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Provision of a palatable feed in automated milking systems (AMS) is considered an essential motivating factor to encourage voluntary visits to the milking stall. Although the quantity and composition of AMS concentrates have been previously investigated, the form of the concentrate has not been extensively evaluated. The objective of this study was to evaluate the effects of feeding pelleted (PB; 132.9 ± 56 DIM, 47.4 ± 9.51 kg/d milk yield) versus steam-flaked barley (SFB; 133.0 ± 63 DIM, 40.5 ± 8.23 kg/d milk yield) in an AMS on dry matter intake, AMS visits, milk and milk component yield, and partial mixed ration (PMR) feeding behavior. Twenty-nine Holstein cows of varying parities were enrolled in this study. Cows were housed in freestall housing with a feed-first guided-flow barn design; 7 cows were housed in a separate freestall pen to enable individual PMR intake and feeding behavior monitoring. This study was conducted as a 2-way crossover, with two 21-d periods in which each cow received the same basal PMR but was offered 2 kg/d (dry matter basis) of PB or SFB in the AMS. Cows receiving the SFB had fewer voluntary AMS visits (2.71 vs. 2.90 ± 0.051, no./d), tended to have a longer interval between milkings (541.7 vs. 505.8 ± 21.02 min), spent more time in the holding pen before entering the AMS (139.9 vs. 81.2 ± 11.68 min/d), and had lower total box time (19.7 vs. 21.4 ± 0.35 min/d) than cows fed PB. Despite changes in AMS attendance, there were no differences for average milk (44.0 kg/d), fat (1.62 kg/d), and protein (1.47 kg/d) yields or AMS concentrate intake (2.02 kg/d). These behavioral changes indicate that offering SFB as an alternative to PB may reduce motivation for cows to voluntarily enter the AMS.

The objective of this study was to evaluate associations of dry off management factors, milking activity and production data, and somatic cell count prior to dry off and early in the subsequent lactation of cows milked by automated systems. Data were collected for 342 cows from five farms, for two milk tests prior to dry off, and for the two milk tests post calving. The results suggest that the post-calving milking performance of cows milked by automated systems may be more associated with individual cow traits than with their dry off management.

Studies have been conducted to assess the lactation productivity of Kholmogory breed Tatarstan type first-calf cows with robotic milking systems. The frequency of milking distribution during the day was observed: in the interval of 4-6 hours, the minimum number of milking occurs (6.4 %), in the interval of 12-14 hours, the maximum number (10.7 %). Most milking (34.7 %) is carried out in 6-8 hours after the previous one. More frequent milking (after 4-6 hours) is rarely observed is in 6 % of cases. 48.6 % of milking occurs after the 8-12 hour interval. For an interval of more than 12 hours occurs for 10.7 % of milking. With an increase in the milking interval, the milk yield also increases. At 4-6 hour interval, the milk yield is 5.2 kg, at 6-8 hour is 6.0 kg. An increase in the milking interval by 2 hours is accompanied by an increase in the single milk yield by 0.8-1.3 kg. During the first month, the cows were milked the least number of times, which was 75, or 2.4 times per day; during the second month, they were milked 86 times (2.8 per day). By the 4th month, milkings reached a peak of 93 times (3 per day). By the 5th month, a decline was observed (by 11 % compared to the 4th month). The peak milk production takes place during the second month of lactation, 681.3 kg. By the third month, a decline was observed by 4.8 %, to 648.1 kg. The maximum decrease in milk production, by 12.4 %, occurred between the 5th and 6th month of the tested lactation period.

Udder health is at risk when herds transition from a conventional milking system (CMS) to an automatic milking system (AMS). Somatic cell counts (SCC) are generally elevated for several months following a transition. However, such observations were made in studies conducted in the early 2000s. Technical improvements to AMS have likely been made since then, and farm management may have improved, learning from past experiences. This longitudinal observational study quantified national trends in SCC deteriorations in dairy herds that transitioned from a CMS to an AMS. Census data from the Dutch test day recording was used to determine these trends. It consisted of all cow-level SCC measurements conducted in the Netherlands from January 1, 2007 to December 31, 2019. Three udder health indicators, including the natural logarithm of the yield-corrected bulk-milk SCC, the proportion of cows having a composite SCC ≥200,000 cells/mL, and the proportion of cows having a new elevated SCC, were evaluated using multivariable population-averaged generalized estimation equations. Predicted values resulting from these statistical models were interpreted to determine trends in SCC deteriorations from 12 mo before and 12 mo after the transition to an AMS. Decreasing SCC deteriorations were observed during the study period for all 3 udder health indicators. Udder health deteriorations around the transition to an AMS were still observed, but they decreased in magnitude over the course of the study period. Bulk-milk SCC deteriorations were, for instance, 2.5 times lower in 2019 compared with those observed in 2007. Therefore, the effect of transitioning to an AMS on udder health became less severe in more recent years. However, deteriorations in the proportion of new SCC elevations were still evident toward the end of the study period. Efforts to lower udder health deteriorations in herds that transition to an AMS should therefore be continued and should intensify on factors lowering the proportion of cows having a new elevated SCC.

Robotic milking systems (RMS) have been demonstrated to reduce on-farm labour requirements and collect significant individual-level data in relation to animal health, welfare and production, but they are still largely underutilised. Studies on the relationship between heat stress, animal behaviour and robotic milking performance in a RMS are still insufficient. To model such a relationship, this study focused on analysing the data collected from a RMS system. Animal response indicators of heat stress assessment were rumination time (RT), milk temperature (MT) and daily milk yield (DMY). In addition, RMS milking behaviour, i.e. time of milking (TM), milking frequency (MF), milking duration (MD), milking speed (MS) and milk yield per milking (MY) were also monitored. A new index of rumination efficiency (REI) was created to evaluate rumination efficiency under heat stress, defined as the ratio between DMY and RT. Using multiple broken-line regression, it was found that a 1 °C rise in daily mean temperature could reduce RT by 5.12 min, decrease REI by 0.07 kg·cow−1·h−1, and increase low efficiency milking by 1%. Moreover, the study also found cows prefer to milk between 7:00–9:00AM, and 86% of milking events happened during this period. No significant correlation was found between heat stress and milking behaviour. However, delaying the first milking event of the day and controlling milking intervals to

