Randomized-controlled study assessing the effect of milking permission settings and concentrate supplementation on milking frequency and milk yield in a pasture-based automatic milking system

This study aimed to verify the effect of milking permission (MPE) and concentrate supplementation (CS) on milking frequency (milkings per cow per day) and milk yield (kilograms per cow per day) in a farm using a pasture-based automatic milking system (AMS). Sixty-eight cows milked using this AMS unit were randomly assigned to 1 of 4 groups homogeneous for parity, DIM, and milk yield. Treatments used were frequent or restricted MPE, that granted cows permission to milk after 6 to 8 h or 9.6 to 14 h of the previous milking, respectively; and low (LC) or high (HC) CS of 0.5 kg or 3.5 kg/cow per day, respectively. The combination of the 2 levels of MPE and the 2 levels of CS resulted in the 4 treatment combinations (frequent HC [FHC], restricted HC [RHC], frequent LC [FLC], and restricted LC [RLC]). This study was designed as a 2 × 2 factorial arrangement with treatment crossover: each of the 4 cow groups was randomly assigned to 1 of the 4 treatment combinations for a 5-wk experimental period (1 pretreatment week and 4 treatment weeks), and after each 5-wk period groups crossed over to another treatment combination until they experienced all. Statistical analysis assessed the effect of MPE, CS, and their interaction on milk yield, milking frequency, box time, milking time, and average milk-flow rate. This was done using a mixed model analysis with repeated measures to account for repeated observations on the experimental unit (cow). Milk yield per cow per day and milkings per cow per day were significantly higher with the frequent compared with the restricted MPE (1.5 kg and 0.65 milkings, respectively). Milk yield per cow per day and milkings per cow per day were significantly higher with the HC compared with the LC CS (3.1 kg and 0.25 milkings, respectively). Additionally, milk yield per cow per day was affected by the interaction of MPE and CS and it was highest with the FHC (20.1 kg) treatment combination, followed by RHC (18.2 kg) treatment combination. The number of milkings per cow per day were also affected by the interaction of MPE and CS. The highest estimated number of milkings per cow per day was recorded for the FHC (2.12) and the FLC (1.77) treatment combinations, followed by the RHC (1.38) and RLC (1.23) treatment combinations. Similarly, milking interval was 2.5 h longer for the RLC treatment combination compared with RHC. The shortest milking interval was observed for the FHC (11 h) and FLC (12.8 h) treatment combinations. In conclusion, the study showed that allowing access to the robot between 6 to 8 h after the previous milking was sufficient (even with a minimal level of CS) to achieve acceptable milk production and milking performance in a pasture-based AMS.

A Literature Review of Modeling Approaches Applied to Data Collected in Automatic Milking Systems

Automatic milking systems (AMS) have played a pioneering role in the advancement of Precision Livestock Farming, revolutionizing the dairy farming industry on a global scale. This review specifically targets papers that focus on the use of modeling approaches within the context of AMS. We conducted a thorough review of 60 articles that specifically address the topics of cows' health, production, and behavior/management Machine Learning (ML) emerged as the most widely used method, being present in 63% of the studies, followed by statistical analysis (14%), fuzzy algorithms (9%), deterministic models (7%), and detection algorithms (7%). A significant majority of the reviewed studies (82%) primarily focused on the detection of cows' health, with a specific emphasis on mastitis, while only 11% evaluated milk production. Accurate forecasting of dairy cow milk yield and understanding the deviation between expected and observed milk yields of individual cows can offer significant benefits in dairy cow management. Likewise, the study of cows' behavior and herd management in AMSs is under-explored (7%). Despite the growing utilization of machine learning (ML) techniques in the field of dairy cow management, there remains a lack of a robust methodology for their application. Specifically, we found a substantial disparity in adequately balancing the positive and negative classes within health prediction models.

Effect of Minimum Milking Interval on Traffic and Milk Production of Cows Milked by a Pasture Based Automatic Milking System

Simple Summary

Several studies have demonstrated that combining grazing and robotic milking is possible. However, there is often a decrease in milking frequency, which leads to a decrease in milk production. The objective of this study was to investigate the effect of two strategies to improve traffic in a pasture-based automatic milking system. Therefore we formed four groups differing based on concentrate allocation and based on minimum milking interval (MMI) necessary to access the robot for milking. Therefore four groups (high concentrate–short MMI, high concentrate–long MMI, low concentrate–short MMI, low concentrate–long MMI) were constituted. We compared these four groups with regard to traffic parameters (milkings, refused milkings) and animal production. The study highlighted the positive effect of high concentrate–short MMI on traffic to the robot by reducing the number of refused milkings. High concentrate allocation allowed to maintain high milk production over the experiment duration.

