Factors associated with intensity of technology adoption and with the adoption of 4 clusters of precision livestock farming technologies in Irish pasture-based dairy systems

Precision livestock farming (PLF) technologies have been widely promoted as important tools to improve the sustainability of dairy systems due to perceived economic, social, and environmental benefits. However, there is still limited information about the level of adoption of PLF technologies (percentage of farms with a PLF technology) and the factors (farm and farmer characteristics) associated with PLF technology adoption in pasture-based dairy systems. The current research aimed to address this knowledge gap by using a representative survey of Irish pasture-based dairy farms from 2018. First, we established the levels of adoption of 9 PLF technologies (individual cow activity sensors, rising plate meters, automatic washers, automatic cluster removers, automatic calf feeders, automatic parlor feeders, automatic drafting gates, milk meters, and a grassland management decision-support tool) and grouped them into 4 PLF technology clusters according to the level of association with each other and the area of dairy farm management in which they are used. The PLF technology clusters were reproductive management technologies, grass management technologies, milking management technologies, and calf management technologies. Additionally, we classified farms into 3 categories of intensity of technology adoption based on the number of PLF technologies they have adopted (nonadoption, low intensity of adoption, and high intensity of adoption). Second, we determined the factors associated with the intensity of technology adoption and with the adoption of the PLF technology clusters. A multinomial logistic regression model and 4 logistic regressions were used to determine the factors associated with intensity of adoption (low and high intensity of adoption compared with nonadoption) and with the adoption of the 4 PLF technology clusters, respectively. Adoption levels varied depending on PLF technology, with the most adopted PLF technologies being those related to the milking process (e.g., automatic parlor feeders and milk meters). The results of the multinomial logistic regression suggest that herd size, proportion of hired labor, agricultural education, and discussion group membership were positively associated with a high intensity of adoption, whereas age of farmer and number of household members were negatively associated with high intensity of adoption. However, when analyzing PLF technology clusters, the magnitude and direction of the influence of the factors in technology adoption varied depending on the PLF technology cluster being investigated. By identifying the PLF technologies in which pasture-based dairy farmers are investing more and by detecting potential drivers and barriers for the adoption of PLF technologies, the current study could allow PLF technology companies, practitioners, and researchers to develop and target strategies that improve future adoption of PLF technologies in pasture-based dairy settings.     

Analysis of adoption trends of in-parlor technologies over a 10-year period for labor saving and data capture on pasture-based dairy farms

The use of precision technology is increasingly seen as an option to improve productivity, animal welfare, resource use efficiency, and workplace features on dairy farms. There is limited research related to longitudinal adoption patterns of precision dairy technologies and reasons for any patterns. The aim of this analysis was to investigate trends in technology adoption regarding both the amount (number of farms with a technology) and intensity (number of technologies per farm) of adoption. Surveys of parlor technology adoption were conducted on New Zealand dairy farms in 2008, 2013, and 2018, with 532, 500, and 500 respondents, respectively. Technologies were grouped into labor-saving (LS, such as automatic cluster removers) or data-capture (DC, such as in-line milk meters) categories. Trends were examined for farms that had only LS, only DC, or LS+DC technologies. Technology adoption increased over time; the likelihood of technology adoption in 2018 (and 2013 in parentheses) increased by 21 (22), 7 (68), and 378% (165) for LS, DC, and LS+DC technology groups, respectively, compared to 2008. Farms with LS+DC technologies also had a greater proportion of LS technologies compared to non-LS+DC farms, although this relationship declined over the 10-yr period. The use of a rotary versus herringbone parlor was estimated to be associated with 356 and 470% increase in the likelihood of adopting LS technologies and LS+DC, respectively, from 2008 to 2018. Regional differences in adoption were also found, with the likelihood of adopting DC and LS+DC technologies found to be 46 and 59% greater, respectively, in the South Island of New Zealand, compared to the base region of Waikato. The results highlight the importance of understanding spatial and temporal farm characteristics when considering future effect and adoption of precision dairy technologies. For example, the analysis indicates the occurrence of 2 trajectories to technology investment on farms, where larger farms are able to take advantage of technology opportunities, but smaller farms may be constrained by factors such as lack of economies of scale, limited capital to invest, and inability to retrofit technology into aging parlor infrastructure.     

