Separating spatial and temporal sources of variation for model testing in precision agriculture

Separating spatial and temporal sources of variation for model testing in precision agriculture The application of crop simulation models in precision agriculture research appears to require only the specification of some input parameters and then running the model for each desired location in a field. Reports in the extensive literature on modeling have described independent tests for different cultivars, soil types and weather, and these have been presumed to validate the model performance in general. However, most of these tests have evaluated model performance for simulating mean yields for multiple plots in yield trials or in other large-area studies. Precision agriculture requires models to simulate not only the mean, but also the spatial variation in yield. No consensus has emerged about how to test model performance rigorously, or what level of performance is sufficient. In addition, many measures of goodness of fit between the observed and simulated data (i.e., model performance) depend on the range of variation in the observed data. If, for example, inter-annual and spatial sources of variation are combined in a test, poor performance in one might be masked by good performance in the other. Our objectives are to: (1) examine research aims that are common in precision agriculture, (2) discuss expectations of model performance, and (3) compare several traditional and some alternative measures of model performance. These measures of performance are explained with examples that illustrate their limitations and strengths. The risk of relying on a test that combines spatial and temporal data was shown with data where the overall fit was good (r 2  > 0.8), but the fit within any year was zero. Information gained using these methods can both guide and help to build confidence in future modeling efforts in precision agriculture. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Separating spatial and temporal sources of variation for model testing in precision agriculture

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Publisher
Springer US
Copyright
Copyright © 2007 by Springer Science+Business Media, LLC
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.
10.1007/s11119-007-9046-9
Publisher site
See Article on Publisher Site

Abstract

The application of crop simulation models in precision agriculture research appears to require only the specification of some input parameters and then running the model for each desired location in a field. Reports in the extensive literature on modeling have described independent tests for different cultivars, soil types and weather, and these have been presumed to validate the model performance in general. However, most of these tests have evaluated model performance for simulating mean yields for multiple plots in yield trials or in other large-area studies. Precision agriculture requires models to simulate not only the mean, but also the spatial variation in yield. No consensus has emerged about how to test model performance rigorously, or what level of performance is sufficient. In addition, many measures of goodness of fit between the observed and simulated data (i.e., model performance) depend on the range of variation in the observed data. If, for example, inter-annual and spatial sources of variation are combined in a test, poor performance in one might be masked by good performance in the other. Our objectives are to: (1) examine research aims that are common in precision agriculture, (2) discuss expectations of model performance, and (3) compare several traditional and some alternative measures of model performance. These measures of performance are explained with examples that illustrate their limitations and strengths. The risk of relying on a test that combines spatial and temporal data was shown with data where the overall fit was good (r 2  > 0.8), but the fit within any year was zero. Information gained using these methods can both guide and help to build confidence in future modeling efforts in precision agriculture.

Journal

Precision AgricultureSpringer Journals

Published: Nov 29, 2007

References

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