Technical note: Assessment of milk temperature measured by automatic milking systems as an indicator of body temperature and fever in dairy cows

Recent Advances on Early Detection of Heat Strain in Dairy Cows Using Animal-Based Indicators: A Review

In pursuit of precision livestock farming, the real-time measurement for heat strain-related data has been more and more valued. Efforts have been made recently to use more sensitive physiological indicators with the hope to better inform decision-making in heat abatement in dairy farms. To get an insight into the early detection of heat strain in dairy cows, the present review focuses on the recent efforts developing early detection methods of heat strain in dairy cows based on body temperatures and respiratory dynamics. For every candidate animal-based indicator, state-of-the-art measurement methods and existing thresholds were summarized. Body surface temperature and respiration rate were concluded to be the best early indicators of heat strain due to their high feasibility of measurement and sensitivity to heat stress. Future studies should customize heat strain thresholds according to different internal and external factors that have an impact on the sensitivity to heat stress. Wearable devices are most promising to achieve real-time measurement in practical dairy farms. Combined with internet of things technologies, a comprehensive strategy based on both animal- and environment-based indicators is expected to increase the precision of early detection of heat strain in dairy cows.

Sampling strategy and measurement device affect vaginal temperature outcomes in lactating dairy cattle

Body temperature (BT) is widely used to evaluate health and heat load status in cattle. Despite its importance, studies vary in how BT is measured and in the biological interpretation of the results. Costs, practicality, labor, and welfare concerns can affect how BT is measured, including frequency of measurement and the type of device used. Inaccurate BT outcomes may have implications for cattle welfare; for example, animals may only receive treatment when fever is identified. Our objectives were (1) to compare measurement of vaginal temperature (VT) using relatively small, inexpensive, and low-accuracy loggers (±0.5 to ±1°C, iButton range; Embedded Data Systems, Lawrenceburg, KY) to a high-accuracy logger (±0.1°C; StarOddi, Gardabaer, Iceland), and (2) to evaluate how different BT sampling strategies correspond to 24-h VT in lactating dairy cows. To address the first objective, VT data from 54 cows were recorded every 45 min for 12 d/cow, on average, using 2 different types of temperature loggers (StarOddi DST centi-T and iButton DS1921H or DS1922L) attached to a shortened, hormone-free controlled internal drug release insert. Average VT obtained from both loggers were compared using mixed models and regression analyses. In addition, we tested the consistency of the low-accuracy loggers in detecting cows with elevated BT using the kappa coefficient of concordance. To address the second objective, VT data from 20 cows were recorded every min for 9 to 11 d/cow using StarOddi loggers. Using these data, we estimated average VT using 11 sampling strategies (every 5, 10, 15, 30, 45, 60, and 120 min, 1×/d recorded in the morning or afternoon, 2×/d, or 3×/d). Estimates and observed means were compared using linear regression. Compared with StarOddi loggers, the iButtons either underestimated (H model: 38.7 vs. 38.0 ± 0.06°C) or overestimated VT (L model: 38.7 vs. 39.2 ± 0.04°C). When considering elevated or fever VT thresholds, iButtons did not correctly classify animals; kappa coefficients of concordance were ≤0.35. Measuring VT as often as every 120 min resulted in more accurate estimates compared with strategies that recorded it once to thrice per day. These results indicate that the type of device (i.e., data logger) and sampling strategies affect BT outcomes and that these decisions affect the interpretation of BT data.