Wearable precision dairy monitoring (PDM) technologies currently used to detect estrus may provide insight into disease detection. However, the incorporation of PDM into farm management and its perceived usefulness for dairy producers have not been explored. As the targeted end users of these products, information is needed on how producers use generated disease alerts as well as barriers to adoption and usefulness. The objective of this research was to assess the perceived usefulness producers attributed to alerts from a daily generated alert list designed to identify sick or injured cows or cows that experienced a major management change. Data from 1,171 cows on 4 commercial farms in Kentucky were collected from October 2015 to October 2016. Each cow was equipped with 2 PDM technologies: a leg tag (measuring activity in steps/d and lying time in h/d) and a neck collar (measuring eating time in h/d). Alerts were generated based on an individual cow's decrease of ≥30% in activity, lying, and eating time compared with each cow's 10-d moving mean. Producers sorted alerts into 3 overall categories: (1) the cow alert was perceived to be true and the cow was visually checked, (2) the cow alert was perceived to be true, but the cow was not visually checked, and (3) the cow alert behavior change was doubted by the producer and the cow was not visually checked. Further subdivisions were also provided to explain the choice for an overall category. Over the year, 24,012 cow alerts were generated (eating time, n = 9,543; lying time, n = 9,777; activity, n = 1,590; or a combination of behaviors, n = 3,102). Only 8% of the alerts were doubted by the producer. Although 55% of alerts were perceived to be true, producers visually assessed cows based on only 21% of the alerts with a large variation between farms (2 to 45% of the alerts visually assessed). Producers were more likely to completely ignore alerts over time. Producers were more likely to perceive cow alerts to be true and visually check cows when ≤20 alerts occurred per day, cows were fresh or in early lactation, alerts occurred during the work week, or when cow alerts were for eating time, activity, or a combination of multiple behaviors. Behavioral disease alerts must be improved and correspond to an actionable change for producers to use them. Incorporating herd management software, creating and managing alerts by lactation stage, and focusing on behaviors producers already find useful could improve future alert utilization.

The increased uptake of sensor technologies and precision farming tools for the dairy cattle sector is enabling real-time monitoring of animal health, welfare, and productivity. These digital advancements provide high-frequency, objective, and large-scale phenotypic data for breeding purposes. This review explores the potential of sensor-derived data to improve genetic and genomic evaluations in dairy cattle and outlines key challenges, opportunities, and approaches associated with their implementation. While these data streams have great potential for genetic evaluations, their integration into national and international breeding programs remains limited due to fragmentation across sensor brands, lack of standardization, and challenges related to data accessibility, data access and portability rights, business interests, and governance. A crucial aspect of leveraging digital technologies in dairy cattle breeding is data harmonization and integration. We highlight the importance of establishing standardized data collection and data sharing protocols, implementing robust quality control and data cleaning methodologies, as well as defining novel sensor-based traits and estimating their genetic background. In this context, we compiled heritability estimates for novel traits derived from data recorded by sensors and other technologies in dairy cattle populations. The development of phenomics in breeding programs, which involves integrating multisource data-including sensor-based, genomic, and management information-will be key to accelerating genetic progress, especially for traits related to animal welfare, health, resilience, and efficiency. This review presents a roadmap for the effective use of sensor-derived data in genetic evaluations, advocating for centralized data infrastructures, transparent data-sharing agreements, and the role of different stakeholders from academia and industry, including organizations such as the International Committee on Animal Recording (ICAR) in establishing global standards and guidelines. By addressing these challenges, dairy breeding programs can fully harness precision dairy farming technologies to enhance production and environmental efficiency, improve animal health and welfare, and drive sustainable genetic advancements in the dairy cattle sector.

Heat stress abatement is a challenge for dairy producers in the United States, especially in the southern states. Thus, managing heat stress is critical to maintain dairy cow performance in the summer. The ability to employ a metric to measure heat stress and evaluate abatement strategies may benefit dairy producers by providing meaningful feedback on the effectiveness of current and future management strategies with the goal of improving heat stress management. Therefore, this study aimed to explore the use of the summer to winter performance ratio metric to quantify and compare farm performance variables among regions of the United States. Monthly performance data recorded by the Dairy Herd Improvement Association from 2007 to 2016, for all US Dairy Herd Improvement Association herds processing records through Dairy Records Management Systems (Raleigh, NC), were obtained. Season dates were based on the astronomical definition of the Northern Hemisphere with summer as June 21 to September 21 and winter as December 21 to March 19. States were grouped into regions based on climate zone classification. Performance records included a total of 16,573 herds [Northeast (n = 7,955), Midwest (n = 6,555), Northern Plains (n = 305), Southeast (n = 1,370), and Southern Plains (n = 388) regions]. Herd test day performance variables energy-corrected milk, somatic cell score, milk fat and protein percentage, conception rate, heat detection rate, and pregnancy rate in summer and winter were used to calculate summer to winter ratios for each region. The MIXED procedure of SAS 9.4 (SAS Institute Inc., Cary, NC) was used to compare test day performance variables. The effects of year, mean days in milk, mean 150-d milk, mean herd size, and number of milkings per day were included as covariates in the models. Dairy cattle performance in all climate regions was negatively affected by summer heat stress, but some regions greater than others. A difference was also observed among regions when comparing summer to winter ratios for each performance parameter. This indicates that summer performance varies by climate region identified by the summer to winter ratio and demonstrates usefulness of the metric to monitor degree of heat stress based on performance.

