Feeding Patterns and Performance of Cows in Controlled Cow Traffic in Automatic Milking Systems

Impact of dairy cow personality traits and concentrate allowance on their response to training and adaptation to an automated milking system

The objectives of this study were to determine: 1) if dairy cow personality traits and concentrate allowance are associated with the behavior and performance of cows during training to use an automated milking system (AMS); and 2) if these factors were associated with the behavior and performance of cows after AMS training. Twenty-nine mid- to late-lactation Holstein cows (218 ± 49 DIM), who were milking on a rotary parlor and had never previously been milked in an AMS, were enrolled in this study. Cows were assigned to 1 of 2 dietary treatments, consisting of a basal partial mixed ration (PMR) common to both treatment groups, with a concentrate allowance (on dry matter basis) of: 1) 2.0 kg/d in the AMS (L-Tx); or 2) 6.0 kg/d in the AMS (H-Tx). Cows were trained to use the free-traffic AMS, with supervised milkings, over 72 h and were milked in this system for 63 d after training was complete. Variables relating to feeding behavior, milking activity, and production were measured from the start of AMS training until the end of the study. Between 42 and 63 d after AMS introduction, each cow was assessed for personality traits using a combined arena test consisting of exposure to a novel environment, novel object, and novel human. Principal components analysis of behaviors observed during the personality assessment revealed 2 factors (interpreted as boldness and activeness traits) that together explained 85% of the variance; each cow received a score for each trait. Associations between dietary treatment and personality traits with feeding behavior, milking activity, and production were analyzed using mixed-effect linear and logistic regression models. Cows with greater scores for the active trait produced less milk during the 3 d of AMS training compared with cows with lower scores. Within the H-Tx, more active cows had a 3.92 times greater risk of kicking off teat cups during AMS training than less active cows. However, during the 8 wk after training, more active cows had a 1.37 times lesser risk of teat cup kickoffs than those that were less active. Cows on the H-Tx produced 4.4 kg/d more energy-corrected milk compared with cows on the L-Tx in the 8 wk after training. During the 8 wk after AMS training the cows on the H-Tx consumed an average of 21.4 kg/d of PMR and were delivered 4.6 kg/d of AMS concentrate, while the L-Tx cows consumed 23.4 kg/d PMR and were delivered 2.0 kg/d of AMS concentrate. The results indicate that both dairy cow personality traits and AMS concentrate allocation influence their response to AMS training and subsequent feeding and milking behavior and production.

Minimally Invasive Markers of Stress and Production Parameters in Dairy Cows before and after the Installation of a Voluntary Milking System

Automatic milking systems (AMS) are a low-labour alternative to conventional parlours, with previous studies demonstrating that cows vary in their ability to cope with the change to AMS. Cortisol expression can be combined with other measures to assess stress: saliva and hair have the advantage of requiring minimally invasive sampling. No work has investigated the long-term impact of introduction of AMS. The aims of the study were to assess short-term and chronic stress associated with a change in milking system by measuring salivary and hair cortisol levels and to assess the impact on health and production parameters. Cows from one farm changing their milking system were recruited to the study and sampled for saliva (n = 10) and hair (n = 12) before and after installation. Cortisol levels were measured using a salivary cortisol enzyme immunoassay kit. Body condition, lameness and milk parameters of the whole herd were regularly assessed. Salivary cortisol showed no diurnal pattern but was affected by lameness and gestation. Non-lame cows showed a reduction in salivary cortisol after AMS introduction (p < 0.001). Hair cortisol levels increased after AMS, but it was unclear if this change was seasonal. Milk yield increased by 13% and somatic cell count reduced by 28%. Body condition score was consistently good, but lameness remained high throughout the study. Production values alone do not represent high welfare. The high lameness and associated cortisol levels suggest that cow stress requires consideration when changing milking systems.

Designing Automated Milking Dairy Facilities to Maximize Labor Efficiency

As automatic milking systems grow in popularity in North America, questions about how to design the barn to improve labor efficiency arise. Multiple considerations such as cow flow traffic type, robot positioning within the pen, the number of cows per pen, and how cows are managed around the robots must be discussed during the barn planning period. This article focuses on barn design and pen layout to maximize labor efficiency in herds with single-box automatic milking systems.

