Over the last 100 yr, the dairy industry has incorporated technology to maximize yield and profit. Pressure to maximize efficiency and lower inputs has resulted in novel approaches to managing and milking dairy herds, including implementation of automatic milking systems (AMS) to reduce labor associated with milking. Although AMS have been used for almost 20 yr in Europe, they have only recently become more popular in North America. Automatic milking systems have the potential to increase milk production by up to 12%, decrease labor by as much as 18%, and simultaneously improve dairy cow welfare by allowing cows to choose when to be milked. However, producers using AMS may not fully realize these anticipated benefits for a variety of reasons. For example, producers may not see a reduction in labor because some cows do not milk voluntarily or because they have not fully or efficiently incorporated the AMS into their management routines. Following the introduction of AMS on the market in the 1990s, research has been conducted examining AMS systems versus conventional parlors focusing primarily on cow health, milk yield, and milk quality, as well as on some of the economic and social factors related to AMS adoption. Additionally, because AMS rely on cows milking themselves voluntarily, research has also been conducted on the behavior of cows in AMS facilities, with particular attention paid to cow traffic around AMS, cow use of AMS, and cows' motivation to enter the milking stall. However, the sometimes contradictory findings resulting from different studies on the same aspect of AMS suggest that differences in management and farm-level variables may be more important to AMS efficiency and milk production than features of the milking system itself. Furthermore, some of the recommendations that have been made regarding AMS facility design and management should be scientifically tested to demonstrate their validity, as not all may work as intended. As updated AMS designs, such as the automatic rotary milking parlor, continue to be introduced to the dairy industry, research must continue to be conducted on AMS to understand the causes and consequences of differences between milking systems as well as the impacts of the different facilities and management systems that surround them on dairy cow behavior, health, and welfare.

Large-scale phenotyping of animal behaviour traits is time consuming and has led to increased demand for technologies that can automate these procedures. Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable farm settings presents challenges. The aim of this review is to provide a systematic overview of the advances that have occurred in automated, high throughput image detection of farm animal behavioural traits with welfare and production implications. Peer-reviewed publications written in English were reviewed systematically following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After identification, screening, and assessment for eligibility, 108 publications met these specifications and were included for qualitative synthesis. Data collected from the papers included camera specifications, housing conditions, group size, algorithm details, procedures, and results. Most studies utilized standard digital colour video cameras for data collection, with increasing use of 3D cameras in papers published after 2013. Papers including pigs (across production stages) were the most common (n = 63). The most common behaviours recorded included activity level, area occupancy, aggression, gait scores, resource use, and posture. Our review revealed many overlaps in methods applied to analysing behaviour, and most studies started from scratch instead of building upon previous work. Training and validation sample sizes were generally small (mean±s.d. groups = 3.8±5.8) and in data collection and testing took place in relatively controlled environments. To advance our ability to automatically phenotype behaviour, future research should build upon existing knowledge and validate technology under commercial settings and publications should explicitly describe recording conditions in detail to allow studies to be reproduced.

Objective—To determine whether therapeutic riding resulted in higher levels of stress or frustration for horses than did recreational riding and whether therapeutic riding with at-risk individuals was more stressful for the horses than was therapeutic riding with individuals with physical or emotional handicaps. Design—Observational study. Animals—14 horses in a therapeutic riding program. Procedure—An ethogram of equine behaviors was created, and horses were observed while ridden by 5 groups of riders (recreational riders, physically handicapped riders, psychologically handicapped riders, atrisk children, and special education children). Number of stress-related behaviors (ears pinned back, head raised, head turned, head tossed, head shaken, head down, and defecation) was compared among groups. Results—No significant differences in mean number of stress-related behaviors were found when horses were ridden by recreational riders, physically handicapped riders, psychologically handicapped riders, or special education children. However, mean number of stress-related behaviors was significantly higher when horses were ridden by the at-risk children. Conclusions and Clinical Relevance—Results suggest that for horses in a therapeutic riding program, being ridden by physically or psychologically handicapped individuals is no more stressful for the horses than is being ridden in the same setting by recreational riders. However, at-risk children caused more stress to the horses, suggesting that the time horses are ridden by at-risk children should be limited both daily and weekly.

Lying down and resting are important for optimal cow health, welfare, and production. In comparison with free stall farms with a milking parlor, farms with automated milking systems (AMS) may place less constraint on how long cows can lie down. However, few studies report lying times on AMS farms. The aims of this study were to describe the variation in lying times of dairy cows in AMS farms and to understand how much of the variation in individual lying times is related to cow-level factors, including lameness, the presence of hock and knee lesions, and body condition score (BCS). We visited 36 farms in Canada (Quebec: n = 10; Ontario: n = 10; British Columbia: n = 4; and Alberta: n = 5), and the United States (Michigan: n = 7). Gait scores, presence of hock and knee lesions, and BCS were recorded for 40 Holstein cows from each herd. Parity and days in milk were retrieved from farm records. Lying time was recorded across 4d using accelerometers (n = 1,377). Multivariable analysis was performed. Of scored cows, 15.1% were lame (i.e., obviously limping; 203 of 1,348 cows). Knee lesions were found in 27.1% (340 of 1,256 cows) and hock lesions were found in 30.8% (421 of 1,366 cows) of the animals. Daily lying time varied among cows. Cows spent a median duration of 11.4 h/d lying down (25th-75th percentile = 9.7-12.9 h), with a lying bout frequency of 9.5 bouts/d (25th-75th percentile = 7.5-12 bouts/d) and a median bout duration of 71 min (25th-75th percentile = 58-87 min/bout). Lameness was associated with cows lying down for 0.6 h/d longer in fewer, longer bouts. Increased lying time was also associated with increased parity, later stage of lactation and higher BCS. Older cows (parity ≥ 3) spent about 0.5 h/d more lying down compared with parity 1 cows, and cows with BCS ≥ 3.5 lay down on average 1 h/d longer than cows with BCS ≤ 2.25. Hock lesions were associated with shorter lying times in univariable models, but no associations were found in the multivariable models. We concluded that only a small proportion of the variation between cows in lying time is explained by lameness, leg lesions, and BCS.