TraWel was started to address an important challenge in modern dairy farming: large amounts of valuable data are collected every day but are not fully used to improve animal health and welfare. More than half of the milk in Norway is produced on farms with automatic milking systems (AMS). These systems continuously collect information about each cow, creating a unique opportunity to detect disease earlier and support better herd management.
One of the biggest health challenges in dairy farming is subclinical mastitis, a hidden udder infection that often develops without visible signs. If it is not detected early, it can progress to clinical mastitis, causing pain for the cow, reducing milk production, increasing treatment costs, and shortening the animal’s productive life. Existing monitoring methods do not fully exploit the rich information available from modern milking systems.
TraWel will develop new methods that combine artificial intelligence (AI), dynamic modelling, and advanced data management to analyse biosensor data collected by AMS. By identifying hidden patterns and predicting disease development, the project will create an early warning and decision-support system for subclinical mastitis at both the individual cow and herd level.
SINTEF contributes expertise in artificial intelligence, mathematical modelling, and data analytics. Together with partners in veterinary science and dairy production, SINTEF develops advanced methods for analysing large datasets and transforming them into practical decision-support tools. The project will help farmers detect disease earlier, improve animal health and welfare, reduce antibiotic use, and contribute to more sustainable and knowledge-based dairy production in Norway.