Context. Accurate prediction of phenological and morphological traits in alfalfa (Medicago sativa L.) is essential to improve yield performance and increase selection efficiency in breeding programs. Aims. To identify morpho-phenological traits driving total dry matter yield in local alfalfa genotypes and to evaluate the predictive ability of multiple machine-learning (ML) algorithms. Methods. Field data were collected over two years with three replicates per trait (n = 360 observations). Correlations between yield and traits were assessed using Pearson’s r. Four ML algorithms—Random Forest (RF), Elastic Net Regression (ENet), Extreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR)—were trained to predict yield; performance was compared using R² and RMSE. Trait importance was examined across models. Key results. Yield was strongly positively associated with winter dormancy, post-harvest regrowth rate and plant resistance (r ≈ 0.67–0.68). RF achieved the best predictive performance (R²=0.61; RMSE=26.49). Five traits (winter dormancy, post-harvest regrowth, resistance, plant form and root-crown bud number) consistently ranked as the most influential predictors. Conclusions. RF best captured the partially non-linear, interaction-driven yield structure, and pinpointed a coherent set of morpho-phenological predictors aligned with classical correlation outcomes. Implications. Integrating morpho-phenological traits with ML substantially improves yield predictability and provides a practical, data-driven framework to prioritise traits (e.g. winter dormancy, regrowth, resistance, plant form, root-crown buds) for selecting high-yielding, stress-resilient alfalfa genotypes.
albayrak et al. (Thu,) studied this question.