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<p>Identifying stable maize hybrids helps mitigate GxE effects, which are prominent in tropical agriculture. Genomic prediction (GP) enhances breeding efficiency, and incorporating GxE effects improves accuracy. However, multi-environmental GP is computationally intensive and time-consuming. This study aimed to identify efficient strategies for genomic selection by directly predicting the stability of single-cross maize hybrids. Using a balanced dataset of 128 hybrids across six environments, nine indices were estimated, and genomic predictions were conducted with a Bayesian Ridge Regression model. Ten rounds of tenfold cross-validation were performed to evaluate predictive ability (PA) and accuracy (ACC). Considering the nature of the dataset, seven out of nine indices present satisfactory PA and ACC, fluctuating from 0.17 to 0.31, and from 0.33 to 0.61, respectively. On average, indices predicting outperform the Bayesian Multi-Environment approach by 29.7%, considering the seven well-predicted indices. Wricke’s ecovalence index and the main effect of genotypes present better performance, however, considering larger datasets, the Euclidian Distance and the Harmonic Mean of the Relative Performance of the Breeding Values (MHPRVG) indices should be more adequate.</p>

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Efficient strategies for direct genomic prediction of grain yield stability in maize hybrids

  • 期刊论文

  • 发表日期：

    2025年10月2日

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  • Eric Vinicius Vieira Silva,
  • Carlos Pereira da Silva,
  • Yasmin Vasques Berchembrock,
  • Renzo Garcia Von Pinho

摘要

Identifying stable maize hybrids helps mitigate GxE effects, which are prominent in tropical agriculture. Genomic prediction (GP) enhances breeding efficiency, and incorporating GxE effects improves accuracy. However, multi-environmental GP is computationally intensive and time-consuming. This study aimed to identify efficient strategies for genomic selection by directly predicting the stability of single-cross maize hybrids. Using a balanced dataset of 128 hybrids across six environments, nine indices were estimated, and genomic predictions were conducted with a Bayesian Ridge Regression model. Ten rounds of tenfold cross-validation were performed to evaluate predictive ability (PA) and accuracy (ACC). Considering the nature of the dataset, seven out of nine indices present satisfactory PA and ACC, fluctuating from 0.17 to 0.31, and from 0.33 to 0.61, respectively. On average, indices predicting outperform the Bayesian Multi-Environment approach by 29.7%, considering the seven well-predicted indices. Wricke’s ecovalence index and the main effect of genotypes present better performance, however, considering larger datasets, the Euclidian Distance and the Harmonic Mean of the Relative Performance of the Breeding Values (MHPRVG) indices should be more adequate.