A systematic review on the application of precision livestock farming technologies to detect lying, rest and sleep behavior in dairy calves.

Welfare studies are increasingly involving the application of Precision Livestock Farming (PLF) sensors, rather than the use of animal-based indicators directly assessed. PLF technology has the advantage to constantly monitor behavior over a long period of time, thus enabling the assessor to identify changes in animal time budgets in real-time. In calves, lying behavior is essential: new-borns have been reported to spend 70-80% of their daily time lying. Growing up, calves progressively reduce the time spent lying; at 3 months, lying behavior occupies around the 50% of their day. Several studies emphasize how lying behavior can be considered as a potential indicator of positive welfare in ruminants, including calves. The aim of this study was to critically revise scientific literature regarding the application of precision livestock farming technologies to measure lying, rest and sleep behaviors in dairy calves. A systematic literature search based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was conducted through Scopus and Web of Science databases to retrieve full peer-reviewed papers written in English on different PLF technologies applied to measure lying behavior in dairy calves. Literature search retrieved 731 records. After duplicate removal and the application of inclusion criteria, a total of 16 papers were considered eligible for the evaluation. Different PLF technologies and approaches were reported to be used: triaxial accelerometers, machine learning with accelerometer data, computer vision with video cameras, wearable cameras and real-time locating system. Most of the papers (10 out of 16) reported the use of accelerometers, placed on different parts of body of the animal (hind leg, neck, head, ear). Considering the importance that lying behavior has for maintaining homeostasis and development of calves, the possibility to monitor it constantly and reliably with PLF technology would certainly provide a better understanding of calves' behavior and positive welfare. However, our findings underline PLF technologies still show some practical limitations. Therefore, we must ensure that the sensors are valid and reliable before applying them in practice to detect changes that can be linked with welfare status of calves.