[Measurement of the status of trace elements in cattle using liver biopsy samples]. / Het bepalen van de status van de spoorelementenvoorziening bij rundvee met behulp van leverbiopten.

Serum, plasma, or urine samples are usually used for the measurement of the trace elements copper; zinc, iron, selenium, because these samples are easy to obtain; however; these samples are not always appropriate. For example, it is not possible to measure molybdenum, the major antagonist of copper; in blood or urine. Therefore measurement of trace elements in liver tissue is considered the gold standard. For the assessment of selenium the method of choice remains determination of glutathion peroxidase in erythrocytes and for the assessment of magnesium determination of magnesium in urine. We determined the accuracy and repeatability of measuring trace elements in liver biopsies and whole liver homogenates. The levels of trace elements measured were similar in both preparations (92% agreement). Liver biopsy in live animals is a relatively simple procedure but not common in The Netherlands. Reference levels of trace elements, classified as too low, low, adequate, high, and too high, were established on the basis of our research and information in the literature. In a second study we investigated the practical aspects of obtaining liver tissue samples and their use. Samples were collected from cattle on a commercial dairy farm. Liver biopsy provided additional information to that obtained from serum and urine samples. We prepared a biopsy protocol and a test package, which we tested on 14 farms where an imbalance of trace minerals was suspected. Biopsy samples taken from 4 to 6 animals revealed extreme levels of trace elements.

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.