The aim of this study was to characterize patterns of energy balance through lactation of cows kept under constant feeding conditions. Danish Holstein, Danish Red, and Jersey cows were studied during consecutive lactations and remained on the same dietary treatment throughout. They were fed a normal (13.55 MJ of digestible energy/kg of dry matter) or a lower energy diet (12.88 MJ of digestible energy/kg of dry matter) ad libitum throughout lactation. Energy balance was calculated using the effective energy (EE) system in such a way that energy balance equated to body energy reserve change. In the EE system the energy values assigned to feeds are directly equivalent to the energy requirements of the animal; 1 MJ of EE supply has the same energy value as 1 MJ of lipid loss from the body. The resulting body energy change data were analyzed using a linear spline model. There was no evidence to suggest that different combinations of breed and parity required different knot placements. The Holstein mobilized significantly more body energy in early lactation than the Danish Red and Jersey breeds. Parity 1 cows mobilized significantly less than parity 2 and 3 cows. There was a significant interaction between breed and parity in the first half of lactation due to parity 1 Jersey cows having a greater mobilization than would be expected of the difference between parities in the other breeds. As lactation progressed, the differences between parities and between breeds decreased. Cows on the higher energy diet had a more positive energy balance. Within breed and parity, the following possible predictors of individual differences in body energy change were examined: fatness-corrected live weight, condition score at calving, and genotype. There was no difference in the predicted cow effect or residual energy balance profile when grouped according to quartiles of corrected live weight or according to condition score at calving. During the period of most negative energy balance (d 14) there was no significant relationship between live weight and intake, suggesting that, within diet type, the systematic patterns of body energy change through lactation in cows that were kept under stable and sufficient nutritional conditions cannot be accounted for by environmental factors such as constrained intake or condition score at calving. Thus, these patterns appear to have a genetic basis. The proportion of the phenotypic variation (remaining after accounting for fixed effects) accounted for by additive genetic effects varied through lactation from 4.2 to 13.0%. Genetic correlations between early and late lactation energy balances were low and close to zero, suggesting that body energy changes in early and late lactation are genetically independent traits.

Electrical conductivity (EC) of milk has been introduced as an indicator trait for mastitis over the last decade, and it may be considered as a potential trait in a breeding program where selection for improved udder health is included. In this study, various EC traits were investigated for their association with udder health. In total, 322 cows with 549 lactations were included in the study. Cows were classified as healthy or clinically or subclinically infected, and EC was measured repeatedly during milking on each quarter. Four EC traits were defined; the inter-quarter ratio (IQR) between the highest and lowest quarter EC values, the maximum EC level for a cow, IQR between the highest and lowest quarter EC variation, and the maximum EC variation for a cow. Values for the traits were calculated for every milking throughout the entire lactation. All EC traits increased significantly (P < 0.001) when cows were subclinically or clinically infected. A simple threshold test and discriminant function analysis was used to validate the ability of the EC traits to distinguish between cows in different health groups. Traits reflecting the level rather than variation of EC, and in particular the IQR, performed best to classify cows correctly. By using this trait, 80.6% of clinical and 45.0% of subclinical cases were classified correctly. Of the cows classified as healthy, 74.8% were classified correctly. However, some extra information about udder health status was obtained when a combination of EC traits was used.

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.

Intramammary infusion of lipopolysaccharide (LPS) in cows induces udder inflammation that partly simulates mastitis caused by infection with Gram-negative bacteria. We have used this animal model to characterize the quantitiative response in the milk proteome during the time course before and immediately after the LPS challenge. Milk samples from three healthy cows collected 3 h before the LPS challenge were compared with milk samples collected 4 and 7 h after the LPS challenge, making it possible to describe the inflammatory response of individual cows. Quantitative protein profiles were obtained for 80 milk proteins, of which 49 profiles changed significantly for the three cows during LPS challenge. New information obtained in this study includes the quantified increase of apolipoproteins and other anti-inflammatory proteins in milk, which are important for the cow's ability to balance the immune response, and the upregulation of both complement C3 and C4 indicates that more than one complement pathway could be activated during LPS-induced mastitis. In the future, this analytical approach may provide valuable information about the differences in the ability of individual cows to resist and recover from mastitis.

