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Towards practical application of sensors for monitoring animal health; design and validation of a model to detect ketosis

Published online by Cambridge University Press:  19 May 2017

Machteld Steensels ,
Ephraim Maltz ,
Claudia Bahr ,
Daniel Berckmans ,
Aharon Antler  and
Ilan Halachmi
Machteld Steensels
Affiliation:
Institute of Agricultural Engineering – Agricultural Research Organization (ARO) – The Volcani Center, PO Box 6, Bet-Dagan 50250, Israel Department of Biosystems (BIOSYST), KU Leuven, Kasteelpark Arenberg 30 – bus 2456, 3001 Heverlee, Belgium
Ephraim Maltz
Affiliation:
Institute of Agricultural Engineering – Agricultural Research Organization (ARO) – The Volcani Center, PO Box 6, Bet-Dagan 50250, Israel
Claudia Bahr
Affiliation:
Department of Biosystems (BIOSYST), KU Leuven, Kasteelpark Arenberg 30 – bus 2456, 3001 Heverlee, Belgium
Daniel Berckmans
Affiliation:
Department of Biosystems (BIOSYST), KU Leuven, Kasteelpark Arenberg 30 – bus 2456, 3001 Heverlee, Belgium
Aharon Antler
Affiliation:
Institute of Agricultural Engineering – Agricultural Research Organization (ARO) – The Volcani Center, PO Box 6, Bet-Dagan 50250, Israel
Ilan Halachmi*
Affiliation:
Institute of Agricultural Engineering – Agricultural Research Organization (ARO) – The Volcani Center, PO Box 6, Bet-Dagan 50250, Israel
*
*For correspondence; e-mail: halachmi@volcani.agri.gov.il
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Abstract

The objective of this study was to design and validate a mathematical model to detect post-calving ketosis. The validation was conducted in four commercial dairy farms in Israel, on a total of 706 multiparous Holstein dairy cows: 203 cows clinically diagnosed with ketosis and 503 healthy cows. A logistic binary regression model was developed, where the dependent variable is categorical (healthy/diseased) and a set of explanatory variables were measured with existing commercial sensors: rumination duration, activity and milk yield of each individual cow. In a first validation step (within-farm), the model was calibrated on the database of each farm separately. Two thirds of the sick cows and an equal number of healthy cows were randomly selected for model validation. The remaining one third of the cows, which did not participate in the model validation, were used for model calibration. In order to overcome the random selection effect, this procedure was repeated 100 times. In a second (between-farms) validation step, the model was calibrated on one farm and validated on another farm. Within-farm accuracy, ranging from 74 to 79%, was higher than between-farm accuracy, ranging from 49 to 72%, in all farms. The within-farm sensitivities ranged from 78 to 90%, and specificities ranged from 71 to 74%. The between-farms sensitivities ranged from 65 to 95%. The developed model can be improved in future research, by employing other variables that can be added; or by exploring other models to achieve greater sensitivity and specificity.

Keywords

Dairy cowketosissensorsrumination durationactivitymilk yieldlogistic regression model
Type
Research Article
Information
Journal of Dairy Research , Volume 84 , Issue 2 , May 2017 , pp. 139 - 145
Copyright
Copyright © Proprietors of Journal of Dairy Research 2017 

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