Automatic Milking Systems Farms Research Articles

Optimising profitability and productivity of pasture-based dairy farms with automatic milking systems

There is a large variability in profitability and productivity between farms operating with automatic milking systems (AMS). The objectives of this study were to identify the physical factors associated with profitability and productivity of pasture-based AMS and quantify how changes in these factors would affect farm productivity. We utilised two different datasets collected between 2015 and 2019 with information from commercial pasture-based AMS farms. One contained annual physical and economic data from 14 AMS farms located in the main Australian dairy regions; the other contained monthly, detailed robot-system performance data from 23 AMS farms located across Australia, Ireland, New Zealand, and Chile. We used linear mixed models to identify the physical factors associated with different profitability (Model 1) and partial productivity measures (Model 2). Additionally, we conducted a Monte Carlo simulation to evaluate how changes in the physical factors would affect productivity. Our results from Model 1 showed that the two main factors associated with profitability in pasture-based AMS were milk harvested/robot (MH; kg milk/robot per day) and total labour on-farm (full-time equivalent). On average, Model 1 explained 69% of the variance in profitability. In turn, Model 2 showed that the main factors associated with MH were cows/robot, milk flow, milking frequency, milking time, and days in milk. Model 2 explained 90% of the variance in MH. The Monte Carlo simulation showed that if pasture-based AMS farms manage to increase the number of cows/robot from 54 (current average) to ∼ 70 (the average of the 25% highest performing farms), the probability of achieving high MH, and therefore profitability, would increase from 23% to 63%. This could make AMS more attractive for pasture-based systems and increase the rate of adoption of the technology.

The AMS Integrated Management Model: A decision-support system for automatic milking systems

• First decision-support system developed for pasture-based automatic milking systems. • Uses data from 37 farms across Australia, Ireland, New Zealand, Chile and Argentina. • Differences between actual and predicted physical performance ranged from 2 to 14%. • Farmers agreed that the decision-support system is useful and easy to use. • Applications include investment planning and system performance optimisation. There is a significant opportunity to improve profitability and productivity in pasture-based automatic milking systems ( AMS ). A decision-support system ( DSS ) is required for AMS that can integrate key mechanics of dynamic biological processes with farm economics. Here we developed and evaluated a web-based DSS named the Integrated Management Model ( IMM ) designed for assisting AMS farmers and their advisors to evaluate and improve the physical and economic performance of their businesses ( https://bit.ly/MilkingEdgeAMSTool ). The IMM comprises a series of empirically determined predictive equations derived from the main drivers of productivity and profitability in AMS, together with stochastic simulation and optimisation modelling. The equations and models in the IMM were developed using two data sources available through the Milking Edge project: (a) an annual physical and economic dataset collected from 14 AMS farms across Australia (from years 2015 to 2018), and (b) a monthly physical dataset from 23 AMS farms located across Australia, Ireland, New Zealand, and Chile (years 2015 to 2019). Model predictions were evaluated using two similar datasets: (a) an annual physical and economic dataset from 11 Australian AMS farms (2018 to 2020) and a monthly physical dataset from 20 AMS from Australia, New Zealand, Ireland, and Argentina (2019 to 2020). The DSS was tested by 11 AMS farmers that provided feedback using the technology acceptance model framework. Results from the model evaluation showed the accuracy of the equations and simulations to predict physical variables such as milk harvested (kg milk/robot.d) or milking frequency (milkings/cow.d) was reasonably good (2–14% differences between observed and predicted values). The economic equations, which predicted operating profit margin ( OP ) (%) and return on total assets ( ROTA ) (%), could determine the relative changes and direction of profitability when physical variables change. However, the accuracy of these equations to estimate absolute values was low (ROTA: R 2 0.26 and MAE 2.0%; OP: R 2 0.36 and MAE 13.5%), probably due to the empirical nature of the equations and the relatively small number of farms involved in their development. Overall, farmers agreed that the IMM was a useful and easy-to-use DSS that can be used confidently to simulate physical scenarios and optimise the system performance. Whilst this DSS was designed for AMS farmers and farmers considering investing in AMS, future applications could include research, training, extension, teaching, consultancy or even innovation and development of new technology.

