Comparison of Functional Aspects in Two Automatic Milking Systems and Auto-Tandem Milking Parlors

Farm-level nutritional factors associated with milk production and milking behavior on Canadian farms with automated milking systems

The objective of this study was to describe the nutritional strategies used on Canadian dairy farms with automated milking systems (AMS), both at the feed bunk and the concentrate offered at the AMS, as well as to determine what dietary components and nutrients, as formulated, were associated with milk production and milking behaviors on those farms. Formulated diets (including ingredients and nutrient content) and AMS data were collected from April 1, 2019, until September 30, 2020, on 160 AMS farms (eastern Canada [East] = 8, Ontario [ON] = 76, Quebec [QC] = 22, and western Canada [West] = 54). Both partial mixed ration (PMR) and AMS concentrate samples were collected from May 1 to September 30, 2019, on 169 farms (East = 12, ON = 63, QC = 42, West = 52). We collected AMS milking data for 154 herds. For each farm (n = 161), milk recording data were collected and summarized by farm to calculate average milk yield and components. Multivariable regression models were used to associate herd-level formulated nutrient composition and feeding management practices with milk production and milking behavior. Milk yield (mean ± SD = 37.0 ± 0.3 kg/d) was positively associated with the PMR ether extract (EE) concentration (+0.97 kg/d per percentage point [p.p.] increase) and with farms that fed barley silage as their major forage source (n = 16; +2.18 kg/d) as compared with haylage (n = 42), whereas farms that fed corn silage (n = 96; +1.23 kg/d) tended to produce more milk than farms that fed haylage. Greater milk fat content (4.09 ± 0.28%) was associated with a greater PMR-to-AMS concentrate ratio (+0.02 p.p. per unit increase) and total diet net energy for lactation (+0.046 p.p. per 0.1 Mcal/kg increase), but a lesser percentage of NFC of the PMR (-0.016 p.p. per p.p. increase of NFC percentage). Milk protein content (3.38 ± 0.14%) was positively associated with the forage percentage of the PMR (+0.003 p.p. per p.p. increase of forage percentage) and the total diet starch percentage (+0.009 p.p. per p.p. increase of starch percentage), but was negatively associated with farms feeding corn silage (-0.1 p.p. compared with haylage) as their major forage. Greater milking frequency (2.77 ± 0.40 milkings/d) was observed on farms with free-flow cow traffic systems (+0.62 milkings/d) and was positively associated with feed push-up frequency (+0.013 milkings/d per additional feed push-up), but negatively associated with PMR NFC content and forage percentage of the total ration (-0.017 milkings/d per p.p. increase of forage percentage). Lastly, greater milking refusal frequency (1.49 ± 0.82 refusals/d) was observed on farms with free-flow cow traffic systems (+0.84 refusals/d) and farms feeding barley silage (+0.58 refusals/d) than with guided flow and farms feeding either corn silage or haylage, respectively. These data give insight into the ingredients, nutrient formulations and type of diets fed on AMS dairy farms across Canada and the association of those factors with milk production and milking behaviors.

Behavior of Cows in the Lying Area When the Exit Gates in the Pens Are Opened: How Many Cows Are Forced to Get Up to Go to the Milking Parlor?

Equipping a farm with a milking parlor requires moving groups of cows from their pens to the part of the barn where milking takes place. The task of moving cows, carried out two or three times each day, shows links to the lying area of the barn. When the cows are taken from the pen to the milking parlor, some of them may be lying down, and this lying down must be interrupted. The forced standing up of cows can be considered in terms of their welfare. The aim of the study was to examine the number of cows lying in the stalls at the time of opening the exit gates in the pens in order to take the cows to the milking parlor. The study covered four pens, each with 12 cows. The behavior of the cows in the pens before morning and afternoon milking was recorded over 26 days. In the analysis, the dependent variable was the number of lying cows, and the independent variables were the time of milking and the level of sand in the lying stalls. The results of the study showed the significance of differences in the number of lying cows for stalls with a low and high level of sand, both in the case of morning and afternoon milking. Differences in the number of lying cows were also found when comparing the time before morning and afternoon milking. To compare the tendency of individual cows to lie down before going to milking, an index of forced standing up was proposed. The research conducted showed differences in the behavior of cows before leaving the pen to the milking parlor. The stage to reduce the forced standing up of cows is to equip the farm with an automatic milking system (AMS) instead of using a milking parlor. In barns with AMS, cows have full freedom to get up and approach the milking stall. The results of the observations are thus an additional argument confirming the benefits of using an automatic milking system, considered in terms of the welfare of dairy cows, regarding their lying down and getting up.

