The influence of previous medical treatments on milking order in dairy cows

Animal health issues are a problem on many dairy farms, and much is already known about clinical treatments and the behaviour of sick animals. Animal health issues can influence behaviour seen around the milking parlour, but less is known about the relationship between the number of previous medical treatments and the queuing to be milked, the ‘milking order’. Information was collected on five afternoon milking sessions, the individual cows’ age and the medical treatment history of each cow in a group of 100 cows. The question addressed was whether the age and the medical treatment history of each cow in the herd affected its milking order. In addition, milking order was tested day to day, and was found to be consistent. A significant positive correlation was found between medical treatment history and milking order rank, meaning that cows with a higher medical treatment history tended to enter the milking parlour later than cows with a lower medical treatment history. In contrast with this finding, it was found that older cows were more likely to enter the milking parlour early when compared to younger animals, a finding which is supported by previous studies. These somewhat contradictory effects of (a) age and (b) medical treatment history on milking order suggest that health disorders may have long-term measurable effects on the position of a cow in the milking order, even when the effect of age on milking order is accounted for.     

Farm characteristics related to on-farm cow mortality in dairy herds: a questionnaire study

Dairy cow mortality is an important animal welfare issue that also causes financial losses. The objective of this study was to identify farm characteristics and herd management practices associated with high on-farm cow mortality in Swedish dairy herds. A postal questionnaire was sent to farmers that had either high or low mortality rates for 3 consecutive years. The questionnaire consisted of five sections: ‘About the farm’, ‘Milking and housing’, ‘Feeding’, ‘Routines’ and ‘Lame and sick cows’. A total of 145 questionnaires were returned (response rate=33%). Ten of the 77 characteristics investigated met the inclusion criteria for multivariable analysis. The final logistic regression model included: herd size, breed, use of natural service bull, bedding improvement frequency and pasture system. Herds with Swedish Holstein as the predominant breed (odds ratio (OR) 22.9, 95% confidence interval (CI) 5.2 to 101.8) or with mixed breeds (OR 5.5, 95% CI 1.7 to 17.5) had a greater risk of being high mortality herds than herds that were predominantly Swedish Red (OR 1). Herds larger than 100 cows (OR 19.6, 95% CI 3.5 to 110.4) and herds with 50 to 99 cows (OR 13.8, 95% CI 3.2 to 60.6) had greater risk of mortality than herds numbering 35 to 50 cows (OR 1). Being a high mortality herd was also associated with having cows on exercise lots during the summer season (OR 3.6, 95% CI 1.3 to 9.9) compared with on pasture. A missing answer on the question of bedding improvement frequency was associated with high mortality herds. Overall, this study suggests that characteristics that are related to intensification of the dairy industry are also associated with high on-farm mortality of dairy cows.     

Robot utilisation of pasture-based dairy cows with varying levels of milking frequency

Achieving a consistent level of robot utilisation throughout 24 h maximises automatic milking system (AMS) utilisation. However, levels of robot utilisation in the early morning hours are typically low, caused by the diurnal feeding behaviour of cows, limiting the inherent capacity and total production of pasture-based AMS. Our objective was to determine robot utilisation throughout 24 h by dairy cows, based on milking frequency (MF; milking events per animal per day) in a pasture-based AMS. Milking data were collected from January and February 2013 across 56 days, from a single herd of 186 animals (Bos taurus) utilising three Lely A3 robotic milking units, located in Tasmania, Australia. The dairy herd was categorised into three equal sized groups (n=62 per group) according to the cow’s mean daily MF over the duration of the study. Robot utilisation was characterised by an interaction (P< 0.001) between the three MF groups and time of day, with peak milking time for high MF cows within one h of a fresh pasture allocation becoming available, followed by the medium MF and low MF cows 2 and 4 h later, respectively. Cows in the high MF group also presented for milking between 2400 and 0600 h more frequently (77% of nights), compared to the medium MF group (57%) and low MF group (50%). This study has shown the formation of three distinct groups of cows within a herd, based on their MF levels. Further work is required to determine if this finding is replicated across other pasture-based AMS farms.     