Comparison of different applications of automatic herd control systems on dairy farms – a review

Recent years have seen the rapid development of different devices which can be helpful in the daily work of livestock farmers. The growing size of livestock herds has led farmers to lose individual contact with their animals, while behavioral studies show that breeders can effectively and precisely monitor a herd of up to 100 cows. This was the main motivation for this study, which aims to identify and test various electronic devices which provide useful herd management data, including estrus detection, individual activity and body temperature measurement, monitoring rumen pH levels, milk quality and content as well as milk temperature and somatic cell count measurements. Some devices can detect the metabolic status of animals with a reasonable level of precision. Contemporary animal farms are offered a large number of systems for monitoring the behavior of the animals in the herd and helping to identify those that are intended for insemination or are too active or excessively apathetic. Monitoring devices support herd management and help to reduce costs through the early detection of animal diseases and nutritional problems. This review aims to compile and summarize the information currently available on the use of automatic herd control systems on dairy farms, as well as to discuss the interpretation of the results, providing a useful diagnostic tool in nutritional evaluations of dairy herds. © 2018 Society of Chemical Industry     

Can we identify key characteristics associated with grazing-management dairy systems from survey data?

Discriminant analysis was used to identify farms using confinement and grazing-production systems from mail survey data of 2074 dairy farmers in Pennsylvania, Vermont, Virginia, and North Carolina. Survey respondents included 45.1% of the farms using confinement management; 13.5% of farms practicing intensive grazing, defined as moving cows to new pasture at least every 3 d; and 41.4% of farms using nonintensive grazing. Farmers using confinement management had significantly more cows, higher milk production, more crop acreage, higher debt, used automatic takeoff milking units (ATO), fed total mixed rations (TMR), and were more satisfied. In general, dairy farmers who grazed their milking cows had smaller herds, fewer acres, but had more acres per cow and made less use of technology. However, farmers practicing intensive grazing were significantly younger, more educated, less experienced, more likely to use computers, and farmed less acreage than other graziers or farmers on confinement farms. The discriminant function correctly classified 70% of the total sample when divided into confinement and overall grazing categories. However, the discriminant function correctly classified only 36% of intensive-grazing farms in comparison to confinement farms. Significant variables identified using ordinary least squares as being related to confinement management were milk per cow, acres of corn, use of ATO and TMR, debt greater than 40%, and residence in North Carolina. Significant variables associated with grazing management were acres of pasture, future use of pasture, education, and residence in Vermont. The analysis indicated that the discriminant function could correctly classify confinement and nonintensive-grazing management but was unable to reliably differentiate between confinement and intensive-grazing farms.     

Short communication: Drying-off practices and use of dry cow therapy in Finnish dairy herds

The objective of this study was to survey drying-off practices and use of dry cow therapy (DCT) in Finland through an online questionnaire. The questionnaire was accessible to all dairy farmers of the Finnish dairy herd recording system in 2016 (approximately 5,400 farms). In total, 715 dairy producers across the country, representative of the Finnish dairy industry, participated in the survey. Cows were dried off gradually in most of the farms. Most farms (78%) reported using selective DCT, whereas 9% of farms did not use any DCT, and 13% of farms applied blanket DCT. A significant trend was observed with increasing herd size and proportion of farms using blanket DCT. Percentage of farms using blanket DCT was also higher in farms with automatic milking system. Farmer's own experience was the most commonly reported reason for choosing a particular approach to DCT. Microbiological testing of milk samples at dry-off was the preferred method of selecting cows for DCT; 82 and 64% of farms using selective and blanket DCT approach, respectively, reported testing milk samples before treatment. The second most common criteria for using antibiotic DCT were clinical mastitis history and high somatic cell count. A high number of farms using selective DCT reported treating only up to one-fourth of their cows at dry-off. Information acquired on drying-off practices in Finland allows for future monitoring of prudent antimicrobial usage at dry-off.     

