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<p>This research aims to find resilient and productive hulless barley genotypes for present climate change in Ethiopia through applied several statistical models. A simple lattice design with two replications was used for investigating 25 genotypes across 6 environments. Applied substantial statistical models, including multivariate, univariate, mixed, and stability indices. Grain yield was significantly (<i>p</i> &lt; 0.001) affected by genotype, environment, and GEI, in accordance to the AMMI analysis of variances, with contributions to variation of 12.1%, 21.2%, and 34.3%, respectively. The GGE biplot analysis grouped the six test conditions into three mega-environments, with G3 and G14 were the most resilient and productive genotypes across the test environments. Annicchiarico measures show that genotypes G3 and G14 outperform in both favourable and unfavourable conditions in terms of productivity and confidence. Moreover, univariate statistical models demonstrated that G14 and G13 are vibrant, high-yielding genotypes that are resilient to in range of stressors. The integrated models of multivariate, univariate, and stability selection indices show that G6 and G13 genotypes are adaptive to particular locations, whereas G14 and G3 genotypes are robust and high yielders in all test conditions. This investigation confirmed that G14 and G3 genotypes are productive and resilient in diverse environments. It was recommended that a nationwide performance trial could be evaluated these two genotypes and made widely available to small-scale farmers in western Ethiopia and similar agro-ecologies throughout the country.</p>

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A multi-model approach to predict a resilient and high productive hulless barley genotypes in the context of current climate change

  • 期刊论文

  • 发表日期：

    2025年6月16日

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  • Geleta Gerema,
  • Meseret Tola,
  • Chemeda Birhanu

摘要

This research aims to find resilient and productive hulless barley genotypes for present climate change in Ethiopia through applied several statistical models. A simple lattice design with two replications was used for investigating 25 genotypes across 6 environments. Applied substantial statistical models, including multivariate, univariate, mixed, and stability indices. Grain yield was significantly (p < 0.001) affected by genotype, environment, and GEI, in accordance to the AMMI analysis of variances, with contributions to variation of 12.1%, 21.2%, and 34.3%, respectively. The GGE biplot analysis grouped the six test conditions into three mega-environments, with G3 and G14 were the most resilient and productive genotypes across the test environments. Annicchiarico measures show that genotypes G3 and G14 outperform in both favourable and unfavourable conditions in terms of productivity and confidence. Moreover, univariate statistical models demonstrated that G14 and G13 are vibrant, high-yielding genotypes that are resilient to in range of stressors. The integrated models of multivariate, univariate, and stability selection indices show that G6 and G13 genotypes are adaptive to particular locations, whereas G14 and G3 genotypes are robust and high yielders in all test conditions. This investigation confirmed that G14 and G3 genotypes are productive and resilient in diverse environments. It was recommended that a nationwide performance trial could be evaluated these two genotypes and made widely available to small-scale farmers in western Ethiopia and similar agro-ecologies throughout the country.