Digitalisation opportunities for livestock welfare monitoring with a focus on heat stress

Abstract

Rising global temperatures due to climate change pose an increased risk of heat stress (HS) in livestock, which requires accurate prediction methods to improve animal welfare and mitigate heat-related economic losses. In this literature review, we aimed to critically examine the potential of digitalisation in precision livestock farming from the perspective of assessing heat stress in cattle. Particular attention was paid to the possibility of using sensors to record changes in behaviour, physiological parameters, and feed and water consumption as predictors for detecting and preventing heat stress and its consequences in animals. The use of already proven sensors, such as those that record animal behaviour, to assess physiological parameters related to heat stress may be promising, using innovative data processing technologies, which requires additional investigation in future studies. Precision livestock farming (PLF) technologies that enable real-time monitoring of herds under specific conditions, such as disease or stress, play a key role in increasing the efficiency of herd management. Sensors, cameras, microphones, GPS, accelerometers and RFID tags are commonly used to collect data on animal behaviour and physiological parameters, helping to detect stress. Machine learning algorithms applied to sensor data can distinguish between different states of declining cow welfare, helping farmers make quick decisions. In summary, the integration of sensors, artificial intelligence and precision devices can help prevent heat stress in farm animals, improving animal welfare and productivity, while mitigating the negative effects of heat stress caused by climate change.