Lameness, a disease often observed in loose-housed dairy cattle herds, affects animal welfare in general and reduces cow locomotion. As cow traffic may be affected by restricted locomotion, lameness may be a significant problem in herds with automatic milking systems (AMSs). Between January and August 2002, a field study was conducted to evaluate animal health in eight herds with an AMS. Herd sizes ranged from 60 (n = 5 herds with one automatic milking unit [AMU]) to 120 cows (n = 3 herds with two AMUs). Four visits were made, during which 40-50 cows were randomly assigned for clinical examination of body condition, cleanliness, claw length, disorders of claws and legs, lameness, pressure lesions, and disorders of udder and teats. Lameness was observed in 14% of cows, ranging from 5% to 28% between herds. Approximately 60% of cows had pressure lesions on the hock and 23% of cows had overgrown claws. Preliminary results show that overgrown claws, pressure sores with swellings, early stage of lactation, and high milk yield significantly increased the risk of lameness. Lameness significantly reduced the number of voluntary milkings per day.

A welfare indicator protocol integrating a total of 38 measures from 4 information sources: housing system, management, animal behaviour and clinical health has been developed for decision support in Automatic Milking System (AMS) herds. Two expert opinion studies focusing on the relevance of the welfare indicator protocol as basis for on-farm welfare assessment have been carried out; one study focused on the opinion of 21 AMS researchers; the other on that of 14 AMS production advisors and 15 veterinary practitioners. The researchers were asked to score the individual welfare relevance of the 38 measures. Furthermore, both the panels were asked to prioritise the 10 most important measures. The 21 researchers generally appreciated the listed protocol measures as highly relevant. The researcher and advisor panels agreed on prioritised measures from all 4 information sources among the 10 most important welfare indicators. In summary, researchers as well as production advisors and veterinarians supported the suggested measures for inclusion in a welfare assessment system. This has demonstrated that integration of different information is considered decisive for operational welfare assessment at herd level.

This research was carried out to quantify the effects of a range of variables on milk fat globule (MFG) size for a herd of Holstein-Friesian cows managed through an automatic milking system with year-round calving. We hypothesised that the overall variation in average MFG size observed between individual animals of the same herd cannot sufficiently be explained by the magnitude of the effects of variables that could be manipulated on-farm. Hence, we aimed to conduct an extensive analysis of possible determinants of MFG size, including physiological characteristics (parity, days in milk, days pregnant, weight, age, rumination minutes, somatic cell count) and milk production traits (number of milkings, milk yield, fat yield, protein and fat content, fat-protein ratio) on the individual animal level; and environmental conditions (diet, weather, season) for the whole herd. Our results show that when analysed in isolation, many of the studied variables have a detectable effect on MFG size. However, analysis of their additive effects identified days in milk, parity and milk yield as the most important variables. In accordance with our hypothesis, the estimated effects of these variables, calculated using a multiple variable linear mixed model, do not sufficiently explain the overall variation between cows, ranging from 2.70 to 5.69 µm in average MFG size. We further show that environmental variables, such as sampling day (across seasons) or the proportion of pasture and silage in the diet, have limited effects on MFG size and that physiological differences outweigh the effects of milk production traits and environmental conditions. This presents further evidence that the selection of individual animals is more important than the adjustment of on-farm variables to control MFG size.

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.

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.

This study aimed to examine the influence of feed delivery frequency and environmental conditions on daily time budget of lactating dairy cows. The study was carried out in two commercial dairy farms with Holstein herds. Fifty lactating dairy cows milked in automatic milking units (AMS farm) and 96 primiparous lactating dairy cows milked in a conventional milking parlour (conventional farm) were exposed to different frequencies of feed delivery replicated in different periods of the year (warm and mild) that were characterized by different temperature-humidity indices (THI). On each farm, feeding treatments consisted of two different feed delivery frequencies (1× and 2× on the AMS farm; 2× and 3× on the conventional farm). All behaviours of the cows were monitored for the last 8 d of each treatment period using continuous video recording. The two data sets from different farm systems were considered separately for analysis. On both farms, environmental conditions expressed as THI affected time budgets and the pattern of the behavioural indices throughout the day. The variation in the frequency of feed delivery seems to affect the cow's time budget only in a limited way. Standing time of cows on the conventional farm and the time spent by cows in the milking waiting area on the AMS farm both increased in response to increased feeding frequency. Although feed delivery frequency showed limited influence on cow's time budget, the effect on standing time could be carefully considered, especially on farms equipped with AMS where the type of cow traffic system (e.g., milking first) might amplify the negative consequences of more frequent feed delivery. Further investigations are required to evaluate the effect of THI and feed delivery frequency on other aspects of behavioural activity.

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.

Automatic, or robotic, milking systems have the potential to significantly change the way milk is produced on U.S. dairy farms. However, there is a high degree of uncertainty associated with adoption of this new technology. A real options approach is used to analyze the decision to replace an operational milking system with an automatic milking system. The most important source of uncertainty is shown to be the length of the technology's useful life. Under our assumptions, the automatic system is always an optimal investment if it is certain that it will last longer than the operational system being replaced.

Early detection of post-calving health problems is critical for dairy operations. Separating sick cows from the herd is important, especially in robotic-milking dairy farms, where searching for a sick cow can disturb the other cows’ routine. The objectives of this study were to develop and apply a behaviour- and performance-based health-detection model to post-calving cows in a robotic-milking dairy farm, with the aim of detecting sick cows based on available commercial sensors. The study was conducted in an Israeli robotic-milking dairy farm with 250 Israeli-Holstein cows. All cows were equipped with rumination- and neck-activity sensors. Milk yield, visits to the milking robot and BW were recorded in the milking robot. A decision-tree model was developed on a calibration data set (historical data of the 10 months before the study) and was validated on the new data set. The decision model generated a probability of being sick for each cow. The model was applied once a week just before the veterinarian performed the weekly routine post-calving health check. The veterinarian’s diagnosis served as a binary reference for the model (healthy–sick). The overall accuracy of the model was 78%, with a specificity of 87% and a sensitivity of 69%, suggesting its practical value.

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.

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.

In pasture-based automatic milking systems (AMS), feed is the main incentive that can be managed to encourage reliable and consistent voluntary and distributed cow traffic. Modifying timing, placement and size of feed allocations is expected to have an effect on cow behavior that could avoid the occurrence of extended milking intervals, which have a negative effect on milk yield. Therefore, behavioral studies provide information on how cows modify their actions under different management regimes and can help explain the impact of those regimes. Behavioral observations were conducted in spring 2011 at the FutureDairy AMS research farm, as part of a study where a herd of 175 cows was split into two groups that received supplementary feed either before (PRE), or immediately after (POST) milking. In addition, all cows were offered access to two daily pasture allocations. Observations were conducted in the pasture allocation on 15 focal cows from each treatment group during four periods of 24 h to detect presence and behavior (grazing, ruminating, idling and other) every 15 min. In addition, bite rate and pasture biomass were measured every hour. Overall, despite the finding that more POST cows than PRE cows entered the pasture allocation during the first 8 h of active access, there was no difference in the total proportion of cows that had gained access by the end of the active access period (average 68% for both treatments). Cows in the PRE treatment started exiting the pasture allocation just 6 h after entering, compared with 8 h for POST cows, although their behaviors in the pasture allocation did not differ. Behaviors and bite rate were more dependent on pasture biomass than on supplementary feeding management.