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.

Fallopia japonica (Japanese knotweed, Reynoutria japonica or Polygonum cuspidatum) is considered an extremely invasive plant worldwide and a bioindicator of heavy metals. Yet, its potential as a crop for honeybees is still underevaluated. This study employs atomic absorption spectrometry to quantitatively analyze the concentration of macro-elements, namely, calcium (Ca), potassium (K) and magnesium (Mg); micro-elements, such as copper (Cu), iron (Fe), manganese (Mn) and selenium (Se); and trace elements, i.e., cadmium (Cd), chromium (Cr), nickel (Ni) and lead (Pb) in different anatomic parts of Fallopia japonica (FJ) plants (roots, rhizomes, stems, leaves) and their traceability into honey. This research encompasses a thorough examination of samples collected from the northwestern and western part of Romania, providing insights into their elemental composition. The results showed that the level of trace elements decreases in terms of traceability in honey samples (Pb was not detected in any of the analyzed samples, while Cd had a minimum content 0.001 mg/kg), ensuring its quality and health safety for consumption. Moreover, the data generated can function as a valuable resource to explore the plant’s positive eco-friendly impacts, particularly in relation to its honey.

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.

The aim of this work was to evaluate the welfare of sheltered dogs using behavioral indicators that indicate negative and positive emotional states (provoked and unprovoked behavior). The behavior of the selected animals was evaluated by direct observation of the indicators. The data collected was used for computing a life quality score (LQ). Forty-three behavioral indicators (23 negative indicators and 20 positive indicators) were identified and analyzed in 20 dogs housed for more than two years in the shelter. Six negative indicators (tail chasing, circling, escape attempt, chewing bars, coprophagy and lifting a front leg) were not identified in any of the 20 evaluated dogs. An average LQ score of 0.115 was obtained, with values between -0.35 and 0.4. The results showed that 55% of the assessed dogs had higher LQ scores than the mean value. Canine behavior can be assessed within a reasonable amount of time by recording the presence or absence of certain behavioral indicators. These recordings can then be processed to obtain a quality of life score for each animal.

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.