Feeding behavior improves prediction of dairy cow voluntary feed intake but cannot serve as the sole indicator

Low-cost feeding-behavior sensors will soon be available for commercial use in dairy farms. The aim of this study was to develop a feed intake model for the individual dairy cow that includes feeding behavior. In a research farm, the individual cows’ voluntary feed intake and feeding behavior were monitored at every meal. A feed intake model was developed based on data that exist in commercial modern farms: ‘BW,’ ‘milk yield’ and ‘days in milking’ parameters were applied in this study. At the individual cow level, eating velocity seemed to be correlated with feed intake (R²=0.93 to 0.94). The eating velocity coefficient varied among individuals, ranging from 150 to 230 g/min per cow. The contribution of feeding behavior (0.28) to the dry matter intake (DMI) model was higher than the contribution of BW (0.20), similar to the contribution of fat-corrected milk (FCM)/BW (0.29) and not as large as the contribution of FCM (0.49). Incorporating feeding behavior into the DMI model improved its accuracy by 1.3 (38%) kg/cow per day. The model is ready to be implemented in commercial farms as soon as companies introduce low-cost feeding-behavior sensors on commercial level.     

Bimodal milk flow and overmilking in dairy cattle: risk factors and consequences

To maximise the return on capital invested in the milking parlour, the largest number of cows should be milked gently and completely in the shortest possible time. Bimodal milk flow and overmilking negatively influence the efficiency of the milk removal process and teat health. This observational study had the objective of investigating the prevalence of bimodal milk flow and overmilking, determining which individual and farm-related variables are associated with these occurrences, and determining the association of overmilking and bimodal milk flow with milk yield and with short- and long-term teat changes. Twenty-one farms were visited once during the study period, wherein the milking routine was timed, the teat condition was assessed, and dynamic evaluation of the milking vacuum was performed. A total of 606 vacuum graphic records were obtained, with an average of 29 ± 3 records per farm, in order to indirectly evaluate the milk flow and thus determine the occurrence of bimodal milking and overmilking time. The average percentage of bimodality per farm was 41.7%. The median overmilking time was 59 seconds, and on average, 78.3% of the cows in a herd were overmilked longer than 30 seconds. An association was found at cow level between the occurrence of bimodal milk flow and days in milk, the total stimulation time, parity, and the preparation lag time. The increase in the mean total stimulation time and the number of passes during preparation were associated with a decrease in the proportion of bimodality in the herd. Parity, reattachment of the milking unit and milking in manual mode were associated with an increase in overmilking time of an individual cow. The presence of a clogged air bleed hole in the claw and the reduction of the cluster removal milk flow threshold were associated with an increase in the herd’s median overmilking time. The average milk flow decreased with the increase in overmilking time and with the occurrence of bimodal milk flow. An association was also found between the occurrence of bimodal milk flow and decreased milk yield. A mean of 78.4% of cows per farm had short-term teat changes in at least one teat, and 33.6% of evaluated cows per farm displayed at least one teat with hyperkeratosis. These results emphasise the association of bimodality and overmilking on milking efficiency and reinforce the importance of the milkers’ actions and the functioning of the milking parlour for its prevention.     

The effect of omitted milking on the behaviour of cows in the context of cluster attachment failure during automatic milking