Forecasting Milking Efficiency of Dairy Cows Milked in an Automatic Milking System Using the Decision Tree Technique

In barns equipped with an automatic milking system, the profitability of production depends primarily on the milking efficiency of a cow (ME; kg/min) defined as cow milk yield per minute of box time. This study was carried out on 1823 Polish Holstein−Friesian cows milked by the automatic milking system (AMS) in 20 herds. Selected milking parameters recorded by the AMS were analyzed in the research. The aim of the study was to forecast ME using two statistical techniques (analysis of variance and decision trees). The results of the analysis of variance showed that the average ME was 1.67 kg/min. ME was associated with: year of AMS operation (being the highest in the first year), number of cows per robot (the highest in robots with 61−75 cows), lactation number (highest for multiparas), season of calving (the highest in spring), age at first calving (>36 months), days in milk (151−250 days) and finally, rear quarter to total milk yield ratio (the highest between 51% and 55%). The decision tree predicted that the highest ME (2.01 kg/min) corresponded with cows that produced more than 45 kg of milk per day, were milked less than four times/day, had a short teatcup attachment time (<7.65 s) and were milked in robots that had an occupancy lower than 56 cows.

Farm-level factors associated with lameness prevalence, productivity, and milk quality in farms with automated milking systems

Impaired locomotion (lameness) may negatively affect the ability and desire of cows to milk voluntarily, which is a key factor in success of automated milking systems (AMS). The objective of this study was to identify factors associated with herd-level lameness prevalence and associations of lameness and other farm-level factors with milking activity, milk yield, and milk quality in herds with AMS. From April to September 2019, 75 herds with AMS in Ontario, Canada, were visited, and data on barn design and farm management practices were collected. Data from AMS were collected, along with milk recording data, for the 6-mo period before farm visits. Farms averaged 98 ± 71 lactating cows, 2.3 ± 1.5 robot units/farm, 43.6 ± 9.4 cows/robot, 36.4 ± 4.9 kg/d of milk, a milking frequency of 3.01 ± 0.33 milkings/d, and a herd average geometric mean SCC of 179.3 ± 74.6 (× 1,000) cells/mL. Thirty percent of cows/farm (minimum of 30 cows/farm) were scored for body condition (1 = underconditioned to 5 = over conditioned) and locomotion (1 = sound to 5 = lame; clinically lame ≥3 out of 5 = 28.3 ± 11.7%, and severely lame ≥4 out of 5 = 3.0 ± 3.2%). Clinical lameness (locomotion score ≥3) was less prevalent on farms with sand bedding, with increased feed bunk space per cow, and on farms with non-Holstein breeds versus Holsteins, and tended to be less prevalent with lesser proportion of underconditioned cows (with body condition score ≤2.5). Severe lameness occurrence (farms with any cows with locomotion score ≥4) was associated with a greater proportion of underconditioned cows and in farms with stalls with greater curb heights. Herd average milk yield/cow per day increased with lesser prevalence of clinical lameness (each 10-percentage-point decrease in clinical lameness prevalence was associated with 2.0 kg/cow per day greater milk yield) and greater milking visit frequency per day, and tended to be greater with increased feed push-up frequency. Lesser herd average somatic cell count was associated with lesser clinical lameness prevalence, herd average days in milk, and proportion of overconditioned cows, and somatic cell count tended to be lesser for farms with sand bedding versus those with organic bedding substrates. The results highlight the importance of minimizing lameness prevalence, using of sand bedding, ensuring adequate feed access and feed bunk space, and maintaining proper cow body condition to optimize herd-level productivity and milk quality in AMS herds.

