Impact of Mid-to-Late Gestational Overfeeding on Maternal Performance and Calf Outcomes in Hanwoo Cattle: A Machine Learning Approach.

This study evaluated the effects of maternal overfeeding during mid-to-late gestation on maternal productivity, metabolic status, reproductive recovery, and calf performance in Hanwoo cattle using conventional statistics and machine learning (ML) approaches. A total of 243 pregnant cows were assigned to either a control group or an overfeeding group from gestation day 90 to parturition. The overfeeding treatment increased nutrient supply to approximately 140-145% of the control level. Maternal body weight (BW), body condition score (BCS), serum metabolites, and reproductive traits were evaluated throughout gestation and postpartum, while calf growth, morphometrics, and metabolic traits were assessed at birth and weaning. Calves were further classified into growth- or meat-quality-oriented genotypes using SNP-based profiling. Overfeeding increased maternal BW gain and BCS during gestation and reduced circulating non-esterified fatty acid concentrations, indicating improved maternal energy status. However, overfed cows showed a longer interval to postpartum estrus return. Calf birth weight was not significantly affected by maternal overfeeding, whereas calf growth and morphometric traits at weaning were more strongly influenced by parity, sex, and genotype. Machine learning models identified gestational BW, metabolic indicators, calf feed intake, and genotype as major predictors of maternal and calf outcomes, with random forest and XGBoost showing superior predictive performance compared with linear models. These findings suggest that parity- and genotype-informed nutritional management combined with ML-based prediction may support precision feeding strategies in beef cattle production systems.