Adoption of technology, management practices, and production systems in US milk production

The introduction of new technology, management practices, and alternative production systems has resulted in rapid structural change in the US dairy industry. This paper examines adoption rates and adopter characteristics for the following dairy technologies, practices, and systems: holding pen with an udder washer, milking units with automatic take-offs, genetic selection technologies, recombinant bovine somatotropin, membership in the Dairy Herd Improvement Association, computerized feed delivery systems, computerized milking systems, use of a nutritionist to design feed rations, grazing, milking cows 3 times daily, and milking parlors. Four of these were used on a greater percentage of farms in 2005 than in 2000, but increased farm sizes and the interaction of farm size with adoption suggest a greater percentage of milk being produced under each, with the exception of grazing. Except for grazing, technologies were generally complementary.     

Understanding the adoption of smartphone apps in dairy herd management

The number of decision support tools available to farmers, including dairy herd management smartphone apps, has been steadily increasing. The existing literature does not cover topics concerning the adoption and use of herd management smartphone apps or which specific functions of such apps are perceived as most useful by dairy farmers. It is unclear whether technology adoption can only be explained by economic reasoning, because the beliefs about a technology also play a role in decision-making. Therefore, this study seeks to determine whether an extended technology acceptance model can explain adoption and use of herd management smartphone apps. Results about the adoption and use of dairy herd management smartphone apps are derived from an online survey conducted in 2018 with 280 German dairy farmers. To model farmers' frequency of use of herd management smartphone apps, we applied partial least squares structural equation modeling and an ordered logit model. Our results show that 93% of the dairy farmers in our sample use a smartphone and 61% already use a herd management smartphone app. Daily use is reported by 38% of the adopters. Dairy farmers rated functions related to the observation of animal health, reproduction management, and data gathering as most useful, which should be in focus by developers and providers for future development. The key attitudinal components of the technology acceptance model, namely perceived ease of use and perceived usefulness, both positively influence the intention to use such apps. This ultimately has a positive effect on the actual usage behavior. Besides other factors, dairy farmers' education and knowledge of herd management smartphone apps have a positive effect on perceived ease of use. Our model explains 33% of the variance in the actual usage behavior related to herd management smartphone apps. Because perceived ease of use and perceived usefulness positively influence the intention to use such apps and ultimately the actual usage behavior, developers and providers should highlight the benefits of using herd management smartphone apps and also keep the interface of the apps as simple as possible.     

Assessment of the current performance of grazing infrastructure across Irish dairy farms

The increased average Irish dairy herd size in a post-quota environment has put heightened pressure on grazing infrastructure. In a rotational grazing system, grazing infrastructure consists of the paddock system, which delineates the grazing areas into appropriately sized grazing parcels, and the roadway network, which connects these paddocks to the milking parlor. Where herd size has increased without corresponding adaptations to the infrastructure, farm management and roadway network performance has been affected. The links between suboptimal grazing infrastructure and roadway network efficiency are poorly understood and not widely documented. The aims of this study were to (1) analyze the effect of herd expansion and paddock size on pasture allocations per paddock, (2) identify the factors that affect the total distance walked per year, and (3) create a metric to compare the efficiency of roadway networks across farms of varying grazing platforms. A sample population of 135 Irish dairy farms with a median herd size of 150 cows was used for this analysis. Herds were split into the following 5 categories: <100 cows, 100 to 149 cows, 150 to 199 cows, 200 to 249 cows, and ≥250 cows. Herds with ≥250 cows had a greater number of paddocks per farm and rotated around the grazing paddocks more frequently, with 46% of paddocks only suitable for 12 h allocations relative to herd size, compared with just 10% to 27% of paddocks for herds with <100 cows to herds with 200–249 cows. When predicting the total distance walked per year on each study farm, the mean distance from a paddock to the milking parlor was the strongest indicator (R² = 0.8247). Other metrics, such as herd size, have failed to account for the location of the milking parlor relative to the grazing platform. The creation of the relative mean distance from a paddock to milking parlor (RMDMP) metric allowed the calculation of a farm's roadway network efficiency for moving the herd between paddocks and the milking parlor. The analyzed farms increased their efficiency in terms of RMDMP (0.34–40.74%) as they increased herd size post quota. However, the position of new additional paddocks relative to the milking parlor substantially affected their RMDMP.     