Survival of Polish Holstein-Friesian Cows to Second, Third and Fourth Lactation in Conventional and Automatic Milking Systems

The main objective of the study was to determine the effect of transition from a conventional milking system (CMS) to an automatic milking system (AMS) on survival of 6361 Polish Holstein-Friesian cows to second (SL2), third (SL3) and fourth (SL4) lactation as well culling reasons. The cows were born between 2002 and 2015 and calved between 2004 and 2018. All data for the survival analysis and culling reasons of cows in 17 herds during operation of CMS and AMS were extracted from the SYMLEK official milk recording system. Cow survival (SL2, SL3 and SL4) was analysed with multiple logistic regression using the following effects in the model: milking system (MS), first calving season (CS), age at first calving (AFC), ease of first calving (CE), birth of a dead calf at first calving (DC), milk yield (MY) for full first lactation (MY – this effect was ignored in SL2 analysis), herd (H), and MS × H interaction. In the next stage of the study, χ2 test was used to analyse culling reasons of cows (udder diseases, low fertility – infertility and reproductive disorders, locomotor diseases, low milk yield, other diseases – metabolic, digestive and respiratory diseases, accidents and chance events) in the first, second and third lactation and collectively in the first three lactations. Logistic regression analysis indicated a significant effect of MS, AFC, DC on SL2 and SL3, and of MY on SL3 and SL4. Moreover, H and MS × H interaction had a highly significant effect on SL2, SL3, and SL4. Cows used in AMS barns were characterized by significantly worse SL2 and SL3 compared to CMS (odds ratio), by 27.8% and 31.0%, respectively. It was also observed that the effect of switching from CMS to AMS on cow survival was determined by herd membership – in most herds this effect was unfavourable. A distinctly positive effect of milking automation on cow survival (SL2, SL3, SL4) was noted in only one barn (herd) – it was a new barn with a considerably expanded number of milked cows, where the lying area was covered with straw. When analysing the reasons for culling in the first three lactations collectively, it was found that after the AMS system was introduced into the herds, there were increases in the rate of culling for locomotor diseases (by 0.85 percentage points (p.p.)), low milk yield (1.36 p.p.) and other diseases (3.01 p.p.). It was also observed that the automation of milking reduced culling due to udder diseases by 0.37 p.p., low fertility by 3.24 p.p., and accidents and chance events by 1.60 p.p.

Impact of Music Played in an Automatic Milking System on Cows’ Milk Yield and Behavior—A Pilot Study

Practical experience suggests that music can have a positive effect on the welfare of dairy cows, which for some other animal species has been shown in earlier studies. Music could, furthermore, be a useful tool to support, for example, daily milking routines. In this pilot study we explored effects of music inside an automatic milking system (AMS) on cows’ milk yield and behavior. The experiment was conducted on a Finnish dairy farm with 56 cows in loose housing. Over two 2-day periods without and with selected music pieces played inside the AMS, data on daily milk yield (DMY), selection gate passing frequency (GP), milking frequency (MF), and milking interval (MI) were recorded. For analyses, data of 17 Holstein-Friesian cows were used. At cow level, mean values over repeated measurements without and with music were calculated, and analyzed by paired t-tests (DMY, MF) or Wilcoxon tests (MI, GP). During intervals with music versus without, cows passed the selection gate more often (15.8 versus 13.8) and had higher MF (3.0 vs. 2.8). No differences were found in MI (07:49:21 vs. 08:37:38) and DMY (36.5 vs. 37.0). The latter might be explained by a ceiling effect. Overall, the results suggest that the investigated sample of cows perceived the selected music as attractive and that playing music might be a practical tool to reduce necessary efforts of driving cows to milking.

Challenges and Tendencies of Automatic Milking Systems (AMS): A 20-Years Systematic Review of Literature and Patents

Over the last two decades, the dairy industry has adopted the use of Automatic Milking Systems (AMS). AMS have the potential to increase the effectiveness of the milking process and sustain animal welfare. This study assessed the state of the art of research activities on AMS through a systematic review of scientific and industrial research. The papers and patents of the last 20 years (2000-2019) were analysed to assess the research tendencies. The words appearing in title, abstract and keywords of a total of 802 documents were processed with the text mining tool. Four clusters were identified (Components, Technology, Process and Animal). For each cluster, the words frequency analysis enabled us to identify the research tendencies and gaps. The results showed that focuses of the scientific and industrial research areas complementary, with scientific papers mainly dealing with topics related to animal and process, and patents giving priority to technology and components. Both scientific and industrial research converged on some crucial objectives, such as animal welfare, process sustainability and technological development. Despite the increasing interest in animal welfare, this review highlighted that further progress is needed to meet the consumers' demand. Moreover, milk yield is still regarded as more valuable compared to milk quality. Therefore, additional effort is necessary on the latter. At the process level, some gaps have been found related to cleaning operations, necessary to improve milk quality and animal health. The use of farm data and their incorporation on herd decision support systems (DSS) appeared optimal. The results presented in this review may be used as an overall assessment useful to address future research.

