Akademik Veri Yönetim Sistemi
CROP & PASTURE SCIENCE, cilt.77, sa.3, 2026 (SCI-Expanded, Scopus)
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 lucerne genotypes and to evaluate the predictive ability of multiple machine-learning (ML) algorithms.Methods Field data were collected over 2 years with three replicates per trait (n = 360 observations). Correlations between yield and traits were assessed using Pearson's correlation coefficient, 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 coefficient of determination (R2) and root mean square error (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 approximate to 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 lucerne genotypes.