Stability Analysis of Grain Yield and Yield Contributing Traits in Maize (Zea mays L.) Hybrids Across Diverse Agro-climatic Environments of Chhattisgarh, India
Dinesh Kumar Thakur *
Guru Ghasidas Regional Agriculture Research Station, Kawardha (Kabirdham) C.G. 491995, India.
Santosh Kumar Sinha
RMD College of Agriculture and Research Station, Ambikapur (Surguja) C.G. 497001, India.
Devarchan Nirala
Guru Ghasidas Regional Agriculture Research Station, Kawardha (Kabirdham) C.G. 491995, India.
*Author to whom correspondence should be addressed.
Abstract
The present investigation evaluated the stability of grain yield and agronomic traits in maize (Zea mays L.) hybrids across diverse agro-climatic environments of Chhattisgarh, India, during Kharif 2022. A total of 40 experimental single-cross hybrids along with three check hybrids [NK-30, CoH(M)-8, and CGASM-1] were evaluated at three locations, namely Ambikapur, Kawardha, and Raipur, using a randomised complete block design (RCBD) with three replications. Fourteen agronomic and yield-related traits were recorded, and genotype × environment (G × E) interaction was assessed using the Eberhart and Russell (1966) stability model. Pooled analysis of variance revealed significant environmental effects for all traits and significant G × E interactions for ear height, 100-grain weight, ear length, and grain yield, confirming differential responses of hybrids across environments. Grain yield ranged from 44.50 to 69.48 q ha⁻¹, with AMH-2022-126 recording the highest mean grain yield (69.48 q ha⁻¹), followed by NK-30 (68.87 q ha⁻¹) and AMH-2022-101 (68.03 q ha⁻¹). Based on mean grain yield, regression coefficient (bᵢ), and deviation from regression (S²dᵢ), three hybrids—AMH-2022-126, AMH-2022-108 and AMH-2022-133—were identified as widely adapted and stable across environments. Hybrids AMH-2022-101 and AMH-2022-119 showed specific adaptation to favourable environments.
Keywords: Genotype × environment interaction, stability analysis, Eberhart and Russell model, grain yield, maize hybrids, multi-environment evaluation