Invited review: Learning from the future—A vision for dairy farms and cows in 2067

The world's population will reach 10.4 billion in 2067, with 81% residing in Africa or Asia. Arable land available for food production will decrease to 0.15 ha per person. Temperature will increase in tropical and temperate zones, especially in the Northern Hemisphere, and this will push growing seasons and dairy farming away from arid areas and into more northern latitudes. Dairy consumption will increase because it provides essential nutrients more efficiently than many other agricultural systems. Dairy farming will become modernized in developing countries and milk production per cow will increase, doubling in countries with advanced dairying systems. Profitability of dairy farms will be the key to their sustainability. Genetic improvements will include emphasis on the coding genome and associated noncoding epigenome of cattle, and on microbiomes of dairy cattle and farmsteads. Farm sizes will increase and there will be greater lateral integration of housing and management of dairy cattle of different ages and production stages. Integrated sensors, robotics, and automation will replace much of the manual labor on farms. Managing the epigenome and microbiome will become part of routine herd management. Innovations in dairy facilities will improve the health of cows and permit expression of natural behaviors. Herds will be viewed as superorganisms, and studies of herds as observational units will lead to improvements in productivity, health, and well-being of dairy cattle, and improve the agroecology and sustainability of dairy farms. Dairy farmers in 2067 will meet the world's needs for essential nutrients by adopting technologies and practices that provide improved cow health and longevity, profitable dairy farms, and sustainable agriculture.     

Reproductive management practices on dairy farms: The Canadian National Dairy Study 2015

The objectives of this cross-sectional study were to characterize reproductive management practices on Canadian dairy farms and describe differences based on regional and demographic factors. A questionnaire was offered to all licensed Canadian dairy producers and included 189 questions regarding producer and farm background information, herd dynamics, biosecurity, disease prevalence, calf health, animal welfare, milking practices, reproduction, and internet use. Twenty-four questions were related to estrus detection, hormonal protocols for reproduction, insemination, and pregnancy diagnosis. A total of 1,373 producers responded to the survey, representing a response rate of 12.5%. Estrus detection practices in lactating cows were associated with herd size, barn type, region, organic production, breeding method, and age of respondent. The most commonly used estrus-detection method in cows was visual (51.0% of farms for first insemination; 45.5% for subsequent inseminations). Estrus detection for nulliparous heifers was associated with herd size, barn type, region, and breeding method, with visual detection also the most common method for heifers (71.3% of farms). Eighty percent of farms used strictly artificial insemination, 2.8% used natural service only, and 16.8% used a combination of artificial insemination and natural service. Breeding method was associated with herd size, barn type, region, and education level of the respondent. Pregnancy diagnosis method was associated with herd size, barn type, region, and organic production. Ultrasound was the most commonly used method of pregnancy diagnosis (used by 52.2% of farms). Sixty-nine percent of farms rechecked cows for pregnancy, and rectal palpation was the most commonly used method (employed by 48.7%). Reproductive management practices vary considerably among Canadian dairy farms and decisions are associated with farm-level factors, including region, herd size, and barn type, as well as producer-level factors, such as age, managerial role, and education level.     

