Assessment and visualization of spatial interpolation of soil pH values in farmland

Assessment and visualization of spatial interpolation of soil pH values in farmland Site-specific farming entails fine-scale detection of field parameters that affect yield coupled with directing appropriate management inputs to select areas that improve field-scale cropping system profitability. Currently, limited technologies are available to evaluate spatial variability in soil properties on a fine scale (submeter resolution). Therefore, information is typically generated by collecting discrete samples and utilizing spatial interpolation to estimate data for the unsampled locations. In this study, soil pH samples were collected from a 12.15 ha agricultural field in northwest Missouri using two grid-sampling regimes: 0.11 ha with 110 samples and 0.98 ha with 12 samples. Three spatial interpolation methods (inverse distance weighted, spline and kriging) were tested to evaluate the effects of interpolation on unsampled locations. In addition to quantitative validation evaluations, results were also assessed by 2D visualization and 3D visualization. Although each assessment approach provided useful information, the inverse distance weighted technique overall better-estimated soil pH values as determined by a combination of all three approaches. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Assessment and visualization of spatial interpolation of soil pH values in farmland

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Publisher
Springer Journals
Copyright
Copyright © 2013 by Springer Science+Business Media New York
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/s11119-013-9316-7
Publisher site
See Article on Publisher Site

Abstract

Site-specific farming entails fine-scale detection of field parameters that affect yield coupled with directing appropriate management inputs to select areas that improve field-scale cropping system profitability. Currently, limited technologies are available to evaluate spatial variability in soil properties on a fine scale (submeter resolution). Therefore, information is typically generated by collecting discrete samples and utilizing spatial interpolation to estimate data for the unsampled locations. In this study, soil pH samples were collected from a 12.15 ha agricultural field in northwest Missouri using two grid-sampling regimes: 0.11 ha with 110 samples and 0.98 ha with 12 samples. Three spatial interpolation methods (inverse distance weighted, spline and kriging) were tested to evaluate the effects of interpolation on unsampled locations. In addition to quantitative validation evaluations, results were also assessed by 2D visualization and 3D visualization. Although each assessment approach provided useful information, the inverse distance weighted technique overall better-estimated soil pH values as determined by a combination of all three approaches.

Journal

Precision AgricultureSpringer Journals

Published: May 10, 2013

References

  • A model for agro-economic analysis of soil pH mapping
    Adamchuk, VI; Morgan, MT; Loewenberg-Deboer, JM
  • A comparison of prediction methods for the creation of field-extent soil property maps
    Bishop, TF; McBratney, AB

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