Simple Summary The aim of this study was to verify the effect of temperature stress on dairy cows milked in an automatic milking system in the temperate climate of central Europe. The study showed that there was not a decrease in the daily milk yield per milking if the temperature-humidity index value was lower than 68. This was accomplished with the dairy cows having an average daily milk yield of 29 kg to 31 kg. A decrease of the daily milk yield per cow was recorded if the temperature-humidity index value was greater than 72. A higher average milk speed, as well as a higher maximum milk speed, may occur with a temperature-humidity index value higher than 68. Abstract The values of the temperature-humidity index and its influence on the performance parameters of dairy cows were monitored on four farms located in the southern part of the central Slovakia during a period of three years. The observed parameters included: the milk yield per cow per day, average milk speed and maximum milk speed. The thermal-humidity index was calculated based on a formula. The individual periods were divided according to the achieved THI. The results of dairy cows with a milk yield of 29 kg to 31 kg show that there is not a decrease in the milk yield per milking if the THI value is lower than 68. It was also found that there was a decrease in the milk yield per dairy cow in the robotic milking parlor for a THI value greater than 72. The influence of a THI value higher than 68 in these dairy cows results in a higher average milk speed, as well as a higher maximum milk speed. These two parameters are not yet in the main area of research interest. This study enriches the area with new knowledge, according to which dairy cows can show thermal stress by increasing the milk speed as well as the maximum milk speed.

There are several differences in how an automatic milking system (AMS; milking equipment not requiring human intervention for the milk harvesting process) and a conventional milking system (CMS) are managed, where the effect of milking system type on the human-animal relationship has remained unexplored. A survey and observations from 5 Australian dairy farms transitioning from CMS to AMS were taken twice, 1 yr apart, before and after transition to an AMS. The farmers completed a survey and had all farmer-cow interactions documented for 3 d. In addition, a random selection of lactating cows had their avoidance distance (the distance at which they move away from an approaching person) recorded and were involved in a handling test during both visits. The survey findings indicated that basic management practices remained mostly unchanged, whereas records of farmer-cow interactions showed 4 out of 5 farms had less interaction time after AMS transition. This was caused by a reduction in milk harvesting tasks, where a small portion of this time was re-invested into time that farmers spent around the cows without directly interacting with them and into tasks involving close cow contact. Overall, an approximate 27% decline was observed in avoidance distances of cows from an AMS compared with the CMS. A handling test was performed on 4 of the 5 farms before and after AMS transition, where the farmers were asked to move a selection of cows through a gate one at a time. In the AMS more vocal effort was required to move the cows, and the cows responded with a reduced occurrence of running past the farmer and reduced occurrence of slipping in an attempt to avoid the farmers compared with the CMS. Overall, results show that farmers spent less time interacting with cows in the AMS, and that cows were less fearful around people as seen by reduced avoidance distances and reduced stress responses to close handling.

Measurement of electrical conductivity (EC) is a method frequently used in dairy cows during milking in milking parlours, but especially in robotic milking as a low-cost mastitis detection method. The aim of this study was to evaluate the relationship between somatic cell count (SCC) and EC of milk in sheep reared in Slovakia as factors for monitoring subclinical mastitis on the basis of a bacteriological examination of udder health. Samples were collected individually from both halves of the udder from 295 sheep of different breeds from eight farms during evening milking. Based on SCC, the samples (590) were divided into classes (SCC < 2 × 105, 2 × 105 ≤ SCC < 4 × 105, 4 × 105 ≤ SCC < 6 × 105, and SCC ≥ 6 × 105 cells.mL-1), (SCC < 7 × 105 and SCC ≥ 7 × 105 cells.mL-1) and (SCC < 1 × 105 and SCC ≥ 1 × 105 cells.mL-1) respectively. Based on the presence of pathogens in the udder halve, they were classified as “major pathogens” (14), “minor pathogens” (161) and “without pathogens” (415). The presence of a pathogen had a significant effect on the increase in EC, SCC and protein content and decrease in content of lactose. We found a significant correlation between EV and SCC at first classification only in cases where all data was analysed jointly (r = 0.531), SCC ≥ 6 × 105 (r = 0.403) and SCC < 2 × 105 (r = 0.214). In the second and third classification, we found significant correlations in both cases, the SCC < 7 × 105 (r = 0.270) and the SCC ≥ 7 × 105 (r = 0.382) and SCC < 1 × 105 (r = 0.136) and the SCC ≥ 1 × 105 (r = 0.557). The electrical conductivity showed a stronger correlation with the lactose and protein content than LogSCC. We can argue that measuring the electrical conductivity of sheep milk may be a possible alternative for mastitis detection in sheep. EC can be useful in detecting animals with level of SSC greater than 6 × 105 cells.mL-1.

The studies were conducted in the conditions of LLC “Milk Group” of Sukhinichsky district of Kaluga region. The object of research is robotic milking machines, Swedish breed cows and Danish red breeds. Equality of development of the front and rear lobes, the udder index, the average milk flow rate and the frequency of delivery can be achieved only with long-term selection by type, good severity of signs of milkiness. Based on the selection of cows for milking on a robotized milking plant, the following requirements were put forward: high milk productivity and milk yield, tightly attached udder, nipples of the same size, the lower point of which should not be below 33 cm from the floor level, the minimum distance between the rear nipples in 3 cm, between the front nipples - 12.5 - 30 cm, the thickness of the nipples in the range of 1.5-3.5 cm, the rear nipples should be located 3 cm lower than the lower part of the udder, the minimum distance between the front and rear These udder teats - 7 cm, an angle of deviation from vertical teats should not exceed 30 °. The main reasons for the insignificant loss of milk during robotic milking are the milking of cows that do not have a full-fledged milk-giving reflex or the reflex was inhibited at the beginning of the milking of milk with a delay in putting the teat on the teats of the udder. The size of one-time milk yield, with the exception of the program indicators of this restriction on the milk yield, is determined by the frequency of effective visits to the robot (i.e., the multiplicity of milking during the day). In robotic milking, the frequency of milkings varies by day periods relatively little and drops to a minimum only in the second half of the night, in our opinion, due to the relatively late distribution of feed mixtures. It is also noted that cows of all ages have the same number of milkings on a robotized unit within 2 times.

The studies were carried out on black-and-white cattle of the Ural intra-breed type, bred at the enterprises of the Sverdlovsk region. The studied groups of animals are formed depending on the milking technology (milking in a milk pipe, in milking parlors, robotic milking). The level of hormones in the groups of cows, where intensive milking technologies were used, is higher than in the groups of linear milking in the milk pipeline: prolactin by 17.2 ng / ml; adrenocorticotropic hormone by 10.3 pg / ml; cortisol by 5.3 nmol / l. The number of heifers with a high type of stress tolerance is on average 6.3% less in groups where intensive milking technologies were used in comparison with linear milking in a milk pipe.

