# Milk Biomarkers and Herd Welfare Status in Dairy Cattle: A Machine Learning Approach

## TL;DR

This study explores if milk biomarkers can reflect dairy cow welfare using machine learning, finding that certain biomarkers correlate with welfare scores.

## Contribution

The novel contribution is using routine milk data and machine learning to predict herd welfare status, offering a faster alternative to traditional assessments.

## Key findings

- Farms with higher welfare scores had lower somatic cell indicators, higher lactose, and increased milk yield.
- Machine learning models correctly classified farm welfare status in about 70% of cases.
- SCC, DSCC, lactose, and milk yield were the most important predictors across models.

## Abstract

Evaluating animal welfare on dairy farms is essential for ensuring good management, consumer confidence and sustainable milk production. However, comprehensive protocols such as Welfare Quality® (WQ®) are time-consuming and require trained assessors, limiting their frequent use on farms. This study explored whether commonly recorded milk biomarkers, which are routinely measured on most dairy farms, can reflect herd welfare status. We assessed 43 dairy farms using the full Welfare Quality® (WQ®) protocol and compared welfare outcomes with common milk biomarkers such as somatic cell count (SCC), differential somatic cell count (DSCC), lactose, urea, fat-to-protein ratio and milk yield. Farms with higher welfare scores showed lower somatic cell indicators, higher lactose concentrations and increased milk yield. We also tested six machine learning models to see whether milk data alone could classify farms into welfare categories. The best models correctly identified farm welfare status in about 70% of cases. Across all models, SCC, DSCC, lactose and milk yield were the most important predictors. These findings should be regarded as preliminary and exploratory, providing initial evidence rather than immediate tools for routine welfare assessment or on-farm decision-making.

Routine milk-recording data may provide valuable insights into dairy cow welfare, although their ability to accurately reflect herd-level welfare outcomes remains unclear. This study explored the associations between routinely collected milk biomarkers and farm-level welfare status using a comparative machine learning approach. Using the Welfare Quality® (WQ®) protocol, 43 commercial dairy farms were classified as Enhanced, Acceptable, or Not Classified. Farm-level milk variables included somatic cell count (SCC), differential somatic cell count (DSCC), fat-to-protein ratio (FPR), fat, protein, casein, lactose, urea, β-hydroxybutyrate (BHB), acetone, total plate count (TPC), and morning milk yield. Kruskal–Wallis tests revealed significant differences among welfare classes for DSCC, SCC, lactose, and milk yield (False Discovery Rate-adjusted p < 0.05). Six machine learning algorithms were trained using 10-fold stratified cross-validation. The Elastic-Net (ENET) model showed the highest mean performance (Accuracy = 0.72 ± 0.19; Kappa = 0.56 ± 0.31), followed by Random Forest and Multilayer Perceptron (Accuracy = 0.70). Model accuracy exhibited substantial variability across cross-validation folds, reflecting the limited sample size and class imbalance. Across models, the most influential variables were SCC, DSCC, lactose, milk yield, FPR, fat, and urea. Overall, the findings provide preliminary and exploratory evidence that routine milk biomarkers capture welfare-relevant patterns at the herd level, supporting their potential role as complementary indicators within data-driven welfare assessment frameworks.