The dairy industry faces significant challenges in data integration and analysis, which are critical for informed decision-making, operational optimization, and sustainability. Data integration—combining data from diverse sources, such as herd management systems, sensors, and diagnostics—remains difficult due to the lack of standardization, infrastructure barriers, and proprietary concerns. This commentary explores these issues based on insights from a multidisciplinary group of stakeholders, including industry experts, researchers, and practitioners. Key challenges discussed include the absence of a national animal identification system in the US, high IT resource costs, reluctance to share data due to competitive disadvantages, and differences in global data handling practices. Proposed pathways forward include developing comprehensive data integration guidelines, enhancing farmer awareness through training programs, and fostering collaboration across industry, academia, and technology providers. Additional recommendations involve improving data exchange standards, addressing interoperability issues, and leveraging advanced technologies, such as artificial intelligence and cloud computing. Emphasis is placed on localized data integration solutions for farm-level benefits and broader research applications to advance sustainability, traceability, and profitability within the dairy supply chain. These outcomes provide a foundation for achieving streamlined data systems, enabling actionable insights, and fostering innovation in the dairy industry.

The purpose of this prospective observational study was to determine whether dairy cattle housing types were associated with staphylococcal and mammaliicoccal populations found on teat skin, bedding, and in bulk tank milk. Twenty herds (n = 10 sand-bedded freestall herds; n = 10 compost-bedded pack herds) were enrolled. Each herd was visited twice for sample collection, and at each visit, 5 niches were sampled, including bulk tank milk, composite teat skin swab samples collected before premilking teat preparation, composite teat skin swab samples collected after premilking teat preparation, unused fresh bedding, and used bedding. All samples were plated on Mannitol salt agar and Columbia blood agar and staphylococcal-like colonies were selected for further evaluation. Bacterial colonies were speciated using MALDI-TOF mass spectrometry. All species were grouped into 4 categories included host-adapted, opportunistic, environmental, and unclassified. Absolute numbers and proportions of each genus and species were calculated. Proportional data were compared between groups using Fisher's exact test. Data representing 471 staphylococcal-like organisms were analyzed. Overall, 27 different staphylococcal and mammaliicoccal species were identified. Staphylococcus chromogenes was the only species identified from all 20 farms. A total of 20 different staphylococcal-like species were identified from bulk tank milk samples with the most prevalent species being S. chromogenes, followed by Staphylococcus aureus and Mammaliicoccus sciuri. Overall, more staphylococcal and mammaliicoccal isolates were identified among used bedding than unused bedding. The increased numbers of isolates within used bedding were primarily from used sand bedding samples, with 79% (76/96) of used bedding isolates being identified from sand bedding and only 20.8% (20/96) from used compost-bedded pack samples. When comparing categories found among sample types, more unclassified species were found in used sand bedding than in used compost-bedded pack samples. This finding is possibly related to the composting temperatures resulting in reduced growth or destruction of bacterial species. The prevalence of S. aureus was high in bulk tank milk for all herds, regardless of herd type, which may represent the influence of unmeasured management factors. Overall, staphylococcal and mammaliicoccal species were highly prevalent among samples from both farm types.

Standards for data generation and collection are important for integration and for achieving data-driven actionable insights in dairy farming. Data integration and analysis are critical for advancing the dairy industry, enabling better decision-making, and improving operational efficiencies. This commentary paper discusses the challenges of and proposes pathways for standardizing data generation and collection based on insights from a multidisciplinary group of stakeholders. Drawing from a series of meetings of industry experts, academics, and farmers organized under the Dairy Brain Project’s Coordinated Innovation Network (CIN), we explore the benefits of creating uniform data generation and collection protocols to ensure compatibility and reliability across different data sources. Key insights include the importance of defining standardization at both farm and industry levels, the role of education and incentives, and the potential for using existing frameworks such as the International Committee for Animal Recording. Additionally, we highlight industry-specific case studies, including successful examples from Brazil such as GERAR, which focuses on reproductive performance data, and Labor Rural, which integrates data from multiple farms to provide valuable insights to farmers and milk processors. The paper concludes with recommendations for implementing these protocols and highlights the need to foster collaboration among stakeholders for the successful implementation and adoption of standardized data generation and collection protocols in the dairy industry.