Physical and economic comparison of pasture-based automatic and conventional milking systems

Automatic milking systems (AMS) have the potential to increase dairy farm productivity and profitability; however, adoption rates, particularly in pasture-based systems, have been lower than expected. The objectives of this study were to compare the physical and economic performance of pasture-based AMS with conventional milking systems (CMS) and to identify gaps for improving AMS productivity and profitability. We used data from 14 AMS and 100 CMS located in the main Australian dairy regions and collected over 3 yr (2015-2016, 2016-2017, 2017-2018). Farms within similar regions and herd sizes were compared. Results showed that all the main physical performance indicators evaluated such as milk production per cow, milk production per hectare, pasture grazed per hectare, or milk solids per full-time equivalent were similar between systems. The AMS farms had higher overhead costs such as depreciation and repairs and maintenance; however, no differences in total labor costs were observed between systems. Profitability, measured as earnings before interest and tax, operating profit margin, and return on total assets, was not significantly different between AMS and CMS. Opportunities for improving pasture utilization, labor efficiency, and robot utilization in AMS farms were identified. Improving efficiency in these areas could improve productivity and profitability of these systems, and therefore increase the interest of this technology.

Modeling variability of the lactation curves of cows in automated milking systems

Historically, cow selection criteria were developed for conventional milking systems that have regular milking intervals (MI). However, in automatic milking systems (AMS), there is variability in MI within and between cows. These sources of variability provide an opportunity to identify cows with high daily milk yield (DY) and long MI. An extended MI (longer than 16 h in pasture-based systems) has a negative effect on DY. Cows that tolerate extended MI and maintain high DY can be considered more efficient than cows with low DY and long MI, or with high DY but short MI, thereby improving robotic system use. Knowledge of the behavior and parameters of lactation curves of cows in AMS could help farmers to identify cows with a specific lactational phenotype. The objective of this study was to identify individual cows with high DY and long MI within herds, which could reflect increased tolerance to milk accumulation under AMS. A database containing records for 773,483 milking events for one year (July 2016-June 2017) from 4 pasture-based AMS farms was used. Lactation curves within each herd were fitted using several mixed models including fixed effects for the parameters of the lactation curve and random cow effects. Predicted curves of average DY according to parity (multiparous and primiparous) were obtained. The best linear unbiased prediction of the random cow effect allowed us to categorize lactations as having either high or low milk production. The median MI of each lactation was then used to categorize cows as having either short or long MI. Daily yield at the peak of lactation, days to peak and 305-d cumulative milk production were used to compare the effect of DY and MI categories, as well as the DY × MI interaction. Milk production by multiparous and primiparous cows with high DY and long MI was between 35 and 45% higher than that of the low DY and short MI. From all lactations analyzed, the incidence of animals with high DY and long MI across farms was 7.5%. We have identified and quantified a new, AMS-specific, phenotype (the combination of a relatively higher DY with relatively longer MI) with potential to increase use of AMS units. Identifying more efficient animals should help generate new approaches for differential management and for selecting cows in AMS.

Dairy farmers’ job satisfaction and the influence of automatic milking systems

Innovation and implementation of new technology in farming is considered important to meet challenges for agriculture to increase sustainability and improve efficiency in production. Less emphasise has been on how the farmers experience the new technology. This paper responds to this gap and explores how Automatic Milking Systems (AMS) influence farmers’ job satisfaction. The research questions are: Are there differences in the experienced level of job satisfaction between AMS farmers and farmers applying Conventional Milking Systems (CMS)? Which factors determine the level of job satisfaction in dairy farming? Do these factors vary on AMS farms compared to CMS farms? The empirical data is based on a survey to a sample of dairy farmers with AMS and CMS. The results show that the most important factors which influence job satisfaction positively are common for AMS and CMS; Increased income, new cowshed, there is a successor present, farmer wants to continue farming. Contrary, higher education and being a male reduces job satisfaction. Further, the results show that AMS farmers are more satisfied with their working day, their occupational safety and their working environment. Other factors which influence job satisfaction for dairy farmers are; Less paper work, working together, considering technological competence less important, being appreciated, considering economic competence less important, increasing milk quota, loneliness and health worries. However, these factors can to a varying degree be explained from an AMS/CMS perspective, even though they differ between the two technologies.