In robotic milking there is always a slight chance of failure to attach the milking cluster. Attachment failure is most likely for cows whose udder conformation is less convenient for robot attachment. In general, after milking failure cows try to revisit the milking robot if they are not sent to a separate area. Since it is difficult to estimate the effect of milking failure on such a cow and her welfare in conditions of robotic milking, a specific 16-day trial was conducted on 12 cows. These cows were milked in a milking parlour with six milking stalls. Each afternoon milking, three cows were not milked. All the cows were closely observed in the cubicle house for 1 h after the afternoon milking. Thereafter, all cows were brought to the milking parlour the third time and the three unmilked cows were milked. In total, each cow was observed 12 times after milking and four times after omitted milking. The following behavioural traits were registered: time budget for the 1 h, occurrence and time until eating, drinking, lying, urination and defecation, and aggressive interactions. Milking order was defined on the basis of how often a cow came to the milking parlour in the first batch of six cows. Moreover, the data related to the milk yield and the use of the automatic feeding installation with the complete diet were analysed. After omitted milking, only the cows from the first batch stood longer in cubicles (14.2 min of 1 h) and lay less (5.4 min of 1 h) than milked cows of the same batch (respectively 7.0 min and 16.3 min for standing and lying in cubicles) (P<0.01). After omitted milking, cows urinated earlier and more frequently (64.5%) than milked cows (36.3%) (P<0.002) (both batches). There were no statistically significant differences in eating time and feed intake after milking and omitted milking. Milk yield per cow averaged 24.9 kg during days with omitted (delayed by 1 h) milking and 25.3 kg during the days without omitted milking (P<0.05). It was concluded that cows show some signs of discomfort after omitted milking (urination); this discomfort seemed to be greater in cows coming earlier to the milking parlour (afterwards they preferred to stand rather than to lie). The 60% of cases of milk leakage found after omitted milking indicates that failed cluster attachment can be accompanied by an extra risk factor for the occurrence of mastitis. However, omitted milking as a treatment did not influence feeding and aggressive behaviour or milking order when unmilked cows were brought to the milking parlour the third time together with the milked cows. Our methods and results can be useful for estimating the effects of robot milking failures on a cow. Future studies should pay particular attention to high-yielding cows and to longer periods of delayed milking.     

Review: Milking robot utilization,a successful precision livestock farming evolution

Automatic milking systems (AMS), one of the earliest precision livestock farming developments, have revolutionized dairy farming around the world. While robots control the milking process, there have also been numerous changes to how the whole farm system is managed. Milking is no longer performed in defined sessions; rather, the cow can now choose when to be milked in AMS, allowing milking to be distributed throughout a 24 h period. Despite this ability, there has been little attention given to milking robot utilization across 24 h. In order to formulate relevant research questions and improve farm AMS management there is a need to determine the current knowledge gaps regarding the distribution of robot utilization. Feed, animal and management factors and their interplay on levels of milking robot utilization across 24 h for both indoor and pasture-based systems are here reviewed. The impact of the timing, type and quantity of feed offered and their interaction with the distance of feed from the parlour; herd social dynamics, climate and various other management factors on robot utilization through 24 h are provided. This novel review draws together both the opportunities and challenges that exist for farm management to use these factors to improved system efficiency and those that exist for further research.     

Which preventive measures against mastitis can increase the technical efficiency of dairy farms?

The aim of this paper was to explore if, and in that case how, various preventive measures against mastitis influenced the whole-farm economic outcome, measured as technical efficiency, of a sample of specialised dairy farms in Sweden. In particular, the paper aimed at analysing whether a change to preventive measures applied by fully efficient farms would be a way for inefficient farms to become fully efficient. First, technical efficiency was assessed for each farm in the sample based on farm-level accounting data and the data envelopment analysis. In a second step, the effects of preventive measures against mastitis (collected through a mail questionnaire) on technical efficiency were assessed with logistic regression. Keeping cows in a loose-housing barn, stimulating udders manually during milking, and having cows standing on clean bedding during milking were found to significantly increase the probability of a farm being fully efficient. Once the farmer considers the somatic cell count (SCC) to be too high, undertaking measures such as contacting a veterinarian, checking overall hygiene routines, and culling cows with high SCC were found to significantly increase the probability of a farm being fully efficient. Thus, these measures may be plausible targets for advisory services aimed at assisting farmers to become fully efficient, especially if they are confirmed in future studies. Several common preventive measures against mastitis, that is, choice of bedding material, frequency of cleaning stalls, pre-milking, post-milking teat disinfection, applying a milking order based on the SCC of cows, and milking high-SCC cows with a separate cluster, were found to have no statistically significant effect on farm economic outcome. However, these measures may still be valuable for non-economic goals, such as increased animal welfare, and the results imply that they can be implemented without negative impact on the economic performance of the farm.     

