[Show abstract][Hide abstract] ABSTRACT: Frequently updated energy balance (EB) estimates for individual cows are especially useful for dairy herd management, and individual-level estimates form the basis for group-level EB estimates. The accuracy of EB estimates determines the value of this information for management decision support. This study aimed to assess EB accuracy through ANOVA components and by comparing EB estimates based either on milk composition (EBalMilk) or on body condition score (BCS) and body weight (BW) (EBalBody). Energy balance based on milk composition was evaluated using data in which milk composition was measured at each milking. Three breeds (Danish Red, Holstein-Friesian, and Jersey) of cows (299 cows, 623 lactations) in parities 1 to 4 were used. Milk data were smoothed using a rolling local regression. Energy balance based on milk composition was calculated using a partial least squares (PLS) model based on milk fat, protein, and lactose contents and yields, and the daily change in these variables at each day. Energy balance based on BCS and BW was calculated from changes in body condition and BW scored weekly or fortnightly. Equations for calculation of EBalMilk and EBalBody used no common variables and were, therefore, assumed mathematically independent. Traits were analyzed within 3 stages of lactation expected to have high mobilization of body tissue (1, early), almost balanced (2), and deposition of body energy (3, mid to late lactation). In general, EBalMilk and EBalBody followed similar expected changes through lactation. Estimates of covariance were obtained using single-trait mixed models with random regression terms describing the change with time and used for calculation of repeatability as intraclass correlations. Within stage, EBalMilk was less repeatable than EBalBody (0.53, 0.41, 0.43 vs. 0.93, 0.91, 0.86, respectively, for stages 1, 2, and 3), mainly because of a larger residual variance for EBalMilk. Correlations between individual-level estimates of EBalMilk and EBalBody were close to zero. However, correlations between EB estimates in different lactation stages tended to be stronger for EBalMilk than for EBalBody, although correlations for both EB traits were small. It is concluded that EB estimates based on milk composition are less accurate than those based on body traits, but EBalMilk can compensate partly for this inaccuracy by being updated more frequently.
[Show abstract][Hide abstract] ABSTRACT: In this paper, a new uterine discharge index (D-index) was created and tested. It was based on a principal component analysis (PCA) of clinical findings of classical uterine discharge symptoms and rectal temperature during the postpartum period of dairy cattle. The PCA analysis revealed how uterine discharge features relate to each other and how they cluster together possibly representing different degrees of uterine inflammation. The D-index was the result of the multivariate PCA-analysis, and the D-index gives a continuous value between 0 and 10. It was demonstrated that the same scale, i.e. the D-index, can be used without any adjustment from 1 to 6 weeks post-calving. It is valid for any type of uterine discharge without defining the type of infection or differentiating between infection and contamination. The D-index was tested using the uterine involution data. Uterine involution was significantly delayed in the high-D-index group of cows. Similarly, in the test with all cows, involution was progressively delayed and the rate of involution of the pregnant horn was slowed down with the increase of the D-index values. It is concluded that the D-index can be a new practical, universal, tool for improved management of dairy cows in the postpartum period under commercial conditions.
[Show abstract][Hide abstract] ABSTRACT: This study tested a model for predicting reproductive status from in-line milk progesterone ;measurements. The model is that of Friggens and Chagunda [Theriogenology 64 (2005) 155]. Milk progesterone measurements (n = 55 036) representing 578 lactations from 380 cows were used to test the model. Two types of known oestrus were identified: (1) confirmed oestrus (at which insemination resulted in a confirmed pregnancy, n = 121) and (2) ratified oestrus (where the shape of the progesterone profile matched that of the average progesterone profile of a confirmed oestrus, n = 679). The model detected 99.2% of the confirmed oestruses. This included a number of cases (n = 16) where the smoothed progesterone did not decrease below 4 ng/ml. These cows had significantly greater concentrations of progesterone, both minimum and average, suggesting that between cow variation exists in the absolute level of the progesterone profile. Using ratified oestruses, model sensitivity was 93.3% and specificity was 93.7% for detection of oestrus. Examination of false positives showed that they were largely associated with low concentrations of progesterone, fluctuating around the 4 ng/ml threshold. The distribution of time from insemination until the model detected pregnancy failure had a median of 22 days post-insemination. In this test, the model was run using limited inputs, the potential benefits of including additional non-progesterone information were not evaluated. Despite this, the model performed at least as well as other oestrus detection systems.
