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Comparison of locomotion scoring for dairy cows by experienced and inexperienced raters using live or video observation methods
- A Schlageter-Tello, EAM Bokkers, PWG Groot Koerkamp, T Van Hertem, S Viazzi, CEB Romanini, I Halachmi, C Bahr, D Berckmans, K Lokhorst
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- Journal:
- Animal Welfare / Volume 24 / Issue 1 / February 2015
- Published online by Cambridge University Press:
- 01 January 2023, pp. 69-79
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Lameness is considered a major problem in dairy production. Lameness is commonly detected with locomotion scores assigned to cows under farm conditions, but raters are often trained and assessed for reliability and agreement by using video recordings. The aim of this study was to evaluate intra- and inter-rater reliability and agreement of experienced and inexperienced raters for locomotion scoring performed live and from video, and to calculate the influence of raters and the method of observation (live or video) on the probability of classifying a cow as lame. Using a five-level locomotion score, cows were scored twice live and twice from video by three experienced and two inexperienced raters for three weeks. Every week different cows were scored. Intra- and inter-rater reliability (expressed as weighted kappa, kw) and agreement (expressed as percentage of agreement, PA) for live/live, live/video and video/video comparisons were determined. A logistic regression was performed to estimate the influence of the rater and method of observation on the probability of classifying a cow as lame in live and video observation. Experienced raters had higher values for intra-rater reliability and agreement for video/video than for live/live and live/video comparison. Inexperienced raters, however, did not differ for intra- and inter-rater reliability and agreement for live/live, live/video and video/video comparisons. The logistic regression indicated that raters were responsible for the main effect and the method of observation (live or from video) had a minor effect on the probability for classifying a cow as lame (locomotion score ≥ 3). In conclusion, under the present experimental conditions, experienced raters performed better than unexperienced raters when locomotion scoring was done from video. Since video observation did not show any important influence in the probability of classifying a cow as lame, video observation seems to be an acceptable method for locomotion scoring and lameness assessment in dairy cows.
Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing
- T. Van Hertem, C. Bahr, A. Schlageter Tello, S. Viazzi, M. Steensels, C. E. B. Romanini, C. Lokhorst, E. Maltz, I. Halachmi, D. Berckmans
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The objective of this study was to evaluate if a multi-sensor system (milk, activity, body posture) was a better classifier for lameness than the single-sensor-based detection models. Between September 2013 and August 2014, 3629 cow observations were collected on a commercial dairy farm in Belgium. Human locomotion scoring was used as reference for the model development and evaluation. Cow behaviour and performance was measured with existing sensors that were already present at the farm. A prototype of three-dimensional-based video recording system was used to quantify automatically the back posture of a cow. For the single predictor comparisons, a receiver operating characteristics curve was made. For the multivariate detection models, logistic regression and generalized linear mixed models (GLMM) were developed. The best lameness classification model was obtained by the multi-sensor analysis (area under the receiver operating characteristics curve (AUC)=0.757±0.029), containing a combination of milk and milking variables, activity and gait and posture variables from videos. Second, the multivariate video-based system (AUC=0.732±0.011) performed better than the multivariate milk sensors (AUC=0.604±0.026) and the multivariate behaviour sensors (AUC=0.633±0.018). The video-based system performed better than the combined behaviour and performance-based detection model (AUC=0.669±0.028), indicating that it is worthwhile to consider a video-based lameness detection system, regardless the presence of other existing sensors in the farm. The results suggest that Θ2, the feature variable for the back curvature around the hip joints, with an AUC of 0.719 is the best single predictor variable for lameness detection based on locomotion scoring. In general, this study showed that the video-based back posture monitoring system is outperforming the behaviour and performance sensing techniques for locomotion scoring-based lameness detection. A GLMM with seven specific variables (walking speed, back posture measurement, daytime activity, milk yield, lactation stage, milk peak flow rate and milk peak conductivity) is the best combination of variables for lameness classification. The accuracy on four-level lameness classification was 60.3%. The accuracy improved to 79.8% for binary lameness classification. The binary GLMM obtained a sensitivity of 68.5% and a specificity of 87.6%, which both exceed the sensitivity (52.1%±4.7%) and specificity (83.2%±2.3%) of the multi-sensor logistic regression model. This shows that the repeated measures analysis in the GLMM, taking into account the individual history of the animal, outperforms the classification when thresholds based on herd level (a statistical population) are used.