Profitability on dairy farms with automatic milking systems compared to farms with conventional milking systems

The objective of this study was to explore differences in profitability between farms with automatic milking systems (AMS) and farms with conventional milking systems (CMS). To explore profitability, we analysed the gross farm income from dairy cows. Accounting and production data for over a thousand dairy farms were collected. Using kernel-matching, we made CMS farms more comparable to AMS farms. We then used ordinary least squares regression to estimate the effect of AMS relative to farm size and time passed since last investment in milking systems. The results show that farms must have 35 to 40 cows before AMS becomes more profitable than CMS. Further, any profitability gains will only be visible after a transitional period of approximately four years. Milk revenues are higher on AMS farms, and the difference increases with the size of the farm. Production-related costs are also higher on AMS farms.

Farm-level risk factors for bovine mastitis in Dutch automatic milking dairy herds

Automatic milking systems (AMS) are installed on a growing number of dairy farms worldwide. Management to support good udder health might be different on farms with an AMS compared with farms milking with a conventional milking system, as risk factors for mastitis on farms using an AMS may differ. The aim of this study was to identify farm level factors associated with mastitis on Dutch dairy farms using an AMS. In 2008, risk factor data were collected using a questionnaire combined with on-farm recordings of cow, stall, and AMS hygiene on 135 farms. These risk factor data were linked to 4 udder-health-associated dependent variables: average herd somatic cell count (HeSCCav), variance of the average herd somatic cell count (SCC) on test days (HeSCCvar), the average proportion of new high SCC cases (NHiSCC), and the farmer-reported annual incidence rate of clinical mastitis (IRCM). We employed regression models using multiple imputation to deal with missing values. Due to the high dimensionality of the risk factor data, we also performed nonlinear principal component analysis (NLPCA) and regressed the dependent variables on the principal components (PC). Good hygiene of cows and of AMS were found to be related to a lower HeSCCav and less NHiSCC. Effective postmilking teat disinfection was associated with a lower NHiSCC. A higher bulk tank milk SCC threshold for farmers' action was related to more NHiSCC. Larger farm size was related to lower HeSCCvar but higher NHiSCC. Negative attitude of farmers to animal health, higher frequency of checking AMS, and more time spent on viewing computer data were all positively related to higher IRCM. An NLPCA with 3 PC explained 16.3% of the variance in the risk factor variables. Only the first 2 PC were associated with mastitis. The first PC reflected older and larger farms with poor cow hygiene and AMS hygiene, and was related to higher HeSCCav and NHiSCC, whereas the second PC reflected newly built smaller farms with poor cow hygiene and low milk production, and was associated with higher HeSCCvar and NHiSCC, but lower IRCM. Our study suggests that many of the risk factors on conventional milking system farms are applicable to AMS farms, specifically concerning hygiene of the cows and the milking machine, but on large AMS farms, udder health may need more attention than on smaller AMS farms. Multiple imputation is instrumental to deal with missing values and NLPCA is a useful technique to process high dimensional data in our study.

The effect of temporal variation in feed quality and quantity on the diurnal feeding behaviour of dairy cows

The diurnal feeding patterns of dairy cows affects the 24 h robot utilisation of pasture-based automatic milking systems (AMS). A decline in robot utilisation between 2400 and 0600 h currently occurs in pasture-based AMS, as cow feeding activity is greatly reduced during this time. Here, we investigate the effect of a temporal variation in feed quality and quantity on cow feeding behaviour between 2400 and 0600 h as a potential tool to increase voluntary cow trafficking in an AMS at night. The day was allocated into four equal feeding periods (0600 to 1200, 1200 to 1800, 1800 to 2400 and 2400 to 0600 h). Lucerne hay cubes (CP = 19.1%, water soluble carbohydrate = 3.8%) and oat, ryegrass and clover hay cubes with 20% molasses (CP = 11.8%, water soluble carbohydrate = 10.7%) were offered as the ‘standard’ and ‘preferred’ (preference determined previously) feed types, respectively. The four treatments were (1) standard feed offered ad libitum (AL) throughout 24 h; (2) as per AL, with preferred feed replacing standard feed between 2400 and 0600 h (AL + P); (3) standard feed offered at a restricted rate, with quantity varying between each feeding period (20:10:30:60%, respectively) as a proportion of the (previously) measured daily ad libitum intake (VA); (4) as per VA, with preferred feed replacing standard feed between 2400 and 0600 h (VA + P). Eight non-lactating dairy cows were used in a 4 × 4 Latin square design. During each experimental period, treatment cows were fed for 7 days, including 3 days habituation and 4 days data collection. Total daily intake was approximately 8% greater (P < 0.001) for the AL and AL + P treatments (23.1 and 22.9 kg DM/cow) as compared with the VA and VA + P treatments (21.6 and 20.9 kg DM/cow). The AL + P and VA treatments had 21% and 90% greater (P < 0.001) dry matter intake (DMI) between 2400 and 0600 h, respectively, compared with the AL treatment. In contrast, the VA + P treatment had similar DMI to the VA treatment. Our experiment shows ability to increase cow feeding activity at night by varying feed type and quantity, though it is possible that a penalty to total DMI may occur using VA. Further research is required to determine if the implementation of variable feed allocation on pasture-based AMS farms is likely to improve milking robot utilisation by increasing cow feeding activity at night.

