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Use of ai technician scores for body condition, uterine tone and uterine discharge in a model with disease and milk production parameters to predict pregnancy risk at first ai in holstein dairy cows

Authors
Journal
Theriogenology
0093-691X
Publisher
Elsevier
Publication Date
Volume
51
Issue
7
Identifiers
DOI: 10.1016/s0093-691x(99)00071-0
Keywords
  • Fertility
  • Estrus
  • Body Condition
  • Metritis
  • Cystic Ovaries
  • Mastitis
Disciplines
  • Biology
  • Medicine

Abstract

Abstract Technicians recorded body condition score (BCS) and several parameters related to estrus and/or metritis for 1694 first insemination cows on 23 farms. Additional variables for modeling the adjusted odds ratios (OR) for pregnancy were data on disease prior to or within 21 days of Al and test day milk yields. Significant predictors for pregnancy were farm, year and season, BCS, uterine tone, contaminated insemination gun after AI, fat-protein corrected kilograms milk (FPCM), days in milk (DIM), and diseases. Vaginal mucus, ease of cervical passage, and lameness were not significant predictors for pregnancy. Pregnancy risk at AI increased with increasing DIM, reaching a near optimum after 82 days. Lack of uterine tone was associated with a lowered pregnancy risk (OR = 0.69) as was contaminated insemination gun (OR = 0.67), first-parity lactation, FPCM > 33kg (OR=0.71), BCS 2.5 at AI (OR= 0.65), clinical mastitis (OR=0.53), cystic ovarian disease (OR =0.53), and metritis (OR = 0.74). It was concluded that data on BCS and uterine findings, as collected by AI technicians, are significant predictors of AI outcome. Dairy producers and veterinarians should jointly examine the potential costs and value of such AI technician-based data to improve herd fertility.

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