[Show abstract][Hide abstract] ABSTRACT: High-producing dairy cows experience negative energy balance in early lactation. Dry-cow feeding management will affect the performance and metabolic status of dairy cows in the following early lactation. The present study evaluates dry-cow feeding strategies for priming lipid metabolism in the dairy cow to overcome the metabolic challenges in the following early lactation. Five weeks before expected calving, 27 cows were assigned to 1 of 3 isonitrogenous and isoenergetic dietary treatments: a low-fat control diet (dry-control); a high saturated fat diet (dry-HSF); and a high linseed diet (dry-HUF). The cows were fed the same TMR lactation diet after calving. The treatments were evaluated by performance and metabolic parameters in blood and liver. The cows fed dry-HSF and dry-HUF had significantly greater plasma nonesterified fatty acid concentrations compared with dry-control, and the dry-HUF cows had the greatest C18:3 concentrations in plasma in the prepartum period. Further, the cows fed dry-HSF and dry-HUF diets had a tendency for the greatest capacity for incomplete beta-oxidation of fatty acids in the liver in wk 3 prepartum. The plasma cholesterol concentration was greatest for cows fed dry-HSF in the prepartum period compared with those fed dry-control and dry-HUF. The cows fed dry-HSF had the lowest plasma nonesterified fatty acid and liver fat concentrations in early lactation compared with the cows fed dry-control and dry-HUF. Data in the literature and the present experiment indicate that supplementing dry cows with a saturated fatty acid source is a positive strategy for priming dairy cows for body fat mobilization in the following early lactation.
[Show abstract][Hide abstract] ABSTRACT: Milk composition varies with energy status and was proposed for measuring energy balance on-farm, but the accuracy of prediction using monthly samples is not high. With automated sampling and inline milk analysis, a much higher measurement frequency is possible, and thus improved accuracy of energy balance determination may be expected. Energy balance was evaluated using data in which milk composition was measured at each milking. Three breeds (Danish Holstein, Danish Red, and Jerseys) of cows (623 lactations from 299 cows) in parities 1, 2, and 3+ were used. Data were smoothed using a rolling local regression. Energy balance (EBal) was calculated from changes in body reserves (body weight and body condition score). The relationship between EBal and milk measures was quantified by partial least squares regression (PLS) using group means data. For each day in lactation, the within-breed and parity mean EBal and mean milk measures were used. Further PLS was done using the individual cow data. The initial PLS models included 25 combinations of milk measures allowing a range of nonlinear effects. These combinations were as follows: days in milk (DIM); DIM raised to the powers 2, 3, and 4; milk yield; fat content; protein content; lactose content; fat yield; protein yield; lactose yield; fat:protein ratio; fat:lactose ratio; protein:lactose ratio; and milk yield:lactose ratio, together with 10 "diff()" variables. These variables are the current minus the previous value of the milk measure in question. Using group means data, a very high proportion (96%) of the variability in EBal was explained by the PLS model. A reduced model with only 6 variables explained 94% of the variation in EBal. This model had a prediction error of 3.82 MJ/d; the 25-variable model had a prediction error of 3.11 MJ/d. When using individual rather than group means data, the PLS prediction error was 17.3 MJ/d. In conclusion, the mean Ebal of different parities of Holstein, Danish Red, and Jersey cows can be predicted throughout lactation using 1 common equation based on DIM, milk yield, milk fat, and milk protein measures.
[Show abstract][Hide abstract] ABSTRACT: The aim of this study was to test a model for mastitis detection using a logic that allows examination of time-related changes and a progressive scale of mastitis state (i.e., not using specificity/sensitivity). The model produces a mastitis risk (MR) for individual cows on a scale from 0 (completely healthy) to 1 (full-blown mastitis). The main model input was lactate dehydrogenase (LDH; mumol/min per L) x milk yield. Test data containing 253 mastitis cases were used. Proportional samples were collected from each cow at each milking and analyzed for LDH and somatic cell count (SCC). The basis for the health definitions was veterinary treatment records. A refinement of the basic health definitions was made using systematic positive deviations in log(SCC) to indicate untreated infections. Two subsets of cows were identified: mastitic cows and cows completely free of mastitis (healthy controls). The time-profiles of these 2 groups in a 60-d window relative to day of veterinary treatment were examined. Model reliability throughout all stages of lactation and degrees of infection was examined using SCC as a continuous measure of degree of mastitis. The time-profile for the health controls was flat throughout the 60-d window with a median MR of 0.02. In contrast, the profile of the mastitic cows increased above the control cows' baseline from about -6 d, rising to a MR value of 0.20 at d 0, and declining to the control level after treatment. There were significant differences between mastitic and healthy cows from -4 to +2 d relative to veterinary treatment. When cases were time-aligned to peak of infection, rather than veterinary treatment, there was a much sharper peak to the time-profile of mastitic cows. The median MR at peak was 0.62 and the mean was 0.80. Using these data, the MR value of 0.62 had a <1% likelihood of actually coming from a healthy control. Testing against SCC, on the whole data set, showed that only 2.1% of all MR values had an error >0.7. These estimates of model reliability are comparable with the greatest values reported in the literature and, additionally, the model was able to detect significant differences between mastitic and healthy cows 4 d before treatment. It was also found that specificity/sensitivity calculations are inappropriate for evaluating time-related changes and a progressive scale of predicted mastitis state.