Monitoring predictive indicators of stressors in laying hens

Traditionally, laying hen farmers monitor health, welfare and productivity of their flock based on feed and water intake of the birds, several climate factors, the productive output of the flock (egg production and bird weight) and observations on behavioural performance. Due to the growing number of birds per layer farm and the decreased availability of qualified personnel, it becomes increasingly difficult to safeguard and control animal health and welfare. Concurrently, there is a global trend towards more sustainable livestock farming with profitable animal production and efficiency, maintaining good animal health and welfare and food safety and a low ecological footprint. To keep up with these developments, farmers can benefit from state-of-the art sensor technology, serving as artificial nose, ears and eyes that gather 24/7 data on flock health, welfare and productivity.

This research project aims to improve laying hen welfare in aviaries by early detection of stressors based on continuous assessment of reliable, predictive animal-based indicators. Relevant stressors and their predictive indicators will be identified based on interviews with laying hen farmers and poultry experts. Additionally, the predictive value of egg quality and manure odour profiles, combined with continuous sensor-enabled data on feed and water intake, climate parameters and egg production, in on-farm welfare assessment will be identified.

The potential of odour sensors, acoustic sensors and camera’s to detect stress-induced changes in hen behaviour and health will be determined. Patterns in sensor data will be discovered with ‘machine learning’ models in a commercial setting. Based on developed algorithms, relationships between sensor output and changes in hen welfare, health and productivity will be detected. Ultimately, this information can be used to develop a predictive monitoring platform for the poultry farmer, to support farm management decisions and to validate effects of data-driven decisions on laying hen health, welfare and productivity.