Abstract

In dairy farms automatic milking systems and grazing, traffic to the robot is the cornerstone of profitability as higher milking frequency enhances milk yield. In this study, we investigated whether shortening the minimum milking interval (MMI), i.e., the required time between two milkings for an animal to get access to the milking unit, coupled with high concentrate allocation, could increase the daily milking frequency (MF, milking/cow/day) and consequently the milk yield of grazing cows. Two groups of cows (n = 19 and n = 20) belonging to the same herd were discriminated based on concentrate supply (high vs. low: 4 vs. 2 kg/cow/day) and then further divided on the basis of MMI (4 h vs. 6 h) so that four groups were formed (HC4 h–HC6 h–LC4 h and finally LC6 h). Higher concentrate allocation induced a rise in milk yield (MY, kg/cow/day) and allowed to stabilize it in periods of grass shortage but did not influence milking frequency, while shorter MMI (4 h) was correlated with higher MF without effect on MY. A combination of both strategies (4 h and high concentrate) improved the traffic globally to the robot. This result was linked to a reduction of refused milking and, therefore, the decrease in returns to the robot. This strategy could be advised to maximize the system’s efficiency during periods of high milk sales. When the economic conditions do not favour the increase in concentrate supply, short MMI could facilitate the traffic and increase the efficiency of returns.

The effect of milking frequency and intervals on milk production and functional properties of the cows’ udder in automatic milking systems

The objective of this study was to describe the association between the milking frequency and milk production and to determine the effect of milking intervals on the functional properties of the udder of cows in automatic milking systems. Thousand eight milking recordings were enrolled in the study, in total, 106 Holstein cows were observed. The results of studies showed that at a daily milk yield per cow of 22.0 ± 0.6 (means ± SD) kg, the milking frequency was 2 times (13.5 % of the total number of milkings), 32.7 ± 0.4 kg – 3 times (57.2 %), 37.7 ± 0.6 kg – 4 times (28.0 %), 51.3 ± 4.1 kg – 5 times (1.3 %). An increase in the daily milk yield due to a reduction in the milking interval has been established (p<0.001). However, milk yield per milking has the opposite tendency (p<0.001). The average and maximum milk flow rates increased with an increase in the milking interval and reached the highest values in the interval of 7.50–8.99 hours – 2.36 and 3.36 kg/min, respectively (p<0.001). Cows with a low indicator of maximum milk flow rate (3.01 ± 0.06 kg/min) had greater milk production (36.6 ± 0.47 kg). With an increase in the milking interval from 4.50–5.99 to 7.50–8.99 hours, it was accompanied by a decrease in the latency period milk flow in the quarters of the udder, when the milking interval reached 9.00–10.49 and 10.50–11.99 hours, the latency period milk flow increased.

Evaluation of automatic milking system variables in dairy cows with different levels of lactation stage and reproduction status

In this study, we hypothesized that differences of automatic milking systems (AMS) variables in dairy cows during estrus and through diverse stages of lactation can be suggested as alternative indicators to support the pregnancy in dairy farms using automatic milking systems. The key objectives were: (1) to determine the variation of automatic milking system indicators during lactation and to estimate the relationship with reproduction status in dairy cows; (2) to test the hypothesis that milking traits of cows can be influenced by estrus and conceiving, and can be used as a predictor of the likelihood of reproductive success in dairy herds. Estrus synchronization was performed in 368 healthy Lithuanian Black and White cows. All cows (n = 368) were synchronized and inseminated for the first time on the 91st day in milk (DIM). Cows not pregnant (17.39%) were synchronized and inseminated again at 132 DIM. After the first insemination pregnant (n = 304) cows were identified as group 1, after the second insemination pregnant (n = 58) cows - as group 2. Overall, 12 01 713 records of udder quarters in cows from 5 to 305 DIM were evaluated. The results revealed the reduction in milk yield during estrus 11.05% on 91 DIM and 13.89% on 132 DIM (P < 0.001) and an increment in milk flow traits in cows after 91 DIM (P < 0.05), also a slight decline in milk flow traits on 132 DIM. Furthermore, milking frequency (MF) of cows decreased significantly (P < 0.001) after conceiving. The interval between milkings (MI) increased (40.30%) during estrus of cows in group 1 (P < 0.001), and thereafter gradually increased, however in group 2 there was a temporary increment (6.06%) on the 91 DIM and steady rise (42.13%) on 132 DIM was noticed. The results highlight that changes in AMS indicators of cows may be considered as an additional tool for improvement of reproductive management in dairy herds, but further research-based studies are necessary before practical application.

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.

The effect of herd mentality on dairy heifers conditioned to traffic through audio cues

The objective for the present trial was to understand whether dairy heifers could be trained to respond to an audio cue paired with a feed reward. The use of acoustic conditioning to induce cattle movement has not previously been tested with animal-mounted devices to call cattle both individually and as a group. Five heifers underwent testing for 6 days as part of an 18-day field trial (12 days of conditioning). The 6-day testing and data-collection period involved the heifers being called via a smartphone device mounted on the cheek strap of a halter. Heifers were called either as individuals or as a group. When the audio cue was sent, heifers were expected to traffic from a group-holding area to a feeding area (~80-m distance) to receive an allocation of a grain-based concentrate. Heifers were significantly (P = 0.001) more likely to approach the feeding area when called as a group (91% response rate) than when they were called as individuals (67% response rate). When heifers did respond to being called, their time to traffic to the feed area was quicker (P < 0.001) when they were called as a group (77.9 ± 55.4 s) than when they were called as individuals (139.3 ± 89.2 s). The present trial has shown that animals can be trained to respond to an audio cue paired to a feed reward, highlighting the potential for acoustic conditioning to improve voluntary cow movement with an animal-mounted device. It also highlights the limitations of cattle responding to being called individually compared with being called as a group.

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 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.