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Increasing milk yields in modern dairy cows cause concern that high yield may impair the cows' health and welfare, for example, via negative effects on metabolic status and hypothalamo-pituitary-adrenocortical (HPA) function. This study aims to investigate whether a high level of milk production, and the associated metabolic status, affects HPA function in dairy cows and changes their adaptive capacity. Additionally, it aims to establish whether possible effects of milk production level only show under challenging conditions. Holstein-Friesian cows, which produced on average 11,443 and 7727 kg of fat and protein-corrected milk (FPCM)/305 d in their previous lactation, were compared. During the dry period, the cows were fed to requirements or overfed. High milk yield and the concomitant large energy deficit were associated with 1) increased pituitary (re)activity, i.e., increased ACTH baseline concentrations and higher ACTH concentrations after corticotropin-releasing hormone (CRH) administration, and 2) decreased adrenocortical reactivity, i.e., lower cortisol responses after ACTH administration. Although significant, the effects of milk production level on HPA function were relatively small. Animals showed seemingly normal hormonal responses to CRH and ACTH administration. Also, cortisol baseline concentrations were unaffected. It seems, therefore, unlikely that the adaptive capacity of the high-producing cows was significantly impaired compared with their low-producing herdmates. [ABSTRACT FROM AUTHOR]

Lameness is a major problem in modern dairy husbandry and has welfare implications and other negative consequences. The behavior of dairy cows is influenced by lameness. Automated lameness detection can, among other methods, be based on day-to-day variation in animal behavior. Activity sensors that measure lying time, number of lying bouts, and other parameters were used to record behavior per cow per day. The objective of this research was to develop and validate a lameness detection model based on daily activity data. Besides the activity data, milking data and data from the computerized concentrate feeders were available as input data. Locomotion scores were available as reference data. Data from up to 100 cows collected at an experimental farm during 23 mo in 2010 and 2011 were available for model development. Behavior is cow-dependent, and therefore quadratic trend models were fitted with a dynamic linear model on-line per cow for 7 activity variables and 2 other variables (milk yield per day and concentrate leftovers per day). It is assumed that lameness develops gradually; therefore, a lameness alert was given when the linear trend in 2 or more of the 9 models differed significantly from zero in a direction that corresponded with lameness symptoms. The developed model was validated during the first 4 mo of 2012 with almost 100 cows on the same farm by generating lameness alerts each week. Performance on the model validation data set was comparable with performance on the model development data set. The overall sensitivity (percentage of detected lameness cases) was 85.5% combined with specificity (percentage of nonlame cow-days that were not alerted) of 88.8%. All variables contributed to this performance. These results indicate that automated lameness detection based on day-to-day variation in behavior is a useful tool for dairy management. [ABSTRACT FROM AUTHOR]

During the transition phase, dairy cows are susceptible to develop postpartum diseases. Cows that stay healthy or recover rapidly can be considered to be more resilient in comparison to those that develop postpartum diseases. An indication of loss of resilience will allow for early intervention with preventive and supportive measures before the onset of disease. We investigated which quantitative behavioral characteristics during the dry period could be used as indicators of reduced resilience after calving, using noninvasive Smart Tag neck and Smart Tag leg sensors in dairy cows (Nedap N.V.). We followed 180 cows during 2 wk before until 6 wk after parturition at 4 farms in the Netherlands. Serving as proxy for loss of resilience, as defined by the duration and severity of disease, a clinical assessment was performed twice weekly and blood samples were taken in the first and fifth week after parturition. For each cow, clinical and serum value deviations were aggregated into a total deficit score (TDS total). We also calculated TDS values relating to inflammation, locomotion, or metabolic problems, which were further divided into macro-mineral and liver-related deviations. Smart Tag neck and leg sensors provided continuous behavioral activity signals of which we calculated the average, variance, and autocorrelation during the dry period. Diurnal patterns in the behavioral activity signals were derived by fast Fourier transformation and the calculation of the nonperiodicity. To select significant predictors of resilience, we first performed a univariate analysis with TDS as dependent variable and the behavioral characteristics that were measured during the dry period, as potential predictors with cow as experimental unit. We included parity group as fixed effect and farm as random effect. Next, we performed multivariable analysis with only significant predictors, followed by a variable selection procedure to obtain a final linear mixed model with an optimal subset of predictors with parity group as fixed effect and farm as random effect. The TDS total was best predicted by average inactive time, nonperiodicity ruminating, nonperiodicity of bouts standing up and fast Fourier transformation stand still. Average inactive time was negatively correlated with average eating time, and these 2 predictors could be exchanged with only little difference in model performance. Our best performing model predicted TDS total at a cutoff level of 60 points, with a sensitivity of 79.5% and a specificity of 73.2% with a positive predicted value of 0.69 and a negative predicted value of 0.83. The models to predict the other TDS categories showed a lower predictive performance as compared with the TDS total model, which could be related to the limited sample size and therefore, low occurrence of problems within a specific TDS category. Furthermore, more resilient dairy cows are characterized by high averages of eating time with high regularity in rumination and low averages of inactive time. They reveal high regularity in standing time and transitions from lying to standing, in the dry period. These behaviors can be used as indicators of resilience and allow for preventive intervention during the dry period in vulnerable dairy cattle. However, further examination is still required to find clues for adequate intervention strategies., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

