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This article in AJ

  1. Vol. 95 No. 2, p. 365-379
     
    Received: Mar 29, 2002


    * Corresponding author(s): slesch@ussl.ars.usda.gov
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doi:10.2134/agronj2003.3650

Using the Dual-Pathway Parallel Conductance Model to Determine How Different Soil Properties Influence Conductivity Survey Data

  1. S. M. Lesch * and
  2. D. L. Corwin
  1. USDA-ARS George E. Brown, Jr., Salinity Lab., 450 West Big Springs Rd., Riverside, CA 92507-4617

Abstract

The correlation structure between apparent soil electrical conductivity (ECa) and various soil properties can often appear radically dissimilar in different field surveys. Ideally, some type of methodology for survey data validation should be developed that can predict the expected correlation structure between ECa survey data and various soil properties, given information about the soil properties themselves. In this paper, we review an existing model for ECa and hypothesize that this model can be used to accurately predict the expected correlation structure between ECa data and multiple soil properties of interest (such as soil salinity, saturation-paste percentage, and soil water content). Our objective is twofold: (i) to demonstrate how this model can be employed to produce the expected correlation structure and (ii) to extend this ECa model to handle survey data collected under low water content situations by dynamically adjusting the model's assumed water content function. This adjustment can be estimated using acquired ECa signal and soil sample data, and its statistical significance can be determined for each specific survey situation. We demonstrate both of these techniques using acquired electromagnetic induction signal data and measured soil properties of interest from 12 different field salinity surveys performed in California and Colorado and in Alberta, Canada. Results from these 12 surveys suggest that the ordinary model is able to accurately predict the expected correlation structure between conductivity and soil property when the water content is near field capacity and that the dynamically adjusted model is able to substantially improve the accuracy of the predicted correlation structure when the water content is significantly below field capacity.

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Copyright © 2003. American Society of AgronomyPublished in Agron. J.95:365–379.