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<p>Evaluating genotypes in multiple locations helps in measuring the interaction between genotype and environment (GEI) and in identifying genotypes that consistently yield high results, while also categorizing the evaluating locations into mega environments. A total of 237 Recombinant Inbred Lines population derived from GPU 28×GE 1746 (Population ‘A’) and 201 Recombinant Inbred Lines population derived from cross GPU 28×GE 6635 (Population ‘B’) were evaluated in three environments over two seasons viz., <i>Kharif</i> (<i>Kh</i>) and Summer (Su) 2023 using alpha lattice design with two replications. The Additive Main effect and Multiplicative Interaction (AMMI) analysis revealed significant effects of genotype, environment, and their interaction on grain yield. GGE biplot analysis effectively identified mega-environments and discriminative environments, as well as genotypes with superior performance and stability. Genotypes G229, G191 and G297 in Population ‘A’ and G130, G298 and G171 in population ‘B’ have exhibited high mean performance coupled with stability across testing locations. These findings demonstrate the utility of AMMI and GGE models in selecting finger millet genotypes with broad adaptation, and provide a statistical basis for genotype recommendation in targeted environments.</p>

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Ameliorating the Stability and Yield Potential in Finger Millet (Eleusine Coracana (L) Gaertn) Through Genotype × Environment Interaction Studies

  • 期刊论文

  • 发表日期：

    2026年2月16日

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  • Chetana,
  • T. E. Nagaraja,
  • J. Meenakshi,
  • D. N. Vinutha,
  • M. Manjunatha,
  • S. Kavya,
  • Sujata Bhat,
  • I. S. Tilak,
  • R. Madhusudhana

摘要

Evaluating genotypes in multiple locations helps in measuring the interaction between genotype and environment (GEI) and in identifying genotypes that consistently yield high results, while also categorizing the evaluating locations into mega environments. A total of 237 Recombinant Inbred Lines population derived from GPU 28×GE 1746 (Population ‘A’) and 201 Recombinant Inbred Lines population derived from cross GPU 28×GE 6635 (Population ‘B’) were evaluated in three environments over two seasons viz., Kharif (Kh) and Summer (Su) 2023 using alpha lattice design with two replications. The Additive Main effect and Multiplicative Interaction (AMMI) analysis revealed significant effects of genotype, environment, and their interaction on grain yield. GGE biplot analysis effectively identified mega-environments and discriminative environments, as well as genotypes with superior performance and stability. Genotypes G229, G191 and G297 in Population ‘A’ and G130, G298 and G171 in population ‘B’ have exhibited high mean performance coupled with stability across testing locations. These findings demonstrate the utility of AMMI and GGE models in selecting finger millet genotypes with broad adaptation, and provide a statistical basis for genotype recommendation in targeted environments.