Behavioural responses related to increasing core body temperature of grazing dairy cows experiencing moderate heat stress

Characterization of the Temperament and Reactivity of Nelore Cattle (Bos indicus) Associated with Behavior Scores during Corral Management in the Humid Tropics

The evaluation of the reactivity and distress of cattle during corral management, by means of subjective scores, aims at the standardization of behavioral indicators, through non-invasive methods, in addition to enabling the development of more appropriate management practices, thus promoting the comfort and well-being of these animals. Therefore, in this study, we aimed to characterize the temperament and distress of cattle managed in a corral using behavioral indicators during the rainiest period. For this, the experiment was conducted on a property located in the municipality of Mojuí dos Campos, during the rainiest quarter (February-April). Thus, 30 male cattle, not castrated, approximately 29 months of age, clinically healthy, and weighing 310 + 20 kg, were divided into three rearing systems: silvopastoral (SP), traditional (SS), and integrated (SI) systems. There were 10 animals per system. Physiological parameters were collected to evaluate rectal temperature (RT) and respiratory rate (RR), as well as body surface temperature (BST), through thermal windows (head and flank infrared temperature and rump infrared temperature). To evaluate temperament and reactivity, scores indicative of corral behavior were used, namely escape speed (ES), tension score (SS_1), tension score (SS_2), reactivity scale (RS), movement score (MS), and temperament scale (TS). The results showed that there was a thermal amplitude of 5.9 °C on average and 8.6 °C at maximum when comparing the structure of the corral and the trees. In addition, the comparisons between the production systems for the behavioral variables did not differ at the 5% significance level, except for ES, where the traditional system differed from the integrated system and the silvopastoral system, showing intermediate average values for both. In addition, there was a positive correlation between the variables RT and RR (r = 0.72; p < 0.01), RR and SS_2 (r = 0.38; p = 0.04), flank infrared temperature and MS (r = 0.47; p = 0.01), rump infrared temperature and RS (r = 0.37; p = 0.04), SS_1 and RS (r = 0.41; p = 0.02), SS_1 and SS_2 (r = 0.39; p = 0.03), RS and SS_2 (r = 0.58; p = 0.00), RS and MS (r = 0.50; p = 0.01), RS and TS (r = 0.61; p = 0.00), SS_2 and MS (r = 0.51; p = 0.00), SS_2 and TS (r = 0.47; p = 0.01), and MS and TS (r = 0.44; p = 0.02), and a negative correlation between ES and TS (r = -0.42; p = 0.02). The rainy season had a major influence on the evaluation of temperature and distress levels during handling in the corral, as evidenced by the association between physiological and behavioral parameters.

Investigation of Climate Effects on the Physiological Parameters of Dairy Livestock (Cow vs. Buffalo)

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.