This study investigated the relationship between body condition and milk yield of dairy cows. Holstein cows (n = 779) on a commercial dairy farm were scored for body condition weekly beginning at dry-off and continuing until 120 d of lactation. Multiple linear regression and principal component analysis were used to characterize relationships. Mean body condition scores were 2.77 and 2.66 at dry-off and parturition, respectively. Principal component analysis was used to reduce the collinearity among independent variables, to calculate new parameter estimates, and to rank the relationship of each variable with milk yield. Results indicated that change in body condition during the dry period was ranked first, followed by lactation number, and then body condition score at dry-off for multiparous cows. A one-point increase in body condition score between dry-off and parturition was associated with 545.5 kg more milk in the first 120 d of lactation. Each additional point of body condition at dry-off was associated with 300 kg less milk at 120 d of lactation. Results suggested that cows that gained condition during the dry period yielded more milk in the first 120 d of lactation and had an accelerated rate of increase in milk yield. The results of this study indicate that body condition score is an important tool for monitoring dairy herds.

This study investigated the relationship between changes in body condition during the dry period and early lactation and conception to first postpartum AI. Holstein cows (n = 720) on a commercial dairy farm were scored weekly for body condition beginning at dry-off and continuing until first AI. Occurrence of postpartum diseases was recorded. A multiple logistic regression model was a significant predictor of the success or failure of conception for multiparous cows, but not for primiparous cows. Principal component analysis reduced collinearity among independent variables and allowed the variables to be ranked based on their contribution to the interval from first AI to conception. The top three ranking variables were lactation number, milk yield at 120 d of lactation, and change in body condition score between parturition and wk 4 of lactation. Increased milk yield at 120 d of lactation was associated with an increased likelihood of conception, and decreased body condition during the 1st mo of lactation was associated with a decreased likelihood of conception. Health problems were less associated with conception than were body condition or milk yield in this herd. Body condition during the dry period and during the first 30 d of lactation is an important tool to identify cows at risk for failure to conceive at first AI.

This research validated body condition scores with ultrasound measurements of subcutaneous fat. Fifty Holstein cows were evaluated during three sessions in 1993. Cows scored during each session were divided into three groups of 15 or 20 cows. Body condition scores were assigned by one trained individual, utilizing a five-point (1 = thin to 5 = fat) visual scoring technique. Cows were scored to the nearest quarter point. Ultrasound measurements of subcutaneous fat were obtained by another individual at the lumbar, thurl, and tailhead areas of both sides of the cow. Body condition scores and ultrasound measurements were collected on the same day, but obtained independently. Correlations between ultrasound measurements ranged from .36 to .86. Regression models were developed to validate the body condition scoring technique across the three cow groups. Group number and different combinations of ultrasound measurements were independent variables, and body condition score was the dependent variable. Ultrasound measurements were significantly associated with body condition scores. The coefficients of determination for the models ranged from .36 to .65, depending on which ultrasound measurements were included in the model. These results suggest that the body condition scoring technique used in this study was as valid as ultrasound techniques for measurement of subcutaneous fat.

No abstract

A microcomputer expert system for dairy herd reproductive management was developed using an expert system shell and Turbo Pascal. The expert system initially examines the broad areas of days open, days to first breeding, detection of estrus, and conception rate to determine whether a problem exists. Interpretations ranging from "excellent" to "severe" were established for each trait. The system then selects an area for evaluation that has the largest negative influence on days open. Once an area has been selected for further evaluation, the expert system utilizes information from the user and DHI reports developed by the Dairy Records Processing Center in Raleigh, NC. These reports identify problems with conception categorized by production, parity, service number, days in milk, breed, and service sire. In addition, questions are presented by the expert system to isolate problems of accuracy of data, use of natural service, semen handling, AI technique, detection of estrus, signs of estrus, and other management areas. Recommendations and suggestions are given. Ten commercial herds having a conception rate less than 40% were evaluated by the expert system and by an extension reproduction specialist who supplied information for the system. Of 100 areas investigated, the expert system and extension specialist identified 47 as potential problem areas, agreeing on 85% of them. Most discrepancies resulted from the specialist applying a less restrictive standard when values were close to a preselected threshold.