Short communication: The effect of temperature-humidity index on milk yield and milking frequency of dairy cows in pasture-based automatic milking systems

Hot weather is known to negatively affect cow performance primarily through reduced feed intake and milk yield. However, little information is available on how it affects cow milk yield and milking frequency in automatic milking systems (AMS). Milking data were collected from 6 pasture-based AMS farms in Australia to assess the effect of temperature-humidity index (THI) on milk yield and milking frequency. Daily measures of average milk yield per cow and average milking frequency per cow during December to February (Australian summer) were assessed for associations with maximum, minimum, and average THI from d 0, -1, -2, and -3 in relation to the milking data. Average daily milk yield per cow was negatively associated with an increasing maximum, minimum, and average THI (-0.11, -0.08, and -0.15 kg/THI unit increase, respectively) on the collection day and up to 3 d prior. The average daily milking frequency was negatively associated with maximum THI on 1 d (-0.003/THI unit increase) and 2 d (-0.003/THI unit increase) before collection. Our results show that high THI conditions were negatively associated with milking frequency and milk yield in a pasture-based AMS and that research into management and infrastructure (cow cooling) in these systems is warranted to reduce production losses.

Stall and feed bunk stocking rates impact cows' diurnal behavior and activity in automatic milking system farms

Abstract High stall stocking rates have been shown to alter behavior of cows in parlor-milked systems; however, no studies have examined stocking rates for cows milked with automatic milking systems (AMSs). Therefore, we examined the influence of stall and feed bunk overstocking on cows' behavior, displacements, cows' activity, and rumination time in an AMS dairy farm. Each of 2 pens contained 60 cows/pen, 58 free stalls, and 60 headlocks. The effect of stocking rate was examined using 3 treatments applied separately to stalls and feed bunks (100% = 58 stalls or 60 headlocks available for 60 cows; 120% = 50 stalls or 50 headlocks available for 60 cows; and 150% = 40 stalls or 40 headlocks available for 60 cows). Each stall or feed bunk treatment was applied separately for 1 week in a randomized order that was different for each pen, with a 1-week washout period between treatments. Data were collected during the last 2 days of each treatment week. Activity level, time spent ruminating, and the number of displacements were recorded for both feed bunk and lying stall treatments. The number of cows lying, perching, or standing in lying stalls was recorded in response to stall stocking rate treatments, and the number of cows eating or standing at the feed bunk was recorded for feed bunk stocking rate treatments. Statistical analyses were performed using R software (version 3.3.1). Overstocking stalls to 150% reduced the number of cows lying and standing in stalls but increased the number of cows perching in stalls compared with 100% or 120% stocking rates (all P ≤ 0.05). Moreover, overstocking feed bunks to 150% increased the number of cows eating and standing at the feed bunk after delivery of fresh feed compared to 100% or 120% stocking rates (all P ≤ 0.05). Finally, overstocking of either lying stalls or the feed bunk to 150% reduced time spent ruminating and increased cow activity and number of cows displaced from stalls or the feed bunk in comparison with lower stocking rates (all P ≤ 0.05). Thus, changes to cows' behavior when overstocked above 120% in AMS farms may detrimentally affect welfare and health. Producers can use these results to determine optimal stocking rates in AMS dairies, allowing them to optimize facility design and management to maintain good welfare and production without wasting space or resources.

