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.

What is the challenge?

This research will focus on utilizing automated calf feeders that will identify calves with disease sooner in order to mitigate the risks of performance loss and improve therapeutic outcomes.

Addressing the problem:

The main objective of this project is to utilize existing data collected from automated calf milk feeders and validate new technologies to improve the health, welfare, and performance of dairy calves. Automated calf feeders collect data at each visit to the feeder, including information on how fast the calf is drinking, the quantity of milk consumed, the number of visits the calf makes to the feeder and the timing of visits.

Project Impact:

This project aims to improve the understanding of what markers should be routinely evaluated by dairy producers and advisors to identify calves with disease on automated milk feeders.

Key message for decision makers:

This data could be used to identify calves that have disease earlier, leading to improved therapeutic success and a reduced impact on future growth performance.

Partners:

Dr. Michael Steel (co-Investigator)