Buffalo (Bubalus bubalis) is the 5th species bred in the world after cows, sheep, goats and pigs with 203,939,158 heads (FAO 2023). Buffaloes are mainly reared for milk production and, globally, in...

Nowadays climate change is affecting the planet’s biodiversity, and livestock practices must adapt themselves to improve production without affecting animal welfare. This work investigates the influence that some climatic parameters such as Environment Temperature, Relative Humidity, Thermal excursion and Temperature–Humidity Index (THI), can have on milk quantity and quality in two different dairy species (buffaloes and cows) raised on the same farm. A further aim was to understand if THI threshold used for cows could also be used for buffaloes. The climatic parameters were recorded daily through a meteorological station located inside the farm. Milk quantity (converted into ECM) and quality (Fat Percentage—FP; Protein Percentage—PP; Somatic Cell Count—SCC) were measured. Data were analyzed with Spearman’s correlation index, separately for buffaloes and cows. The results indicate a greater sensitivity of cows to heat stress and a strong negative correlation of the ECM with meteorological data (p < 0.01). The results of this study may stimulate the use of integrated technologies (sensors, software) in the dairy sector, since the IoT (sensors, software) helps to enhance animal well-being and to optimize process costs, with a precision livestock farming approach.

In the last years, the livestock sector is moving towards a more sustainable animal-based industry, mitigating the environmental impact of livestock while meeting the demand for high-quality food. To achieve these goals, farms are using a more technological approach, adopting algorithms to manipulate the vast amount of data from sensors and routine operations. The results will be useful for making more objective decisions. In this context, machine learning - a branch of Artificial Intelligence applied to the study of prediction, inference, and clustering algorithms - can be successfully employed. Nowadays, machine learning algorithms are successfully used to solve many issues in the livestock sector, such as early disease detection, and they are expected to be employed in the future for welfare monitoring. This brief review gives an overview of the current state of the art of the most popular applications for dairy science and the most widely used and best-performing algorithms, highlighting the challenges and obstacles for broad acceptance of these techniques in the dairy sector.

This study investigated the use of K-means and hierarchical clustering, to group Italian Mediterranean buffalo using routinely collected test-day records. The analysis was first conducted on a combined dataset comprising three buffalo herds and subsequently on each herd individually. The main objective was to determine whether data-driven groupings could be implemented to support improvements in general herd management strategies. Results indicated that K-means consistently outperformed hierarchical clustering across all datasets, as reflected by average silhouette scores (0.17–0.18 vs. 0.10–0.12 for K-means and hierarchical, respectively), favorable Davies–Bouldin Index (DBI; 2.05–2.16 vs. 2.11–2.5 for K-means and hierarchical, respectively) and Calinski–Harabasz Index values (CHI; 1034–3877 vs. 729–2109 for K-means and hierarchical, respectively). K-means identified two clusters in the combined dataset and in two of the three herds, while three clusters were identified in the remaining herd. Cluster composition analysis revealed that days in milk and milk yield were the main discriminating factors when two clusters were formed. When three clusters emerged, K-means also identified a subgroup of animals that differed from the others in both age and lactation stage. These findings were supported by the analysis of variance (ANOVA), which showed statistically significant differences among most of the evaluated variables.

The intestinal microbiome plays a fundamental role in canine health and well-being, regulating functions, including digestion, immunity, metabolism, and behavior. Dysbiosis refers to the disruption of the balanced composition of resident commensal communities, and gut bacteria can influence behavior via neurological, metabolic, endocrine, and immune-mediated pathways. Growing evidence supports the existence of a bidirectional communication between the gut and the central nervous system, known as the gut–brain axis, through which intestinal microorganisms may influence behavior via neurological, metabolic, endocrine, and immune-mediated pathways. Despite the expanding interest in this field, the contribution of intestinal dysbiosis to the development and severity of behavioral and neurological disorders in companion dogs remains poorly understood. This review aims to critically analyze the literature from 2011 to 18 September 2025 concerning the association between dysbiosis, the gut–brain axis, and both gastrointestinal and non-gastrointestinal illnesses in dogs. To our knowledge, this review represents the first application of Text Mining (TM) in this domain: TM facilitates the identification and analysis of valuable information from extensive datasets, converting unstructured content into structured data, thereby enabling quantitative analysis. We used the following search terms on three bibliographic databases (PubMed, Scopus, and Web of Science): “dysbiosis” AND “canine” OR “dog” AND “gut–brain axis” AND “behavior”. Of the 1176 records retrieved, 35 studies were checked following the PRISMA guidelines, and they met the predefined inclusion criteria in the final analysis.