Genotype × Environment Interaction and Stability Analysis of Commercial Hybrid Grain Corn Genotypes in Different Environments

Abstract

The introduction of superior grain corn genotypes with high and stable yield (YLD) in most environments is important to increase local production and reduce dependency on imported grain corn. In this study, days to tasseling (DT), plant height, and YLD of 11 grain corn genotypes were observed in 10 environments to evaluate the effects of genotype (G), environment (E), and genotype by environment interactions (GEI) using GGE analysis and the stability of genotypes using stability parameters. In each location, grain corn genotypes were arranged in three replications using a randomized complete block design. An analysis of variance showed that all three traits were highly significant toward G and E factors, whereas GEI showed that only DT and YLD were highly significant. Genotype V14 produced the highest YLD of 10,354 kg/ha, followed by V4 (10,114 kg/ha) and V2 (9797.74 kg/ha). These three genotypes also dominated in seven out of 10 tested environments. With regard to stability ranking, genotype V4 was the most stable genotype, with a big gap difference between the second (V14) and third places (V2). Therefore, V14, V4, and V2 were the most promising genotypes because of their great YLD performance and most stable across tested environments, which can be recommended to farmers for high-scale planting.

Deciphering genotype‐by‐environment interaction in new soybean lines based on multiple traits using different adaptability and stability methods

“…This pattern aligns with findings from Adham et al. ( 2022 ), Bilate Daemo et al. ( 2023 ), Esan et al.…”

Influence of Genotype × Environment Interaction on Yield Stability of Maize Hybrids with AMMI Model and GGE Biplot

“…In this study, the effects of environment, genotype, and GEIs on maize yield were significant (p < 0.01), and the environmental factor contributed more variation (49.96%) than genotypes and GEIs, reflecting that there was a greater variability in the environment (Table 3). Similar results were found in Adham et al, Alizadeh et al, and Ansarifard et al that the environment accounted for most of the variance [16,35,36]. Additionally, the GEI effect is largely a response to environmental factors, not genotypic factors [37]; thus, the significant GEI factors demonstrated that the use of AMMI and the GGE biplot was appropriate in yield evaluation [38].…”

Adaptabilidad y estabilidad de mutantes de tomate en Los Santos, Panamá

“…Se realizó un gráfico Biplot (Figura 1) con las puntuaciones de los 2 ejes principales correspondientes a los PCA del análisis Biplot GGE-SReg, el cual, permitió visualizar explícitamente el desempeño de los genotipos evaluados dentro del entorno (cuál-ganó-dónde). De acuerdo con este análisis se formaron 3 grupos ambientales potenciales, resultados similares obtuvieron otros autores en tomate (Djidonou et al 2020) y otros cultivos (Adham et al 2022, Lima et al 2023. El primero fue el ambiente de Villa Lourdes en el cual el genotipo más sobresaliente fue el G14 (T7.RA-15-EN9-7); el segundo estuvo conformado por las localidades de Albina Grande, Tres Quebradas y El Ejido en el cual el cultivar con mejor comportamiento fue el G3 (ICLN-1) y el tercer grupo ambiental y menos productivo debido a la gran cantidad de patógenos de suelo, se identificó para La Villa en el cual el genotipo 9 (DINA.RB-17-EN21-2) presentó un mejor desempeño.…”

“…Si un entorno tiene una alta capacidad de discriminación y representatividad genotípica, entonces es un entorno ideal (Adham et al 2022). Con base en el Biplot de capacidad de discriminación y representatividad, ningún ambiente del estudio ocupa la primera posición en ambos análisis.…”