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

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

Current and potential system performance on commercial automatic milking farms

Dairy farmers considering installing automatic milking systems (AMS) would benefit from adequate contextual information on commercial AMS farm performance. The aim of the present study was to capture key performance indicators related to AMS utilisation on commercial Australian AMS farms on a monthly basis, with the aim of understanding the current and potential system performance. Eight Australian AMS farms were monitored on a monthly basis for a 12-month period. The average number of milking events (milkings/robot.h) was calculated for every hour of the day, on a monthly basis for each of the participating farms. Data exported electronically also allowed the calculation of the number of current and potential extra (both average and maximum) milkings (milkings/robot.day), cows (cows/robot), yield (kg milk/robot.day) and milking time (h/robot.day) for every month on each farm. Despite a wide range in farm performance, the actual milkings (120 milkings/robot.day), cows (51 cows/robot), yield (1263 kg milk/robot.day) and milking time (13.63 h/robot.day) indicated that there is an opportunity to improve these parameters by a maximum of ~60%. To achieve this would require the adoption of a variety of strategies that might be quite farm specific and would be reliant on optimisation of data relating to cow traffic and system utilisation that are relevant to automatic milking systems.

Cow hair allergen concentrations in dairy farms with automatic and conventional milking systems: From stable to bedroom.

Bovine hair and dander are considered to be a notable risk factor for sensitization and allergic symptoms in occupationally exposed cattle farmers due to various IgE binding proteins. Farmers are suspected not only to be exposed during their work inside the stables but also inside their homes as allergens could be transferred via hair and clothes resulting in continued bovine allergen exposure in private areas. In recent years a new sensitive sandwich ELISA (enzyme linked immunosorbent assay) test has been developed to measure the cow hair allergen (CHA) concentration in dust. The aim of the present study was to determine the CHA concentration in airborne and settled dust samples in stables and private rooms of dairy cattle farms with automatic milking systems (AM) and conventional milking systems (CM), also with respect to questionnaire data on farming characteristics. For this purpose different sampling techniques were applied, and results and practicability of the techniques were compared. Dust sampling was performed in the stable, computer room (only AM), changing room, living room and bedroom (mattress) of 12 dairy farms with automatic milking systems (AM group) and eight dairy farms with conventional milking systems (CM group). Altogether, 90 samples were taken by ALK filter dust collectors from all locations, while 32 samples were collected by an ion charging device (ICD) and 24 samples by an electronic dust fall collector (EDC) in computer rooms (AM) and/or changing and living rooms (not stables). The dust samples were extracted and analyzed for CHA content with a sandwich ELISA. At all investigated locations, CHA concentrations were above the limit of detection (LOD) of 0.1 ng/ml dust extract. The median CHA concentrations in dust collected by ALK filters ranged from 63 to 7154 μg/g dust in AM farms and from 121 to 5627 μg/g dust in CM farms with a steep concentration gradient from stables to bedrooms. ICD sampling revealed median CHA contents of 112 μg/g airborne dust in the computer rooms of the AM farms and median CHA loads of 5.6 μg/g (AM farms) and 19.8 μg/g (CM farms) in the living rooms. Passive dust sampling by EDC was performed only at two locations in the AM group resulting in median CHA values of 116 μg/m(2) (computer room) and 55.0 μg/m(2) (changing room). Except for the stable samples the median CHA load was lower in AM farms compared to CM farms. The CHA contents of ALK filter samples were significantly correlated in most locations. Differences between the farming types were not significant. Although allergen transfer to the private area of the farmers has been found and results from several locations were correlated, differences in CHA concentrations were not significant with respect to questionnaire data such as the wearing of stable clothes in living room, free access of pets to stable and home, frequency of hair washing. All sampling techniques seem to being practicable for simple and effective CHA measurement.

