Using Milk Feeder Data to Predict Calf Learning Success

By Melissa Cantor and Breanna Bone

Many dairy producers know the feeling: you move a new pen of calves onto an automated milk feeder (AMF), and within hours, some are happily drinking milk, while others hang back, reluctant to approach. The "fast learners" quickly figure out how to get milk and are independently going up to the feeder. Whereas the "slower learners" require repeated training sessions, extra labor, and sometimes hand-fed bottles to keep them on track.

What if you could tell, within the first couple of days of being on the AMF, which calves were going to be fast learners, and which ones would need more help? Thankfully, the data is already being collected by the AMF; you just need to know what to look for.

The Power of AMF Data

Automated milk feeders are not just a feeding tool; they also provide us with unique insights into the calf's feeding behaviors.

Most AMF systems track the following metrics for each calf:

• Milk Intake (L) – How much milk the calf consumes in a day

• Drinking Speed (L/min) – How quickly the calf drinks its milk allotment
• Rewarded Visits – The number of times the calf visits the feeder and receives milk in a day

• Unrewarded Visits – The number of visits where no milk is allotted (often due to the milk feeding plan)

These data are useful, but further behavioral insight comes when you track how these behaviors change over time.

What is Relative Change and Why It Matters

If you only look at the raw data numbers, you might miss the full story. For example, a calf that drinks 4 liters on day one and 5 liters on day two has had a smaller percentage of change in milk intake than a calf that drinks 1 liter on day one and 3 liters on day two, but the first calf still drank more overall. So, which calf is better?