Follow the data: Wearable sensors bring proactive management to herd health

Activity monitors for humans are built into nearly every device carried during day-to-day routines. Whether it’s a watch, a smartphone or even exercise equipment, humans have quick access to track their heart rate, sleeping patterns and even the number of steps taken in a day. All of these health markers can provide helpful insights to share with a doctor.

In veterinary medicine, particularly within the dairy industry, wearable activity monitors provide similar benefits in research and in practice. High tech tools and data technology increasingly play a key role in enhancing animal health, optimizing production and providing whole herd analysis much faster than could previously be done using manual methods.

Researchers with Dairy at Guelph at the University of Guelph are studying precision technologies, including wearable activity trackers and automatic feeders, to notch up knowledge and optimize their value to farmers.

Much like the way humans use FitBits and Apple watches, monitoring dairy cows starts with fitting a sensor tag or collar onto the cow to measure specific day-to-day activities. Data insights can include how long the cow stands, lies down, walks around or grazes, which over time, helps track its baseline behaviour. Having this information means veterinarians and farmers will be able to tell when a specific cow might be behaving uncharacteristically and can help them through potential problems.

Dr. Stephen LeBlanc, Director of Dairy at Guelph and professor in the Ontario Veterinary College’s (OVC) Department of Population Medicine, notes that several dairy researchers at the U of G are working on validating and optimizing the use of data from wearable sensors and other technologies. “Dairy farmers and veterinarians are at the forefront of implementing automation and technology for milking, feeding, reproductive management and detection of health problems. Our research is helping to identify which data and signals are most useful.”

Often, this information is connected to additional automated processes in a dairy barn, such as automatic feeders, which can read a cow’s sensor and, with a bit of programming from the farmer, deliver a customized diet based on each cow’s individual needs.

Dr. David Renaud, an assistant professor in the Department of Population Medicine, recently worked with colleagues to study automated milk feeders (AMF) used by dairy calves before they are weaned. The research evaluated a range of studies to better understand how AMF systems can predict calf illness in this life phase.

AMFs measure milk consumption, drinking speed and the number of times calves use the feeder. The review of 56 studies found that each of these data points may provide insight into early disease detection in pre-weaned calves.

Beyond using AMFs, Renaud and his graduate students are also looking at other technologies to predict disease. “Right now, we’re doing observational studies at the Ontario Dairy Research Centre to try to predict disease with help from activity monitors,” says Renaud, referring to the unique research site in Elora, Ont. that is owned by the Agricultural Research Institute of Ontario and managed by the University of Guelph through the Ontario Agri-Food Innovation Alliance.

Dr. Tony Bruinjé, a doctoral candidate in OVC’s Department of Population Medicine who is advised by LeBlanc, recently used wearable activity monitors to study whether postpartum health during the time immediately after a cow has a calf is a factor in the monitors detecting when cows are in heat or estrus (the time when they are receptive for breeding) and to identify risk factors that might cause a missed detection.

The timing of a cow’s pregnancy plays an important role in the lactation cycle and milk production. Producers need to identify the single day over a 21-day reproductive cycle that cows are in heat. Wearable sensors have been developed to better detect when a cow is in heat, but they only detect cows that are displaying physical signs of being in heat, such as increased movement and interactions with other cows. Bruinjé’s study will help shed light on why some cows don’t express estrus behaviour in this way, and how their health, after calving, plays a role.

“We examined 1,200 Holstein cows in commercial herds in Ontario for a variety of health conditions from three weeks before calving until nine weeks after calving, following up to check their probability of being detected in heat by the activity monitors during the breeding period,” says Bruinjé.

The study found that cows suffering from things like systemic inflammation (when the immune system kicks into gear to fight infection), hyperketonemia (characterized by elevated levels of blood ketones), reproductive tract disease, or excessive body condition loss after calving, were less likely to be detected in estrus than healthy cows.

“Producers monitoring postpartum health of cows can use these findings to identify cows less likely to be detected in heat by activity monitors and who would benefit from assistance, leaving the rest alone to express estrus naturally,” he adds.

Bruinjé believes the technology will continue to increase its accuracy, become further integrated in on-farm technology and play a role in the relationship between farmers and veterinary care specialists.

“The data collected by these technologies will allow veterinarians and producers to be proactive rather than reactive in herd management,” he says. “It will continue to optimize timely decision-making with less human intervention and improved animal well-being.”

The research on dairy calf weaning using automatic feeders is funded by Dairy Farmers of Ontario and the Ontario Research Fund.

The research on post-partum health monitoring of cows for heat detection is funded by the Ontario Agri-Food Innovation Alliance, a collaboration between the Ontario Ministry of Agriculture, Food and Rural Affairs and the University of Guelph.