Mastitis has detrimental effects on the world's dairy industry, reducing animal health, milk production and quality, as well as income for farmers. In addition, consumers' growing interest in food safety and rational usage of antibiotics highlights the need to develop novel strategies to improve mastitis detection, prevention, and management. In the present study we applied machine learning (ML) analyses to predict presence or absence of subclinical mastitis in Italian Mediterranean buffaloes, exploiting information collected the previous month during routine milk recording procedures, as well as climatic data. The data set included 3,891 records of 1,038 buffaloes from 6 herds located in Basilicata Region (South Italy). Prediction models were developed using 4 different ML algorithms (Generalized Linear Model, Support Vector Machines, Random Forest, and Neural Network) and 2 data set splitting approaches for the creation of the training and test sets (by record or by animal ID number, always with 80% of the data used for model training and the remaining 20% for model testing). Support Vector Machine was the best method to predict high or low somatic cell count at the subsequent test-day record in the validation set, and therefore it was used to estimate the contribution of each feature to the best model. Independently from the data set splitting approach, the most important features were somatic cell score, differential somatic cell count, electrical conductivity, and milk production. Among climatic data, the most informative were temperature and relative humidity. When the data were split by animal ID, an improvement in models' predictive performance on the test set was observed, suggesting this as the most appropriate data splitting approach in data sets with repeated measures to avoid data leakage. According to different metrics, Neural Network was the best method for making predictions on the test set. Our findings confirmed the promising role of ML methods to improve prevention and surveillance of subclinical mastitis, exploiting the large amount of data currently available to identify animals that would possibly have high somatic cell count the subsequent month.

The effect of crocin in the semen extender before cryopreservation was evaluated on sperm parameters of 20 bucks of five different breeds: Garganica (GA), Jonica (JO), Maltese (MA), Mediterranean Red (MR) and Saanen (SA). Semen samples were centrifuged, to remove seminal plasma, divided in two aliquots and diluted with Tris-egg-yolk-based extender, containing 0 (control group) and 1 mM crocin. Crocin concentration was established after a preliminary dose trial. On fresh and frozen-thawed sperm, motility, viability, morphology, membrane integrity, DNA fragmentation and ROS levels were evaluated. The freezing process led to a decrease (p < 0.05) in all the sperm parameters recorded, confirming the deleterious effect of cryopreservation on goat semen. The most interesting result regarding the inclusion of crocin in the extender before cryopreservation was as follows: Crocin significantly improved (p < 0.05) sperm motility in all breeds, except for Mediterranean Red, compared to the control group. Furthermore, 1 mM crocin reduced percentage of spermatozoa with DNA fragmentation with a marked decrement (p < 0.05) in Garganica and Saanen, as compared to the control group. Finally, intracellular ROS decreased (p < 0.01) in the crocin-treated sperm of all breeds, as compared to the control. In conclusion, supplementation of 1 mM crocin in the extender decreased oxidative stress, improving sperm motility and the DNA integrity of frozen-thawed sperm in different breeds.

The temperature–humidity index (THI) has been commonly used to analyse heat stress in dairy cattle, but little is known about its effects on buffaloes. In this study, daily milk yield (MY), fat percentage (FP), protein percentage (PP) and somatic cell count (SCC) data from 808 buffalo cows plus environmental temperature and relative humidity were used to investigate the consequence of heat stress. Two mixed models were used to evaluate the impact of THI on MY, FP, PP and log transformed SCC (SCS). The effect of THI was significant for PP, FP and SCS, whereas its interaction with parity was statistically significant for PP and SCS. The relationship between PP and FP and THI was positive but of different magnitude according to the parity. When THI was below 62, an unfavourable effect was observed, especially in primiparous buffalo cows. A significant interaction between SCS and THI across parities was also observed. The effect of THI on MY across parities was not definite but overall a favourable relationship was observed. Our findings depict a susceptibility of buffaloes to low values of THI, suggesting an optimal THI range for water buffaloes between 59 and 63, although some deleterious effects were observed in primiparous buffaloes at THI values lower than 62. Additional investigations are needed to better elucidate the influence of THI on buffalo species.

In dogs, the phase from mating to the end of weaning lasts about 120 days and encompasses many aspects that, interacting, contribute to increase the allostatic load. The coat and claws, useful for long-term change assessments, have the advantage of being collectable without invasiveness. In the present study, the Cortisol (C) and Dehydroepiandrosterone-sulfate (DHEA-S) concentration monthly changes in the coat and claws were studied in female dogs from mating to the end of weaning to assess Hypothalamic–Pituitary–Adrenal (HPA) axis activation during pregnancy and the post-partum period. The results from 15 Dobermann Pinscher female dogs showed a trend of increase of the coat C from mating to 60 days post-partum, with significant changes between mating and parturition-60 days post-partum (p < 0.01) and between the 30-day pregnancy diagnosis (PD) and 30–60 days post-partum (p < 0.05). The claws C trend showed significant increases between mating and 30–60 days post-partum (p < 0.05) and between the PD and 60 days post-partum (p < 0.01). DHEA-S in both matrices showed non-significant changes. The results suggest that maternity could play a pivotal role in the HPA axis activation, with a subsequent chronic secretion of C determining an increase in the allostatic load in the mothers. Neither maternal parity nor litter size played a significant role in the accumulation of C and DHEA-S in both matrices.

The measurement of milk electrical conductivity (EC) is a relatively simple and inexpensive technique that has been evaluated as a routine method for the diagnosis of mastitis in dairy farms. The aim of this study was to obtain further knowledge on relationships between EC, production traits and somatic cell count (SCC) in Italian Mediterranean Buffalo. The original dataset included 5411 records collected from 808 buffalo cows. Two mixed models were used to evaluate both the effect of EC on MY, PP and FP and EC at test-day, and the effect of EC on somatic cell score (SCS) by using five different parameters (EC_param), namely: EC collected at the official milk recording test day (EC_day₀), EC collected 3 days before official milk recording (EC_day₃), and three statistics calculated from EC collected 1, 3 and 5 days before each test-day, respectively. All effects included in the model were significant for all traits, with the only exception of the effect of EC nested within parity for FP. The relationship between EC and SCS was always positive, but of different magnitude according to the parity. The regression of EC on SCS at test-day using different EC parameters was always significant except when the regression parameter was the slope obtained from a linear regression of EC collected over the 5-day period. Moreover, in order to evaluate how well the different models fit the data, three parameters were used: the Average Information Criteria (AIC), the marginal R₂ and the conditional R₂. According to AIC and to both the Marginal and Conditional R₂, the best results were obtained when the regression parameter was the mean EC estimated over the 5-day period.

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.