Managing Uncertainty in Site-Specific Management: What is the Best Model?

Managing Uncertainty in Site-Specific Management: What is the Best Model? Models which enable the representation of spatially variable crop performance are central to site-specific management. Rarely have these models been considered in relation to the different sources of uncertainty facing the decision maker. This paper describes various sources of uncertainty (temporal, metrical, structural, and translational) in the context of the site-specific management problem and the model types proposed to solve the problem. An example involving the site specific application of nitrogen fertilizer in dryland wheat production is presented to show how these model types might be evaluated for suitability to a given situation. We conclude that in data poor situations, knowledge-driven models may be less accurate but preferred by the farmer, while in data rich situations data-driven models may be more appropriate. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Managing Uncertainty in Site-Specific Management: What is the Best Model?

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Publisher
Springer Journals
Copyright
Copyright © 2000 by Kluwer Academic Publishers
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1023/A:1009984516714
Publisher site
See Article on Publisher Site

Abstract

Models which enable the representation of spatially variable crop performance are central to site-specific management. Rarely have these models been considered in relation to the different sources of uncertainty facing the decision maker. This paper describes various sources of uncertainty (temporal, metrical, structural, and translational) in the context of the site-specific management problem and the model types proposed to solve the problem. An example involving the site specific application of nitrogen fertilizer in dryland wheat production is presented to show how these model types might be evaluated for suitability to a given situation. We conclude that in data poor situations, knowledge-driven models may be less accurate but preferred by the farmer, while in data rich situations data-driven models may be more appropriate.

Journal

Precision AgricultureSpringer Journals

Published: Oct 16, 2004

References

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