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Article

Evaluation of a Binary Classification Approach to Detect Herbage Scarcity Based on Behavioral Responses of Grazing Dairy Cows

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Competitiveness and System Evaluation, Agroscope, Tänikon 1, 8356 Ettenhausen, Switzerland
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Institute of Agricultural Sciences in the Tropics, University of Hohenheim, Fruwirthstrasse 31, 70599 Stuttgart, Germany
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Institute of Animal Nutrition and Physiology, Christian-Albrechts-Universität zu Kiel, Hermann-Rodewald-Strasse 9, 24118 Kiel, Germany
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Division of Animal Husbandry and Ethology, Faculty of Life Sciences, Humboldt University of Berlin, Invalidenstrasse 42, 10115 Berlin, Germany
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Thuenen Institute of Agricultural Technology, Bundesallee 47, 38116 Braunschweig, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Christos Tachtatzis, Craig Michie and Ivan Andonovic
Sensors 2022, 22(3), 968; https://doi.org/10.3390/s22030968
Received: 10 December 2021 / Revised: 18 January 2022 / Accepted: 24 January 2022 / Published: 26 January 2022
(This article belongs to the Special Issue Sensors for Animal Health Monitoring and Precision Livestock Farming)
In precision grazing, pasture allocation decisions are made continuously to ensure demand-based feed allowance and efficient grassland utilization. The aim of this study was to evaluate existing prediction models that determine feed scarcity based on changes in dairy cow behavior. During a practice-oriented experiment, two groups of 10 cows each grazed separate paddocks in half-days in six six-day grazing cycles. The allocated grazing areas provided 20% less feed than the total dry matter requirement of the animals for each entire grazing cycle. All cows were equipped with noseband sensors and pedometers to record their head, jaw, and leg activity. Eight behavioral variables were used to classify herbage sufficiency or scarcity using a generalized linear model and a random forest model. Both predictions were compared to two individual-animal and day-specific reference indicators for feed scarcity: reduced milk yields and rumen fill scores that undercut normal variation. The predictive performance of the models was low. The two behavioral variables “daily rumination chews” and “bite frequency” were confirmed as suitable predictors, the latter being particularly sensitive when new feed allocation is present in the grazing set-up within 24 h. Important aspects were identified to be considered if the modeling approach is to be followed up. View Full-Text
Keywords: precision grazing management; herbage allowance; chewing behavior; rumen fill scoring; milk yield drop; allocating new pasture; decision support precision grazing management; herbage allowance; chewing behavior; rumen fill scoring; milk yield drop; allocating new pasture; decision support
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MDPI and ACS Style

Hart, L.; Dickhoefer, U.; Paulenz, E.; Umstaetter, C. Evaluation of a Binary Classification Approach to Detect Herbage Scarcity Based on Behavioral Responses of Grazing Dairy Cows. Sensors 2022, 22, 968. https://doi.org/10.3390/s22030968

AMA Style

Hart L, Dickhoefer U, Paulenz E, Umstaetter C. Evaluation of a Binary Classification Approach to Detect Herbage Scarcity Based on Behavioral Responses of Grazing Dairy Cows. Sensors. 2022; 22(3):968. https://doi.org/10.3390/s22030968

Chicago/Turabian Style

Hart, Leonie, Uta Dickhoefer, Esther Paulenz, and Christina Umstaetter. 2022. "Evaluation of a Binary Classification Approach to Detect Herbage Scarcity Based on Behavioral Responses of Grazing Dairy Cows" Sensors 22, no. 3: 968. https://doi.org/10.3390/s22030968

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