Researchers at Michigan State University are examining whether artificial intelligence can help dairy producers better understand how calves recover from disbudding — and whether some animals may need additional pain support after the procedure.
Hot-iron disbudding remains common in dairy calf management. Producers use it to prevent horn growth, reduce injury risk and improve facility safety. However, the procedure creates a burn wound around the horn bud, resulting in inflammation and pain that can extend beyond the immediate treatment window.
Veterinary groups including the American Veterinary Medical Association and the American Association of Bovine Practitioners recommend multimodal pain control. That approach combines local anesthesia with non-steroidal anti-inflammatory drugs (NSAIDs) to reduce both acute and lingering discomfort.
Not All Calves Recover the Same Way
Even when farms follow recommended protocols, calves do not respond identically. Pain perception varies, and so does the response to medication. As a result, some animals may continue to experience discomfort after disbudding, while others recover more quickly.
Ongoing pain can influence more than behavior. Research has linked stress and inflammation to suppressed immune function, increased disease susceptibility and reduced growth performance — all critical factors in early-life herd health.
Yet identifying subtle, persistent pain presents a challenge. Visual observation may miss changes in activity patterns that signal discomfort.
Turning Ear Tag Data Into Insight
To address that gap, researchers in the Trindade Lab are using machine-learning models to analyze minute-by-minute activity data from commercial ear-tag sensors already deployed on many dairy farms.
In preliminary trials involving 40 calves, the model distinguished between calves before and after disbudding with 91% overall accuracy. It correctly identified calves likely experiencing pain 86% of the time and correctly recognized calves not in pain 82% of the time.
More notably, at 24, 48 and 72 hours after disbudding, the system flagged approximately one-quarter of calves as still likely experiencing pain — despite receiving multimodal analgesia.
Those findings suggest that standardized pain protocols may not fully address discomfort in every calf.
Toward Individualized Pain Management
Researchers are now exploring how the system could integrate into a practical, on-farm decision-support tool. A future mobile application could notify producers and veterinarians when individual calves show activity patterns consistent with ongoing pain.
Such an approach would allow for targeted treatment decisions, including whether an additional NSAID dose may be appropriate. At the same time, it could reduce unnecessary medication in calves that have recovered as expected.
If validated through larger-scale studies, this precision-monitoring approach could support:
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Improved calf welfare
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Reduced stress-related disease risk
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More efficient labor allocation
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Lower risk of delayed wound healing or complications
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Stronger early-life growth performance
Implications for Herd Health
As dairy producers continue refining calf care practices, tools that detect subtle health and welfare indicators may offer new advantages. By combining established pain management protocols with real-time behavioral data, researchers hope to move toward more individualized and responsive care.
For herd health programs focused on long-term productivity, understanding how calves recover — not just how they are treated — may represent the next step in optimizing early-life management.