The transition period is a demanding phase in the life of dairy cows. Metabolic and infectious disorders frequently occur in the first weeks after calving. To identify cows that are less able to cope with the transition period, physiologic or behavioral signals acquired with sensors might be useful. However, it is not yet clear which signals or combination of signals and which signal properties are most informative with respect to disease severity after calving. Sensor data on activity and behavior measurements as well as rumen and ear temperature data from 22 dairy cows were collected during a period starting 2 wk before expected parturition until 6 wk after parturition. During this period, the health status of each cow was clinically scored daily. A total deficit score (TDS) was calculated based on the clinical assessment, summarizing disease length and intensity for each cow. Different sensor data properties recorded during the period before calving as well as the period after calving were tested as a predictor for TDS using univariate analysis of covariance. To select the model with the best combination of signals and signal properties, we quantified the prediction accuracy for TDS in a multivariate model. Prediction accuracy for TDS increased when sensors were combined, using static and dynamic signal properties. Statistically, the most optimal linear combination of predictors consisted of average eating time, variance of daily ear temperature, and regularity of daily behavior patterns in the dry period. Our research indicates that a combination of static and dynamic sensor data properties could be used as indicators of cow resilience., (The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

Claw health, locomotion, feed intake, milk yield, body weight, activity, and lying and standing behavior of dairy heifers were monitored in a single dairy herd during the first 3 mo after calving. During the first 8 wk after calving, 2 treatments were applied: restricted freestall access by closing the stalls between 2300 h and 0500 h (yes or no) and alley flooring (concrete or rubber topped slatted floors). Apart from treatments, housing was identical. The animals were kept in small groups (n=4 to 6) in adjacent barn pens. Thereafter, the animals were kept in 1 group in a freestall section with concrete slatted floor and unrestricted access to the stalls for 5 wk. All animals were fed the same partial mixed ration. We hypothesized that (1) hard flooring causes high mechanical load of the claws and (2) restricted freestall access causes prolonged standing bouts and reinforced effects of hard flooring on claws. The heifers had only minor claw lesions before first calving, and the prevalence and severity of sole hemorrhages increased during the first 3 mo after calving (from 0.24 ± 0.08 to 1.18 ± 0.14 and from 0.04 ± 0.01 to 0.24 ± 0.02, respectively), particularly in the outer hind claws. Animals kept on rubber alley flooring had lower average hemorrhage scores in wk 9 (0.13 ± 0.03 vs. 0.21 ± 0.03) and wk 14 (0.20 ± 0.03 vs. 0.27 ± 0.03) after calving, had a slower feed intake (3.05 ± 0.14 vs. 3.46 ± 0.14 g/s) and spent more time feeding (7.3 ± 0.3 vs. 6.6 ± 0.3 min/h) than animals kept on hard concrete alley floors. Restricted freestall access resulted in fewer standing bouts per day (14.4 ± 1.0 vs. 17.9 ± 1.0) and more strides per hour (99.8 ± 5.4 vs. 87.2 ± 5.4) without changing overall standing time (15.0 ± 0.3 vs. 14.7 ± 0.3 h/d) and did not affect the occurrence of sole hemorrhages. The animals with no overnight freestall access spent more time standing (55.9 ± 0.9 vs. 35.8 ± 0.9 min/h) and feeding (7.8 ± 0.3 vs. 4.3 ± 0.3 min/h) between 2300 and 0500 h and less during the rest of the 24-h period (31.3 ± 0.8 vs. 37.0 ± 0.8 min/h and 6.8 ± 0.3 vs. 7.6 ± 0.3 min/h). Thus, the animals adapted to restricted freestall access, that caused increased overnight standing, by additional lying down during the day and used part of the extra standing time at night for feeding. The restrictions probably had only a minor effect on the mechanical load of their claws. Therefore, the first part of the hypothesis was confirmed and the second part was rejected., (Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

