# Development of Machine Learning-Based Sub-Models for Predicting Net Protein Requirements in Lactating Dairy Cows

## TL;DR

This paper develops machine learning models to predict protein needs in lactating cows, aiming to improve feeding efficiency and sustainability.

## Contribution

The study introduces and validates machine learning models as efficient predictors of net protein requirements in dairy cows.

## Key findings

- Random forest regression outperformed support vector regression in predicting net protein requirements.
- The models explained over 80% of the variation in both maintenance and lactation protein needs.
- The approach could enhance precision feeding by reducing input variables while maintaining accuracy.

## Abstract

Accurately estimating the protein requirements of lactating dairy cows is crucial for enhancing nutrient utilization and promoting sustainable milk production. This study explored the application of machine learning (ML) techniques to predict two components of protein requirement—net protein for maintenance (NPm) and net protein for lactation (NPl)—utilizing a dataset compiled from published experimental studies on lactating Holstein cows. Random forest regression (RFR) and support vector regression (SVR) algorithms were trained to approximate target values calculated using the NASEM (2021) equations. Among the two approaches, the RFR model consistently outperformed SVR in terms of predictive accuracy, explaining over 80 percent of the variation in both NPm and NPl. These findings demonstrate the potential of ML models to serve as surrogate approximators of mechanistic outputs, offering computational efficiency and practical applicability in precision feeding programs. Further research is warranted to validate these models under field conditions and to explore their integration into hybrid modeling frameworks.

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 (R2 = 0.82, RMSEP = 22.38 g/d, CCC = 0.89) and NPl (R2 = 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.