Forecasting the milk yield of cows on farms equipped with automatic milking system with the use of decision trees

The purpose of this paper was to utilize the decision trees technique to determine the factors responsible for high monthly milk yield in Polish Holstein-Friesian cows from 27 herds equipped with milking robots. The applied statistical method-the decision tree technique-showed that the most important factors responsible for monthly milk yield of dairy cows using robots were, in descending order of importance: milking frequency, lactation number, month of milking, and type of lying stall. At the same time, it has been ascertained that the highest monthly milk yield (47.24 kg) can be expected from multiparous cows kept in barns with a deep bedding that were milked more frequently than three times per day. On the other hand, the lowest milk production (13.56 kg) was observed among dairy cows milked less frequently than two times a day, with an average number of milked quarters lower than 3.97. The application of the decision trees technique allows a breeder to select appropriate levels of environmental factors and parameters that will help to ensure maximized milk production.

The relationships between udder-quarter somatic-cell counts and milk and milking parameters in cows managed with an automatic milking system

Context Some milking parameters such as milk yield, milk flow, milking duration, milk conductivity and somatic-cell count can all be listed as economically important traits in dairy practice. Aims The aim of the study was to investigate the relationships among lactation stage, lactation number, milking season and milk-performance traits at an udder-quarter level, including somatic-cell count (SCC), milk yield (MY), milking duration (MD), time in box (TB), milk flow (MF) and milk conductivity (MC). An additional aim was to analyse milking-parameter levels in milkings with a SCC lower and higher than 400000 cells/mL. Methods The study included an analysis of 1621582 successful milkings obtained from six herds of dairy cattle equipped with milking robots (AMS). Key results The study confirmed that MD and MY differed greatly between front and rear quarters. Rear quarters took longer to be milked but produced more milk. During the first 100 days of lactation, the primiparous cows spent more time in the robot than did multiparous cows; however, in the second and third lactations, older cows were spending more time in the AMS. For primiparous cows, MF increased with time, being the highest at the end of lactation (&gt;200 days in milk). A different trend has been found in the group of multiparous cows, where a steady decrease in MF was observed with subsequent lactation stages. A lower MC was recorded for cows in their first lactation than for multiparous cows. Data obtained from primiparous cows showed the highest MC to occur between 100 and 200 days of lactation. In the group of multiparous cows, MC increased with the lactation stage. It was also shown that the mean values obtained for MY, MD and TB were higher for cows with a lower SCC (&lt;400000). Correlations between lnSCC (the natural logarithm of SCC) and MY and between lnSCC and MD were negative and low, while those between lnSCC and MC and lnSCC and MF were positive. Moderate correlations were found between lnSCC and total MC. Conclusions The study confirmed the differences in the performance of different udder quarters in relation to MY, MD, TB, MF, MS and SCC. Implications AMS provides farmers with vast data on milk and milking parameters. By monitoring changes in these parameters, farmer may be able to predict the level of production of their herd and the health of cows.

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.

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.

Quarter milking in primiparous and multiparous cows

Cows in their first lactation usually have lower milking parameters than multiparous cows. The purpose of this study was to determine changes in milking parameters in primiparous and multiparous cows in a quarter milking system. This is preliminary research which at a later stage will indicate whether primiparous cows milked in an automatic milking system (AMS) throughout their first lactation will have better milking parameters as multiparous cows. The data for statistical analysis was collected from three dairy herds in which Lely Astronaut A4 automatic milking systems were used. In total, data from 718 cows were analysed (531 lactations of primiparous cows and 477 of multiparous cows). The data was collected over a period of four years beginning in 2012, when the automatic milking system was implemented. After eliminating visits to the milking robot which ended in refusal, we analysed 352,708 milking sessions from primiparous cows and 488,711 from multiparous cows. Multifactorial analysis of variance of the numerical data was performed using a linear model taking into account the effect of herd, lactation stage, milking year and season, and the interaction of these factors. The statistical procedure showed that all linear model components had a highly significant or significant impact on the milking parameters considered. During initial stage of lactation the duration of the stay in the milking robot in the two groups was very similar—396-398 s. During this stage the milking time was about 268 s for the primiparous cows and 280 for the multiparous cows, and the average milk yield was 8.38 kg for the former and 10.40 kg for the latter. During the final stage of lactation (after 200 days) the milking time was 214 s in the primiparous cows and 241 in the multiparous cows, with yield of 7.93 and 8.77 kg of milk, respectively. Both primiparous and multiparous cows stayed longer in the milking robot and were milked longer in the spring and winter, which can be linked to higher milk yield. In both groups of cows it took longer to milk the rear quarters; these differences amounted to about 25 s in the primiparous group and about 40 s in the multiparous group.

