This study demonstrates the applicability of an explainable machine learning framework (XGBoost + SHAP) for investigating reproductive performance in dairy cows. Although the predictive performance of the independent test dataset was limited (R² = 0.01), SHAP provided transparent global- and individual-level explanations of model predictions, highlighting the methodological potential of explainable artificial intelligence for veterinary data analysis.