Development of Machine Learning-Based Sub-Models for Predicting Net Protein Requirements in Lactating Dairy Cows
Mingyung Lee | Dong Hyeon Kim | Seongwon Seo | Luis O. Tedeschi
A reliable estimation of protein requirements in lactating dairy cows is necessary for formulating nutritionally adequate diets, improving feed efficiency, and minimizing nitrogen excretion. This study aimed to develop machine learning-based models to predict net protein requirements for maintenance (NPm) and lactation (NPl) using random forest regression (RFR) and support vector regression (SVR). A total of 1779 observations were assembled from 436 peer-reviewed publications and open-access databases. Predictor variables included farm-ready variables such as milk yield, dry matter intake, days in milk, body weight, and dietary crude protein content. NPm was estimated based on the National Academies of Sciences, Engineering, and Medicine (NASEM, 2021) equations, while NPl was derived from milk true protein yield. The model adequacy was evaluated using 10-fold cross-validation. The RFR model demonstrated higher predictive performance than SVR for both NPm (R<sup>2</sup> = 0.82, RMSEP = 22.38 g/d, CCC = 0.89) and NPl (R<sup>2</sup> = 0.82, RMSEP = 95.17 g/d, CCC = 0.89), reflecting its capacity to model the rule-based nature of the NASEM equations. These findings suggest that RFR may provide a valuable approach for estimating protein requirements with fewer input variables. Further research should focus on validating these models under field conditions and exploring hybrid modeling frameworks that integrate mechanistic and machine learning approaches.
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