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.

Cover image of the 50th Conference Proceedings: Text of "AABP Annual Conference" with the AABP logo superimposed over an interlocking 50. Yellow background.

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Published

2017-09-14

Issue

2017

Section

Research Summaries

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