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Utilizing data from automated calf feeders: Identifying novel ways to identify disease to improve growth and performance of dairy calves

Disease in dairy calves is crucial to identify as it can affect not only the calf’s welfare but can lead to poor growth performance and milk production which ultimately impacts the profitability of the dairy farm. However, calves are often housed in group settings where it can be difficult to identify a calf experiencing disease. The purpose of this project is to use automated calf feeders that collect data on the calf on each visit to the feeder and use this data to identify calves that have a disease. This early identification of disease will aid in new technologies to improve the health, welfare, and performance of dairy calves.


Principal investigator: David Renaud & Charlotte Winder