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Background <p>Understanding genotype-environment interaction is crucial for optimizing cotton hybrid performance, facilitating consistent yield and stability in semi-arid regions. To address this challenge, a total of 45 hybrids were evaluated across three kharif seasons (2021–2023) at Punjab Agricultural University Regional Research Station, Abohar. The analysis integrated combined analysis of variance (ANOVA), additive main effects and multiplicative interaction (AMMI), and genotype + genotype × environment (GGE) biplot approaches, supplemented with stability indices such as AMMI stability value (ASV), genotype stability index (GSI), and the weighted average of absolute scores (WAASBY) index, to identify high-performing and stable hybrids.</p> Results <p>Seed-cotton yield ranged from 2 122 to 3 168&#xa0;kg·ha<sup>−1</sup>,&#xa0;with environmental effects explaining the&#xa0;largest proportion of phenotypic variation. The significant genotype × environment interaction&#xa0;(GEI) indicated differential hybrid performance across seasons. AMMI and GGE analyses identified hybrids exhibiting both broad and specific adaptation. Stability indices (ASV, GSI, and WAASBY) consistently identified G7, G8, G17, G22, and&#xa0;G45 as both stable and high-yielding, with mean seed cotton yield (SCY) ranging from 2 625&#xa0;to&#xa0;2 996&#xa0;kg·ha<sup>−1</sup>. For yield component traits, G35 and G21 showed the highest sympodia per plant, G20 and G41 exhibited superior boll weight, and G38 had the highest bolls per plant.</p> Conclusion <p>GGE biplot analysis indicated that E1 was both discriminative and representative for SCY, whereas E2 and E3 were most informative for BW and BPP, respectively, reflecting pronounced seasonal variations. Overall, the integrated analytic pipeline (ANOVA → AMMI → GGE → ASV/GSI/WAASBY) effectively partitioned and interpreted complex GEI into robust selection decisions. This approach facilitated the identification of a refined set of hybrids exhibiting temporal stability, high yield potential, and suitability for both wide and season-specific adaptation.</p>

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Stability of yield and its components in upland cotton hybrids across environments: an AMMI–GGE–WAASBY integration

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月10日

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  • Punia Sunayana,
  • Singh Manpreet,
  • Yadav Sunaina

摘要

Background

Understanding genotype-environment interaction is crucial for optimizing cotton hybrid performance, facilitating consistent yield and stability in semi-arid regions. To address this challenge, a total of 45 hybrids were evaluated across three kharif seasons (2021–2023) at Punjab Agricultural University Regional Research Station, Abohar. The analysis integrated combined analysis of variance (ANOVA), additive main effects and multiplicative interaction (AMMI), and genotype + genotype × environment (GGE) biplot approaches, supplemented with stability indices such as AMMI stability value (ASV), genotype stability index (GSI), and the weighted average of absolute scores (WAASBY) index, to identify high-performing and stable hybrids.

Results

Seed-cotton yield ranged from 2 122 to 3 168 kg·ha−1, with environmental effects explaining the largest proportion of phenotypic variation. The significant genotype × environment interaction (GEI) indicated differential hybrid performance across seasons. AMMI and GGE analyses identified hybrids exhibiting both broad and specific adaptation. Stability indices (ASV, GSI, and WAASBY) consistently identified G7, G8, G17, G22, and G45 as both stable and high-yielding, with mean seed cotton yield (SCY) ranging from 2 625 to 2 996 kg·ha−1. For yield component traits, G35 and G21 showed the highest sympodia per plant, G20 and G41 exhibited superior boll weight, and G38 had the highest bolls per plant.

Conclusion

GGE biplot analysis indicated that E1 was both discriminative and representative for SCY, whereas E2 and E3 were most informative for BW and BPP, respectively, reflecting pronounced seasonal variations. Overall, the integrated analytic pipeline (ANOVA → AMMI → GGE → ASV/GSI/WAASBY) effectively partitioned and interpreted complex GEI into robust selection decisions. This approach facilitated the identification of a refined set of hybrids exhibiting temporal stability, high yield potential, and suitability for both wide and season-specific adaptation.