A comparative study was performed to evaluate the differences in behavioral and physiological stress responses during milking between cows that were milked by an automated milking system (AM-cows) and cows that were milked in a conventional tandem parlor (TM-cows). In a randomized design, 36 primiparous Holstein-Friesian dairy cows were observed and blood sampled (1-min intervals) individually during milking. AM-cows spent less time standing with their heads outside the feeding trough than TM-cows and had a lower heart rate. In addition, AM-cows had lower maximum plasma adrenaline and noradrenaline concentrations during milking. No differences were found in the number of steps. After tactile stimulation of the teats either by hand or by the cleaning brush, mean oxytocin concentrations did not differ. In AM-cows, however, elevated oxytocin levels were prolonged at the end of milking. Averaged over the first five blood samples, AM-cows tended to have higher plasma cortisol concentrations than TM-cows, but median fecal concentrations of the cortisol metabolite dioxoandrostane were comparable. Maximum quarter milk flow, maximum udder milk flow and residual milk as a percentage of the total milk volume was comparable. From this study it is concluded that behavioral and physiological responses, both in automatically and in conventionally milked cows, were relatively low and were typical for cows being milked. We therefore conclude that, as far as the welfare of the dairy cow during milking is concerned, automatic milking and conventional milking are equally acceptable.

An experiment was performed in primiparous dairy cows (n = 23) to examine consistency of individual differences in reactivity to milking, and correlations between measures of behavior, physiology, and milk ejection. Responsiveness to milking was monitored during the first machine milking, on d 2 of lactation, and during milkings on d 4 and 130 of lactation. Measurements included kicking and stepping behavior, plasma cortisol and plasma oxytocin, heart rate, milk yield, milking time, milk flow rate, and residual milk obtained after administration of exogenous oxytocin. With repeated early lactation milkings, residual milk and the incidence of abnormal milk flow curves decreased. On d 130 of lactation all heifers exhibited normal milk ejection. Except for higher plasma cortisol concentrations on d 2, all measures were consistent over time between d 2 and 4 of lactation as indicated by significant rank correlations. Individual differences in the behavioral response to udder preparation were consistent over time between early lactation milkings and d 130 of lactation. Residual milk, milk yield, maximum milk flow rate, plasma oxytocin and heart rate during udder preparation were similarly interrelated on d 2 and 4 of lactation. High heart rate responses on d 2 and 4 were associated with enhanced inhibition of milk ejection. In contrast, behavior recorded during the milking process was unrelated to ease of milk removal. Our results indicate that milking at the beginning of lactation may be stressful to some heifers, to the extent that milk ejection is inhibited, but less disturbing to others. The existence of consistent behavioral and physiological responses in the present study suggests that responsiveness of dairy heifers to milking is mediated by stable animal characteristics.

The objective of this study was to estimate genetic correlations among fertility and measures of energy balance, dry matter intake, and live weight (change). Data from 622 first lactation cows (fed ad libitum a complete ration) included milk, fat, and protein yields; energy balance; and live weights for the first 15 wk of lactation. For a subset of the heifers (n = 329) and 91 contemporaries, progesterone values were measured in the milk twice a week and were used to determine the interval between calving and first luteal activity (CLA). To obtain correlations, a series of bivariate animal model analyses was performed. The heritabilities for CLA and energy balance were 0.16 and 0.33, respectively. Heritability for live weights were 0.48 to 0.61 and for live weight changes were 0.17 and 0.24. Genetic correlations of CLA with milk, fat, and protein yields were high and unfavorable (0.51, 0.65, and 0.48, respectively), whereas energy balance, live weight during lactation, and weight gain all had a favorable genetic correlation with CLA (range -0.40 to -0.80). When genetic variation in CLA was adjusted genetically for milk, fat, and protein yields, the correlation with feed intake became -0.49 and explained approximately half of the genetic variance in CLA. This supports that increasing genetic merit for feed intake, at a common genetic merit for yield, improved CLA. Furthermore, selection on an index including protein yield and energy balance or live weight (change) allowed 0.71 to 0.80 of the maximum response in protein yield without a negative selection effect on CLA.

