Applicability of day-to-day variation in behavior for the automated detection of lameness in dairy cows

Abstract

Lameness is a major problem in modern dairy husbandry and has welfare implications and other negative consequences. The behavior of dairy cows is influenced by lameness. Automated lameness detection can, among other methods, be based on day-to-day variation in animal behavior. Activity sensors that measure lying time, number of lying bouts, and other parameters were used to record behavior per cow per day. The objective of this research was to develop and validate a lameness detection model based on daily activity data. Besides the activity data, milking data and data from the computerized concentrate feeders were available as input data. Locomotion scores were available as reference data. Data from up to 100 cows collected at an experimental farm during 23 mo in 2010 and 2011 were available for model development. Behavior is cow-dependent, and therefore quadratic trend models were fitted with a dynamic linear model on-line per cow for 7 activity variables and 2 other variables (milk yield per day and concentrate leftovers per day). It is assumed that lameness develops gradually; therefore, a lameness alert was given when the linear trend in 2 or more of the 9 models differed significantly from zero in a direction that corresponded with lameness symptoms. The developed model was validated during the first 4 mo of 2012 with almost 100 cows on the same farm by generating lameness alerts each week. Performance on the model validation data set was comparable with performance on the model development data set. The overall sensitivity (percentage of detected lameness cases) was 85.5% combined with specificity (percentage of nonlame cow-days that were not alerted) of 88.8%. All variables contributed to this performance. These results indicate that automated lameness detection based on day-to-day variation in behavior is a useful tool for dairy management.

Lameness detection via leg-mounted accelerometers on dairy cows on four commercial farms

“…Previous studies have reported, that lameness affects cow behaviour: lame cows may lie down longer around feeding (Yunta et al, 2012), have longer daily lying-time (Blackie et al, 2011), longer lying bouts (Thomsen et al, 2012) and take fewer steps/day (de Mol et al, 2013). In addition, using data from automated feed stations, lameness has been found to decrease feeding time (Gonzàlez et al, 2008).…”

“…Thus, some studies have used pedometers or the more advanced accelerometers as stand-alone technology to detect lameness (Mazrier et al, 2006;Alsaaod et al, 2012). Others have combined activity data with additional types of sensor data such as milk yield, concentrate left-overs or milking order to detect lameness (de Mol et al, 2013;Kamphuis et al, 2013).…”

Variables of gait inconsistency outperform basic gait variables in detecting mildly lame cows

“…In the threshold setting of Pastell and Kujala (2007), Ito et al (2010) and Viazzi et al (2013Viazzi et al ( , 2014, the group of non-lame cows included the mildly lame cows (locomotion score 2) which were considered in a separate group in our study. de Mol et al (2013) disregarded the mildly lame cows (locomotion score 2) and tested his detection model for non-lame and severely lame cows (locomotion 3 or more). Poursaberi et al (2010) developed an algorithm based on the arched back posture of cows using computer vision techniques with a 2D-camera (side view).…”

Invited review: Cattle lameness detection with accelerometers

“…The specific analysis reported has varied in the literature. The most common measures reported are receiver operator characteristic curve, area under the curve (Kamphuis et al, 2013;Alsaaod et al, 2017), confusion matrices (Martiskainen et al, 2009;Van Hertem et al, 2013), and specificity, sensitivity, positive predictive value, and accuracy (de Mol et al, 2013;Van Hertem et al, 2013).…”