From sensing to sense-making: Exploring sensor-based monitoring of flock-level health and welfare indicators in laying hens

Summary

Good care for laying hens in cage-free aviary systems requires more than just providing extra space and structures compared to cages. Precision Livestock Farming (PLF) technologies, which use sensors and computer programs, could help detect health and welfare problems early in these complex multi-level systems, but they are still rarely used in practice. This study explored flock-level health and welfare monitoring in commercial aviaries through stakeholder interviews and sensor tests on odor, movement, and egg production. We found that manure odor and video-based activity monitoring can reveal acute stress reactions. Adaptive computer models, combined with farmer knowledge, improve problem detection accuracy, especially for detecting egg production issues. Laying hen farmers value simple health and welfare indicators like feed intake, activity, and sound, but are concerned about misuse of (online) data. Our work highlights the need to combine expert knowledge, user-friendly PLF tools, and multiple data sources to improve early detection and promote better hen welfare.