Reproductive hormone use and its association with herd-level factors on Dutch dairy farms

We studied the use of the 3 commonly used reproductive hormones, namely prostaglandins, GnRH, and progesterone, and associated herd-level factors on 760 Dutch dairy farms from 5 veterinary clinics. From 2017 to 2019 we collected data on the sales of reproductive hormones, converted this data into the number of reproductive hormone doses conducted, and expressed this as the annual number of reproductive hormone doses per 100 adult dairy cows. Additional herd-level information was available for 2019. Due to the excess of zeros in the data set (i.e., a substantial number of farms did not use any hormones), we used a zero-inflated negative binomial model to identify related herd-level factors for the use of reproductive hormones. In the entire study period of 2017 to 2019, 5.8% of the dairy farms did not use any reproductive hormones, with the proportion of nonusers varying between 0.0 and 10.3% per veterinary clinic. This proportion was around 13.5% on an annual basis. Prostaglandins were the most frequently used reproductive hormone in Dutch dairy cows (62.9%), followed by GnRH (33.1%) and progesterone (4.0%). Furthermore, participating in a veterinary herd health management program had a significant effect on reproductive hormone use. These farms used more reproductive hormones than farms that did not participate in a herd health management program and were less represented in the group of nonuser farms. Technologies, such as pedometers and automatic milking systems, also had an effect on reproductive hormone use. The presence of pedometers or activity monitors did not reduce the use of the reproductive hormones but was associated with a greater frequency of users. Farms with an automatic milking system used more reproductive hormones than farms with a conventional milking system. With this study, we have made a first step in achieving transparency in the Dutch dairy industry by providing an objective overview of reproductive hormone use on Dutch dairy farms and identifying associations with some herd-level factors.     

The dehorning of dairy calves: practices and opinions of 639 farmers

Calf dehorning is a routine husbandry practice on dairy farms that is justified by safety reasons for stockpersons and by a reduced risk of injures among herdmates. In Europe, the practice is regulated by law but little is known about the methods applied or about farmers’ attitude to the practice. This study aimed to broaden the existing information on dehorning in dairy holdings by processing results of 639 farm questionnaires gathered in a traditional dairy area of northeastern Italy. Farm questionnaires were stratified according to herd size, type of housing, and productive purpose of the predominant reared breed(s). Chi-squared tests were performed to verify the significant association between a given practice or opinion and the 3 classification factors, and odd ratios were calculated. The outcomes of the study showed that dehorning was carried out on 80% of the surveyed farms, and disbudding was the method reported by all the interviewed farmers. Hot-iron cauterization was the preferred method for disbudding (91%). On average, disbudding was performed at 32 d of age and it was more likely in farms with ≥60 cows than in smaller dairy holdings (OR = 7.3). The practice was carried out mainly by farm personnel, but the intervention of a veterinarian was far more likely (OR = 5.98) on farms with ≤30 cows than on larger dairies. Most farmers (70%) stated that they had not received any specific training on how to perform disbudding. Fifty-two percent of the respondents reported that disbudding causes prolonged postoperative pain (≥6 h) but pain management was rare. Only 10% of the farmers used local anesthesia before cauterization, and 5% of the farmers provided calves with postoperative analgesia. Consistent with these results, farmers indicated limited willingness to pay the cost of analgesia or to call a veterinarian to perform the procedure. This low motivation of the respondents toward the adoption of practices able to reduce pain related to disbudding might arise from their insufficient knowledge on long-term negative effects of early painful experiences on behavior and handling of dairy heifers. Farmers in favor of keeping horned cows were asked about the reasons for not carrying out dehorning. Aesthetic motivations (54% of respondents) and lack of time (24%) were the main reasons cited. Moreover, a large majority of these respondents (74%) reported no difficulty in handling horned cattle.     

