As dairy producers increasingly adopt precision livestock farming technologies, the debate between wearable neck sensors (collars/eartags) and internal devices (rumen boluses) has intensified. Both technologies offer transformative insights into herd health, estrus detection and heat stress. However, when the conversation shifts to key profitability drivers – labor efficiency, technology return on investment (ROI) and feed efficiency – several critical distinctions emerge. Based on recent comparative research, neck collars possess several distinct advantages over bolus technology:

  • Ability to automate actions and gain efficiency in management tasks. By leveraging sensor-driven automated sorting, producers can reduce lock time, decrease labor by one to three hours per day and streamline tasks such as breeding and health treatments. Bolus technology typically cannot sort animals.
  • Flexible technology management and ROI. Collars are wearable devices whereas boluses are internal, which creates two key challenges from a cost and management perspective. When a cow exits the herd for any reason, so does the bolus – meaning new cows will require additional investment. Collars can easily be swapped between animals. Similarly, if a bolus stops functioning, repair and replacement options are limited.
  • Accurate measurement of eating behavior and, in the case of AfiCollar, the ability to accurately identify individual cow dry matter intake (DMI). 

The Mechanics of Cow Monitoring: A Closer Look at Eating Behavior

To understand the disparity of eating behavior data, one must look at what is actually being measured. Accelerometer-based collars, such as the AfiCollar, are positioned on the neck to detect specific 3D movement patterns associated with jaw mechanics and head positioning. This allows the sensor’s algorithms to differentiate between distinct behaviors: rumination, grazing/eating, panting and general activity.

In contrast, a bolus sits within the reticulum. While highly effective at measuring internal physiological parameters like reticulorumen temperature and pH, its ability to monitor specific ingestion behavior is limited by its location. Research comparing these technologies highlights a significant data gap. While collars report specific eating time in minutes per day, bolus systems report a generalized Activity Index.

The Accuracy of Intake Prediction

The inability to isolate eating time has cascading effects on farm management. In a recent study, sensors that recorded both rumination and eating behavior (like collars) achieved significantly higher predictive accuracy (Adjusted-R² of up to 0.58) compared to technologies that relied solely on activity or lacked specific eating metrics.

Without a precise measurement of the time spent eating, the equation for intake remains unsolvable. Research explicitly notes that because boluses rely on rumen motility and general accelerometers, they do not capture eating time as directly as head-mounted sensors, limiting their utility.

Advertisement

The Feed Efficiency Blind Spot

The most significant commercial implication of this technological difference lies in the calculation of Feed Efficiency (FE). FE is generally defined as the ratio of milk produced to DMI. As the industry moves toward breeding and managing for more efficient cows to reduce costs and methane emissions, knowing individual DMI is non-negotiable. Since bolus technology does not measure eating time, it cannot estimate individual DMI. Without DMI, a producer cannot calculate feed efficiency. 

Notably, not all collar sensors have the ability to measure individual cow DMI. In 2025, Afimilk became the first company to accurately identify individual cow intake, combine it with milk production data and create a Feed Efficiency and income over feed score per animal. 

Precision Dairy Tactics Demand the Right Data

While both technologies have proven value, their differences become more discernable when nutrition enters the discussion.

Precision is no longer about collecting more data. It is about collecting the right data. In the race toward more efficient, sustainable dairying, understanding how much a cow eats may be the metric that separates observation from optimization.

References

Edwards, J.P., et al. (2024). "On-animal sensors may predict paddock level pasture mass in rotationally grazed dairy systems." Computers and Electronics in Agriculture.

Hofmann, W., et al. (2024). "Assessing the validity of sensor-based predictions of post-grazing residual in dairy systems." Journal of New Zealand Grasslands.

Castaneda, A., et al. (2025). "Investigating rumination and eating time as proxies for identifying dairy cows with low methane-emitting potential." Journal of Dairy Science Communications.