Automated Monitoring of Cattle Heat Stress and Its Mitigation

Abstract

Climate change related global warming is likely to continue, despite all mitigation measures taken by humans, due to the lag effect of long-term anthropogenic activities. Warming of the atmosphere can impact worldwide cattle production directly by compromising health, welfare and productivity, and indirectly by reducing the quality and quantity of animal feed. Under warm thermal conditions, cattle adjust their physiological and behavioural responses as an integral part of thermoregulation to maintain internal body temperature within a safe range. However, a greater intensity and duration of heat exposure can exceed thermoregulatory capacity leading to an increase in internal body temperature beyond the normal limit that ultimately evokes different animal responses to heat. In cattle, response to heat stress can be visually observed as elevated respiration rate or panting, but continuous visual monitoring is labour intensive, time consuming and subjective. Therefore, different weather-based indices have been developed such as the temperature humidity index (THI) and heat load index (HLI) which are commonly used weather-based indices for monitoring cattle heat stress at commercial level. However, the thermal comfort level of cattle based on weather-based indices has limited use at a microclimatic and individual animal level. Varying sensor-based approaches have shown promise to shift the focus of heat stress management to the individual level. Monitoring individual animal response and mitigation strategies for isolated heat-susceptible cattle could save on heat management costs whilst improving animal welfare and productivity. Here we review the technologies that enable automatic, continuous, and real-time cattle heat stress monitoring and mitigation under commercial conditions. Future platforms for autonomous monitoring and mitigation of heat stress in cattle are likely to be based on minimally-invasive smart technologies either singly, or in an integrated system, enabling real-time solutions to animal responses under various production systems and environmental conditions.

Evaluation of the Best Region for Measuring Eye Temperature in Dairy Cows Exposed to Heat Stress

“…Thus, we speculate that the better performance of the left eye might also be partly attributed to the direct exposure to fans. Indeed, evaporative cooling including sprinklers and fans together has shown a significant effect on reducing the body surface temperature of cows by promoting local cutaneous evaporation ( 25 , 26 ). However, further studies are required in which the direction of the air flow relative to the cows is strictly controlled.…”

Practical considerations for adaptive strategies by US grazing land managers with a changing climate

“…Managers could also modify management. For example, they may move livestock onto grazing lands during the night when lower temperatures occur to reduce heat stress (Islam et al., 2021). This would most likely be feasible with small‐scale producers due to labor requirements and pasture sizes.…”

Milking Welfare: Bovis Sacer and the Inclusive Thanatopolitics of the Dairy Farm

“…As discussed previously, technologies like sensors, cameras, RFID, pressure plates, and infrared thermography, often categorised together as Precision Livestock Farming (PLF) (Silva et al 2021), have been used to surveil cows through the translation of their biologies and behaviours into data, which humans then assess to evaluate cows' economic value. The data produced through these technologies have been used with machine learning to identify patterns in the herd and individual cows, and cattle health and comfort (Garcia et al 2020;Islam et al 2021). These datasets have also been used as part of decision support systems (DSS), which collect data from on-farm and off-farm sources to provide analytical insights for farmers (Baldin et al 2021;Bianchi et al 2022).…”

“…2020; Islam et al. 2021). These datasets have also been used as part of decision support systems (DSS), which collect data from on‐farm and off‐farm sources to provide analytical insights for farmers (Baldin et al.…”