Modern livestock production systems can heavily challenge the welfare of animals. A way to manage farm animal welfare in real-time is through the use of Precision Livestock Farming (PLF) systems. The aim of this chapter is to present the authors’ experience in conducting research towards the development of a PLF system that can provide early warnings to pig farmers when the risk of tail damage is high. During our work, we identified challenges related to: (1) choosing the gold standard; (2) choosing the appropriate technology; (3) performing model validation and implementation. Choosing the gold standard for tail damage is challenged by the fact that tail damage will have different stages of development. Also, choosing the optimal time to raise the warning is a compromise between warning early enough to provide farmers’ with sufficient time to prevent the problem and late enough so that the probability of developing into a real problem is high. The gold standard must also be easy to standardise between different observers and conditions. Choice of appropriate technology needs to consider that tail damage is a multifactorial welfare problem with multiple risk factors and thus, several sensor technologies may be needed. Also the nature of the animals to possibly explore and destroy a sensor as well as the harsh environment of livestock buildings is a challenge. Major barriers for performing model validation and implementation are that already in the beginning, data needs to be saved for internal validation, and collaboration contracts with research herds across nations, farmers and information technology companies needs to be in place; to ensure data for external validation and prototype development. Developing a PLF system for welfare monitoring demands that several considerations and decisions are made already in the beginning. These considerations will take time and decisions made will often be a compromise.
Originalsprog
Engelsk
Titel
Practical Precision Livestock Farming : Hands-on experiences with PLF technologies in commercial and R&D settings