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

Technicians recorded body condition score (BCS) and several parameters rela ted to estrus and/or metritis for 1694 first insemination cows on 23 farms. Additional variables for modeling the adjusted odds ratios (OR) for pregna ncy were data on disease prior to or within 21 days of AI and test day milk yields. Significant predictors for pregnancy were farm, year and season, B CS, uterine tone, contaminated insemination gun after AI, fat-protein corre cted kilograms milk (FPCM), days in milk (DLM), and diseases. Vaginal mucus , ease of cervical passage, and lameness were not significant predictors fo r pregnancy. Pregnancy risk at AI increased with increasing DIM, reaching a near optimum after 82 days. Lack of uterine tone was associated with a low ered 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 Al (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 coll ected by AI technicians, are significant predictors of Al outcome. Daily pr oducers and veterinarians should jointly examine the potential costs and va lue of such AI technician-based data to improve herd fertility. (C) 1999 by Elsevier Science Inc.