Automated milking systems (AMS) provide the capability of adjusting the amount of supplemental concentrate offered to cows based on parity, days in milk, and milk yield; in many situations, more supplement is provided to those cows with higher production demands. To determine associations of subclinical ketosis (SCK) and blood β-hydroxybutyrate (BHB) with milk yield, supplement consumed in the AMS, rumination time, and the ratios of milk yield to supplement intake and milk yield to rumination time, we monitored 605 cows from 9 AMS herds, testing blood BHB concentrations 1×/wk for the first 3 wk of lactation. Milk yield, supplement intake, and rumination data were collected from the AMS computer on each farm. For analyses, only multiparous cows from 8 herds were included (n = 172 total) and were matched, within farm, to include an equal number of cows in each of 4 health status groups (n = 43 per group): SCK (BHB ≥1.2 mmol/L at ≥1 of 3 tests, with no other disorder in the first 30 d in milk), SCK+ (BHB ≥1.2 mmol/L at ≥1 of 3 tests, with another health disorder), HLT (BHB always1.2 mmol/L, with no other disorder), or OTH (BHB always1.2 mmol/L, with a health disorder). Milk yield and rumination time varied by health status, but we found no difference in supplement intake by health status group. As a result, milk yield relative to supplement intake and milk yield relative to rumination time differed by health status and were both positively associated with BHB. Cows in SCK had the highest milk yield and ratio of milk yield to supplement intake, but SCK+ cows had the lowest rumination time and the highest milk yield to rumination ratio. These results highlight the differences in milk production (per day and relative to supplement consumption or rumination time) associated with blood BHB and health status. Therefore, potential exists to modify supplementation in AMS to reduce negative energy balance by accounting for milk production of fresh cows.

The aim of the study was to determine the effect of the free approach to automatic milking system of primiparous (PP) Holstein-Friesian cows with varying locomotion score on the milking frequency, yield per milking and daily milk yield in the subsequent phases of lactation, and the consequent production and economical losses. The evaluation of cows’ locomotion was successively repeated in seven consecutive months, using a five-point scale of posture and movement assessment. It has been found that in the case of analysed PP cows with the locomotion score 4 led to reduction in the milk production by 11% in third stage of lactation compared to the healthy PP. PP cows with locomotion score 4 less frequent went to milking robot and was characterized by the lowest daily milk production.

When a farm that was using a conventional milking system introduces an automatic milking system (AMS) possible risk factors can affect milk quality. The aim of the study was to investigate the influence of milking with automatic milking systems on milk quality variables over a long time-period post-installation. Bulk milk total bacterial count (BMTBC) and somatic cell count (BMSCC) were analysed and compared from 2 years before introduction of automatic milking until 4 years after. Differences regarding these quality parameters were contrasted using t-test and one-way analysis of variance (ANOVA) and post hoc comparisons were performed. A significant increase in BMTBC was observed during the first three months after introduction of AMS, counts then declined to equivalent levels pre-AMS installation, from 25,000 to 50,000 cfu mL−1. Although differences were significant for the first two years post-installation, they became non-significant during the following two years. The difference in BMSCC was not statistically significant between pre and post-AMS installation time periods, but by grouping data into annual periods, significantly higher values of BMSCC were found during the first year after introduction. Nevertheless, these values decreased over time and even showed a significant improvement in the third year with respect to pre-introduction. The data show that the installation of AMS had a marked impact on milk quality. However, as soon as farmers become accustomed to managing the new equipment and the adaption of cows is real, a level of milk quality which can be maintained over time is achievable. SI

The motivation of cows to be milked is a key factor in the utilisation of milking robots. If a cow does not voluntarily attend a robot stall, fetching, that requires expensive labour, is required. This research suggests a new concept, herding all the cows to the milking robot using an automatic herding system (AHS). An AHS was built as a system of slow moving mobile fences controlled by an industrial controller. The AHS herds all the cows to the milking robot. The AHS was used in a commercial farm with two milking robots, and the experiment was conducted for three months. The dairy herd was divided into a reference group (43 cows) and an experimental group (38 cows). The AHS was used only with the experiment group. Milking frequency increased in the experimental group by 45.5% (1.89 milkings d−1 vs. 2.75 milkings d−1), while there was no major change in the milking frequency in the reference group 0.4% (2.38 milkings d−1 vs. 2.39 milkings d−1). Milk yield increased in the experiment group 15.7% (35.65 kg d−1 vs. 41.25 kg d−1). There was also no major change in the milk yield in reference group 4% (31 kg d−1 vs. 29.76 kg d−1). There was an 80% decrease in labour time for fetching the cows to the milking robot in the experimental group (5 h day−1 vs. 1 h day−1) while there was no change in labour for the reference group. The AHS was therefore associated with higher milking frequency, higher milk yield and labour reduction, hence economic benefits are expected for the system.

Dairy cows grazing pasture and milked using automated milking systems (AMS) have lower milking frequencies than indoor fed cows milked using AMS. Therefore, milk recording intervals used for herd testing indoor fed cows may not be suitable for cows on pasture based farms. We hypothesised that accurate standardised 24 h estimates could be determined for AMS herds with milk recording intervals of less than the Gold Standard (48 hs), but that the optimum milk recording interval would depend on the herd average for milking frequency. The Gold Standard protocol was applied on five commercial dairy farms with AMS, between December 2011 and February 2013. From 12 milk recording test periods, involving 2211 cow-test days and 8049 cow milkings, standardised 24 h estimates for milk volume and milk composition were calculated for the Gold Standard protocol and compared with those collected during nine alternative sampling scenarios, including six shorter sampling periods and three in which a fixed number of milk samples per cow were collected. Results infer a 48 h milk recording protocol is unnecessarily long for collecting accurate estimates during milk recording on pasture based AMS farms. Collection of two milk samples only per cow was optimal in terms of high concordance correlation coefficients for milk volume and components and a low proportion of missed cow-test days. Further research is required to determine the effects of diurnal variations in milk composition on standardised 24 h estimates for milk volume and components, before a protocol based on a fixed number of samples could be considered. Based on the results of this study New Zealand have adopted a split protocol for herd testing based on the average milking frequency for the herd (NZ Herd Test Standard 8100:2015).

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.

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.

This study investigated the effects of feeding system on diurnal enteric methane (CH4) emissions from individual cows on commercial farms. Data were obtained from 830 cows across 12 farms, and data collated included production records, CH4 measurements (in the breath of cows using CH4 analysers at robotic milking stations for at least seven days) and diet composition. Cows received either a partial mixed ration (PMR) or a PMR with grazing. A linear mixed model was used to describe variation in CH4 emissions per individual cow and assess the effect of feeding system. Methane emissions followed a consistent diurnal pattern across both feeding systems, with emissions lowest between 05:00 and 08:59, and with a peak concentration between 17:00 and 20:59. No overall difference in emissions was found between feeding systems studied; however, differences were found in the diurnal pattern of CH4 emissions between feeding systems. The response in emissions to increasing dry matter intake was higher for cows fed PMR with grazing. This study showed that repeated spot measurements of CH4 emissions whilst cows are milked can be used to assess the effects of feeding system and potentially benchmark farms on level of emissions.

