Optimizing genomic selection models for wheat breeding under contrasting water regimes in a mediterranean environment

Genomic selection models, particularly ridge regression and machine learning approaches, demonstrated reliable predictive performance across environments and traits. Incorporating data from multiple environmental conditions improves prediction accuracy and supports the development of drought-resilient wheat lines. These results reinforce the utility of genomic selection in modern wheat breeding programs for enhancing both productivity and stress tolerance.