ArticleAnimals : an open access journal from MDPI2025
Development of Machine Learning-Based Sub-Models for Predicting Net Protein Requirements in Lactating Dairy Cows.
Article in Animals : an open access journal from MDPI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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
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