In a shared environment, our companion animals became unintended sentinels for pollutant exposure consequences, developing even earlier similar conditions to humans. This review focused on the human–pet cohabitation in an environment we all share. Alongside other species, canine and feline companions are veritable models in human medical research. The latency period for showing chronic exposure effects to pollutants is just a few years in them, compared to considerably more, decades in humans. Comparing the serum values of people and their companion animals can, for example, indicate the degree of poisonous lead load we are exposed to and of other substances as well. We can find 2.4 times higher perfluorochemicals from stain- and grease-proof coatings in canine companions, 23 times higher values of flame retardants in cats, and 5 times more mercury compared to the average levels tested in humans. All these represent early warning signals. Taking these into account, together with the animal welfare orientation of today’s society, finding non-invasive methods to detect the degree of environmental pollution in our animals becomes paramount, alongside the need to raise awareness of the risks carried by certain chemicals we knowingly use.

This paper investigates the effect of different housing conditions on the welfare quality of breeding horses. Using a welfare protocol that included health and behavioral parameters, 330 stallions (kept in tie-stall housing) and 365 broodmares (kept in extensive, mostly free housing) were assessed. The horses were categorized into four welfare categories (“not classified”, “acceptable”, “enhanced” and “excellent”), according to an individual welfare score calculated for each horse. The prevalence of stallions with dyspnea, tendon and joint swellings, abnormal gait and abnormal hoof horn quality was significantly (p < 0.05) higher than that of the broodmares. No significant difference (p > 0.05) was found in the human-related behavioral response of the two categories of breeding horses. The median individual welfare scores were significantly higher (p < 0.05) in the broodmares than in the breeding stallions. The mares had “enhanced” and “excellent” welfare, while the stallions had “acceptable” and “enhanced” welfare. The results revealed differences in the horses’ welfare quality for the different housing conditions. Accordingly, it can be concluded that positive changes in housing management, such as free housing with the use of boxes, could improve the welfare quality of breeding stallions.

Despite an increase in awareness of their essential needs, many stallions continue to be kept in conditions limiting their social interactions and movement. To supplement the studies which highlight the effects of these practices on selected aspects of equine mental and physical wellbeing, we aimed to monitor a group of 32 adult intact stallions during their transition from tethered housing with limited outdoor access to free group housing through the lens of their overall welfare, perceived emotional status, and docility toward humans. Over three visits (before the management change, two weeks, and three months after, respectively), their welfare, qualitative behavior, and docility were assessed. Analysis of the data collected showed an improvement in the stallions’ overall welfare and no decrease in their docility after their group-release, with a constant correlation between these two aspects. The evaluation of their emotional states was less relevant, lacking consistency between the assessments for most of the descriptors used, warranting further research in similar conditions. Although our study covered a relatively short period of time, our results provide encouraging support for stallion owners in deciding on a similar management change for the welfare of their animals.

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.

Despite considerable research regarding the benefits of natural living conditions on several aspects of the health and well-being of dairy cows, the effects of pasture access on their overall welfare are less studied. In this comparative study, the Welfare Quality^® protocol was applied in 22 zero-grazing and 17 grazing access farms with an ulterior statistical exploration of the differences found. Moreover, correlations were calculated between pasture access and animal-based welfare measures. Aside from the multiple benefits of pasturing identified within the welfare measures, criteria, and principles, in the overall classification, the farms with permanent confinement ranked lower than the grazing farms. Although both systems used free-stall barns, allowing the cows’ movement, the grazing animals showed improved overall welfare. Yet, the origin-related adaptation of the animals could play a role. The authors recommend research-based tailoring whenever these conditions are intended to be transposed in technology, especially in intensive systems.

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.