Quantifying walking and standing behaviour of dairy cows using a moving average based on output from an accelerometer

Nielsen, Lars Relund; Pedersen, Asger Roer; Herskin, Mette S.; Munksgaard, Lene

doi:10.1016/j.applanim.2010.08.004

Cited by 86 publications

(56 citation statements)

References 8 publications

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“…The IceTags were attached to a hind leg and thus measured the cow's movements. It is able to detect whether the cow is walking, standing or lying down and to predict the duration of lying bouts as documented in recent studies (Munksgaard et al, 2006;Nielsen et al, 2010;Tolkamp et al, 2010). The activity measure used in this study was the mean number of minutes lying per day during the 2 days before the second herd visit.…”

Section: Methodsmentioning

confidence: 92%

“…Furthermore, 61 observations were excluded because the measured lying time seemed biologically unlikely. Previous studies have validated the automatic monitoring of lying, standing and walking behaviour with IceTags by comparing with direct or video observations of the cows (Munksgaard et al, 2006;Nielsen et al, 2010;Tolkamp et al, 2010). These studies have shown that IceTags are reliable in detecting Figure 1 Predicted probabilities (P(Hyg)) of the three cow leg cleanliness levels stratified by the levels of the risk factors based on the results from an ordinal logistic regression on the risk of having a higher leg cleanliness score in a cross-sectional study of 1315 loose-housed, lactating Danish dairy cows.…”

Section: Discussionmentioning

confidence: 99%

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Identifying risk factors for poor hind limb cleanliness in Danish loose-housed dairy cows

Nielsen

Thomsen²,

Sørensen³

2011

Animal

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The objective of this study was to identify possible risk factors for poor cow hind limb cleanliness in Danish loose-housed, lactating dairy cows. The study was conducted as a cross-sectional study of 1315 cows in 42 commercial Danish dairy herds with primarily Danish Holstein cows. The effect of four cow-level factors (parity, days in milk, daily lying time and lameness) and eight herd-level factors (herd size, milk production, milking system, floor type, access to pasture grazing, floor scraping frequency, hoof bathing frequency and hoof washing frequency) on the risk of having dirtier hind limbs were analysed using ordinal logistic regression fitting a proportional odds model. Cow hind limb cleanliness was scored using an ordinal score from 1 to 4: 1 being clean and 4 being covered in dirt. The odds ratios (ORs) estimated from the proportional odds model depict the effect of a risk factor on the odds of having a higher rather than a lower cleanliness score. First parity cows had an increased risk of being dirtier compared with third parity or older cows (OR 5 1.70). Compared with late lactation, early and mid lactation were associated with an increased risk of being dirtier (OR 5 2.07 and 1.33, respectively). Decreasing the daily time lying by 30 min was associated with an increased risk of being dirtier (OR 5 1.05). Furthermore, an increased risk of being dirtier was found in herds with no pasture access (OR 5 3.75).

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Section: Methodsmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Identifying risk factors for poor hind limb cleanliness in Danish loose-housed dairy cows

Nielsen

Thomsen²,

Sørensen³

2011

Animal

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“…The accelerometer software (IceTagAnalyzer, IceRobotics) computes the number of steps made, and a motion index (MI) per second, that is, the total acceleration within the second. The IceTag accelerometer and software have been validated earlier (Nielsen et al, 2010). The accelerometer data were transferred automatically to a local computer at each milking.…”

Section: Accelerometersmentioning

confidence: 99%

“…From the accelerometer output it was determined whether a cow was lying, standing or walking (Nielsen et al, 2010). The following variables were derived from the accelerometers: lying duration (min/day), standing duration (min/day), walking duration (min/day), number of steps (steps/day), step frequency (steps/daily walking minute) and MI for lying, MI for standing and MI for walking (g/day, here g = acceleration due to gravity; Nielsen et al, 2010). We calculated step frequency as steps per daily walking minute, which is a more precise expression than steps per day or hour, because our definition excludes the steps made while the cow is standing.…”

Section: Accelerometersmentioning

confidence: 99%

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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“…Sensors that measure behavioural or physiological changes more directly related to calving might have additional value for the specific prediction of the start of calving. Such noninvasive sensors include: heart rate monitors (currently used in respiration studies (Machado, et al, 2016)), sensors that measure muscle contractions, sensors that monitor the standing and lying pattern (Nielsen, et al, 2010), and biosensors that measure hormone (residue) levels (currently used for measurements in milk (Brandt, et al, 2010) or detection of pathogens (Casalinuovo, et al, 2006)). Future research should focus on a combination of more sensor variables than the current study did, as adding other variables from different sensors may improve the prediction of calving.…”

Section: Discussionmentioning

confidence: 99%

The utility of sensor technology to support reproductive management on dairy farms

Rutten¹

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Quantifying walking and standing behaviour of dairy cows using a moving average based on output from an accelerometer

Cited by 86 publications

References 8 publications

Identifying risk factors for poor hind limb cleanliness in Danish loose-housed dairy cows

Identifying risk factors for poor hind limb cleanliness in Danish loose-housed dairy cows

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

The utility of sensor technology to support reproductive management on dairy farms

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