Use of a data-driven algorithm to guide selective dry-cow therapy

Authors

  • A. K. Vasquez Department of Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY 14853
  • C. Foditsch Department of Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY 14853
  • M. Wieland department of Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY 14853
  • R. A. Lynch Department of Animal Science, Cornell University, Ithaca, NY 14853
  • S. Eicker Valley Ag. Software, Tulare, CA 93274
  • P. D. Virkler Department of Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY 14853
  • D. V. Nydam Department of Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY 14853

DOI:

https://doi.org/10.21423/aabppro20173334

Keywords:

dry-cow therapy, BDCT, dry-cow intramammary, algorithm, high risk cows, animal health, production

Abstract

Nationally in 2014, 80.3% of dairies applied blanket dry-cow therapy (BDCT) and 93% of cows were treated with dry-cow intramammary (IMM) antimicrobials. However, surveys indicate that >80% of dry quarters yield negative culture results. Selective dry-cow therapy (SDCT) describes the identification and treatment of only cows/quarters infected or at high risk for infection. The purpose of this study was to determine if a data-driven algorithm, without the use of culture, could identify and selectively treat only "high risk" cows (those likely benefitting from DCT) without adverse effects on animal health and production outcomes.

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Published

2017-09-14

Issue

Section

Research Summaries

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