J.A. Hertl

Risk factors for post partum ovarian dysfunction in high producing dairy cows in Belgium: A field study

An epidemiological study of risk factors for postpartal ovarian disturbances was carried out on 334 high-yielding dairy cows in 6 well-managed Belgian herds. Ovarian activity was closely monitored using progesterone profiles, based on twice weekly RIA-analysis for progesterone in milk fat, starting at 10 d after calving and continuing until the confirmation of a new pregnancy. Attention was focused on abnormal cyclicity during the preservice, postpartum period; cows were divided into 6 different categories. Three of these categories (normal profile, delayed cyclicity, and prolonged luteal phase) were of major importance and were analyzed using a multiple variable logistic regression model. Season of calving (stable vs pasture, odds ratio (OR)=5.7), an extended length of the previous dry period (> 77 vs < or = 63 d, OR=2.9), problem calvings (OR=3.6), abnormal vaginal discharge (OR=4.5), health problems during the first month of lactation (clinical disease, OR=5.4; ketosis, OR=11.3), and clinical parameters illustrating the appearance of a severe negative energy balance significantly increased the risk for delayed cyclicity before service. Parity (> or = 4 vs 1, OR=2.5), problem calvings (OR=2.9), occurrence of puerperal disturbances (OR ranged from 3.5 to 11.0), health problems during the first month of lactation (OR=3.1), and an early resumption of ovarian cyclicity after calving (< 19 d vs > 32 d, OR=2.8) increased the risk for prolonged luteal cycles before service.

The Cost of Generic Clinical Mastitis in Dairy Cows as Estimated by Using Dynamic Programming

The objective of this study was to estimate the cost of generic clinical mastitis (CM) in high-yielding dairy cows given optimal decisions concerning handling of CM cases. A specially structured optimization and simulation model that included a detailed representation of repeated episodes of CM was used to study the effects of various factors on the cost of CM. The basic scenario was based on data from 5 large herds in New York State. In the basic scenario, 92% of the CM cases were recommended to be treated. The average cost of CM per cow and year in these herds was

The cost of different types of lameness in dairy cows calculated by dynamic programming.

71. The average cost of a CM case was

Effects of clinical mastitis caused by gram-positive and gram-negative bacteria and other organisms on the probability of conception in New York State Holstein dairy cows

179. It was composed of

Optimizing replacement of dairy cows: modeling the effects of diseases

115 because of milk yield losses,

The cost and management of different types of clinical mastitis in dairy cows estimated by dynamic programming

14 because of increased mortality, and

Analysis of correlated continuous repeated observations: modelling the effect of ketosis on milk yield in dairy cows

50 because of treatment-associated costs. The estimated cost of CM was highly dependent on cow traits: it was highest (

Effects of repeated episodes of generic clinical mastitis on mortality and culling in dairy cows.

403) in cows with high expected future net returns (e.g., young, high-milk-yielding cows), and was lowest (

Correlated time to event data: Modeling repeated clinical mastitis data from dairy cattle in New York State

3) in cows that were recommended to be culled for reasons other than mastitis. The cost per case of CM was 18% higher with a 20% increase in milk price and 17% lower with a 20% decrease in milk price. The cost per case of CM was affected little by a 20% change in replacement cost or pregnancy rate. Changes in CM incidence, however, resulted from changes in these factors, thus affecting whole-farm profitability. The detailed results obtained from this insemination and replacement optimization model can assist farmers in making CM treatment decisions.

Effects of pathogen-specific clinical mastitis on probability of conception in Holstein dairy cows

Traditionally, studies which placed a monetary value on the effect of lameness have calculated the costs at the herd level and rarely have they been specific to different types of lameness. These costs which have been calculated from former studies are not particularly useful for farmers in making economically optimal decisions depending on individual cow characteristics. The objective of this study was to calculate the cost of different types of lameness at the individual cow level and thereby identify the optimal management decision for each of three representative lameness diagnoses. This model would provide a more informed decision making process in lameness management for maximal economic profitability. We made modifications to an existing dynamic optimization and simulation model, studying the effects of various factors (incidence of lameness, milk loss, pregnancy rate and treatment cost) on the cost of different types of lameness. The average cost per case (US