The objective of this work was to evaluate the impact of production factors on milking frequency and the latter’s effect on animal welfare in robotic milking. The experiment was performed with Holstein Friesian cows housed in free-stall barns. To assess the impact of production factors, body condition score, milk yield, and concentrate intake were determined. To determine the effects of the milking frequency on welfare, the locomotion and teat-end scores and serum cortisol level were evaluated. Three experimental groups were formed according to the daily average milking frequency: 1.0 to 1.9, 2.1 to 2.9, and above 3.0 milkings per day. The decrease in milking frequency during lactation was related to the reduction in concentrate intake and milk yield. The increase in milking frequency was related to the reduction in the milk flow and the increase in milking duration. Milking frequency was affected by concentrate intake, locomotion problems, milk yield, and lactation stage. The cortisol level and teat-end conditions were not affected by milking frequency. Milking frequency impacts milk flow, duration of milking, and milk composition; however, there is no effect on the indicators of animal welfare.

Research has been conducted to study the cow’s behavioral acts of the Tatarstan type of Kholmogory breed and their relationship with milk productivity in the robotic milking system. It was found that cows spend 706.7 minutes, or 49.1 % of the daily time, in the lying position, and 733.3 minutes, or 50.1 %, in the standing position. Animals spend 283.7 minutes (19.7 % of the daily time) on taking food, 497.3 minutes (34.5 %) on rumination, of which 150 minutes (10.4 %) – standing, 347.3 (24.1) – lying down. 232.3 minutes (16.1 %) of the time the cows stood in one place and were inactive. The movement took 42.7 minutes or 3 % of the daily time. After dividing the cows into 3 groups by productivity was found that highly productive animals were milked 1.9 times more compared to low-productivity animals (4.7 versus 2.7 times). They also spent more time standing (34.5 %), consumed more food (21.0 %), and chewed less cud (13.9 %). Animals assigned to the active and ultra-active groups by the motor activity index outperformed animals of the infro-passive and passive groups by 26.9 % in daily milking.

Studies have been conducted to assess the lactation productivity of first-calf cows of Kholmogory breed of Tatarstan type using robotic milking systems. The following changes in the amount of milking over the course of 10 months of lactation were observed: during the first month, the cows were milked the least number of times, which was 75, or 2.4 times per day. During the second month, they were milked 86 times (2.8 per day). By the 4th month, milking reached a peak of 93 times (3 per day). By the 5th month, a decline was observed (by 11 % compared to the 4th month). The average number of milking times per day was 2.8 ± 0.05 times. The amount of milking is closely correlated with milk productivity: twicemilked cows had an average productivity of 15.3 kg, 3 times – 19.0 kg, and 4 times – 27.0 kg (r = 0.69). The milking robot spent approximately 108 seconds, or 29 % of the time spent in the milking box, on preparation activities, including: udder cleaning, 39 seconds; scanning the position of the utter, 21 seconds; and connecting milking cups, 48 seconds. Lactation took most of the time, namely 251 seconds, or 66 %. The peak milk production takes place during the second month of lactation, 681.3 kg. By the third month, a decline was observed by 4.8 %, to 648.1 kg. The maximum decrease in milk production by 12.4 % occurred between the 5th and 6th month of the tested lactation period.

Cows in herds equipped with conventional milking parlors follow a structured, consistent, and social milking and feeding routine. Furthermore, in most cases cows in conventional herds receive all their nutrients from a total mixed ration, whereas in herds equipped with robotic or automatic milking systems (AMS) a fraction of their nutrients is provided during milking, mainly as a means to attract cows to the milking system. In this regards, AMS present both a challenge and an opportunity for feeding cows. The main challenge resides in maintaining a minimum and relatively constant milking frequency in AMS. However, milking frequency is dependent on many factors, including the social structure of the herd, the farm layout design, the type of traffic imposed to cows, the type of flooring, the health status of the cow (especially lameness, but also mastitis, metritis, among others), the stage of lactation, the parity, and the type of ration fed at the feed bunk and the concentrate offered in the AMS. Uneven milk frequency has been associated with milk losses and increased risk of mastitis, but most importantly it is a lost opportunity for milking the cow and generating profit. On the other hand, the opportunity from AMS resides in the possibility of milking more frequently and feeding cows more precisely or more closely to their nutrient needs on an individual basis, potentially resulting in a more profitable production system. But, feeding cows in the parlor or AMS has many challenges. On one side, feeding starchy, highly palatable ingredients in large amounts may upset rumen fermentation or alter feeding behavior after milking, whereas feeding high-fiber concentrates may compromise total energy intake and limit milking performance. Nevertheless, AMS (and some milking parlors, especially rotary ones) offer the possibility of feeding the cows to their estimated individual nutrient needs by combining different feeds on real time with the aim of maximizing profits rather than milk yield. This approach requires that not only the amount of feed offered to each cow but also the composition of the feed vary according to the different nutrient needs of the cows. This review discusses the opportunities and pitfalls of milking and feeding cows in an AMS and summarizes different feeding strategies to maximize profits by managing the nutrition of the cows individually.

Facility design can affect the accessibility of an automatic milking system (AMS). In particular, gates and alleys positioned around the AMS may affect cow traffic and cow behavior, potentially affecting the duration of time the AMS is available for milking. Eighty-four Holstein cows of various parities and days in milk were randomly divided between 2 groups, each group having access to its own AMS. Cow locations and behaviors in the AMS entrance and exit areas, as well as in the adjacent holding area, were recorded continuously for 14 d. Cows receiving a "no-milking" decision (i.e., cow is rejected from the milking stall due to a recent milking event) took longer to exit the milking stall (18.2±1.33 s), and were more likely to circle and re-enter the AMS (0.8±0.15) compared with cows receiving a milking decision (16.2±1.09 s; 0.2±0.03). Cows exiting the AMS hesitated for long periods when another cow was near the exit gate (192.93±1.11 s) or in the general holding area (101.04±1.07 s). Cows in late lactation had a greater probability of hesitating in the exit alley for long periods (0.55±0.09) compared with cows in early lactation (0.15±0.07), regardless of whether cows were in the holding area. Primiparous cows were more likely to block other cows trying to exit (0.60±0.13) compared with multiparous cows (0.29±0.09). Occasionally, blocking events led to "back-up" events, in which the AMS became unavailable for new cow access due to a back up of cows through the exit alley into the milking stall. The AMS was empty (not occupied) for 10 and 18% (groups 1 and 2, respectively) of the day; therefore, it was possible that back-up events would simply reduce the amount of time the AMS was empty. The duration of back-up events and AMS empty events had a negative relationship in group 1 (r=-0.74), but no such relationship was observed in group 2. The differences in time budgets between the 2 groups suggest that the effect of back-up events on AMS availability may be dependent on group social dynamics.