Relationship between herd size and measures of animal welfare on dairy cattle farms with freestall housing in Germany

The objective of this study was to examine the association of herd size with animal welfare in dairy cattle herds. Therefore, 80 conventional dairy cattle farms were classified by the number of cows into 4 herd size classes, C1 (<100 cows), C2 (100–299 cows), C3 (300–499 cows), and C4 (≥500 cows), and assessed using multiple animal-based measures of the Welfare Quality Assessment protocol for dairy cattle. Data were recorded from April 2014 to September 2016 by an experienced single assessor in northern Germany. Each farm was visited 2 times at an interval of 6 mo (summer period and winter period) to avoid seasonal effects on the outcome. The average herd size was 383 ± 356 Holstein-Friesian cows (range 45 to 1,629). Only farms with freestall (cubicle) housing and a maximum of 6 h access to pasture per day were included in the study. Data were statistically analyzed using a generalized linear mixed model. None of the farms reached the highest overall rating of “excellent.” The majority of the farms were classified as “enhanced” (30%) or “acceptable” (66%), and at 6 assessments the farms were rated as “not classified” (4%). Regarding single indicators, mean trough length per cow, percentage of cows with nasal discharge, and vulvar discharge increased with increasing herd size, whereas it was vice versa for displacements of cows. Percentage of lean cows, percentage of dirty lower legs, and duration of the process of lying down showed a curvilinear relationship with the number of cows per farm. Herd size was not associated with any other measures of the Welfare Quality protocol. In conclusion, herd size effects were small, and consequently herd size cannot be considered as a feasible indicator of the on-farm animal welfare level. Housing conditions and management practices seem to have a greater effect on animal welfare than the number of dairy cows per farm.     

Management practices and reported antimicrobial usage on conventional and organic dairy farms

The primary objective was to compare reported antimicrobial usage between conventional and organic dairy farms. A secondary objective was to contrast selected management characteristics of conventional and organic dairy herds. A questionnaire was administered on site to selected dairy farmers located in Michigan, Minnesota, New York, and Wisconsin. Organic herds (n = 32) were smaller and produced less milk than conventional herds (n = 99). Lactating cows in organic dairies were more likely to be housed in tie stalls, whereas most conventional dairies housed cows in free stalls and milked in a parlor. Total mixed rations and purchased feeds were used on more conventional dairy farms compared with organic dairy farms. Conventional dairy producers were more likely to use advice from veterinarians for recommendations of treatment, and organic dairy producers were more likely to rely on advice from other farmers. Based on recall of antibiotic usage in the previous 60 d, 5.1, 84.9, 9.1, and 0.9% of farmers with conventional herds reported treatment of none, 1 to 10%, 11 to 25%, and >25% of milk cows, respectively. Most organic farmers (90.6%) reported no antibiotic treatments of milk cows, whereas 9.4% reported treating 1 to 10% of milk cows. Ceftiofur was the most commonly reported antibiotic for both farm types. Milk replacer containing antibiotics was reportedly used on 49.5% of conventional herds but only on one organic herd (3.1%). Antibiotics were used in heifer calves on 74.7% of conventional herds versus 21.9% of organic herds. Antibiotics to treat mastitis were used on 79.8% of conventional herds but on none of the organic herds. Most organic farms were in compliance with standards in advance of implementation of regulations.     

Estimating the effect of different work practices and technologies on labor efficiency within pasture-based dairy systems