Offering a forage crop at pasture did not adversely affect voluntary cow traffic or milking visits in a pasture-based automatic milking system

Feed is a strong incentive for encouraging cows in automatic milking systems (AMS) to voluntarily move around the farm and achieve milkings distributed across the 24 h day. It has been reported that cows show preferences for some forages over others, and it is possible that offering preferred forages may increase cow traffic. A preliminary investigation was conducted to determine the effect of offering a forage crop for grazing on premilking voluntary waiting times in a pasture-based robotic rotary system. Cows were offered one of two treatments (SOYBEAN or GRASS) in a cross-over design. A restricted maximum likelihood procedure was used to model voluntary waiting times. Mean voluntary waiting time was 45.5±6.0 min, with no difference detected between treatments. High and mid-production cows spent <44 min/milking in the premilking yard compared with >55 min/milking for low-production cows, whereas waiting time increased as queue length increased. Voluntary waiting time was 23% and 80% longer when cows were fetched from the paddock or had a period of forced waiting before volunteering for milking, respectively. The time it took cows to return to the dairy since last exiting was not affected by treatment, with a mean return time of 13.7±0.6 h. Although offering SOYBEAN did not encourage cows to traffic more readily through the premilking yard, the concept of incorporating forage crops in AMS still remains encouraging if the aim is to increase the volume or quantity of home-grown feed rather than improving cow traffic.     

Variation in enteric methane emissions among cows on commercial dairy farms

Methane (CH₄) emissions by dairy cows vary with feed intake and diet composition. Even when fed on the same diet at the same intake, however, variation between cows in CH₄ emissions can be substantial. The extent of variation in CH₄ emissions among dairy cows on commercial farms is unknown, but developments in methodology now permit quantification of CH₄ emissions by individual cows under commercial conditions. The aim of this research was to assess variation among cows in emissions of eructed CH₄ during milking on commercial dairy farms. Enteric CH₄ emissions from 1964 individual cows across 21 farms were measured for at least 7 days/cow using CH₄ analysers at robotic milking stations. Cows were predominantly of Holstein Friesian breed and remained on the same feeding systems during sampling. Effects of explanatory variables on average CH₄ emissions per individual cow were assessed by fitting a linear mixed model. Significant effects were found for week of lactation, daily milk yield and farm. The effect of milk yield on CH₄ emissions varied among farms. Considerable variation in CH₄ emissions was observed among cows after adjusting for fixed and random effects, with the CV ranging from 22% to 67% within farms. This study confirms that enteric CH₄ emissions vary among cows on commercial farms, suggesting that there is considerable scope for selecting individual cows and management systems with reduced emissions.     

The effect of temporal variation in feed quality and quantity on the diurnal feeding behaviour of dairy cows

The diurnal feeding patterns of dairy cows affects the 24 h robot utilisation of pasture-based automatic milking systems (AMS). A decline in robot utilisation between 2400 and 0600 h currently occurs in pasture-based AMS, as cow feeding activity is greatly reduced during this time. Here, we investigate the effect of a temporal variation in feed quality and quantity on cow feeding behaviour between 2400 and 0600 h as a potential tool to increase voluntary cow trafficking in an AMS at night. The day was allocated into four equal feeding periods (0600 to 1200, 1200 to 1800, 1800 to 2400 and 2400 to 0600 h). Lucerne hay cubes (CP = 19.1%, water soluble carbohydrate = 3.8%) and oat, ryegrass and clover hay cubes with 20% molasses (CP = 11.8%, water soluble carbohydrate = 10.7%) were offered as the ‘standard’ and ‘preferred’ (preference determined previously) feed types, respectively. The four treatments were (1) standard feed offered ad libitum (AL) throughout 24 h; (2) as per AL, with preferred feed replacing standard feed between 2400 and 0600 h (AL + P); (3) standard feed offered at a restricted rate, with quantity varying between each feeding period (20:10:30:60%, respectively) as a proportion of the (previously) measured daily ad libitum intake (VA); (4) as per VA, with preferred feed replacing standard feed between 2400 and 0600 h (VA + P). Eight non-lactating dairy cows were used in a 4 × 4 Latin square design. During each experimental period, treatment cows were fed for 7 days, including 3 days habituation and 4 days data collection. Total daily intake was approximately 8% greater (P < 0.001) for the AL and AL + P treatments (23.1 and 22.9 kg DM/cow) as compared with the VA and VA + P treatments (21.6 and 20.9 kg DM/cow). The AL + P and VA treatments had 21% and 90% greater (P < 0.001) dry matter intake (DMI) between 2400 and 0600 h, respectively, compared with the AL treatment. In contrast, the VA + P treatment had similar DMI to the VA treatment. Our experiment shows ability to increase cow feeding activity at night by varying feed type and quantity, though it is possible that a penalty to total DMI may occur using VA. Further research is required to determine if the implementation of variable feed allocation on pasture-based AMS farms is likely to improve milking robot utilisation by increasing cow feeding activity at night.     

