Genotype × Environment Interaction Insights for Yield and Yield Components in Castor via AMMI and GGE Biplot Models

Despite its industrial potential, productivity in castor remains constrained by genotype × environment interactions (GEI), which obscure the true genetic potential of hybrids across variable production ecologies. The present investigation sought to elucidate the magnitude and pattern of GEI and to identify stable, high-performing hybrids for yield and yield-contributing traits across diverse agro-climatic conditions of Telangana, India. Eight elite castor hybrids were evaluated across multi-environment trials using AMMI (Additive Main Effects and Multiplicative Interaction) and GGE (Genotype and Genotype × Environment) biplot models to dissect stability, adaptability, and environmental representativeness. Highly significant GEI effects were detected for seed yield, days to 50% flowering, number of nodes, and hundred-seed weight, underscoring the differential response of genotypes across environments. The AMMI biplot effectively captured interaction patterns, identifying Palem (E1) location as the most representative and least interactive environment for yield performance. “Which-won-where” analysis of the GGE biplot delineated mega-environment groupings, with PCH-596 excelling under Tandur (E2) and Tornala (E3) locations, while ICH-5 demonstrated superior adaptability to E1. Yield Stability Index (YSI) and GGE ranking analyses consistently recognized PCH-596 and ICH-5 as the most stable and high-yielding hybrids across the environments. The integration of AMMI and GGE biplot methodologies proved highly effective in unravelling complex GEI patterns, facilitating the identification of genotypes with broad and specific adaptability. These findings provide a quantitative basis for environment-specific hybrid recommendations and contribute to accelerating genetic gains in castor breeding programs targeting enhanced productivity and resilience.