Visits were made to 205 dairy farms in England and Wales between October 2006 and May 2007 by 1 or more of 4 researchers. At each visit, all milking cows were locomotion scored (lameness scored) using a 4-point scale (0=sound locomotion, 1=imperfect locomotion, 2=lame, 3=severely lame). The mean prevalence of lameness (scores 2 and 3) across the study farms was 36.8% (range=0-79.2%). On each farm, the presence within the housing and grazing environments of commonly reported risks for increased lameness was recorded. Each farmer was interviewed to gauge the ability of the farm staff to detect and treat lameness. A multivariable linear regression model was fitted. Risk factors for increased lameness were the presence of damaged concrete in yards, cows pushing each other or turning sharply near the parlor entrance or exit, cattle grazing pasture also grazed by sheep, the use of automatic scrapers, not treating lame cows within 48h of detection, and cows being housed for 61 d or longer at the time they were locomotion scored by the visiting researcher. Having a herd consisting entirely of a breed or breeds other than Holstein-Friesian was associated with a reduction in lameness prevalence compared with having a herd consisting entirely of Holstein-Friesians.

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Background: Advances in bio-telemetry technology have made it possible to automatically monitor and classify behavioural activities in many animals, including domesticated species such as dairy cows. Automated behavioural classification has the potential to improve health and welfare monitoring processes as part of a Precision Livestock Farming approach. Recent studies have used accelerometers and pedometers to classify behavioural activities in dairy cows, but such approaches often cannot discriminate accurately between biologically important behaviours such as feeding, lying and standing or transition events between lying and standing. In this study we develop a decision-tree algorithm that uses tri-axial accelerometer data from a neck-mounted sensor to both classify biologically important behaviour in dairy cows and to detect transition events between lying and standing. Results: Data were collected from six dairy cows that were monitored continuously for 36 h. Direct visual observations of each cow were used to validate the algorithm. Results show that the decision-tree algorithm is able to accurately classify three types of biologically relevant behaviours: lying (77.42 % sensitivity, 98.63 % precision), standing (88.00 % sensitivity, 55.00 % precision), and feeding (98.78 % sensitivity, 93.10 % precision). Transitions between standing and lying were also detected accurately with an average sensitivity of 96.45 % and an average precision of 87.50 %. The sensitivity and precision of the decision-tree algorithm matches the performance of more computationally intensive algorithms such as hidden Markov models and support vector machines.

Claw lesion treatment records were recorded by farmers on 27 dairy farms (3,074 cows, 36,432 records) in England and Wales between February 2003 and February 2004. These were combined with farm environment and management data collected using a combination of direct observations, interviews with farmers, and milk recording data. Multilevel models were constructed for the 3 most frequently reported lesions related to lameness, namely, sole ulcers, white line disease, and digital dermatitis. Risks associated with an increased incidence of sole ulcers were parity 4 or greater, the use of roads or concrete cow tracks between the parlor and grazing, the use of lime on free stalls, and housing in free stalls with sparse bedding for 4 mo or more. The risks for white line disease were increasing parity and increasing herd size, cows at pasture by day and housed at night, and solid grooved concrete floors in yards or alleys. Solid grooved flooring was also associated with an increased risk of digital dermatitis, and cows 6 or more months after calving had a decreased risk of a first case of digital dermatitis. These results improve our understanding of the specific risks for 3 important lesions associated with bovine lameness and could be used as interventions in future clinical studies targeted at the reduction of specific lesions.