Estimating risk probabilities for sickness from behavioural patterns to identify health challenges in dairy cows with multivariate cumulative sum control charts

Dairy cattle housing is characterised by increasing herd sizes and the need for assisting technical tools to monitor the cows’ health. This study investigated the combination of logistic regression models with multivariate cumulative sum (MCUSUM) control charts in healthmonitoring of dairy cattle. Sensor information of 618 cows with 791 lactations (138 438 cow days), nine behavioural variables were included as parts of the behavioural patterns: physical activity (“neck activity”, “leg activity”, “walking duration”), resting (“lying duration”, “standing duration”, “transitions from lying to standing”) and feeding (“feeding duration”, “rumination duration”, “inactivity duration”) behaviour. For each of these behavioural patterns, a logistic regression model with the health status (sick vs not sick) as a dependent variable was designed after a variable selection (herd level) based on the herd dataset with 618 cows (618 lactations; 115 547 cow days), which included the variables of each behaviour pattern and the stage of lactation nested in the number of lactations as explanatory variables. The explanatory variables were added stepwise to the model, with the final model being selected with respect to the lowest values of Akaike’s and Bayes’ information criteria. Each model was then applied to a dataset with 173 cows (22 891 cow days) at cow level, resulting in individual daily risk probabilities for getting sick. Thus, risk probabilities of each behavioural pattern were estimated and included in the MCUSUM control charts to identify cows at risk of disease. The performance of the MCUSUM control charts was cross-validated to identify the best fitting reference value k and the threshold value h. Alerts given within 5 days prior to diagnosis were counted as detected sicknesses. The performance resulted in a block sensitivity of 70.9–81.4%, specificity of 87.9–94.2% and a false-positive rate of 5.8–12.1%. The performance was confirmed while testing the entire algorithm resulting in a mean area under the receiver operating characteristics curve of 0.89. Calculating precision and the F₁-score resulted in a precision of 49.0–60.9% (training: 48.8–63.5%) and an F₁-score of 61.1–65.7% in testing (training: 61.0–67.0%). The precision-recall curve (PRC) was derived from precision and recall with an area under the PRC of 0.70 in training and testing. In summary, the present study was able to develop an algorithm showing good classification potential for the online monitoring of sickness behaviour.     

Individualised automated lameness detection in dairy cows and the impact of historical window length on algorithm performance

Lameness is an important economic problem in the dairy sector, resulting in production loss and reduced welfare of dairy cows. Given the modern-day expansion of dairy herds, a tool to automatically detect lameness in real-time can therefore create added value for the farmer. The challenge in developing camera-based tools is that one system has to work for all the animals on the farm despite each animal having its own individual lameness response. Individualising these systems based on animal-level historical data is a way to achieve accurate monitoring on farm scale. The goal of this study is to optimise a lameness monitoring algorithm based on back posture values derived from a camera for individual cows by tuning the deviation thresholds and the quantity of the historical data being used. Back posture values from a sample of 209 Holstein Friesian cows in a large herd of over 2000 cows were collected during 15 months on a commercial dairy farm in Sweden. A historical data set of back posture values was generated for each cow to calculate an individual healthy reference per cow. For a gold standard reference, manual scoring of lameness based on the Sprecher scale was carried out weekly by a single skilled observer during the final 6 weeks of data collection. Using an individual threshold, deviations from the healthy reference were identified with a specificity of 82.3%, a sensitivity of 79%, an accuracy of 82%, and a precision of 36.1% when the length of the healthy reference window was not limited. When the length of the healthy reference window was varied between 30 and 250 days, it was observed that algorithm performance was maximised with a reference window of 200 days. This paper presents a high-performing lameness detection system and demonstrates the importance of the historical window length for healthy reference calculation in order to ensure the use of meaningful historical data in deviation detection algorithms.     