Herd size expansion combined with the seasonal workload on pasture-based dairy farms has led to an increased focus on techniques that can improve farm labor efficiency such as work practices and technologies. The objective of this study was to identify the work practices and technologies associated with labor efficiency of particular tasks, and estimate the time savings that could be made through their implementation during the period of peak labor input on spring-calving dairy farms. Data from an existing labor time-use study, completed from February 1 to June 30, 2019 (150 d), on 76 Irish dairy farms was used in conjunction with a survey on work practice and technology implementation. One hundred ten work practices and technologies were included in the initial survey, and of these, 59 were found to have an association with labor efficiency for their respective tasks. Best practice, regarding labor efficiency, was identified for the 59 work practices and technologies. An accumulation score was compiled for work practice and technology implementation; each farm received one point for each work practice or technology implemented. On average, farms implemented 31 labor-efficient work practices and technologies (ranging from 10–45). The most labor-efficient 25% of farms implemented a greater number of work practices and technologies (n = 37) than the least labor-efficient 25% of farms (n = 25). Multiple regression models estimated that each additional work practice or technology implemented would improve farm labor efficiency by 0.6 h/cow. Additionally, backward-regression models were used to predict the labor-savings associated with the most important work practices and technologies. Labor-savings were estimated for 12 significant individual work practices and technologies, of which 5 were related to milking, 4 to calf care, 2 to cow care, and one to grassland management. The work practices and technologies that offered the largest labor-savings included having one person in the milking pit during the mid-lactation period (?3.04 h/cow), having automatic cluster removers present (?2.55 h/cow) and contracting slurry spreading (?1.78 h/cow). This study focused on the variety of labor-efficient work practices and technologies available and highlighted those that farmers should focus on to improve labor efficiency. The results indicated that there is scope for improvement in the adoption of labor-saving work practices and technologies on many farms. The positive effect of implementing the identified labor-saving techniques on labor efficiency could be used to support future adoption.     

The Irish dairy industry – Recent history and strategy,current state and future challenges

This study explores drivers and resulting changes in the structure and technical efficiency of Irish dairy farms from 2005 to 2018 (covering pre‐ and post‐milk quotas) during which milk production increased by 54%. Over this period, farm structure changed dramatically (fourfold increase in farmers milking >100 cows) and farmers improved technical efficiency and profitability and reduced the greenhouse gas emission intensity of milk produced. The impact of the adoption of key technologies at farm level and the contribution of strategic direction nationally (the Irish Government's Food Harvest 2020 Strategy) influencing this development are explored as are future sector challenges.     

Management,operational, animal health,and economic characteristics of large dairy herds in 4 states in the Upper Midwest of the United States

Recent trends in dairy farm structure in the United States have included a decreasing number of farms, although farm size has increased, especially the share of milk production from very large herds (>2,500 cows). The objectives of this observational study were to describe common management practices; to characterize labor and operational structure; to measure some aspects of animal health, including lameness, hock lesions, mortality, and mastitis incidence; and to summarize cost of production on farms with more than 2,500 cows in 4 states in the Upper Midwest of the United States. The study included 15 dairy farms in Minnesota, Wisconsin, Iowa, and South Dakota. Farms were visited twice, once each year, and on-farm herd records were collected for those 2 yr. On-farm herd records were used to investigate mortality, culling, pregnancy rate, and clinical mastitis incidence. At least 1 high-producing pen of mature cows and 1 pen of fresh cows were scored for locomotion. Likewise, at least 1 pen of high-producing mature cows was scored for cleanliness and hock lesions. Median herd size was 3,975 cows (range = 2,606–13,266). Milk sold per employee was 1,120,745 kg and the number of cows per employee was 105. Eighty percent of the farms had Holstein cows, 13% had Jersey, and 7% had Jersey-Holstein crosses. All farms used artificial insemination as the sole form of breeding and 100% of the farms used hormonal synchronization or timed artificial insemination programs in their reproductive protocols; 21-d pregnancy rate was 21.7%. Median lameness prevalence was 18.3% and median severe lameness prevalence was 5.1%. Median hock lesion prevalence was 17.4% and median severe hock lesion prevalence was 1.9%; mortality rate was 7.4%. Clinical mastitis incidence was 62.5 cases per 100 cow-years. Feed costs accounted for approximately 53% of the total cost of producing milk, followed by labor at 11%, interest and depreciation expenses at 10%, and replacement costs at 9.5%. Herds in the top 50th percentile for profitability had a net income of $2.40 per hundredweight of milk sold compared with $0.95 per hundredweight for herds in the bottom 50th percentile. Although results of this study were helpful in understanding how large dairy systems operate in the Upper Midwest, more research is yet needed with a larger number of farms and wider variety of management practices to identify factors within these large farms that promote optimal animal health and profitability.