Transitioning a dairy herd to an automatic milking system (AMS) from a conventional parlor system may be stressful for the cow, as many changes occur during this process. Chronic stress may affect the welfare of the cow, and acute stress during milking can decrease milk yield. Therefore, it is important to quantify if and how long stress during adaptation to an AMS might persist. Seventy-seven cows with acceptable udder and teat conformation that would not interfere with adaptation to the AMS and that were lactating n = 18, early [0 to 100 d in milk (DIM)]; n = 27, mid (100 to 200 DIM); and n = 32, late (200+ DIM) for the full duration of the project were chosen for observation. All cows had been milked previously in a double-6 herringbone milking parlor. Four stress-related behaviors [step-kick behavior both before and after attachment of teat cups, elimination (urination and defecation instances), and vocalization] were recorded during milking by trained observers, whereas milk yield was automatically recorded by the AMS. Data were collected for 24-h periods beginning on the day the cows transitioned to milking in the AMS (d 0), and on d 1, 2, 4, 8, 16, and 32 thereafter. Instances of elimination and vocalization were greater on d 0 compared with all other days (elimination: d 0 = 3.1 ± 0.09, d 1 = 0.6 ± 0.07, and 0 ± 0 instances thereafter; vocalization: d 0 = 1.7 ± 0.07, d 1 = 0.05 ± 0.04, and 0 ± 0 instances thereafter). Milk yield increased between d 0 (18.3 ± 1.7 kg) and d 1 (30.9 ± 1.7 kg). Primiparous cows (n=28) were more likely than multiparous cows (n = 49) to display step-kick behaviors both before (8.3 ± 2.5; 5.5 ± 0.6, respectively) and after (15.6 ± 2.4; 13.3 ± 1.3, respectively) teat cup attachment during milking. Eight days after introducing the cows to the AMS, over 60% of the herd was milking voluntarily and 95% of the herd was milking voluntarily within a month, which suggests that cows did not find the AMS aversive. Greater elimination and vocalization behavior and lower milk yield on d 0 relative to subsequent days indicated initial stress and discomfort with the milking process in the new system; however, the cows appeared to adapt within 24h.

The aim of this study was to assess the quality of raw cow’s milk from an automatic milking system. Samples of milk (48) were analyzed chemically and microbiologically and the somatic cell count, freezing point and inhibitor residues were determined. For comparison purposes, milk analysis data from two farms using conventional machine milking and 2008 milk analysis report data for the Czech Republic were used. All physical and chemical characteristics of the study samples were within the established limits. The average content (%) of chemical indicators was following: fat 3.79 ± 0.18, protein 3.46 ± 0.06, casein 2.67 ± 0.09, lactose 4.82 ± 0.04 and NFS 8.96 ± 0.11. The values for freezing point and somatic cells count were 221 ± 46.103·ml-1 and -0.521 ± 0.003 °C respectively. No inhibitor residue was detected in any of the samples. Microbiological values were lower than the limits: the total microbial count (2.3·104 CFU·ml-1), psychrotrophic count (6.3·103 CFU·ml-1), coliform count (2.4·101 CFU·ml-1), Escherichia coli count (3.2·101 CFU·ml-1), enterococci count (8.5·101 CFU·ml-1) and Staphylococcus aureus count (1.1·101 CFU·ml-1). The study provides a comprehensive view on the quality of milk produced by robotic milking. The automatic milking system seems to be excellent in terms of milking and milk hygiene. Robotic milking, somatic cells, freezing point, microbiology The production of high quality milk and keeping the herd in good health are the main objectives in primary milk production. A milking robot automatic milking system (AMS) seems to be a possible solution to meet these objectives. Some authors have reported a 10 15% milk yield increase, e.g. Wade et al. (2004) found the milk yield to be 12.4% higher, but others such as Bi l lon and Touraine (2002) have observed, in contrast, milk yield reduction. Klungel et al. (2000), Rasmussen et al. (2002), de Koning et al. (2003), Svennersten-Sjaunja and Pet tersson (2008) have reported AMS to have negative effects on milk indicators such as increase in the freezing point, in the total microbial count, in the free fatty acid content or in the somatic cell count. Klei et al. (1997) have found more frequent milking in AMS (3 times a day) to result in lower milk fat and protein contents. Others, e.g. Nielen et al. (1992) have reported positive effects of AMS on milk quality, more precisely lower incidence of pathogens, particularly of Staphylococcus aureus, a common cause of mastitis in dairy cow. In keeping with automatic milking system, a low prevalence of mastitis in cows were found, therefore the aim of the study was to test milk from automatic milking system for physical and chemical properties, focusing on the somatic cell count and freezing point, and a broad spectrum of microbiological indicators. The results of our study can be with the values obtained by conventional machine milking systems on the farms with higher incidence of mastitis and also with the milk quality report for the Czech Republic in 2008. ACTA VET. BrNO 2011, 80: 207–214; doi:10.2754/avb201180020207 Address for correspondence: Doc. MVDr. Bohumira Janstova, Ph.D. Department of Milk Hygiene and Technology Faculty of Veterinary Hygiene and Ecology University of Veterinary and Pharmaceutical Sciences Palackeho 1-3, 612 42 Brno Czech Republic Phone: +420 541 562 712 Fax: +420 541 562 711 E-mail: janstovab@vfu.cz http://www.vfu.cz/acta-vet/actavet.htm Materials and Methods Raw cow’s milk samples were collected from a Czech farm that uses the automatic milking system (AMS) Lely Astronaut Evolution (The Netherlands). In total, 48 bulk tank milk samples from second to third lactation Holstein cows were collected and analyzed. The milk samples were collected four times a month for one calendar year. They were added the preservative Broad Spectrum Microtabs®II (D&F Control Systems, Inc., California, USA), except for those intended for freezing point determination and microbiological analysis. After cold transport to the laboratory (< 10 oC), the samples were analysed within 24 h according to the respective standards (see below). The following selected physical and chemical properties of milk were monitored: fat, proteins, casein, lactose, non-fat solids, freezing point, titratable acidity, somatic cell count and inhibitor residues. Milk composition was determined in compliance with Czech standard CSN 57 0536 (1999) using a Bentley 2500 analyzer (Bentley Instruments, Minnesota, USA). The somatic cell count (SCC) was determined by flow cytometry using the fluoroopto-electronic method in compliance with Czech standard EN ISO 13366-3:1998, with a Bentley Somacount 500 (Bentley Instruments, USA). The freezing point (FP) determination was carried out in compliance with Czech standard CSN 57 0538 (1998) using a Funke Gerber Freezing point determiner CryoStar automatic 7160 (Funke Gerber, Germany). The instruments were regularly calibrated and tested for analytical performance. Titratable acidity was determined according to Czech standard CSN 57 0530 (2006). The following microbiological indicators were determined: total microbial count (CSN EN ISO 4833:2003), psychrotrophic count (CSN ISO 17410:2003), coliform count (CSN ISO 4832:1995), Escherichia coli count (CSN ISO 16649-2:2003), enterococcus count 0.2 ml of the sample streaked onto Slanetz and Bartley agar (Oxoid, Basingstoke, UK) and cultured aerobically at 37 °C for 48 h, and Staphylococcus aureus count (CSN EN