Impact of automatic milking systems on dairy cattle producers' reports of milking labour management, milk production and milk quality

Automatic milking systems (AMS), or milking robots, are becoming widely accepted as a milking technology that reduces labour and increases milk yield. However, reported amount of labour saved, changes in milk yield, and milk quality when transitioning to AMS vary widely. The purpose of this study was to document the impact of adopting AMS on farms with regards to reported changes in milking labour management, milk production, milk quality, and participation in dairy herd improvement (DHI) programmes. A survey was conducted across Canada over the phone, online, and in-person. In total, 530 AMS farms were contacted between May 2014 and the end of June 2015. A total of 217 AMS producers participated in the General Survey (Part 1), resulting in a 41% response rate, and 69 of the respondents completed the more detailed follow-up questions (Part 2). On average, after adopting AMS, the number of employees (full- and part-time non-family labour combined) decreased from 2.5 to 2.0, whereas time devoted to milking-related activities decreased by 62% (from 5.2 to 2.0 h/day). Median milking frequency was 3.0 milkings/day and robots were occupied on average 77% of the day. Producers went to fetch cows a median of 2 times/day, with a median of 3 fetch cows or 4% of the herd per robot/day. Farms had a median of 2.5 failed or incomplete milkings/robot per day. Producers reported an increase in milk yield, but little effect on milk quality. Mean milk yield on AMS farms was 32.6 kg/cow day. Median bulk tank somatic cell count was 180 000 cells/ml. Median milk fat on AMS farms was 4.0% and median milk protein was 3.3%. At the time of the survey, 67% of producers were current participants of a DHI programme. Half of the producers who were not DHI participants had stopped participation after adopting AMS. Overall, this study characterized impacts of adopting AMS and may be a useful guide for making this transition.

Robotic milking: Technology, farm design, and effects on work flow

Robotic milking reduces labor demands on dairy farms of all sizes and offers a more flexible lifestyle for farm families milking up to 250 cows. Because milking is voluntary, barn layouts that encourage low-stress access by providing adequate open space near the milking stations and escape routes for waiting cows improve milking frequency and reduce fetching. Because lame cows attend less often, preventing lameness with comfortable stalls, clean alley floors, and effective foot bathing warrants special emphasis in robotic dairies. Variable milking intervals create challenges for foot bathing, sorting and handling, and dealing with special-needs cows. Appropriate cow routing and separation options at the milking stations are needed to address these challenges and ensure that the expected labor savings are realized. Protocols and layout and gating should make it possible for a herd worker to complete all handling tasks alone. Free traffic and guided traffic systems yield similar results when excellent management is applied or when the number of cows is well below capacity. In less ideal circumstances, guided traffic and the use of commitment pens result in longer standing times and stress, particularly for lower ranking cows, and poor management with free traffic results in more labor for fetching.

Milking permission and milking intervals in a pasture-based automatic milking system

In a pasture-based, automatic milking system, a proportion of milking events occur with milking intervals (MI) >16 h (extended MI). Additionally, cows necessarily walk longer distances than in indoor-based systems. The decision to milk a cow is based on milking permission criteria, which are generally based on time since last milking but can often take into account expected yield as well. Any cow arriving at the dairy and that does not receive milking permission is drafted to a pasture allocation, but it is not known whether milking refusal influences total time of return and therefore MI. Data from a 33-day period from the FutureDairy pasture-based, automatic milking system research farm using a prototype robotic rotary were analysed to investigate the hypothesis that a greater proportion of milking events occurring with extended MI would correspond to cows that had experienced a previous milking refusal. If this were the case then management practices could be implemented to deal with cows that visit the dairy soon after the last milking event. Results indicate that one-third of milking events had extended MI, although only 16% of them had a previous milking refusal. The average refusal took place 3 h after the last milking event and caused extended MI in >60% of the cases. This indicated that although attention should be placed on cows that returned to the dairy before milking permission (because they were likely to have an extended MI), milking refusals were not the main cause of extended MI. Therefore, cows that visit the dairy facility earlier than expected could be sorted to a feeding area close to the dairy, yet the greatest impact on overall MI will probably be achieved by reducing time spent in any one pasture allocation.