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Journal of Sensors and Sensor Systems An open-access peer-reviewed journal
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Volume 5, issue 2
J. Sens. Sens. Syst., 5, 301–312, 2016
https://doi.org/10.5194/jsss-5-301-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
J. Sens. Sens. Syst., 5, 301–312, 2016
https://doi.org/10.5194/jsss-5-301-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Regular research article 02 Aug 2016

Regular research article | 02 Aug 2016

Comparing mobile and static assessment of biomass in heterogeneous grassland with a multi-sensor system

Hanieh Safari, Thomas Fricke, Björn Reddersen, Thomas Möckel, and Michael Wachendorf Hanieh Safari et al.
  • Department of Grassland Science and Renewable Plant Resources, University of Kassel, Steinstraße 19, 37213 Witzenhausen, Germany

Abstract. The present study aimed to test a mobile device equipped with ultrasonic and spectral sensors for the assessment of biomass from diverse pastures and to compare its prediction accuracy to that from static measurements. Prediction of biomass by mobile application of sensors explained  > 63 % of the variation in manually determined reference plots representing the biomass range of each paddock. Accuracy of biomass prediction improved with increasing grazing intensity. A slight overestimation of the true values was observed at low levels of biomass, whereas an underestimation occurred at high values, irrespective of stocking rate and years. Prediction accuracy with a mobile application of sensors was always lower than when sensors were applied statically. Differences between mobile and static measurements may be caused by position errors, which accounted for 8.5 cm on average. Beside GPS errors (±1–2 cm horizontal accuracy and twice that vertically), position inaccuracy predominantly originated from undirected vehicle movements due to heaps and hollows on the ground surface. However, the mobile sensor system in connection with biomass prediction models may provide acceptable prediction accuracies for practical application, such as mapping. The findings also show the limits even sophisticated sensor combinations have in the assessment of biomass of extremely heterogeneous grasslands, which is typical for very leniently stocked pastures. Thus, further research is needed to develop improved sensor systems for supporting practical grassland farming.

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This study aimed to explore the potential of a multi-sensor system for assessment of biomass in pastures under different grazing intensities. Prediction accuracy with a mobile application of sensors was always lower than when sensors were applied statically. However accuracy of biomass prediction improved with increasing grazing intensity. Although the limitations associated with the system especially in very lenient pastures, the finding opens up a perspective for future grazing management.
This study aimed to explore the potential of a multi-sensor system for assessment of biomass in...
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