- Research Article
2
- 10.3390/ani15213202
Integrative Multivariate Analysis of Milk Biomarkers, Productive Performance, and Animal Welfare Indicators in Dairy Cows
- Nov 03, 2025
- Animals : an Open Access Journal from MDPI
- Daniela Elena Babiciu + 6 more +6
Simple SummaryConsumers, veterinarians, and farmers are increasingly concerned about animal welfare on dairy farms. Thus, invasive procedures should be avoided along the production flux and also during additional procedures, such as performing welfare measurements. In this study, we investigated whether routinely collected milk composition data could serve as an indicator of cows’ health and well-being. Data from 37 commercial farms were analysed, combining milk composition with direct assessments of health, behaviour, and housing conditions. Our findings indicated that specific milk components, such as the fat-to-protein ratio, energy-related metabolites, and udder health indicators, were significantly associated with markers of favourable or unfavourable welfare. Farms with healthier and more content cows tended to produce milk with a better fat-to-protein balance and lower evidence of disease. Conversely, farms with welfare challenges such as lameness, mastitis, or poor hygiene exhibited distinctive milk profiles. These results demonstrated that routine milk testing, already an integral part of herd management, can provide a simple and non-invasive means of monitoring animal welfare. Using milk characteristics to explore the cows’ health and welfare, farmers and advisors can make quicker, evidence-based decisions that benefit both the animals and society.Animal welfare is increasingly recognised as a core component of sustainable dairy production, yet objective assessment at the herd level remains challenging. This study evaluated whether milk biomarkers can serve as non-invasive indicators of cow welfare. Thirty-seven dairy farms were assessed using the Welfare Quality® protocol and various milk analysis parameters. As a first line of results, Spearman correlations revealed strong associations between milk biomarkers and welfare indicators. For example, a higher fat-to-protein ratio was linked to better feeding, lower prevalence of hunger, and improved human–animal relationships. In contrast, elevated somatic cell count and differential somatic cell count were associated with mastitis, lameness, dirtiness, and reduced emotional well-being. Using Principal Component Analysis (PCA), three dimensions were identified, health–hygiene, socio-behavioural, and metabolic stress, explaining 44.7% of variance. K-means clustering distinguished three herd profiles: feeding–metabolic balance, behavioural–comfort, and clinical–hygiene risk. These findings demonstrated that routine milk biomarkers provide integrated, non-invasive information on herd health, behaviour and, comfort. Incorporating routine milk analysis into welfare assessments can support the early detection of issues, facilitate evidence-based decision-making, and promote sustainable dairy management.
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