Results of robotic milking on selected farms in the Czech RepublicThe introduction of robotic milking into farms in the Czech Republic began in 2003. This paper gives the results on 7 farms with robotic milking machines, with regard to breed (Bohemia Spotted cattle, Holstein), company size (number of robots 1 to 8) and type of ownership (private, cooperative). All farms used Lely Astronaut A3 robots. The companies showed statistical differences in average daily milk performance (P≤0.001) from 21.04 kg of milk/head/day to 40.43 kg of milk/head/day. Small private companies with one robot reached significantly (P≤0.001) higher milk performance of 28.79 kg of milk/head/day. On average, the daily milk performances of the Holstein cattle were 7.17 kg of milk higher than those of the Bohemia Spotted cattle (29.97 kg and 22.80 kg, respectively). The average number of milkings per head and day in individual companies ranged from 1.97 to 2.67. The average number of rejections per head and day (1.11) was low in companies with one robot and is influenced by the number of cows per one robot. The highest number of cows that had to be accompanied to the robot could be seen in large cooperatives - 20.1%. Bohemia Spotted cattle showed the lowest number of breeding-cows needing to be accompanied to the robot (8.8%).

The aim of this study was to continuously monitor rumen pH of loose-housed dairy cattle as affected by feeding pattern and live yeast supplementation. Three multiparous lactating rumen-cannulated cows receiving the same basal ration once daily were supplemented with 5 g/d (equivalent to 1010 CFU/d) of Saccharomyces cerevisiae strain CNCM I-1077 (Levucell SC2, Lallemand, Toulouse, France) alternately for periods of 2 weeks following a cross-over design. The three cows were maintained in loose-house conditions and milked with a robotic milking system. Cows consumed a fraction of their ration in the feed bunk as a mixed ration and about 3 kg/d of concentrate during milking at the robotic milking unit. During the last 8 d of each period, rumen pH was monitored every 15 min. However, the rumen was accessed only once every 3 days. These pH measurements were recorded with a pH meter capable of storing pH values automatically that was placed inside a custom-made polyvinyl cylinder with about 300 g of lead to ensure that the device remained in the ventral part of the rumen throughout all readings. Individual feed intake and feeding patterns were also recorded. The data were analyzed using a mixed-effects model with repeated measures. Average rumen pH was greater (P

Currently, different protocols are used in various countries to estimate the 24-h fat and protein percentages for dairy cows that are milked with an automatic milking system. These protocols include estimating 24-h fat and protein percentages from 1) single samples from all animals in the herd; 2) single samples of fat adjusted for covariates and single samples of protein unadjusted for covariates; or 3) all samples (adjusted and unadjusted) collected on test days where test days vary in length from 10 to 18 h. The accuracy of estimated component percentages in predicting observed percentages was determined via agreement analysis for four protocols. Analysis of the data suggests collecting all samples (unadjusted for covariates) during a sampling period of at least 16 h on test day to be the most accurate protocol when estimating 24-h fat and protein percentages in herds with automatic milking systems.

The objective of this study was to quantify genotype by environment interaction ( G x E) between automatic milking systems (AMS) and conventional milking systems (CMS) for test-day milk, fat, and protein yield and for test-day somatic cell score (SCS) in The Netherlands. The G x E was studied in 2 ways: 1) between AMS farms and CMS farms in the same period and 2) within farms comparing the period before introduction of AMS with the period after introduction of AMS. For both sub-objectives, a separate data set was generated. Test-day records were used to be more flexible with respect to the introduction date of AMS. Multivariate, fixed regression, test-day sire models were used to estimate variance components. Genetic correlations between AMS farms and CMS farms in the same period were 0.93, >0.99, 0.98, and 0.79 for test-day milk yield, fat yield, protein yield, and SCS, respectively. Genetic correlations within farms between the period before and after introduction of AMS were lower for production traits and higher for SCS: 0.89, 0.91, 0.87, and >0.99, respectively, for test-day milk yield, fat yield, protein yield, and SCS. Heterogeneity of variance was observed between AMS and CMS in both data sets. Especially the residual variance increased with automatic milking. As a consequence, the heritability tended to be lower for automatic milking. It was concluded that effects of G x E are small between AMS and CMS. Therefore, AMS farms can select sires accurately based on national rankings.

The aim of this study was to analyze the effect of robotic milking (RM) on fertility and somatic cell counts (SCC) among dairy herds participating in the national Dutch milk recording system. It was hypothesized that RM, and a higher milking frequency in general, would have negative effects on fertility, due to expected and supposed deeper negative energy balance (NEB). Herds increasing milking frequency from two to three times daily consistently had increased production. Milk production during RM was intermediate between the amounts obtained by milking twice versus three times a day. Milking three times a day and the associated higher production had no significant effect on reproductive measures such as nonreturn rate at 56 d post insemination (NR56) or days to first service. Although RM did not affect NR56, use of the robot was associated with an increase in days to first service. An increase in milking frequency from two to three times daily did not affect SCC, but SCC were significantly increased after milking with the robot. Robotic milking has a significant positive effect on production and no negative effect on fertility as measured by NR56. The effect of RM in increasing days to first service appears due to reasons other than increased production and a more NEB. Increased SCC during RM is potentially of concern. From the data available, the relationship of RM to clinical mastitis could not be determined but this aspect needs further attention.

