The ability of dairy farmers to keep their cows for longer could positively enhance the economic performance of the farms, reduce the environmental footprint of the milk industry, and overall help in justifying a sustainable use of animals for food production. However, there is little published on the current status of cow longevity and we hypothesized that a reason may be a lack of standardization and an over narrow focus of the longevity measure itself. The objectives of this critical literature review were: (1) to review metrics used to measure dairy cow longevity; (2) to describe the status of longevity in high milk-producing countries. Current metrics are limited to either the length of time the animal remains in the herd or if it is alive at a given time. To overcome such a limitation, dairy cow longevity should be defined as an animal having an early age at first calving and a long productive life spent in profitable milk production. Combining age at first calving, length of productive life, and margin over all costs would provide a more comprehensive evaluation of longevity by covering both early life conditions and the length of time the animal remains in the herd once it starts to contribute to the farm revenues, as well as the overall animal health and quality of life. This review confirms that dairy cow longevity has decreased in most high milk-producing countries over time and its relationship with milk yield is not straight forward. Increasing cow longevity by reducing involuntary culling would cut health costs, increase cow lifetime profitability, improve animal welfare, and could contribute towards a more sustainable dairy industry while optimizing dairy farmers’ efficiency in the overall use of resources available.

Mastitis is a highly prevalent disease, which negatively affects cow performance, profitability, welfare, and longevity. The objectives of this study were (1) to quantify the impact of the first instance of mastitis, at different stages of lactation, on production and economic performance, and (2) to further quantify the impact of the first instance of mastitis when only cows that remain in the herd for at least 100 d in milk (DIM) and those that remain for 305 DIM are included in the analysis. A retrospective longitudinal study was conducted using data from existing animal health record files and Dairy Herd Improvement records. After editing based on selected inclusion criteria and completeness of health records, data consisted of records from first-lactation Holstein cows, from 120 herds, that calved for the first time between 2003 and 2014, inclusive. Mastitic cows were assigned to 1 of 4 groups based on when in the lactation the first event of mastitis occurred: transition (1-21 DIM), early lactation (22-100 DIM), mid lactation (101-200 DIM), or late lactation (201+ DIM). Mid-lactation and late-lactation mastitic cows were also stratified by cumulative milk yield before the mastitis event. Healthy cows (i.e., no recorded mastitis event) were randomly assigned for each lactation stage, with mid-lactation healthy and late-lactation healthy cows similarly stratified. Production performance (cumulative milk, fat, and protein yield) and economic performance [milk value, margin over feed cost (MOFC), and gross profit] were analyzed using a mixed model with herd as a random effect. Significant losses in cumulative milk yield (-382 to -989 kg) and correspondingly lower fat and protein yields were found in mastitic cows, with transition and late-lactation mastitic cows having the highest losses. Drops in production translated to significant reductions in cumulative milk value (-Can$287 to -Can$591; -US$228 to -US$470), MOFC (-Can$243 to -Can$540; -US$193 to -US$429), and gross profit (-Can$649 to -Can$908; -US$516 to -US$722) for mastitic cows at all stages. Differences between mastitic and healthy cows in the early lactation and transition stages remained for all variables in the 100-DIM analysis, but, aside from gross profit, were nonsignificant in the 305-DIM analysis. Gross profit accounted for all costs associated with mastitis and thus continued to be lower for mastitic cows at all stages, even in the 305-DIM analysis in which culled cows were omitted (-Can$485 to -Can$979; -US$386 to -US$779). The research reflects the performance implications of mastitis, providing more information upon which the producer can make informed culling decisions and maximize both herd profitability and cow longevity.

The objective of this study was to validate the CowManager SensOor ear-tag accelerometer (Agis Automatisering BV, Harmelen, the Netherlands) against visual observations of feeding, rumination, resting, and active behaviors of tiestall-housed dairy cows. Prior validation of the sensor has been published for freestall and grazing dairy herds. However, the behavioral differences that exist among these and a tiestall system necessitate additional validation. Lactating Holstein cows (n = 10) at different lactation stages and parities were included in the study. Cows were monitored both visually and with the sensor for 10 h/d for 4 consecutive days (10 cows × 10 h × 4 d = 400 h of observation total). A single trained observer classified each minute of visual observation into 1 of 13 behaviors and then summarized them into the 4 behavioral categories of eating, rumination, not active, or active. The sensor registered ear movements continuously and, based on a proprietary model, converted them into the behavioral categories. Multivariate mixed models were run to obtain covariance estimates, from which correlation coefficients were computed to assess agreement between visual observation and sensor data. The models included the percentage of time spent performing each behavior per day as the dependent variable and technology (visual observation versus sensor) and day as fixed effects. The models also included the random effects of technology and the repeated effects of technology and day. The correlation strength between visual observation and sensor data varied from poor to almost perfect by behavioral category (eating: r = 0.27; rumination: r = 0.69; eating-rumination: r = 0.83; not active: r = 0.95; and active: r = 0.89). The results suggest that the sensor can be used to accurately monitor active and not-active behaviors of tiestall-housed dairy cows. The results also suggest that although the sensor shows promise for identifying feeding behaviors in general, the independent classification of rumination and eating requires additional sensitivity.

