Using Milk Feeder Data to Predict Calf Learning Success

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?

That's where relative change comes in. Instead of looking just at how much milk a calf drinks or how many rewarded visits they have, you look at the percentage of change in their behavior from their own baseline. Relative change can calculate how quickly a calf learns the feeder when comparing it to its own behavioral data. A percentage increase in relative change of milk intake or rewarded visits demonstrates that the calf has increased its milk intake and has gone up to the feeder more compared to their baseline. No change or decrease in relative change means that the calf decreased or did not change their intake compared to the baseline.

How to Calculate Relative Change

Relative change measures how much a calf's behavior has increased or decreased compared to its selected behavior baseline. To calculate it:

    Relative change = (Baseline – Data from Day of Interest) / (Baseline) 

For example, if a calf drank 3 liters on the baseline day and 5 liters on the day of interest, the relative change for milk intake would be: (5 – 3) ÷ (3) = 0.67, or a 67% increase. This approach lets you compare progress between calves with different starting points, making it easier to see which calves are improving and which calves may need more training guidance. You can apply this calculation to any AMF collected feeding behavior (milk intake, rewarded visits, drinking speed, or unrewarded visits.)

How to Determine a Relative Change Baseline

The baseline you choose will affect how meaningful your calculations are. Research shows calves often take about four days to fully train on an AMF, but if you wait that long to set your baseline, you may miss the chance to identify slow learners earlier on the feeder.

Day two is often a good choice. By this point, calves have had a full 24 hours to adjust to their environment, and differences in learning are starting to emerge. One important note: if you are trying to identify independent learners, be sure to consider whether a calf's increase in milk intake or visits came from it independently approaching the feeder or from being led to the feeder as part of a training routine.

Which Variables Matter Most for Predicting Training Success

Research indicates that the following variables can identify calf training success:

• Milk Intake (L) – Fast learning calves often have higher milk intakes as early as day one on the feeder when compared to slower learning calves
• Rewarded Visits – Fast learning calves tend to have more rewarded visits to the feeder as early three days on the AMF when compared to slower learning calves
• Relative Change in Milk Intake (%) – Fast learning calves tend to have lower relative changes in milk compared to slower learning calves. The reasoning for this is that the calves who understand the feeder tend to have less variation in their overall milk intake on a day basis when compared to slower learning calves.
• Relative Change in Rewarded Visits (%) – Fast learning calves tend to have lower relative changes in rewarded visits compared to slower learning calves. The reasoning for this is that the calves who understand the feeder tend to have less variation in how many times they visit the feeder when compared to slower learning calves.

Additionally, drinking speed, unrewarded visits, and the relative changes in these behaviors still have meaning, but they are less reliable as predictors for training success on the AMF.

Real-World Example

Imagine two calves, both introduced to the AMF on the same day:

Calf A - Has a milk intake of 5 liters on day one and 5.5 liters on day two (baseline). This is a 10% relative change in milk intake. The calf's rewarded visits go from 3 on day one to 4 on day two (baseline), and there is also a small relative change in rewarded visits of 25%. This steadiness is actually a good sign. It shows Calf A was meeting its needs right from the start and didn't need to make large changes in its behavior.

Calf B – Has a milk intake of 2 liters on day one and 6 liters on day two (baseline), which is a 67% relative change. The calf's number of rewarded visits on day one was 1, and then it had 4 visits on day two (baseline), which is a 75% relative change in rewarded visits. The percentage gains might look impressive, but they actually tell you Calf B started behind and is playing "catch-up," and thus, suggesting Calf B may need more training support.

This example highlights why relative change works best when paired with actual intake levels. A calf with low relative change but high consistent intake is probably going to train quickly. Whereas a calf with high relative change but low total intake may still be struggling.

How to Make it Work

Tips to find fast and slow learning calves:

• Focus on new calves for their first four days on the feeder
• Check both the raw data numbers and the relative change for milk intake and rewarded visits
• Look for patterns in behavior, not just single-day variations
• Flag calves with low intakes and high relative change for extra training attention

Take-Aways

Identifying slow learners early doesn't just make the training process easier; it sets calves up for better long-term performance. Calves that meet their nutritional targets quickly are better equipped to fight off illness, maintain steady growth, and reach weaning weights on schedule.

And from a labor standpoint, data-driven calf training saves time. Instead of spending time training every calf, you can focus your efforts on calves that need the most training support.

Automated milk feeders collect valuable data on every calf, every day. By focusing on milk intake, rewarded visits, relative change in milk intake, and relative change in rewarded visits, and by looking at individual calf patterns, you can identify which calves will quickly train on the feeder and which calves will need extra training help.