El objetivo de este trabajo fue evaluar tres métodos para identificar mega‑ambientes, para optimizar el uso del potencial genético de los cultivares de arroz, durante el proceso de selección, y para hacer recomendaciones sobre siembras comerciales en Panamá. Los datos experimentales fueron obtenidos de los ensayos de productividad de cultivares precoces realizados entre 2006 y 2008. Para lograr la estratificación de los ambientes y definir los mega‑ambientes, se utilizaron los métodos del genotipo vencedor mediante el modelo AMMI1, el modelo biplot GGE y el de conglomerado por el método de Ward, complementado con el biplot GGE. Los tres métodos utilizados identificaron dos mega‑ambientes, donde los cultivares sobresalientes fueron Fedearroz 473 e Idiap 145-05. Hubo una coincidencia de 100% en el agrupamiento del conglomerado x el biplot GGE, mientras que entre conglomerado x AMMI1 y biplot GGE x AMMI1 fue de 95,2%. El genotipo más estable, en ambos mega-ambientes, fue el cultivar Idiap 145-05, lo que indica capacidad de adaptación amplia y específica. La capacidad adaptativa de los genotipos superiores y no las condiciones agroclimáticas de las localidades evaluadas fue responsable de la definición de los mega‑ambientes.The objective of this work was to evaluate three methods to identify mega-environments, in order to optimize the use of the genetic potential of rice cultivars during the selection process and to make recommendations for commercial plantations in Panama. Experimental data were obtained from the test performance, between 2006 and 2008, for early maturing cultivars. To achieve the stratification of environments and define mega‑environments, the winner genotype method by the AMMI1 model, GGE biplot model and cluster by Ward’s method supplemented by GGE biplot were used. The three methods used identified two mega-environments, where the outstanding cultivars were Fedearroz 473 e Idiap 145-05. There was 100% coincidence in the grouping of the cluster x the GGE biplot, with 95.2% coincidence between the AMMI1 x cluster and GGE biplot x AMMI1. The most stable genotype, in both mega-environments, was the Idiap 145‑05 cultivar, which indicates its broad and specific adaptive capacity. The adaptive capacity of the superior genotypes and not the agroclimatic conditions of the assessed localities was responsible for defining the mega-environments.The objective of this work was to evaluate three methods to identify mega-environments, in order to optimize the use of the genetic potential of rice cultivars during the selection process and to make recommendations for commercial plantations in Panama. Experimental data were obtained from the test performance, between 2006 and 2008, for early maturing cultivars. To achieve the stratification of environments and define mega‑environments, the winner genotype method by the AMMI1 model, GGE biplot model and cluster by Ward’s method supplemented by GGE biplot were used. The three methods used identified two mega-environments, where the outstanding cultivars were Fedearroz 473 e Idiap 145-05. There was 100% coincidence in the grouping of the cluster x the GGE biplot, with 95.2% coincidence between the AMMI1 x cluster and GGE biplot x AMMI1. The most stable genotype, in both mega-environments, was the Idiap 145‑05 cultivar, which indicates its broad and specific adaptive capacity. The adaptive capacity of the superior genotypes and not the agroclimatic conditions of the assessed localities was responsible for defining the mega-environments
