Precision Technology for Dairy Calf Health and Management

Precision technologies provide a way to study and monitor dairy calf feeding and behavior throughout the day. Automated milk feeders record milk intake, drinking speed, and feeder visits, whereas wearable sensors estimate activity, lying behavior, and in some systems, “rumination” (jaw motion). Research has identified associations between these measurements and calf health, including behavioral changes before the clinical diagnosis of respiratory disease and diarrhea.

Measuring calf feeding and behavior

Automated milk feeders collect individual feeding records while delivering programmed milk allowances. These records can describe how much milk a calf consumes, how quickly it drinks, and how often it visits the feeder. Some visits result in milk delivery, whereas others occur when milk is unavailable under the feeding program. These unrewarded visits provide information about feeding motivation, although their frequency also depends on milk allowance and access to the feeder. Additional technologies can measure starter intake or estimate body weight, expanding the information available for evaluating calf development (Costa et al., 2021).

Wearable sensors use movement data to estimate behaviors. Their usefulness depends first on how accurately those estimates represent the behavior being measured. Swartz et al. (2016) compared a leg-mounted accelerometer with video observations in five group-housed calves and reported strong agreement for steps, lying time, and lying bouts. The findings supported the device’s ability to measure these behaviors under the study conditions.

Validation is specific to the device, behavior, and population evaluated. Accurate measurement of lying time does not establish accurate measurement of feeding or rumination, nor does it establish an ability to detect disease. As summarized by Costa et al. (2021), performance varies among technologies and calf ages, making validation an essential part of interpreting the available data.

Behavioral changes associated with disease

Studies of respiratory disease and diarrhea indicate that illness can be accompanied by measurable changes in feeding and activity before clinical diagnosis.

Cantor and Costa (2022) compared 33 calves with bovine respiratory disease with 33 healthy calves. Affected calves had lower milk and starter intake, spent more time lying, and had fewer steps and lying bouts. Differences in unrewarded feeder visits were detectable on several days before diagnosis. These findings suggest that feeding records and activity measurements capture complementary aspects of the behavioral response to respiratory disease.

Evidence from individually housed calves also supports an association between activity and diarrhea. Guevara-Mann et al. (2023) evaluated 13 matched pairs of calves and found that calves with diarrhea took fewer steps and had lower activity indices. Differences in relative activity patterns were detectable two days before clinical signs. This study demonstrated that activity monitoring can identify disease-associated changes even in calves without access to automated milk feeders.

These results support the potential for earlier recognition of illness; however, feeding and activity changes are not specific to a single disease, and differences between sick and healthy calves do not, by themselves, establish how accurately a system will classify individual animals.

Individual patterns and management effects

Interpretation of precision data is complicated by differences among calves and changes during the preweaning period. Milk intake, activity, and resting behavior vary with age, feeding allowance, feeder training, housing, regrouping, and weaning. Consequently, the same recorded value can have different meanings for different calves or at different stages of development.

Measurements relative to an individual calf’s recent pattern may help account for some of this variation. The diarrhea study by Guevara-Mann et al. (2023), for example, identified differences in relative activity patterns before clinical signs. Nevertheless, establishing a useful baseline requires sufficient observations, which can be difficult for diseases occurring shortly after birth.

Management also affects the interpretation of feeding records. Reduced milk intake may accompany illness, but it can also follow a scheduled reduction in allowance. Unrewarded visits may reflect feeding motivation, hunger, or competition. These overlapping influences help explain why an alert developed under one management system may perform differently elsewhere.

Applications in feeding and weaning

Beyond disease detection, precision technologies offer opportunities to characterize individual feeding patterns and development. Automated feeders can deliver different milk allowances and gradual weaning schedules while documenting consumption. Systems that measure starter intake or body weight provide additional data relevant to the transition from milk to solid feed.

Costa et al. (2021) reviewed the potential for these measurements to support individualized feeding and weaning. The extent of that application depends on which measurements are available and how accurately they are recorded. Milk consumption alone provides a different picture of development than a combination of milk intake, starter intake, and growth.

Overall, the available research shows that precision technologies can measure selected calf behaviors and identify changes associated with disease. Evidence for consistently accurate alerts across farms and for improved outcomes following those alerts remains more limited. Their contribution to calf health and management therefore depends on the distinction between recording behavior, detecting illness, and demonstrating a measurable benefit from the information collected.

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