In the dairy cattle sector, the evaluation of the effects induced by heat stress is still one of the most impactful and investigated aspects as it is strongly connected to both sustainability of the production and animal welfare. On the other hand, more recently, the possibility of collecting a large dataset made available by the increasing technology diffusion is paving the way for the application of advanced numerical techniques based on machine learning or big data approaches. In this scenario, driven by rapid change, there could be the risk of dispersing the relevant information represented by the physiological animal component, which should maintain the central role in the development of numerical models and tools. In light of this, the present literature review aims to consolidate and synthesize existing research on the physiological consequences of heat stress in dairy cattle. The present review provides, in a single document, an overview, as complete as possible, of the heat stress-induced responses in dairy cattle with the intent of filling the existing research gap for extracting the veterinary knowledge present in the literature and make it available for future applications also in different research fields.

In the dairy cattle sector, the evaluation of the effects induced by heat stress is still one of the most impactful and investigated aspects as it is strongly connected to both sustainability of the production and animal welfare. On the other hand, more recently, the possibility of collecting large dataset made available by the increasing technology diffusion is paving the way for the application of advanced numerical techniques based on machine learning or big data approaches. In this scenario driven by rapid change there could be the risk of dispersing the relevant information represented by the physiological animal component, which should maintain the central role in the development of numerical models and tools. In the light of this, the present literature review aims to consolidate and synthesize existing research on the physiological consequences of heat stress in dairy cattle. The present review provides, in a single document, an overview as complete as possible of the heat stress-induced responses in dairy cattle with the intent of filling the existing research gap for extracting the veterinary knowledge present in the literature and make it available to those who are developing numerical models and tools.

This study investigates the application of ChatGPT-4 in extracting and classifying behavioral data from scientific literature, focusing on the daily time-activity budget of dairy cows. Accurate analysis of time-activity budgets is crucial for understanding dairy cow welfare and productivity. Traditional methods are time-intensive and prone to bias. This study evaluates the accuracy and reliability of ChatGPT-4 in data extraction and data categorization, considering explicit, inferred, and ambiguous labels for the data, compared with human analysis. A collection of 55 papers on dairy cow behavior were used in the studies. Data extraction for eating, ruminating, and lying behaviors was performed manually and via ChatGPT-4. The artificial intelligence (AI) model's accuracy and labeling performance were assessed through descriptive and statistical analyses. Mixed model analysis was used to compare human and AI outcomes. Artificial intelligence and human time budget data showed significant differences for eating and ruminating but not for lying. ChatGPT-4 estimated daily eating time at 22.3% compared with 23.8% by humans. For ruminating, AI reported 33.4% against 31.7% by humans. Daily lying times were nearly identical, with AI at 44.4% and human analysis at 44.2%. The global accuracy in data extraction was ∼75%, and labeling accuracy reached 67.3%, with significant variability across behavioral categories. In general, the AI model demonstrates moderate accuracy in extracting and categorizing behavioral data, particularly for inferred and ambiguous data. However, explicit data extraction posed challenges, highlighting AI's dependence on input quality and structure. The consistency between AI and human analyses for lying behavior underscores AI's potential for specific applications. ChatGPT-4 offers a promising complementary tool for behavioral research, enabling efficient and scalable data extraction. However, improvements in AI algorithms and standardized reporting in scientific literature are essential for broader applicability. The study advocates for hybrid approaches combining AI capabilities with human oversight to enhance the reliability and accuracy of dairy cow behavioral research.

The aim of this study was to evaluate the effects of supplementing grape pomace (GP) in lambs’ diets. A total of 30 lambs homogeneous for body weight (13.1 ± 2.1 kg) and age (25–30 days) were randomly allocated into two groups. The control group (CTR) received a standard diet for 45 days, while in the same period the experimental group (GP+) was fed with a diet containing 10% GP on a dry matter (DM) basis. The meat samples from the two groups showed no significant differences in drip loss, cooking loss, meat color and total lipid amount. However, the experimental feeding strategy influenced the meat fatty acid composition, with an increase in the relative percentages of stearic, vaccenic and rumenic acids. In particular, the increase in rumenic acids is associated with several health benefits attributed to its high bioactive properties. In cooked meat samples stored for 5 days at 4 °C, the dietary GP supplementation induced an increase in nonanal and 1-octen-3-ol and a significant reduction of hexanal, an indicator of oxidation; this improved resistance to oxidation in the GP+ samples and was also confirmed by the thiobarbituric acid reactive species (TBARS) test. In summary, the present study showed that the dietary GP supplementation was effective in improving the fatty acid composition and the oxidative stability of lamb meat. The use and valorization of the GP as a matrix of interest for zootechnical nutrition can, therefore, represent a suitable strategy for improving the qualitative aspects of animal production.

The aim of this study was to examine variations in cow milk composition as a function of breeding system and seasonality. This study was carried out in 16 dairy farms located in the Abruzzo region (Central Italy), equally distributed between farms that adopt grazing in the spring and summer months, and farms where the intensive system is exploited. Milk was sampled in all seasons in each of the farms involved and was analyzed with particular attention given to the quality of the lipid and protein fractions. A lower concentration of saturated fatty acids and an increase in rumenic, vaccenic and oleic acids were registered for milk samples coming from outdoor grazing, in which was also observed the greatest presence of α and β caseins. The opposite result was instead observed for κ casein, which showed the highest values from intensive farming. Evaluations also focused on retinol, which significantly increased in concentration during summer in both breeding systems. The present results suggest positive insights into the role of the outdoor breeding system in improving the main qualitative trait of bovine milk in warm seasons.