Prediction of Winter Wheat Cultivar Performance Using Mixed Models and Environmental Mean Regression from Multi-Environment Trials for Cultivar Recommendation to Reduce Yield Gap in Poland

Accurate prediction of cultivar performance across diverse environments is crucial for breeding and recommendation systems, helping to reduce the yield gap, the difference between potential and actual yields, which is often widened by poor cultivar selection. This study assessed the adaptability of winter wheat (Triticum aestivum L.) cultivars using a linear mixed-model framework combined with environmental mean regression. The model was trained on yield data from 19 locations over nine years (2015–2023) and validated independently using 2024 data. To ensure robustness, outliers were removed and cultivars with fewer than 30 observations excluded. The model accounted for genotype-by-environment (G×E) interactions and produced adjusted means for each location–year–management combination. These were used in cultivar-specific regressions to estimate yield response across environments. The approach showed strong predictive performance, with a Pearson correlation of 0.958 between predicted and observed yields in the validation year. Results highlight the model’s potential to inform cultivar recommendations, including for less-tested cultivars. This framework offers a practical tool for data-driven decision-making in plant breeding and agronomy, especially under variable growing conditions.