The effects of time of disease occurrence, milk yield, and body condition on fertility of dairy cows.

The associations between occurrence of diseases, milk yield, and body condition score on conception risk after first artificial insemination (AI) were analyzed in an observational study on a convenience sample of 43 farms participating in a herd health program. Data were taken from 9369 lactations, from 4382 cows inseminated between 20 and 180 d in milk from 1990 to 1996. Two logistic regression models, one containing data from all lactations and a subset containing data from 1762 lactations with body condition scoring, were used to determine pregnancy risk at first AI. The effects of herd deviation in test-day milk yield, body condition score loss, and milk fat to protein ratio changes in early lactation were significant predictors of pregnancy risk, independent of disease; days in milk; farm; and seasonal factors. Three different methods of disease parameterization (incidence rates, binomial classes dependent on the interval in days since last occurrence with respect to AI, and a linear variable weighted for this interval) produced similar results. Metritis, cystic ovarian disease, lameness, and mastitis gave odds ratios for pregnancy risk ranging from 0.35 to 1.15, largely dependent on the interval in days from final disease occurrence to first AI. Displaced abomasum, milk fever, and retained fetal membranes resulted in odds ratios for pregnancy risk of 0.25, 0.85, and 0.55, respectively. These diseases showed little relationship between fertility and the number of days since last occurrence. Results of this study confirm the negative effects of milk yield, body score condition loss, and disease on dairy cow fertility. The effects of some diseases on first service conception were strongly dependent on the interval since last disease occurrence. This was especially valid for clinical mastitis, which has an extremely weak effect on conception if occurring prior to AI and is associated with > 50% reduction in pregnancy risk if occurring in the 3 wk directly after AI.

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 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 AI 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 >33 kg (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.

Predicting optimal time of insemination in cows that show visual signs of estrus by estimating onset of estrus with pedometers.

An experiment was designed to estimate the optimal interval from the beginning of estrus to artificial insemination (AI). The data were analyzed by means of a mathematical model. The analysis was based on pedometer readings and results of rectal palpation at 42 to 49 d post-AI of 171 breedings in 121 cows. The chance of conception was highest between 6 and 17 h after increased pedometer activity; the estimated optimum was at 11.8 h. In this data file, the effects of disease, inseminator, time of AI (a.m. or p.m.), and bull did not contribute to the improvement of the model. The effects of disease were not significant because of the low incidence of any specific disease. Activity measurements can be used as a tool for AI strategy to improve conception in groups of healthy cows and heifers already showing visual signs of estrus.

[The dairying veterinary approach to a high bulk milk cell count caused by Streptococcus agalactiae]. / De bedrijfsdiergeneeskundige aanpak van een te hoog tankmelkcelgetal veroorzaakt door Streptococcus agalactiae.

The herd health approach for dairy herds with a high bulk milk somatic cell count, in which Streptococcus agalactiae plays a major role, was evaluated. After introduction of the standard mastitis prevention programme, all quarters of infected cows were treated during lactation. In three of the four herds investigated, the bulk milk somatic cell count dropped below the limit of 400,000 cells/ml for a long period of time. The herd in which there were many infections with Staphylococcus aureus and Streptococcus agalactiae was an exception. The management, somatic cell count, and prevalence of subclinical mastitis in the different herds is discussed. It is concluded that for infection with Streptococcus agalactiae at the herd level, treatment during lactation can be an effective method to lower the bulk milk somatic cell count. In essence, however, the approach to the problem lies in the standard mastitis prevention programme.