Automated calf feeding systems are becoming more common on US dairy farms. The objective of this study was to evaluate calf health in these systems and to identify risk factors associated with adverse health outcomes on farms in the Upper Midwest United States. Over an 18-mo period on bimonthly farm visits to 38 farms, calves (n = 10,179) were scored for attitude, ear, eye, and nasal health, as well as evidence of diarrhea (hide dirtiness score of perianal region, underside of the tail, and tailhead). For all health score categories, a score of 0 represented an apparently healthy animal. Rectal temperatures were taken in calves scoring a ≥2 in any category, and those with a temperature>39.4°C were categorized as having a fever (n = 550). Associations were determined between farm-level variables and health scores to identify risk factors for higher (worse) scores. All health outcomes were associated with season of measurement, with fall and winter seasons increasing the odds of a high health score or detected fever. High bacterial counts measured in the milk or milk replacer were associated with increased odds for higher attitude and ear scores, and higher odds for calves having a detected fever. Higher peak milk allowance (L/d) was associated with lower hide dirtiness score, whereas a longer period of time (d) to reach peak milk allowance was associated with increased odds of higher scores for attitude, ear, eye, and hide dirtiness, as well as fever. Higher fat content in milk was associated with increased odds of high eye score. Less space per calf (m/calf) was associated with higher ear and eye scores, whereas larger group sizes were associated with increased odds of higher nasal score and decreased odds of higher hide dirtiness score. Rectangular pen shape was associated with decreased odds of higher eye score. Absence of a positive pressure ventilation tube was associated with increased odds of having a calf detected with a fever. Based on these results, we hypothesize that these factors could be managed to improve health outcomes for dairy calves on automated feeding systems.

Dairy farm decision support systems (DSS) are tools which help dairy farmers to solve complex problems by improving the decision-making processes. In this paper, we are interested in newer generation, integrated DSS (IDSS), which additionally and concurrently: (1) receive continuous data feed from on-farm and off-farm data collection systems and (2) integrate more than one data stream to produce insightful outcomes. The scientific community and the allied dairy community have not been successful in developing, disseminating, and promoting a sustained adoption of IDSS. Thus, this paper identifies barriers to adoption as well as factors that would promote the sustained adoption of IDSS. The main barriers to adoption discussed include perceived lack of a good value proposition, complexities of practical application, and ease of use; and IDSS challenges related to data collection, data standards, data integration, and data shareability. Success in the sustainable adoption of IDSS depends on solving these problems and also addressing intrinsic issues related to the development, maintenance, and functioning of IDSS. There is a need for coordinated action by all the main stakeholders in the dairy sector to realize the potential benefits of IDSS, including all important players in the dairy industry production and distribution chain.

Automated calf feeding systems are increasing in use across the United States, yet information regarding health and mortality outcomes of animals in these systems is limited. The objective of this study was to investigate the relationship between farm management practices, housing, and environmental factors with mortality and health treatment rates of preweaned dairy calves housed in groups with automated feeding systems. Farm records were collected for health treatments and mortality on 26 farms in the Upper Midwest of the United States. Relationships between factors of interest and mortality or treatment rate were calculated using a correlation analysis. Overall median annual mortality rate was 2.6 (interquartile range = 3.6; range = 0.24-13.4%), and 57% of farms reported mortality rates below 3%/yr. Farms that disinfected the navels of newborn calves had lower mortality rate (mean = 3.0%; standard error = 0.8; 78% of farms) than farms that did not disinfect (mean = 7.3%; standard error = 1.6; 22% of farms). Farm size (number of cows on site) was negatively associated [correlation coefficient (r) = -0.53], whereas the age range in calf groups was positively associated (r = 0.58) with mortality rate. Average serum total protein concentration tended to be negatively associated with annual mortality rate (r = -0.39; median = 5.4; range = 5.0-6.4 g/dL). Health treatment rate was positively associated with coliform bacterial count in feeder tube milk samples [r = 0.45; mean ± standard deviation (SD) = 6.45 ± 4.50 ln(cfu/mL)] and the age of calves at grouping (r = 0.50; mean ± SD = 5.1 ± 3.6 d). A positive trend was detected for coliform bacterial count of feeder mixing tank milk samples [r = 0.37; mean ± SD = 3.2 ± 6.4 ln(cfu/mL)] and calf age at weaning (r = 0.37, mean ± SD = 57.4 ± 9.6 d). Seasonal patterns indicated that winter was the season of highest treatment rate. Taken together, these results indicate that, although automated feeding systems can achieve mortality rates below the US average, improvements are needed in fundamental calf care practices, such as colostrum management and preventing bacterial contamination of the liquid diet and the calf environment.

Data governance is a growing concern in the dairy farm industry because of the lack of legal regulation. In this commentary paper, we discuss the status quo of the available legislation and codes, as well as some possible solutions. To our knowledge, there are currently four codes of practice that address agriculture data worldwide, and their objectives are similar: (1) raise awareness of diverse data challenges such as data sharing and data privacy, (2) provide data security, and (3) illustrate the importance of the transparency of terms and conditions of data sharing contracts. However, all these codes are voluntary, which limits their adoption. We propose a Farmers Bill of Rights for the dairy data ecosystem to address some key components around data ownership and transparency in data sharing. Our hope is to start the discussion to create a balanced environment to promote equity within the data economy, encourage proper data stewardship, and to foster trust and harmony between the industry companies and the farmers when it comes to sharing data.

Automated milk feeders are used by dairy producers to manage preweaned calves in group housing, but little is known about how these feeding systems are being used in the United States. To better understand how US dairy producers are operating these systems, this study investigated characteristics of barn design, environment, and management practices on 38 farms in the Upper Midwest of the United States via a questionnaire and on-farm measurements. Farms using automated feeders ranged in size from 7 to 300 calves on site. Natural ventilation was used on 50% of the farms, followed by barns with mechanical ventilation (39.5%), tunnel ventilation (7.9%), or outdoor facilities (sheltered plastic domes; 2.6%). Calves were kept in groups of 17.6 ± 9.8 animals (range: 5.9 to 60.5) with an average space allowance of 4.6 ± 2.0 m/animal (range: 1.6 to 11.9). Calves on these farms received 3.7 ± 0.75 L (range: 2 to 6) of colostrum, but 22% of the tested calves had serum total protein values lower than 5.0 g/dL. Calves had an initial daily allowance of 5.4 ± 2.1 L (range: 3 to 15 L) of milk or milk replacer, rising to a peak amount of 8.3 ± 2.0 L (range: 5 to 15 L) over 18 ± 11.4 d (range: 0 to 44 d). Milk replacer was fed to calves on 68.4% of the farms compared with whole milk supplemented with nutrient balancer on 23.7% and whole milk alone on 7.9% of the farms. Calves were completely weaned at 56.8 ± 9.0 d of age (range: 40 to 85.5) and 52.1 ± 7.5 d (range: 40 to 79) since introduction into the group pen with the feeder. Notably, bacterial contamination of milk was common; the median coliform count was 10,430 cfu/mL (interquartile range: 233,111; range: 45 to 28,517,000) and standard plate count was 2,566,867 cfu/mL (interquartile range: 15,860,194; range 6,668 to 82,825,000) for samples collected from the feeder tube end (or feeder hose). Some areas of deficiency might be of concern as they might be influencing the success of using automated calf feeding systems. In particular, a better understanding of the dynamics of pathogen load is needed in both the group pen area and in the automated feeder unit itself, as these reservoirs represent significant risk to calf health and welfare.