Combination of Sensor Data and Health Monitoring for Early Detection of Subclinical Ketosis in Dairy Cows

Sturm, Valentin; Efrosinin, Dmitry; Öhlschuster, M.; Gusterer, Erika; Drillich, M.; Iwersen, M.

doi:10.3390/s20051484

Cited by 15 publications

(9 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The system algorithm calculated the time difference of arrival-that is, the time it took the signal to travel from the ear tags to at least 4 receivers and triangulate the location of the cow within the barn. Further information on the localization feature of the SB system is found in Byrne et al (2019) and Sturm et al (2020).…”

mentioning

confidence: 92%

Use of a real-time location system to detect cows in distinct functional areas within a barn

Chapa

Lidauer²,

Steininger³

et al. 2021

JDS Communications

View full text Add to dashboard Cite

Cow location and time spent in relevant areas of the barn are important parameters that can be indicative of health and welfare status. Real-time location systems (RTLS) have the potential to measure these parameters on an individual level. We evaluated the ability of the Smartbow (Smartbow GmbH) RTLS to locate and measure the time cows spent in relevant areas of the barn. This work demonstrated that RTLS are capable of identifying individual cows in important areas of the barn and consequently are able to measure time budgets of cows. Highlights• The RTLS achieved high accuracy in locating cows in alleys, feed bunk and cubicles.• Location and time spent in important barn areas can be automatically determined and used as indicators of health. • The potential of combining RTLS with other sensors technologies was discussed.

show abstract

mentioning

confidence: 92%

Use of a real-time location system to detect cows in distinct functional areas within a barn

Chapa

Lidauer²,

Steininger³

et al. 2021

JDS Communications

View full text Add to dashboard Cite

show abstract

“…Activity (59.42%, p < 0.001), down time (45.40%, p < 0.001) and activity change (77.78%, p < 0.001) were lower in HG (Figure 1). Sturm et al 2020 [16] reported that cows at risk for SKC are slower, i.e., fewer moves compared to healthy cows. Long lying times and a reduction in activity (i.e., paresis) are common sickness behaviours and are clinical symptoms of milk fever [17].…”

Section: Resultsmentioning

confidence: 99%

Relation of Subclinical Ketosis of Dairy Cows with Locomotion Behaviour and Ambient Temperature

Antanaitis

Juozaitienė

Televičius

et al. 2020

Animals

View full text Add to dashboard Cite

Rumination time, chewing time and drinking time are indicators that can be assessed in case of cow disease. In this research, two groups of cows were formed: cows with subclinical ketosis (SCK; n = 10) and healthy cows (HG; n = 10). Behaviour such as walking activity, feeding time with head position up, feeding time with head position down, change of activity and average, minimal and maximal ambient temperature of cows were recorded by the RumiWatch noseband system (RWS; RumiWatch System, Itin+Hoch GmbH, Liestal, Switzerland). The RWS comprises a noseband halter with a built-in pressure sensor and a liquid-filled pressure tube. Data from each studied cow were recorded for 420 h. According to the results of our study, it was determined that cows diagnosed with subclinical ketosis showed a tendency to change their activity more frequently. Our data indicates that minimal and maximal ambient temperatures are related with SCK.

show abstract

“…of previous studies concerned sensors-based validations [15,40,41], observations of physiological behaviors [20,42,43] or comparison between PDF technologies outputs and the available animals' health parameters derived from the farms' databases [29,44,45], only a few studies focused on a strict comparison between the devices' output and individual clinical-diagnostic activities [46][47][48], such as performed in the current investigation (Fig 1 and Supplementary file-S3 Table ).…”

Section: Plos Onementioning

confidence: 99%

Assessment of feeding, ruminating and locomotion behaviors in dairy cows around calving – a retrospective clinical study to early detect spontaneous disease appearance

et al. 2022

View full text Add to dashboard Cite

The study aims to verify the usefulness of new intervals-based algorithms for clinical interpretation of animal behavior in dairy cows around calving period. Thirteen activities associated with feeding-ruminating-locomotion-behaviors of 42 adult Holstein-Friesian cows were continuously monitored for the week (wk) -2, wk -1 and wk +1 relative to calving (overall 30’340 min/animal). Soon after, animals were retrospectively assigned to group-S (at least one spontaneous diseases; n = 24) and group-H (healthy; n = 18). The average activities performed by the groups, recorded by RumiWatch® halter and pedometer, were compared at the different weekly intervals. The average activities on the day of clinical diagnosis (dd0), as well as one (dd-1) and two days before (dd-2) were also assessed. Differences of dd0 vs. dd-1 (ΔD1), dd0 vs. wk -1 (ΔD2), and wk +1 vs. wk -1 (Δweeks) were calculated. Variables showing significant differences between the groups were used for a univariate logistic regression, a receiver operating characteristic analysis, and a multivariate logistic regression model. At wk +1 and dd0, eating- and ruminating-time, eating- and ruminate-chews and ruminating boluses were significantly lower in group-S as compared to group-H, while other activity time was higher. For ΔD2 and Δweeks, the differences of eating- and ruminating-time, as well as of eating-and ruminate-chews were significantly lower in group-S as compared to group-H. Concerning the locomotion behaviors, the lying time was significantly higher in group-S vs. group-H at wk +1 and dd-2. The number of strides was significantly lower in group-S compared to group-H at wk +1. The model including eating-chews, ruminate-chews and other activity time reached the highest accuracy in detecting sick cows in wk +1 (area under the curve: 81%; sensitivity: 73.7%; specificity: 82.4%). Some of the new algorithms for the clinical interpretation of cow behaviour as described in this study may contribute to monitoring animals’ health around calving.

show abstract

Combination of Sensor Data and Health Monitoring for Early Detection of Subclinical Ketosis in Dairy Cows

Cited by 15 publications

References 34 publications

Use of a real-time location system to detect cows in distinct functional areas within a barn

Use of a real-time location system to detect cows in distinct functional areas within a barn

Relation of Subclinical Ketosis of Dairy Cows with Locomotion Behaviour and Ambient Temperature

Assessment of feeding, ruminating and locomotion behaviors in dairy cows around calving – a retrospective clinical study to early detect spontaneous disease appearance

Contact Info

Product

Resources

About