The prevalence of lameness on New Zealand dairy farms: A comparison of farmer estimate and locomotion scoring

Fabian, J; Laven, Richard; Whay, HR

doi:10.1016/j.tvjl.2014.05.011

Cited by 83 publications

(75 citation statements)

References 31 publications

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“…As expected, the sensitivity of a combination of milk yield and milk composition sensing with activity measurements was much lower (38.0% ± 2.5%) than that obtained in the previous study due to the different reference taken (lame treated cows v. locomotion scored cows), nevertheless, when incorporated into a model including a video-based detection system, it elevated the sensitivity to 52.1% ± 4.7%, twice as high (25% to 33%) as by farmer observations (Fabian et al, 2014), which improves the chances for early lameness detection. Sensitivity of the video-based detection system alone reached 48.4% ± 3.3%.…”

Section: Discussioncontrasting

confidence: 67%

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Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing

Hertem

Bahr

Tello

et al. 2016

animal

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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 videobased 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 Θ 2 , 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.

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Section: Discussioncontrasting

confidence: 67%

“…The prevalence of lameness is often underestimated (Bruijnis et al, 2012;Fabian et al, 2014), and is affected by many different factors (Becker et al, 2014). The most common method to obtain a herd lameness prevalence rate is visual locomotion scoring (Flower and Weary, 2009).…”

Section: Introductionmentioning

confidence: 99%

Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing

Hertem

Bahr

Tello

et al. 2016

animal

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“…The current detection method used in practice, visual detection, results in farmers detecting only 1 in 4 severely lame cows when compared with expert scoring Fabian et al, 2014). As only those cows detected as lame will be treated, a high number of lame cows do not get the necessary treatment.…”

Section: Effects Of Detection Performance and Lameness Management On mentioning

confidence: 99%

“…Similar to the study of Bruijnis et al (2010), the average cost of an undetected mild lameness case was €13 (€1,107/86 cases per 65 cows). As farmers' estimated prevalence is only onefourth of the true prevalence Fabian A total cost was calculated for each lameness case and detection subgroup ( Figure 3, Table 1). Correctly detected non-lame cows cost nothing, whereas falsely positive (detected mildly or severely lame) non-lame cows are unnecessarily trimmed, implying extra costs for trimming (preventive effect not taken into account at this stage).…”

Section: From Conceptual To Operational Frameworkmentioning

confidence: 99%

Farm-specific economic value of automatic lameness detection systems in dairy cattle: From concepts to operational simulations

Gucht

Saeys

Meensel³

et al. 2018

Journal of Dairy Science

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Although prototypes of automatic lameness detection systems for dairy cattle exist, information about their economic value is lacking. In this paper, a conceptual and operational framework for simulating the farm-specific economic value of automatic lameness detection systems was developed and tested on 4 system types: walkover pressure plates, walkover pressure mats, camera systems, and accelerometers. The conceptual framework maps essential factors that determine economic value (e.g., lameness prevalence, incidence and duration, lameness costs, detection performance, and their relationships). The operational simulation model links treatment costs and avoided losses with detection results and farm-specific information, such as herd size and lameness status. Results show that detection performance, herd size, discount rate, and system lifespan have a large influence on economic value. In addition, lameness prevalence influences the economic value, stressing the importance of an adequate prior estimation of the on-farm prevalence. The simulations provide first estimates for the upper limits for purchase prices of automatic detection systems. The framework allowed for identification of knowledge gaps obstructing more accurate economic value estimation. These include insights in cost reductions due to early detection and treatment, and links between specific lameness causes and their related losses. Because this model provides insight in the trade-offs between automatic detection systems' performance and investment price, it is a valuable tool to guide future research and developments.

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“…Being outdoors also carries some risks. Interdigital skin softening and trauma caused by poor outdoor conditions, poorly designed outdoor pens, concrete roads and poorly maintained outdoor walking tracks can increase lameness due to foot injuries or diseases (digital dermatitis or interdigital necrobacillosis) (Chesterton et al, 1989;Clarkson and Ward, 1991;Tranter and Morris, 1991;Alban, 1995;Alban et al, 1996;Fabian et al, 2014;Holzhauer et al, 2006;O'Driscoll et al, 2008a;Barker et al, 2009;Burow et al, 2014). Heath stress may also increase the probability of lameness in outdoor settings by altering feeding (predisposing to subclinical ruminal acidosis) and behavioural changes (increased standing time) (Somers et al, 2005).…”

Section: Housing Type Including Pastures or Outdoor Pensmentioning

confidence: 99%

Scientific Opinion on the assessment of dairy cow welfare in small‐scale farming systems

2015

EFS2

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This opinion reviews information on small-scale dairy cow farming systems in Europe, including the impact of production diseases on welfare of cows, and proposes a methodology for welfare assessment in those systems. To address specific expectations of consumers that food be produced locally or regionally or maintaining acceptable animal welfare conditions, in addition to herd size, criteria to define farms as "non-conventional" were proposed. Several sources were investigated for identifying criteria for the description and categorisation of small-scale farms, including dairy umbrella organisations and literature. In addition to herd size (up to 75 cows), proposed criteria related to small-scale farming comprise the workforce source, input level, indigenous breed use and production type certification. To cover the large diversity of farming systems across Europe, it was proposed that farms meeting at least two of these criteria be considered non-conventional. To adapt the welfare assessment to small-scale farms, the same risk factors and welfare consequences, as measured by corresponding animalbased measures identified in previous opinions for intensive farming systems were considered to be also relevant for small-scale systems. In addition, factors related to resources provided on pasture (e.g. shelter), management of pasture (e.g. mixing herds) and management of the cows (e.g. use of local breeds) were considered more likely to be present in small-scale systems. An on-farm survey was run to collect data for welfare assessment from 124 European farms. The distribution of risk factors and animal-based measures varied across the full range in study farms and showed similar patterns in farms with different grazing systems (from no time to full year on pasture). The animal-based measures identified for intensive farming are well suited for application in smallscale dairy farms. Production disease impact on the individual animal's welfare state does not depend on herd size or farming system.

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The prevalence of lameness on New Zealand dairy farms: A comparison of farmer estimate and locomotion scoring

Cited by 83 publications

References 31 publications

Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing

Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing

Farm-specific economic value of automatic lameness detection systems in dairy cattle: From concepts to operational simulations

Scientific Opinion on the assessment of dairy cow welfare in small‐scale farming systems

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