Information on accuracy of milk-sampling devices used on farms with automated milking systems (AMS) is essential for development of milk recording protocols. The hypotheses of this study were (1) devices used by AMS units are similarly accurate in estimating milk yield and in collecting representative milk samples compared with devices used by certified milk recording providers on farms with conventional milking systems (CMS) and (2) devices used on both AMS and CMS comply with accuracy criteria described by the New Zealand Standard and by the International Committee of Animal Recording (ICAR). Milk recording data from 5 AMS farms were collected during 13 milk recording test days between December 2011 and February 2013. Milk yield was estimated by ICAR-approved milk meters on AMS units. Milk samples were collected over a 48-h period and submitted to an off-site certified laboratory for milk composition analysis. Data were also collected manually from 5 to 10 cows per AMS unit; a complete milking of a cow was weighed to serve as gold standard for milk yield, and 3 milk samples per cow milking were collected and analyzed in the laboratory to serve as gold standards for milk composition. A similar procedure was used during 6 milk recording occasions with devices used during conventional milk recording at a CMS research farm. Farm type, breed, season, and region did not appear to affect accuracy of devices used on AMS units. Milk meters used by AMS units complied with ICAR limits in 12.5 and 25% of the milk recording test days for test bucket weights between 2 and 10 kg and for test bucket weights >10 kg, respectively. These percentages were 52 and 42%, respectively, for devices used on CMS. Analyzing all samples as one milk recording test day, 1.4% fell outside the 20% difference band for AMS compared with 1.1% of the milk samples for CMS. Devices used by AMS complied with ICAR in 73% of the milk recording test days for fat percentage, compared with 42% of the milk recording test days by devices used at the CMS farm. When analyzing all milk samples as one milk recording test day, 3.5% of the milk samples fell outside the 99% ICAR limit for AMS compared with 17.2% of the milk samples for CMS. Applying the ICAR standards for fat percentage to crude protein percentage and SCC, devices used on AMS were accurate in estimating crude protein percentage but not in estimating SCC. Thus, devices on AMS units did not comply with national nor ICAR standards with regard to milk yield and fat percentage. However, devices used on AMS were similarly or more accurate compared with devices used during conventional milk recording. It is proposed that devices used on AMS units, when calibrated regularly and when set up according to the manufacturer’s instruction, have similar or improved accuracy compared with CMS devices. Because the New Zealand industry accepts data from devices currently used by certified providers for milk recording on CMS farms, results imply that the AMS devices should also be permitted to be used for milk recording.

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 1,964 individual cows across 21 farms were measured for at least 7 days per 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 coefficient of variation 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.

Milking routines in automatic milking systems (AMS) differ from those in conventional milking. While milking intervals are constant and teat cups are attached simultaneously in conventional milking, in AMS milking intervals are variable and attachment of teat cups can be sequentially delayed. Experiments were performed to test effects of pre-milking teat cleaning, of different milking intervals in different lactational stages, of delayed teat cup attachment and individual teat cup removal on oxytocin release, milk ejection and milk removal. Teat cleaning caused oxytocin release and induced milk ejection. The lag time from start of teat stimulation until milk ejection occurred increased with decreasing milking interval and was longer in late than in early lactation. In addition, milk fat content increased towards the end of lactation and was higher after short than after long intervals from previous milking. Sequentially delayed attachment of teat cups every 20 s or every 60 s did not reduce oxytocin release. However, total interruption of teat stimulation for 2 min between pre-stimulation and start of milking resulted in transiently decreasing oxytocin concentration and increasing amounts of residual milk. The quarters in which teat cups were last removed had non-significantly the smallest amounts of stripping and residual milk. The stimulatory effect of single teat cups during sequentially delayed attachment is sufficient to maintain adequate oxytocin release and maximum milk ejection.

Milk yield is a function of the number of mammary secretory cells and their metabolic activity. Both are not static, but change during the course of lactation. The rate at which they change may be influenced by farm management practices, such as feeding, photoperiod, hormonal treatment (e.g., bovine somatotropin), and milking frequency. By applying such practices, or a combination thereof, at any time during lactation, the farmer has tools to alter the shape of the lactation curve. These practices could be applied after peak lactation to increase milk yield and perhaps slow down the rate of postpeak decline in milk yield, but can also be used to promote mammary involution near drying-off. The present review focuses on milking frequency. Compared with twicedaily milking, milking three times a day or more often (robotic milking) will increase milk yield by 18%, whereas once-daily milking decreases milk output by 20%. Although frequent milking is practiced more often than once daily, the once-daily milked gland provides an excellent model to study functional changes related to milking frequency. The effects of increasing and decreasing milking frequency on mammary functioning are discussed, with emphasis on functional changes in the once-daily milked gland with regard to processes such as changing cell number or activity, feedbackinhibition, tight junction leakiness, apoptosis, and cisternalalveolar milk storage.

Seven cows were monitored for 10 days during which hyperketonaemia was experimentally induced in five cows. Concentrations of acetone in breath, ketones in milk and volume of milk yield were recorded at intervals throughout the period. The concentrations of acetone exhaled by the cows whose feed was reduced rose above 5 μg/l within 7 h of feed being reduced. Five repeated samples each from a ketotic and healthy cow had means of 46 and 21 μg/l with 6.5 μg/l standard error of difference ( P =0.005). Ketone bodies (acetoacetate and acetone) in milk did not rise above 1 mmol/l until 48 h after reducing feed. The milk secretion rate dropped by 10% approximately 36 h after the feed was cut. The onset of ketosis could be detected automatically before clinical signs appeared. Further work is required to determine the statistical validity of different techniques of automatic monitoring, to improve and automate the collection of representative breath samples, to develop sensor systems and to develop a better disease model.

This paper discusses some of the factors which will contribute to the rate of adoption of the robotic milking system by large dairy farms in the United States, if and when current mechanical problems regarding machine attachment and voluntary cow entry for the entire dairy herd are solved. The most relevant factors are the high investment cost of the automatic system and necessary housing changes needed to make the system a serious factor in future milking systems in the United States.

This paper deals with the integration of robotic milking in the dairy farm and focusses on the following aspects: (a) required milking capacity of robotic milking units, (b) cow traffic and visits to the milking units and (c) design of total systems. The capacity of a milking stall in milkings per hour depends on the milking time per milking, the occupation time of the milking stall and the accessability of the milking parlour. Based on milk yields of 14.1, 10.7 and 8.1 and 7.0 min per milking were established. It is expected that because of cows loitering at or obstructing the entry or exit and because of a certain daily pattern in the AMS visits, an effective occupation of about 60% of the milking stall will be possible. The capacity is then 4.2, 4.5 and 5.2 milkings per hour. It was assumed that an AMS-stall is vailable 20 h per day for milking and 4 h per day for cleaning. For a herd of 100 lactating cows, three times daily milking and an occupation time of the milking stall of 60% of the available 20 h, four milking stalls are needed. Based on experiences gained in various experiments a lay out is suggested for an automatic milking system with automatic voluntary cow traffic. For automatic milking in a milking parlour with milking of cows in batches (human controlled cow traffic) the number of milking stalls will depend mainly on the chosen group size. For labour reasons this group size should not be too small and may vary between 50 and 100 cows. Based on the requirement that a group of cows has to be milked within approximately 1 h and an automatic milking stall with a 100% occupation time, the required number of milking stalls for three times per day milking and for herd sizes of 50, 100 and 200 cows are respectively 6, 6 and 8. Plans for cubicle houses with automatic cow traffic are provided.

