayacaja Daniel Hernández Morillo
• Repositorio: Tayacaja
• Lenguaje: español
• OAI Identifier: oai:ojs.unat.edu.pe:article/149
• Enlace del recurso: https://revistas.unat.edu.pe/index.php/RevTaya/article/view/149
• Nivel de acceso: acceso abierto
• Materia: Genotype environment interaction; Zea mays L.; adaptability; performance; productive potential; Interacción genotipo ambiente; adaptabilidad; rendimiento; potencial productivo

The genotype by environment interaction (IGA) is the main limitation to select the best genotypes for different environments. The objective of this study was to use the additive main effects and multiplicative interaction (AMMI) model to evaluate the IGA of 25 varieties of starchy maize. The information was generated in four trials established in contrasting environments in the province of Tayacaja, Peru, in the 2019-2020 crop cycle. The 5x5 alpha lattice design was used, with three replications, where the experimental units consisted of two rows 4 m long, with spatial arrangements of 0.80 m between rows and 0.20 m between planting points. The IGA was measured through the grain yield, adjusted to 15% humidity. Once the importance of the IGA in the experiments was verified, the multivariate analysis was carried out, to obtain the singular values ​​of the AMMI terms that were significant for genotypes and environments. The IGA was highly significant and explained around 33% of the phenotypic variation in performance. The AMMI model explained around 92% of the variation due to the IGA, where the first two axes concentrated all this variation and allowed to identify varieties with specific adaptation and others with broad adaptation to the test environments. The results suggest that the AMMI model was appropriate to evaluate the IGA and to identify the best varieties for each test environment.