Capturing the Power of Data on Dairy Farms to Reduce Antimicrobial Use

Since the 1970s it’s been a common practice on dairy farms to use “blanket” dry cow therapy; that is to administer antimicrobial drugs that prevent and treat costly mammary infections to all cows as they enter a dry off period.  Although the practice may have been warranted as it began, animal care and the milking process have become much more sophisticated over time with many dairies keeping detailed cow specific records. Daryl Nydam DVM, PhD and his collaborative team have harvested the power of that data to predict which cows do—and don’t—need treatment.

This article was originally published in NYFVI’s 2018 annual report. The NYFVI dairy education program emphasized adoption of this practice as a potential topic in its most recent RFP.

The most common infectious disease of dairy cows is mastitis. Nationally 65% of antimicrobial drug usage on dairy farms is for treatment or prevention of mastitis and one-third of those antibiotics are used for dry-off treatment.

Working with New York dairy farms, Nydam has developed and tested a computer algorithm that will provide dairy farmers with the information they need to move to “selective” dry cow therapy (SCDT) protocols to prevent and treat mastitis. This approach can save farm managers $6 per cow across the milking herd and meet consumer desires for more judicious use of antimicrobials.

How does it work? Using a farm’s data, the prototype software applies a predictive SCDT model that sorts the high-risk cows from their low-risk herd mates. The farm data is accessed from widely used industry herd management tools Dairy-Comp 305 and Dairy Herd Improvement Association.

The new protocol is designed to maximize treatment of cows that exhibit clinical or subclinical mastitis and minimize unnecessary treatment of cows that will likely remain healthy throughout the dry period.

Using the algorithm tested by Nydam and coworkers, a herd using a SDCT protocol that has a somatic cell count of 200,000 or less and an adequate diet that maintains the immune system could expect to reduce antimicrobial use by 60% without adversely affecting production and clinical health outcomes. 

In the validation of the model, Nydam was able to confirm that there was no increased risk of new infection in the dry period and similar milk production in the next lactation period among the SCDT group. There was also no increased risk of culling or new mastitis infection in the first 30 days after freshening.

Six New York dairies enrolled in the project and 600 cows were sorted according to the algorithm. Results were excellent in most cases. Five of the six participating farms have indicated that they will continue to use the protocol in the future.

The ability to select only cows that need treatment in an accurate, fast and economical manner makes the practice farmer-friendly and likely to be adopted by New York dairy farms as it becomes available. The work is attracting significant national and international attention as farmers across the country and around the world seek new ways to manage their herd’s health.

“We changed our procedure to follow the selective algorithm for a more controlled treatment plan and reduced the number of cows treated without any negative effect. That represents savings in money, time and labor; reduces the risk of infection to our cows and lessens the opportunity for antibiotic resistance buildup.”

Doug Young
Spruce Haven Farm
Union Springs, New York