Electronic detection of lameness in dairy cows through measuring pedometric activity and lying behavior

Alsaaod, Maher; Römer, Christoph; Kleinmanns, Jens; Hendriksen, Kathrin; Rose-Meierhöfer, Sandra; Plümer, Lutz; Büscher, Wolfgang

doi:10.1016/j.applanim.2012.10.001

Cited by 69 publications

(58 citation statements)

References 30 publications

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“…In an automated lameness detection context, this means that using the cow as her own control is better than comparing with a baseline established for a normally walking cow, as also noted by Alsaaod et al (2012). Also, lame cows (LS4) exhibited much more Activity variability compared with cows with lower LS.…”

Section: Discussionmentioning

confidence: 98%

“…Recently, a small study showed that activity differences between cows may be larger than the activity difference between the lame and non-lame state of the same cow. Moreover, lameness elicited both increases and decreases in activity depending on cow (Alsaaod et al, 2012).…”

Section: Introductionmentioning

confidence: 93%

“…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).…”

Section: Introductionmentioning

confidence: 99%

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

Thorup

Munksgaard

Robert

et al. 2015

Animal

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Lameness in dairy herds is traditionally detected by visual inspection, which is time-consuming and subjective. Compared with healthy cows, lame cows often spend longer time lying down, walk less and change behaviour around feeding time.Accelerometers measuring cow leg activity may assist farmers in detecting lame cows. On four commercial farms, accelerometer data were derived from hind leg-mounted accelerometers on 348 Holstein cows, 53 of them during two lactations. The cows were milked twice daily and had no access to pasture. During a lactation, locomotion score (LS) was assessed on average 2.4 times (s.d. 1.3). Based on daily lying duration, standing duration, walking duration, total number of steps, step frequency, motion index (MI, i.e. total acceleration) for lying, standing and walking, eight accelerometer means and their corresponding coefficient of variation (CV) were calculated for each week immediately before an LS. A principal component analysis was performed to evaluate the relationship between the variables. The effects of LS and farm on the principal components (PC) and on the variables were analysed in a mixed model. The first four PC accounted for 27%, 18%, 12% and 10% of the total variation, respectively. PC1 corresponded to Activity variability due to heavy loading by five CV variables related to standing and walking. PC2 corresponded to Activity level due to heavy loading by MI walking, MI standing and walking duration. PC3 corresponded to Recumbency due to heavy loading by four variables related to lying. PC4 corresponded mainly to Stepping due to heavy loading by step frequency. Activity variability at LS4 was significantly higher than at the lower LS levels. Activity level was significantly higher at LS1 than at LS2, which was significantly higher than at LS4. Recumbency was unaffected by LS. Stepping at LS1 and LS2 was significantly higher than at LS3 and LS4. Activity level was significantly lower on farm 3 compared with farms 1 and 2. Stepping was significantly lower on farms 1 and 3 compared with farms 2 and 4. MI standing indicated increased restlessness while standing when cows increased from LS3 to LS4. Lying duration was only increased in lame cows. In conclusion, Activity level differed already between LS1 and LS2, thus detecting early signs of lameness, particularly through contributions from walking duration and MI walking. Lameness detection models including walking duration, MI walking and MI standing seem worthy of further investigation.

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Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

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

Thorup

Munksgaard

Robert

et al. 2015

Animal

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“…In addition, cows showing impaired locomotion may not always express all traits described by a certain locomotion-scoring method (Schlageter-Tello et al, 2015). Automated and objective methods of lameness detection are an attempt to mimic locomotion scoring by measuring different traits using different types of sensors (Alsaaod et al, 2012;Van Hertem et al, 2014;Beer et al, 2016). Consequently, methods that objectively analyze cattle locomotion could provide useful information for (1) early and more accurate lameness detection, (2) early foot pathology detection, (3) improved intra-and interobserver agreement of gait scoring, (4) monitoring the effect of treatment strategies, and (5) assessing the influence of environmental conditions on locomotion.…”

Section: Introductionmentioning

confidence: 99%

The cow pedogram—Analysis of gait cycle variables allows the detection of lameness and foot pathologies

Alsaaod

Luternauer

Hausegger

et al. 2017

Journal of Dairy Science

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Changes in gait characteristics are important indicators in assessing the health and welfare of cattle. The aim of this study was to detect unilateral hind limb lameness and foot pathologies in dairy cows using 2 high-frequency accelerometers (400 Hz). The extracted gait cycle variables included temporal events (kinematic outcome = gait cycle, stance phase, and swing phase duration) and several peaks (kinetic outcome = foot load, toe-off). The study consisted of 2 independent experiments. Experiment 1 was carried out to compare the pedogram variables between the lateral claw and respective metatarsus (MT; n = 12) in sound cows (numerical rating system <3, n = 12) and the differences of pedogram variables across limbs within cows between lame cows (numerical rating system ≥3, n = 5) and sound cows (n = 12) using pedogram data that were visually compared with the synchronized cinematographic data. Experiment 2 was carried out to determine the differences across limbs within cows between cows with foot lesions (n = 12) and without foot lesions (n = 12) using only pedogram data. A receiver operator characteristic analysis was used to determine the performance of selected pedogram variables at the cow level. The pedogram of the lateral claw of sound cows revealed similarities of temporal events (gait cycle duration, stance and swing phases) but higher peaks (toe-off and foot load) as compared with the pedogram of the respective MT. In both experiments, comparison of the values between groups showed significantly higher values in lame cows and cows with foot lesions for all gait cycle variables. The optimal cutoff value of the relative stance phase duration for identifying lame cows was 14.79% and for cows with foot lesions was 2.53% with (both 100% sensitivity and 100% specificity) in experiments 1 and 2, respectively. The use of accelerometers with a high sampling rate (400 Hz) at the level of the MT is a promising tool to indirectly measure the kinematic variables of the lateral claw and to detect unilateral hind limb lameness and hind limb pathologies in dairy cows and is highly accurate.

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“…Some authors even suggest its incidence to be on the rise in most countries (Bicalho and Oikonomou, 2013). In addition, new technology is being used to diagnose and score lameness and has continuously confirmed that even subclinical lameness is an important cause of pain and suffering in dairy cows (Chambers et al, 1994;Chapinal et al, 2010;Pastell et al, 2010;Schulz et al, 2011;Alsaaod et al, 2012;Bruijnis et al, 2012;Van Hertem et al, 2013). Therefore, lameness should continue to be considered the main threat to welfare in dairy cows and one of the main causes of loss (e.g.…”

Section: Impact Of Lameness Mastitis Metabolic and Reproductive Dismentioning

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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Electronic detection of lameness in dairy cows through measuring pedometric activity and lying behavior

Cited by 69 publications

References 30 publications

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

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

The cow pedogram—Analysis of gait cycle variables allows the detection of lameness and foot pathologies

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

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