Eighteen cows had been selected for their responsiveness to psychological stress during the first lactation and were classified as having low (n = 10) or high (n = 8) cortisol concentrations in response to isolation-induced stress. In the present study these cows, now in their second lactation, were used to determine the effect of social isolation stress on the permeability of mammary tight junctions. During the experiment, each cow was isolated from the rest of the herd for 55 h. After the 1st h of isolation, each cow received a bolus infusion of endotoxin in one hind quarter in order to challenge tight junctions. Blood samples were taken throughout to measure lactose, which was used as an indicator of tight-junction leakiness. After 1 h of isolation, stress caused an increase in tight junction permeability in both groups, which was further enhanced by the endotoxin treatment. Although the permeability did not differ significantly between the two groups, it was consistently higher in the high-cortisol group, which was also the most stress-responsive group. Thus, psychological stress may adversely affect milk quality by allowing serum components to leak into milk.

In the analysis of test day records for dairy cattle, covariance functions allow a continuous change of variances and covariances of test day yields on different lactation days. The equivalence between covariance functions as an infinite dimensional extension of multivariate models and random regression models is shown in this paper. A canonical transformation procedure is proposed for random regression models in large-scale genetic evaluations. Two methods were used to estimate covariance function coefficients for first parity test day yields of Holsteins: 1) a two-step procedure fitting covariance functions to matrices with estimated genetic and residual covariances between predetermined periods of lactation and 2) REML directly from data with a random regression model. The first method gave more reliable estimates, particularly for the periphery of the trajectory. The goodness of fit of a random regression model based on covariables describing the shape of the lactation curve was nearly the same as random regression on Legendre polynomials. In the latter model, two and three regression coefficients were sufficient to fit the covariance structure for additive genetic and permanent environment, respectively. The eigenfunction pattern revealed the possibility of selection for persistency. Covariance functions can be usefully implemented in large-scale test day models by means of random regressions.

Effects of the administration of monensin via concentrates to dairy cows were studied in two trials. In one trial, 64 Holstein cows were assigned to four groups that received 0, 150, 300, or 450 mg/d of monensin from 5 to 24 wk postpartum. Milk production tended to increase (4.0, 3.3, and 5.4%, respectively) for the three groups of treated cows. Fat content was decreased by 0.09, 1.89, and 4.09 g/kg, respectively, for these same three groups. The effect on protein content was small and nonsignificant. Feed intake was reduced in treated cows, although not significantly, and feed efficiency was improved by monensin. In a confirmatory trial, 58 Holstein and 22 Jersey cows were allocated either to a control group or to a treatment group that received 300 mg/d of monensin from 5 to 36 wk postpartum during the first lactation and from 2 wk before calving to 36 wk postpartum during a subsequent lactation. During the first lactation, cows in the treatment group showed a 7% increase in milk production, a relative decrease (1.4 g/kg) in milk fat content, and equal protein content compared with cows in the control group. Body weight gain and body condition scores near the end of the treatment period were higher for cows in the treatment group. A decrease in blood ketone concentrations was found between 7 and 56 d of lactation. Treatment effects on milk production differed between breeds and within genetic lines. Jersey cows were less responsive than were Holstein cows, and Holstein cows with a high ratio of breeding values for protein and fat showed larger milk production responses. Results from the second lactation showed similar differences between the two groups as did those from the first lactation.

Data on 305-d milk yield from the Dutch dairy evaluation were used to obtain breeding value estimates from an animal model for 1984 to 1992. Changes in sire evaluations were investigated for bias and realized accuracy. Evaluations based on progeny records were generally lower than their expectation based on parent average. The average decrease was 157 kg for Black and White bulls and 73 kg for Red and White bulls. Evaluations based on test daughters changed about -50 kg when second and third lactations became available, but estimates for breeding value changed insignificantly when records on daughters from the breeding period of the bull were used. The standard deviation of changes from evaluations for first to second batch was about 14% larger than expected from population parameters. Breeding values of imported bulls, based on Dutch data, decreased 75 kg when more information became available in subsequent evaluations. Heterogeneity of variance was estimated by a quasi-likelihood approach with a model that accounted for sampling variance on estimates of variances within herd. The coefficient of variation of the variances within herd-year was 31%. A simple method for standardization of variances within herd-year decreased bias of parent averages by about 20%, and fluctuations of breeding values were within the expected range. A correction for heterogeneity of variance within herd may not remove all bias of parent averages, but a general improvement of bias and accuracy of breeding values can be expected.