Use of chromium mordanted neutral detergent residue as a predictor of fecal output to estimate intake in grazing high producing Holstein cows

Two experiments were conducted to evaluate use of chromium mordanted neutral detergent residue (Cr-NDr) and cobalt EDTA (Co-EDTA) as predictors of dry matter intake (DMI) in high producing grazing dairy cows. The first experiment was conducted with 10 lactating Holstein cows individually fed a total mixed ration (TMR) in confinement, and dosed with Cr-NDr and Co-EDTA twice daily at milking times for 12-days to validate the markers used for the second experiment. The Cr-NDr accounted for 96% of the variation (r²) in DMI, while Co-EDTA underpredicted DMI by 43% (r²=0.65). The second experiment was conducted on a pasture-based dairy farm, to evaluate the use of Cr-NDr to predict DMI of grazing dairy cows. 15 and 14 high producing dairy cows in trial 1 and 2, respectively, were dosed twice a day at milking times with Cr-NDr for 12-days. Mean total DMI estimated from marker recoveries were unrealistically high (5.95 and 5.52% of body weight for trials 1 and 2, respectively). It was concluded that either diurnal variation in fecal excretion of the marker or a failure in the technique of collecting pasture samples that reflected the cows’ true grazing selection in order to determine pasture composition occurred.     

The effect of concentrate allocation on traffic and milk production of pasture-based cows milked by an automatic milking system

Increased economic, societal and environmental challenges facing agriculture are leading to a greater focus on effective way to combine grazing and automatic milking systems (AMS). One of the fundamental aspects of robotic milking is cows’ traffic to the AMS. Numerous studies have identified feed provided, either as fresh grass or concentrate supplement, as the main incentive for cows to return to the robot. The aim of this study was to determine the effect of concentrate allocation on voluntary cow traffic from pasture to the robot during the grazing period, to highlight the interactions between grazed pasture and concentrate allocation in terms of substitution rate and the subsequent effect on average milk yield and composition. Thus, 29 grazing cows, milked by a mobile robot, were monitored for the grazing period (4 months). They were assigned to two groups: a low concentrate (LC) group (15 cows) and a high concentrate (HC) group (14 cows) receiving 2 and 4 kg concentrate/cow per day, respectively; two allocations per day of fresh pasture were provided at 0700 and 1600 h. The cows had to go through the AMS to receive the fresh pasture allocation. The effect of concentrate level on robot visitation was calculated by summing milkings, refusals and failed milkings/cow per day. The impact on average daily milk yield and composition was also determined. The interaction between lactation number and month was used as an indicator of pasture availability. Concentrate allocation increased significantly robot visitations in HC (3.60±0.07 visitations/cow per day in HC and 3.10±0.07 visitations/cow per day in LC; P<0.001) while milkings/cow per day were similar in both groups (LC: 2.37±0.02/day and HC: 2.39±0.02/day; Ns). The average daily milk yield over the grazing period was enhanced in HC (22.39±0.22 kg/cow per day in HC and 21.33±0.22 kg/cow per day in LC; P<0.001). However the gain in milk due to higher concentrate supply was limited with regards to the amount of provided concentrates. Milking frequency in HC primiparous compared with LC was increased. In the context of this study, considering high concentrate levels as an incentive for robot visitation might be questioned, as it had no impact on milking frequency and limited impact on average milk yield and composition. By contrast, increased concentrate supply could be targeted specifically to primiparous cows.     

