Interpreting Soil Electrical Conductivity and Terrain Attribute Variability with Soil Surveys

Interpreting Soil Electrical Conductivity and Terrain Attribute Variability with Soil Surveys Utilizing soil electrical conductivity (EC) measurements and terrain attributes for precision management will require secondary soil information for adequate interpretation. The objective of this study was to determine whether readily available second-order soil surveys were of adequate quality to aid with interpreting soil EC and terrain data. For three locations in Kentucky, USA, first-order soil surveys were created, second-order surveys reports were obtained, elevation was measured and used to calculate terrain attributes (slope, aspect, plan curvature, profile curvature), and bulk soil electrical conductivity was measured. Three analytical methods (an ordinary least squares analysis and two random field analyses), visual map assessment, and examination of least-squares means were used to assess the relationships between soil EC measurements, terrain attributes and first- and second-order soil surveys. The OLS and random field analyses were problematic. However, the ranking of the OLS F-statistics appeared to reflect the general relationship between landscape variables and first-order soil surveys. The landscape variables related particularly well with soil properties that had been impacted by past soil erosion. Unfortunately, however, second-order soil surveys in this study were not created at suitable scales to adequately interpret EC and terrain data regarding erosion history or other attributes. While these surveys may provide some useful information, field measurements, sampling, and observations will likely be required to develop high quality interpretations of soil EC and terrain attribute data. Precision Agriculture Springer Journals

Interpreting Soil Electrical Conductivity and Terrain Attribute Variability with Soil Surveys

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Kluwer Academic Publishers
Copyright © 2005 by Springer Science+Business Media, Inc.
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
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