Changes in milking parameters with robotic milking

Abstract. The aim of this present study is to describe changes occurring during the milking of cows in various periods following the introduction of an AMS (automatic milking system). The following cow milking parameters were analysed: milkings per cow per day, milking yield, milking speed and milking duration. An increase in milk yield in AMS barns has been found to be possible, but it is affected by a number of factors related to cow milking performance. Milk yield was observed to gradually grow with time after the installation of the robots. Older cows in their third and fourth lactations achieved higher milking parameter values as compared to cows in their first and second lactations. The average milk yield for the whole period was on a similar level, but, due to the fact that the duration of lactation in herd B was more than 100 days longer, that herd achieved a higher milk yield. The use of AMSs in barns enables farmers to monitor cow performance traits and study the relationships between them; farmers should try to select for traits ensuring high performance and directly related to milk yield. This study found a positive relationship between milking duration and milk yield. On the other hand, a highly negative relationship was found between milking duration and milking speed, which means that these parameters should be closely monitored. This study found that the optimal number of milkings per cow per day was in the range of 2.6 to 2.8 milkings a day with a 2.6 kg min−1 milking speed.

Differences in Voluntary Cow Traffic between Holstein and Illawarra Breeds of Dairy Cattle in a Pasture-based Automatic Milking System

Automatic milking systems (AMS) rely upon voluntary cow traffic (the voluntary movement of cattle around a farm) for milk harvesting and feed consumption. Previous research on conventional milking systems has shown differences between dairy cow breeds for intake and milk production, however, the ability to manipulate voluntary cow traffic and milking frequency on AMS farms through breed selection is unknown. This study investigated the effect of breed (Holstein Friesian versus Illawarra) on voluntary cow traffic as determined by gate passes at the Camden AMS research farm dairy facility. Daily data on days in milk, milk yield, gate passes and milking frequency for 158 Holstein Friesian cows and 24 Illawarra cows were collated by month for the 2007 and 2008 years. Illawarra cows had 9% more gate passes/day than Holstein cows over the duration of the study; however, the milking frequency and milk yield of both breeds were similar. Gate passes were greatest for both breeds in early lactation and in the winter (June to August) and summer (December to February) seasons. These findings highlight an opportunity to translate increased voluntary cow movement associated with breed selection into increased milking frequencies, milk production and overall pasture-based AMS performance.

Invited review: udder health of dairy cows in automatic milking

Automatic milking (AM) is increasing in modern dairy farming, and over 8,000 farms worldwide currently use this technology. Automatic milking system is designed to replace conventional milking managed by a milker in a milking parlor or in tie stalls. Cows are generally milked more frequently in AM than in conventional milking, and milking is quarter-based instead of udder-based. Despite improvements in the milking process and often building of a new barn before the introduction of AM, udder health of the cows has not improved; on the contrary, problems may appear following conversion from conventional milking to AM. This review focuses on udder health of dairy cows in AM, and we discuss several aspects of cow and milking management in AM associated with udder health. Finally, adequate management methods in AM are suggested. According to several studies comparing udder health between automatic and conventional milking or comparing udder health before and after the introduction of automatic milking in the same herds, udder health has deteriorated during the first year or more after the introduction of AM. Automatic detection of subclinical and clinical mastitis and cleaning the teats before milking are challenges of AM. Failures in mastitis detection and milking hygiene pose a risk for udder health. These risk factors can partly be controlled by management actions taken by the farmer, but AM also needs further technical development. To maintain good udder health in AM, it is imperative that the barn is properly designed to keep the cows clean and the cow traffic flowing. Milking frequency must be maintained for every cow according to its stage of lactation and milk production. Careful observation of the cows and knowledge of how to use all data gathered from the system are also important. "Automatic" does not mean that the role of a competent herdsman is in any way diminished.