Robot milking and relationship with culling rate in dairy cows

Cow routines and behavioral responses are altered substantially following the installation of robot milking. The present study was designed to analyze the effect that switching from milking parlor to automatic milking system (AMS) had on the culling rate (due to various causes) of dairy cattle. For this purpose, culling records and causes for culling were tracked in 23 dairy farms in the Galicia region (NW Spain). The animals in these farms were monitored for 5 years. For the present study, that length of time was divided into three different stages, as follows: 2 years before switching from a milking parlor to AMS (stage 1), the 1^st year following the implementation of AMS (stage 2) and the 2^nd and 3^rd years succeeding the implementation of AMS (stage 3). Cox models for survival analysis were used to estimate the time to culling due to different reasons during stage 1 in relation to stages 2 and 3. The data indicated that the risk of loss due to death or emergency slaughter decreased significantly following the installation of AMS. In contrast, the risk of culling due to low production, udder problems, infertility or lameness increased significantly. Low-production cows (such as cows in advanced lactation due to infertility) or sick cows (such as mastitic or lame cows) allegedly have a noticeable effect both on the performance and the amortization of the cost of AMS, which in turn would lead to a higher probability of elimination than in conventional systems.     

Monitoring and remediation of on-farm and off-farm ground current measured as step potential on a Wisconsin dairy farm: A case study

ABSTRACT

Ground current commonly referred to as “stray voltage” has been an issue on dairy farms since electricity was first brought to rural America. Equipment that generates high-frequency voltage transients on electrical wires combined with a multigrounded (electrical distribution) system and inadequate neutral returns all contribute to ground current. Despite decades of problems, we are no closer to resolving this issue, in part, due to three misconceptions that are addressed in this study. Misconception 1. The current standard of 1 V at cow contact is adequate to protect dairy cows; Misconception 2. Frequencies higher than 60 Hz do not need to be considered; and Misconception 3. All sources of ground current originate on the farm that has a ground current problem. This case study of a Wisconsin dairy farm documents, 1. how to establish permanent monitoring of ground current (step potential) on a dairy farm; 2. how to determine and remediate both on-farm and off-farm sources contributing to step potential; 3. which step-potential metrics relate to cow comfort and milk production; and 4. how these metrics relate to established standards. On-farm sources include lighting, variable speed frequency drives on motors, radio frequency identification system and off-farm sources are due to a poor primary neutral return on the utility side of the distribution system. A step-potential threshold of 1 V root mean square (RMS) at 60 Hz is inadequate to protect dairy cows as decreases of a few mV peak–peak at higher frequencies increases milk production, reduces milking time and improves cow comfort.     

IgG1 variations in the colostrum of Holstein dairy cows

High-immune quality colostrum (IgG1 concentration ⩾50 g/l) is crucial for the health and development of the young calf. Studies on colostrum quality tend to focus on external factors such as breed, parity or dry period length, but few have focused on within-cow variations. Here we ran experiments to gain a deeper insight into within-cow variation in IgG1 concentrations in dairy cow colostrum. Trials were performed in an experimental farm, located in the Western part of France. Colostrum from each quarter and a composite sample (mix of four quarters) were concomitantly collected on 77 Holstein dairy cows just after calving to assess the influence of sample type on IgG1 concentrations. Variation in IgG1 concentrations during the first milking was studied on samples from nine cows collected every minute from the start of milking. Repeatability of colostral IgG1 concentration was estimated from 2009 and 2010 data on 16 healthy cows. IgG1 concentrations were tested using a radial immunodiffusion method. Sensitivity and specificity were similar regardless of sample type tested (individual quarter or composite milk). Mean average IgG1 concentration was 54.1 g/l in composite colostrum, and was significantly higher in hind quarter teats (56.2 g/l) than front quarter teats (53.1 g/l). Average IgG1 concentration did not change significantly during colostrum milking, and the variations observed (15% or less) were likely due to the laboratory method (CV 15%). IgG1 concentrations in dam colostrum increased slightly from 2009 to 2010 due to BW and parity effects. In 56% of cases, colostrum quality could have been assessed on either individual or composite colostrum samples collected at any time during the first milking without affecting the reliability of the measurement. However, in other cases, differences were significant enough to mean that estimates of average IgG1 concentration in colostrum from any one quarter would not be reliable. It is concluded that colostrum quality, from an IgG1 concentration point of view, could be assessed with a composite sample taken at any time during the first milking.     

