Since the mid-1800s, farmers have been housing livestock. What began as a part-time solution for cold winters, stormy days, or injured animals has evolved into the main or only area in which cows spend their adult lives. With this change, farmers, academic researchers, and industry innovators have shaped the farm landscape, literally. Over the last 100 years, changes have been made for productivity, health, milk quality, reproduction, animal well-being, and farm profitability. We review a snapshot of those changes and look ahead to the future of lactating dairy cattle housing. All housing systems are moving toward improved cow comfort. Stalls in tiestall and freestall systems are now designed to accommodate cows based on body size and, in some cases, stage of lactation. Farmers may choose to build a compost bedded or traditional bedded-pack barn to maximize cattle rest or accommodate various breeds or sizes of cows. Looking to the future, external pressure and public perception may push farmers to consider other alternatives to total confinement. Future housing plans may include access to pasture or exercise lots, allowing cows to express their preferences for being outside or inside. Housing that allows natural expression of behavior while maintaining cow cleanliness and health may improve the lives of cows and farmers.

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 objective of this study was to observe relationships among somatic cell count (SCC) and bacteria counts in milk, on teat ends of lactating cows, and in compost samples from the aerated layer of the compost bedded pack. Twenty-nine lactating cows were used in this study. Clinically mastitis cows were not selected for the trial. The correlation between total bacteria count (TBC) and Streptococcus spp. of teat end and hygiene score was (r = 0.49) and (0.44, P = 0.01), respectively. In addition, there was a positive correlation (0.40, P = 0.03) between TBC on teat ends and somatic cell score. When analysing bacterial populations on teat ends and in milk, there was a positive correlation (0.39, P = 0.03) between Escherichia coli at the teat end and coliform counts in milk and also a positive correlation (0.38, P = 0.04) between coliform counts at the teat end and milk. Furthermore, Streptococcus spp. counts on teat end were positively correlated (0.38, P = 0.04) with TBC in milk. Although correlations were observed between hygiene score and SCC with bacterial population, all correlations were moderate. Therefore, hygiene score was not an efficient tool to estimate bacterial populations on teat end and milk.