Abstract
Heat stress in animals affects productivity, health, and reproduction, with particularly pronounced effects in dairy cows. Identifying heat stress requires understanding both physiological and environmental indicators, such as increased heart rate, respiratory rate, and rectal temperature, which reflect the animal's thermal condition within its environment. Thermoregulation in cows involves behavioral and physiological adjustments to maintain homeothermy, aiming to stabilize their internal thermal state. To assess the thermal condition of animals, machine learning models have been developed, leveraging both environmental and physiological indicators for more accurate stress detection. Among the various indices of thermal environment, the Temperature and Humidity Index (THI) is the most widely used. Cooling strategies for animals and their environments are essential to mitigate the effects of heat stress. One effective approach involves the use of evaporative adiabatic cooling combined with forced ventilation systems in feeding alleys and pre-milking holding areas. This method enhances evaporative exchanges and facilitates heat dissipation between the animal and its surroundings, thereby alleviating heat stress and improving both the welfare and productivity of dairy cows.