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Oakwood Dairy herdsmen Greg Kowalewski, Steve Taber and Esdras Ramirez check several cows flagged with health alerts Glenn Gaston photography
RESPONDING TO ALERTS: Oakwood Dairy herdsmen Greg Kowalewski (left), Steve Taber and Esdras Ramirez respond to cows flagged for attention by the farm’s automated health monitoring system.

New York dairy farm embraces health monitoring technology

Oakwood Dairy is now able to better track cow health, as well as save money on labor.

What was once just a trial is now a new cow wellness and farm labor management tool at Oakwood Dairy in Auburn, N.Y.

“Everything we do leads to milk production. Automated health monitoring technology helps us catch issues before they present to the eye and keeps our cows healthier and in production, and is a great addition to our genetics program, staff training and farm management aimed at doing the best we can for cow health,” says Garrett Miller, herd manager.

Julio O. Giordano, a veterinarian and associate professor of dairy cattle biology and management at Cornell University, led the project that tracked 1,200 cows over a year at the 2,100-cow dairy.

“The objective was to evaluate the feasibility of cow health monitoring based primarily on an automated system, and its impact on milk production, culling dynamics, reproduction, labor and costs,” Giordano says.

The research was funded by the New York Farm Viability Institute.

Farm technology

Half of the cows wore a collar-attached sensor to track rumination behavior and physical activity. Clinical exams, auscultation and bloodwork produced data on body temperature, urine ketones and other health and disease indicators. In-line milk monitoring sensors installed at the milking parlor monitored daily changes in milk production.  

The cows in the group were evaluated based on a combination of alerts from neck and parlor sensors. The system uses an algorithm to calculate a health index (HI) score for each cow. When HI reaches or falls below 86 out of 100, an alert is sent to the farm’s computer and to smartphones. A change in production rate between two milking sessions generated an alert from the milk yield sensor.\

From 30 days before calving to 35 days in milk, the farm’s staff observed 600 cows as usual while 600 other cows were automatically tracked for issues such as displaced abomasum, ketosis, indigestion, metritis and mastitis.

“We have used pedometers to measure cow activity related to milk production, but we could see how adding the rumination tracking enhances proactive cow health and stress management and reduced labor,” Miller says.

Glenn Gaston photographyClose up of a cow's neck-attached data sensor used to track rumination behavior and physical activity

COW CONNECTION: Neck-attached data sensors track rumination behavior and physical activity on cows and heifers at Oakwood Dairy.

Tracking cow health through the AHM system did not result in a different proportion of cows identified with health disorders up to 35 days in lactation. “Importantly, there were no differences for milk average daily production, total production up to 22 weeks, milk quality, nor for first AI reproduction performance,” Giordano says.

He adds that a statistical tendency for more cows identified with clinical ketosis in the traditionally monitored group “was likely the result of the more intensive monitoring; however, we are uncertain about the implications of this difference as we did not observe substantial difference in herd performance.”

Less stress, labor savings

“This system increases farm efficiency by focusing attention in the fresh pen only on those cows flagged for attention and allows more time for cows in need of specialized care, for checking the herd for any lameness and such tasks as trimming tail hair,” Miller says. “We used to check 15 to 35 cows a day in the headlock. With the automated system, it is zero to 10, cutting labor by two-thirds, reducing cow time in headlocks and letting more of our cows just be cows.”

Staff hours spent checking cows was 711 hours per worker for the nonautomated group and 284 hours per worker for cows tracked by sensor.

“Our staff works hand-in-hand with the system, but I think they also are working to see if they can outsmart it by catching issues before the system alerts,” he says.

The automated system occasionally allows workers to be able to complete tasks and go home early.

Glenn Gaston photographyGarrett Miller checks a computer screen for automated health alerts with other herdsmen

COMPUTER CONNECTION: Garrett Miller (second from left) along with some of his herdsmen check the computer for alerts from the automated health monitoring system on the farm.

“We could potentially add 300 cows without necessarily adding new labor,” he says.

Upon completing the trial, Oakwood Dairy purchased the automated system and now tracks heifers of breeding age.

“The automated tracking supports the opportunity for first-calving at 22-23 months, which makes more milk over the cow’s lifetime,” he says.

The farm is exploring opportunities to add more technology, particularly for its milking systems, as well as a parlor upgrade.

Before you buy

Which farms can benefit the most from automated health monitoring? A previous survey of Giordano’s group of 60 dairy farms showed that most farms checked fresh cows once per day; 36% checked cows twice daily; and 5% did not monitor due to issues with labor, time and untrained personnel.

“The potential value of using automated health monitoring for farms that do not use intensive monitoring by farm personnel is in identifying more cows with health issues. For farms that do use intensive monitoring, the opportunity exists to reduce labor and cow handling,” Giordano says.

“This project provided an opportunity to understand how an automated health monitoring system could benefit both cow health and business economics at the farm level, particularly in light of the tight labor market,” says David Grusenmeyer, executive director of the New York Farm Viability Institute.

Considerations for purchasing an automated health monitoring system include identifying the best sensor tag; calculating average tag replacement cost; labor hours to monitor and respond to system alerts; time and cost to develop compliance, procedure and staff training; cost of troubleshooting the technology; and factoring in the cost of health issues the system may not yet catch efficiently, such as milder cases of mastitis and metritis.

The system used at Oakwood Dairy cost approximately $8,000, plus $165 per sensor tag, and is expected to have a lifespan of four to six years depending on farm conditions and usage.

Giordano’s research team calculated the technology’s breakeven points for systems with an average three, five or seven-year lifespan. To learn more about Giordano’s work, contact him at 607-255-0136, email jog25@cornell.edu, or visit blogs.cornell.edu/giordano.

Dunn writes from her farm in Mannsville, N.Y.
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