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Economics of reducing antibiotic usage for clinical mastitis and metritis through genomic selection.

  • Kaniyamattam, K1
  • De Vries, A2
  • Tauer, L W3
  • Gröhn, Y T4
  • 1 Section of Epidemiology, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853. Electronic address: [email protected]
  • 2 Department of Animal Sciences, University of Florida, Gainesville 32611.
  • 3 Charles H. Dyson School of Applied Economics and Management, College of Agriculture and Life Sciences and Cornell S. C. Johnson College of Business, Cornell University, Ithaca, NY 14853.
  • 4 Section of Epidemiology, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853.
Published Article
Journal of Dairy Science
American Dairy Science Association
Publication Date
Jan 01, 2020
DOI: 10.3168/jds.2018-15817
PMID: 31629507


Antibiotics use (ABU) in animal agriculture has been implicated in the emergence of antibiotic resistance, a global public health threat. Economically justifiable antibiotic reduction strategies can motivate farmers to reduce ABU for clinical mastitis (CM) and metritis, the most common reasons for ABU on dairy farms. Our objective was to quantify the reduction in incidence of CM, metritis, and ABU, and the herd performance of a representative US herd that uses genomic selection for Lifetime Net Merit 2018 (NM$) selection index, compared with genetic selection based only on the mastitis (MAST) or metritis resistance (METR) trait or a health trait subindex (HTH$). A stochastic dynamic simulation model of a 1,000-cow herd with multi-trait genetics for 19 correlated traits included in the NM$ affected the performance of animals. The incidence of CM and metritis for each animal was affected by the genetic and environmental components of the MAST or METR, along with a standard phenotypic function that calculated the daily underlying herd probability to contract CM or metritis. Selection decisions were made based on genomic estimated breeding values of the traits of interest. A strategy named AI-NM$ based decisions on the NM$ trait so that the correlated genetic trends in MAST and METR are improved. Three other strategies named AI-MAST, AI-METR, and AI-HTH$ maximized respectively MAST, METR, and HTH$ genetic merit, but with a tradeoff in NM$ genetic merit. The cumulative true breeding values (TBV) of NM$ for 15 yr showed a difference of $4,947 per cow between the AI-NM$ (best strategy for NM$) and AI-METR (worst strategy for NM$). However, the 15-yr cumulative TBV of MAST was 26.50 percentage points (PP) higher in AI-MAST, and 18.5 PP higher for METR in AI-METR, compared with AI-NM$. As a result, the 15-yr cumulative phenotypic CM and metritis incidence was respectively 94.03 PP and 58 PP lower in AI-MAST and AI-METR compared with AI-NM$. Therefore the corresponding 15-yr cumulative ABU decreased by 42% in AI-MAST and by 53% in AI-METR. We found that AI-MAST had the lowest CM incidence across the 15 yr, whereas AI-METR had the lowest incidence of metritis and the smallest total ABU for 15 yr. To achieve the lowest incidence of CM, metritis, and ABU strategies AI-MAST, AI-METR, and AI-HTH$ had to incur 15-yr discounted cumulative losses per cow of $1,486, $1,434, and $1,130, respectively, compared with AI-NM$. Hence, AI-NM$ had the best financial performance, despite having slightly higher incidence of CM, metritis, and ABU. Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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