Lameness is one of the most important welfare and productivity concerns in the dairy industry. Our objectives were to obtain producers' estimates of its prevalence and their perceptions of lameness, and to investigate how producers monitor lameness in tiestall (TS), freestall with milking parlor (FS), and automated milking system (AMS) herds. Forty focal cows per farm in 237 Canadian dairy herds were scored for lameness by trained researchers. On the same day, the producers completed a questionnaire. Mean herd-level prevalence of lameness estimated by producers was 9.0% (±0.9%; ±SE), whereas the researchers observed a mean prevalence of 22.2% (±0.9%). Correlation between producer- and researcher-estimated lameness prevalence was low (r = 0.19) and mean researcher prevalence was 1.6, 1.8, and 4.1 times higher in AMS, FS, and TS farms, respectively. A total of 48% of producers thought lameness was a moderate or major problem in their herds (TS = 34%; AMS =53%; FS = 59%). One third of producers considered lameness the highest ranked health problem they were trying to control, whereas two-thirds of producers (TS = 43%; AMS = 63%; FS = 71%) stated that they had made management changes to deal with lameness in the past 2 yr. Almost all producers (98%) stated they routinely check cows to identify new cases of lameness; however, 40% of producers did not keep records of lameness (AMS = 24%; FS = 23%; TS = 60%). A majority (69%) of producers treated lame cows themselves immediately after detection, whereas 13% relied on hoof-trimmer or veterinarians to plan treatment. Producers are aware of lameness as an issue in dairy herds and almost all monitor lameness as part of their daily routine. However, producers underestimate lameness prevalence, which highlights that lameness detection continues to be difficult in in all housing systems, especially in TS herds. Training to improve detection, record keeping, identification of farm-specific risk factors, and treatment planning for lame cows is likely to help decrease lameness prevalence.

Lameness is a persistent and underreported health and welfare problem in the dairy industry, resulting in reduced cow performance and profitability as well as early culling. The study objectives were (1) to quantify the impact of the first instance of lameness, at different stages of lactation, on production and economic performance, and (2) to further quantify the impacts of the first instance of lameness when only cows that remain in the herd for at least 100 d in milk (DIM) and those that remain for 305 DIM are included in the analysis. A retrospective longitudinal study was conducted using pre-existing data from animal health records and Dairy Herd Improvement Association records. Data were edited based on selected inclusion criteria, yielding a data set containing records from 15,159 first-lactation Holstein cows from 120 herds with year of first calving between 2003 and 2014. Lame cows were assigned to 1 of 4 groups based on when in the lactation the first event of lameness occurred: transition (1-21 DIM), early lactation (22-100 DIM), mid-lactation (101-200 DIM), or late lactation (201+ DIM). Mid- and late-lactation lame cows were also stratified by cumulative milk yield before the lameness event. Healthy cows (i.e., no recorded lameness event) were randomly assigned for each lactation stage, with mid-lactation healthy and late-lactation healthy cows similarly stratified. Production performance (cumulative milk, fat, and protein yield) and economic performance [milk value, margin over feed cost (MOFC), and gross profit] were analyzed using a mixed model with herd as a random effect. Cumulative milk yields were 811 to 1,290 kg lower for lame cows than for healthy cows, with milk component yields undergoing similar reductions. Reductions in milk yield contributed to losses in milk value (-Can$527 to -Can$1,083; -US$419 to -US$862) and MOFC (-Can$510 to -Can$774; -US$406 to -US$616). Higher losses were reported using gross profit (-Can$753 to -Can$1,052; -US$599 to -US$837), which includes all lameness-related costs. Production and performance losses were smaller when 100 DIM and 305 DIM thresholds were applied (i.e., exclusion of cows culled before 100 and 305 DIM, respectively), however, mid- and late-lactation lame cows maintained high levels of significant losses for all 6 variables analyzed. Lameness also led to higher levels of culling, masking losses for transition and early-lactation lame cows in the 305-DIM analysis. Increasing producer understanding of the costs associated with lameness not only serves to provide insight to producers for more informed culling decisions, but may also help producers weigh the costs of adopting new methods and technologies targeted at reducing on-farm lameness.

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.