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Selective Dry Cow Therapy

What is SDCT?

Since the 1960s, it has been a common practice on dairy farms to use “blanket” dry cow therapy--to administer antimicrobial drugs that prevent and treat costly mammary infections to all cows as they enter a dry off period. That practice was warranted at the time. But since then, great improvements in animal care, milking techniques, and animal specific record keeping have reduced both infection rates during lactation and the need for blanket treatment at dry off. 

Collaborators at Cornell University, University of Minnesota, Dairy Health and other private veterinary practices, Quality MIlk Production Service (QMPS), CADE, and other industry allies have validated a Selective Dry Cow Therapy or SDCT protocol that allows dairy farmers to reduce their use of dry cow antibiotic treatments by 50-80+% without negatively impacting cow health or production, while saving money on treatments. 




This protocol uses herd health records (Dairy Herd Information Association Somatic Cell Count (SCC) data or DHIA SCC and mastitis event data) to identify the cows that are likely to not have subclinical mastitis and thus do not need antibiotic treatment at dry off. Thus, farmers need only treat dairy animals with an active mastitis infection or at a higher risk for infection at dry-off, as opposed to treating all animals at the end of their lactation. Cows can be designated as high or low-risk by using somatic cell count (SCC) and milk culture data prior to dry-off, as well as herd records. This approach recommends treatment based on specific criteria such as elevated SCC and history of mastitis.


For farms that are using DairyCOMP305 (DC305) software and are on DHIA test, the command “ECON\SDCT” has been created to easily identify animals and is already available for use (see DC305 Quick Start Guide for command instructions). For farms that are not using that platform, the recommendations can be calculated offline with herd health records or even with bacteriologic culture results.

Assessing Risk Through Somatic Cell Counts
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