Differential response to stocking rates and feeding by two genotypes of Holstein-Friesian cows in a pasture-based automatic milking system

The throughput of automatic milking systems (AMS) is likely affected by differential traffic behavior and subsequent effects on the milking frequency and milk production of cows. This study investigated the effect of increasing stocking rate and partial mixed ration (PMR) on the milk production, dry matter intake (DMI), feed conversion efficiency (FCE) and use of AMS by two genotypes of Holstein-Friesian cows in mid-lactation. The study lasted 8 weeks and consisted in a factorial arrangement of two genotypes of dairy cattle, United States Holstein (USH) or New Zealand Friesian (NZF), and two pasture-based feeding treatments, a low stocking rate system (2 cows/ha) fed temperate pasture and concentrate, or a high stocking rate system (HSR; 3 cows/ha) fed same pasture and concentrate plus PMR. A total of 28 cows, 14 USH and 14 NZF, were used for comparisons, with 12 cows, six USH and six NZF, also used for tracking of animal movements. Data were analyzed by repeated measure mixed models for a completely randomized design. No differences (P>0.05) in pre- or post-grazing herbage mass, DMI and FCE were detected in response to increases in stocking rate and PMR feeding in HSR. However, there was a significant (P<0.05) grazing treatment×genotype×week interaction on milk production, explained by differential responses of genotypes to changes in herbage mass over time (P<0.001). A reduction (P<0.01) in hours spent on pasture was detected in response to PMR supplementation in HSR; this reduction was greater (P=0.01) for USH than NZF cows (6 v. 2 h, respectively). Regardless of the grazing treatment, USH cows had greater (P=0.02) milking frequency (2.51 v. 2.26±0.08 milkings/day) and greater (P<0.01) milk yield (27.3 v. 16.0±1.2 kg/day), energy-corrected milk (24.8 v. 16.5±1.0 kg/day), DMI (22.1 v. 16.6±0.8 kg/day) and FCE (1.25 v. 1.01±0.06 kg/kg) than NZF cows. There was also a different distribution of milkings/h between genotypes (P<0.001), with patterns of milkings/h shifting (P<0.001) as a consequence of PMR feeding in HSR. Results confirmed the improved FCE of grazing dairy cows with greater milk production and suggested the potential use of PMR feeding as a tactical decision to managing HSR and milkings/day in AMS farms.

Sustainability evaluation of automatic and conventional milking systems on organic dairy farms in Denmark

Organic dairy farmers in Denmark currently are implementing automatic milking systems (AMS) to save labour costs. As organic agriculture aims at sustainable production, the introduction of a new technology such as AMS should be evaluated regarding its economic viability, environmental impact, and social acceptability, i.e., its contribution to sustainable development. The objective of this research, therefore, was to evaluate sustainability of AMS use on organic dairy farms in Denmark, by comparing results of a set of sustainability indicators for nine farms using AMS with nine farms using conventional milking systems (CMS). Sustainability indicators were quantified for economic performance of the farm, on-farm eutrophication, on-farm biodiversity, animal welfare (including health), grazing time, milk composition and labour time. Milk yield per cow per year was higher for AMS farms (9021kg energy corrected milk [ECM] per cow per year) than for CMS farms (7664kg ECM), but did not result in a higher net profit or gross margin per cow for AMS farms. Nitrogen surplus per hectare of available land was higher for AMS farms (110kgNha−1) than for CMS farms (66kgNha−1). This difference was not due to the use of AMS but was caused by a higher export of manure by the CMS farms. The number of veterinary treatments per cow per year was unaffected by AMS use, but culling rate was higher for the AMS farms (38%) than for the CMS farms (32%). There was no difference between the AMS and CMS farms in milk composition indicators such as somatic cell count, clostridium spores, and urea. The acid degree value (ADV), measuring free fatty acids (FFA) in the milk, was higher in the milk from the AMS farms (0.78meql−l) compared with the CMS farms (0.49meql−l). Labour time measured in hours of work per dairy cow per day, was only half for the AMS compared with the CMS users; i.e., 2.3min per cow per day. Grass intake by grazing as percentage of total feed intake was reduced by AMS (5.1kg DM per cow per day for the AMS farms against 6.8kg DM per cow per day for the CMS farms). From this quantification of selected sustainability indicators it can be concluded that organic dairy farms using AMS, in spite of the substantial decrease in grazing time, show the potential of economic and environmental sustainable development within the range of herd sizes investigated (65–157 cows per farm). Even though the lower number of grazing hours per cow per year on the AMS farms did not affect indicator scores for animal health or milk quality, this reduction in grazing hours might be a problem for consumers to accept AMS use.