Productive,economic and risk assessment of grazing dairy systems with supplemented cows milked once a day

Milking cows once a day (OAD) is a herd management practice that may help to reduce working effort and labour demand in dairy farms. However, a decrease in milk yield per cow occurs in OAD systems compared with twice a day (TAD) systems and this may affect profitability of dairy systems. The objective of this study was to assess productive and economic impact and risk of reducing milking frequency from TAD to OAD for grazing dairy systems, using a whole-farm model. Five scenarios were evaluated by deterministic and stochastic simulations: one scenario under TAD milking (TADAR) and four scenarios under OAD milking. The OAD scenarios assumed that milk yield per cow decreased by 30% (OAD30), 24% (OAD24), 19% (OAD19) and 10% (OAD10), compared with TADAR scenario, based on experimental and commercial farms data. Stocking rate (SR) was increased in all OAD scenarios compared to TADAR and two levels of reduction in labour cost were tested, namely 15% and 30%. Milk and concentrate feeds prices, and pasture and crop yields, were allowed to behave stochastically to account for market and climate variations, respectively, to perform risk analyses. Scenario OAD10 showed similar milk yield per ha compared with TADAR, as the increased SR compensated for the reduction in milk yield per cow. For scenarios OAD30, OAD24 and OAD19 the greater number of cows per ha partially compensated for the reduction of milk yield per cow and milk yield per ha decreased 21%, 15% and 10%, respectively, compared with TADAR. Farm operating profit per ha per year also decreased in all OAD scenarios compared with TADAR, and were US$684, US$161, US$ 303, US$424 and US$598 for TADAR, OAD30, OAD24, OAD19, OAD10, respectively, when labour cost was reduced 15% in OAD scenarios. When labour cost was reduced 30% in OAD scenarios, only OAD10 showed higher profit (US$706) than TADAR. Stochastic simulations showed that exposure to risk would be higher in OAD scenarios compared with TADAR. Results showed that OAD milking systems might be an attractive alternative for farmers who can either afford a reduction in profit to gain better and more flexible working conditions or can minimise milk yield loss and greatly reduce labour cost.     

Factors associated with the concentration of immunoglobulin G in the colostrum of dairy cows

Transfer of sufficient immunoglobulin G (IgG) to the neonatal calf via colostrum is vital to provide the calf with immunological protection and resistance against disease. The objective of the present study was to determine the factors associated with both colostral IgG concentration and colostral weight in Irish dairy cows. Fresh colostrum samples were collected from 704 dairy cows of varying breed and parity from four Irish research farms between January and December 2011; colostral weight was recorded and the IgG concentration was determined using an ELISA method. The mean IgG concentration in the colostrum was 112 g/l (s.d. = 51 g/l) and ranged from 13 to 256 g/l. In total, 96% of the samples in this study contained >50 g/l IgG, which is considered to be indicative of high-quality colostrum. Mean colostral weight was 6.7 kg (s.d. = 3.6 kg) with a range of 0.1 to 24 kg. Factors associated with both colostral IgG concentration and colostral weight were determined using a fixed effects multiple regression model. Parity, time interval from calving to next milking, month of calving, colostral weight and herd were all independently associated with IgG concentration. IgG concentration decreased (P < 0.01) by 1.7 (s.e. = 0.6) g/l per kg increase in the colostral weight. Older parity cows, cows that had a shorter time interval from calving to milking, and cows that calved earlier in spring or in the autumn produced colostrum with higher IgG concentration. Parity (P < 0.001), time interval from calving to milking (P < 0.01), weight of the calf at birth (P < 0.05), colostral IgG concentration (P < 0.01) and herd were all independently associated with colostral weight at the first milking. Younger parity cows, cows milked earlier post-calving, and cows with lighter calves produced less colostrum. In general, colostrum quality of cows in this study was higher than in many previous studies; possible reasons include use of a relatively low-yielding cow type that produces low weight of colostrum, short calving to colostrum collection interval and grass-based nutritional management. The results of this study indicate that colostral IgG concentration can be maximised by reducing the time interval between calving and collection of colostrum.