Prediction of nutrient partitioning is a long-standing problem of animal nutrition that has still not been solved. Another substantial problem for nutritional science is how to incorporate genetic differences into nutritional models. These two problems are linked as their biological basis lies in the relative priorities of different life functions (growth, reproduction, health, etc.) and how they change both through time and in response to genetic selection. This paper presents recent developments in describing this biological basis and evidence in support of the concepts involved as they relate to nutrient partitioning. There is ample evidence that at different stages of the reproductive cycle various metabolic pathways, such as lipolysis and lipogenesis, are up or down regulated. The net result of such changes is that nutrients are channelled to differing extents to different organs, life functions and end-products. This occurs not as a homeostatic function of changing nutritional environment but rather as a homeorhetic function caused by the changing expression of genes for processes such as milk production through time. In other words, the animal has genetic drives and there is an aspect of nutrient partitioning that is genetically driven. Evidence for genetic drives other than milk production is available and is discussed. Genetic drives for other life functions than just milk imply that nutrient partitioning will change through lactation and according to genotype -i.e. it cannot be predicted from feed properties alone. Progress in describing genetic drives and homeorhetic controls is reviewed. There is currently a lack of good genetic measures of physiological parameters. The unprecedented level of detail and amounts of data generated by the advent of microarray biotechnology and the fields of genomics, proteomics, etc. should in the long-term provide the necessary information to make the link between genetic drives and metabolism. However, gene expression, protein synthesis etc, have all been shown to be environmentally sensitive. Thus, a major challenge in realising the potential afforded by this new technology is to be able to be able to distinguish genetically driven and environmentally driven effects on expression. To do this we need a better understanding of the basis for the interactions between genotypes and environments. The biological limitations of traditional evaluation of genotype £ environment interactions and plasticity are discussed and the benefits of considering these in terms of trade-offs between life functions is put forward. Trade-offs place partitioning explicitly at the centre of the resource allocation problem and allow consideration of the effects of management and selection on multiple traits and on nutrient partitioning.Keywords: cattle, genotype environment interaction, nutrient partitioning, plasticity IntroductionIn its broadest sense, the term 'nutrient partitioning' refers to the processes by which available nutrients are channelled, in varying proportions, to different met...

The effects of energy density in the diet [low = 0.86 SFU/kg dry matter (DM) or high = 1.06 SFU/kg DM] and daily milking frequency (two or three times) in early lactation on plasma concentrations of metabolites and hormones were evaluated in 40 Holstein dairy cows arranged in a 2 x 2 factorial block design. The four treatment combinations were L2, L3, H2 and H3, and the experimental period comprised the first 8 weeks of lactation. Plasma glucose, insulin and insulin-like growth factor (IGF)-I concentrations were on average 8 (3.43 versus 3.19 mmol/l), 114 (41.6 versus 19.4 pmol/l) and 60% (91.9 versus 57.4 ng/ml) higher, whereas beta-hydroxybutyrate (BOHB), plasma urea nitrogen (PUN) and growth hormone (GH) concentrations were on average 18 (0.73 versus 0.89 mmol/l), 14 (7.18 versus 8.35 mmol/l), and 63% (1.0 versus 2.6 ng/ml) lower for cows fed diet H than for cows fed diet L. Cows milked three times daily had a 6% (3.20 versus 3.42 mmol/l) lower plasma glucose concentration and a 19% (0.88 versus 0.74 mmol/l) higher plasma concentration of BOHB compared with cows milked two times daily. Plasma non-esterified fatty acid (NEFA) concentration was not affected by either treatment. Overall, it is concluded that increasing the daily milking frequency creates a higher metabolic imbalance in early lactation. Cows in early lactation will benefit from receiving a high energy density diet and thereby avoid a too high metabolic imbalance when mobilizing body tissue in support of milk production.

A dynamic deterministic biological model was developed that generates, for a given cow on a given day, a value for her risk of having mastitis. The model combines real-time information from a mastitis indicator measured in milk with additional factors that are other known risk factors of mastitis but that are not reflected in the indicator. l-Lactate dehydrogenase (LDH), an enzyme whose activity is increased because of mastitis, is used as an example of a mastitis indicator. The additional factors incorporated in the model are days from calving, breed, parity, milk yield, udder characteristics, other disease records, electrical conductivity, and herd characteristics. The model is designed to run each time a new LDH value is recorded and can run in the absence of the additional factors. Electrical conductivity measurements and disease records, where available, also trigger the model to run. As an input, milk LDH activity values (micromol/min per L) are multiplied by milk yield (L) to produce the amount of LDH (micromol/min) and are then smoothed using an extended Kalman filter before being processed by the biological model. The output comprises a risk of acute mastitis and a relative degree of chronic mastitis. The model also produces a days-to-next sample value that allows sampling frequency to be either increased or reduced depending on the risk of mastitis. The days-to-next sample value was designed to make the best use of opportunities afforded by automated, inline sampling technology. The model functionality was investigated using simulated data, and real-farm data of naturally occurring mastitis were then used to validate the model. The results demonstrated that the model is robust to sampling frequency and random noise in the LDH measurements. It was able to detect mastitis reasonably well: Using a threshold mastitis risk of 0.7, sensitivity for detecting clinical mastitis was 82%. Specificity, that is, the ability to avoid misclassifying healthy observations as mastitis, was 99%.

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.