Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 532013
Title The potential of using sensor data to predict the moment of calving for dairy cows
Author(s) Rutten, C.J.; Steeneveld, W.; Kamphuis, C.; Huijps, K.; Hogeveen, H.
Source In: Precision Livestock Farming Applications / Halachmi, Ilan, Wageningen : Wageningen Academic Publishers - ISBN 9789086862689 - p. 161 - 167.
Department(s) Business Economics
Animal Breeding & Genomics
Publication type Peer reviewed book chapter
Publication year 2015
Abstract On dairy farms, management of calving is important for the health of dairy cows and the survival rate of calves born. Although an expected calving date is known, farmers need to check their cows regularly to estimate the moment when a cow will start calving. A sensor system which predicts the moment of calving could help farmers to check cows effectively for the occurrence of dystocia. In this study, a total of 450 cows on two farms were equipped with Agis SensOor sensors (Agis Automatisering B.V., Harmelen, the Netherlands), which measure rumination activity, activity and temperature hourly. Data were collected over a one-year period. During that period, the exact moment of 417 calvings was recorded using camera images of the calving pen taken every 5 minutes. In total 110 calvings could be linked with sensor data. The moment when calving started was defined as the hour in which the camera images showed the cow having contractions or labour initially started. Two logit models were developed: a reduced model with the expected calving date as the independent variable and a full model which additionally included independent variables based on sensor data. The areas under the Receiver Operating Characteristic curves were 0.682 and 0.878 for the reduced and full model with, at a false positive rate of 10%, sensitivities of 22 and 69%, respectively. Results indicated that the inclusion of sensor data improved prediction of the start of calving and thus that the sensor data used have some potential for predicting the moment of calving.
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