The feasibility and short term consequences of a robotic milking and concentrates supplementation routine in an automatic milking system dairy were tested. The system comprised a selection unit and a milking unit equipped with a milking robot. In a two-phase 34 day experiment with 16 Friesian-Holsteins, cows reported voluntarily to the selection unit throughout the day. Concentrates could be allocated in both the milking and selection unit. During the last 11 days, the milking robot was available for milking for 24 h a day. A set of variables and mathematical equations describing the visiting pattern and concentrates supplementation was devised. The selection unit occupation time (about 10 h/d), together with the number of visits (about 95 visits/d), provided a good performance measure of the system capacity. It proved possible to maintain daily automatically controlled milking and concentrates supplementation. Not all concentrates (about 90% of planned) were supplemented as planned. To achieve the planned allocation of all concentrates the system must allow for revision of allocation decision during the day based on individual consumption and visiting patterns. The use of a selection unit enables control of cow traffic and the concentrates allocation, but might slow down the traffic between the selection and milking units by a period of up to 5 min passage time. A mean shorter passage of 3·8 min/visit between the selection and milking unit, as compared with the exit time when cows are referred to the feeding area might indicate that cows prefer the milking unit to the feeding area. With a twice daily milking regime 95% of the milking visits to the selection unit were voluntary, which means that cows were brought to the unit only in 5% of all milkings. It is predicted that about 10% of all cows would be unable to adapt to the automatic milking system routine, and would have to be culled from the herd.

A database consisting of 35291 milking records from 83 cows was built over a period of 10 months with the objectives of studying the effect of teat cup attachment failures and milking interval regularity on milk production with an automated milking system (AMS). The database collected records of lactation number, days in milk (DIM), milk production, interval between milkings (for both the entire udder and individual quarters in case of a teat cup attachment failure) and average and peak milk flows for each milking. The weekly coefficient of variation (CV) of milking intervals was used as a measure of milking regularity. DIM, milking intervals, and CV of milking intervals were divided into four categories coinciding with the four quartiles of their respective distributions. The data were analysed by analysis of variance with cow as a random effect and lactation number, DIM, the occurrence of a milking failure, and the intervals between milkings or the weekly CV of milking intervals as fixed effects. The incidence of attachment failures was 7·6% of total milkings. Milk production by quarters affected by a milking failure following the failure was numerically greater owing to the longer interval between milkings. When accounting for the effect of milking intervals, milk production by affected quarters following a milking failure was 26% lower than with regular milkings. However, the decrease in milk production by quarters affected by milking failures was more severe as DIM increased. Average and peak milk flows by quarters affected by a milking failure were lower than when milkings occurred normally. However, milk production recovered its former level within seven milkings following a milking failure. Uneven frequency (weekly CV of milking intervals >27%) decreased daily milk yield, and affected multiparous more negatively than primiparous cows.

Changes in milking performance and milk quality were followed on 45 multiparous Holsteins before and after implementation of a robotic or automatic milking system (AMS). After an initial monitoring week, approximately half of the herd was randomly assigned to be milked with the robot; the remaining half remained as control cows and continued to be milked twice daily in the parlor. Records of milking performance were followed for a total of four weeks on all cows; records of milk quality were followed for a total of three months on all cows. No significant changes in milk yield were observed between the robot and control cows during the first three weeks of AMS use; however, average daily machine-on time increased significantly for the robot cows in weeks two and three while average flow rate decreased significantly for the robot cows in week two. No significant differences were found in the geometric mean of individual cow somatic cell counts between the robot and control groups after one and two months of AMS use.

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. (C) 2000 Elsevier Science B.V.

SummaryWhen six cows were milked every 4 h over 48 h the average milk yield per cow increased by 1O·7% for the last 24 h period compared with the previous 5 d. Milk composition was similar to pre-experimental values although fat content was 0·5% lower in the last 24 h. The somatic cell count rose to twice the pre-experimental value over the first 24 h but was not significantly different by the second 24 h. It appears that cows responded quickly to an increased frequency of milking and milk of an acceptable composition was produced. This has implications for management of milk production where quotas are in operation and for robotic milking systems.

Lactating Holstein cows (52 multiparous and 90 primiparous) were monitored over a period of 10 mo to observe effects of grouping primiparous cows (PPC) separately from multiparous cows (MPC) on performance, feeding behavior, feed intake, feed efficiency, and milk production of PPC. Cows were kept in 2 symmetrical pens each equipped with a robotic milking unit, 2 waterers, and 28 feeding spaces. Typically, 100 lactating cows were present at a time, thereby ensuring 1.78 cows per feeding place in each pen. One pen (PP) was composed exclusively of PPC whereas the other pen (PM) included 30% PPC and 70% MPC. Primiparous cows were evenly distributed to each pen by days in milk and daily milk production. As they calved, additional primiparous cows were assigned sequentially to each of the 2 treatment groups; multiparous cows calving during the study were allocated to the PM group. Both PP and PM groups were managed equally and were fed the same basal ration twice daily plus 3 kg/d of concentrate during milking. Individual eating behavior and feed consumption at each visit were monitored electronically. Milk production was recorded daily, and milk composition monthly. Observed arithmetic means and standard errors are presented but application to other management situations is limited because animals within pen were not independent. Total dry matter intake (18.7 vs. 18.1 +/- 0.9 kg/d) and milk production (25.9 vs. 25.6 +/- 0.8 kg/d) of PPC were similar in both the PM and PP groups, respectively. Primiparous cows in the PP group had numerically more visits to the robotic milking unit (3.26 vs. 2.68 +/- 0.15) and to the feed troughs (4.91 vs. 4.02 +/- 0.43), but apparently spent less time eating (2.72 vs. 3.22 +/- 0.1 h/d) than did PPC in the PM group. Differences in feed efficiency were low but PPC in the PP group had numerically higher feed efficiency at times through 200 d in milk. Alternative grouping strategies illustrate potentially important differential responses among primiparous cows that warrant further study.