HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Free Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in ISI Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (11) Citing Articles via Google Scholar Google Scholar Articles by Lesch, S. M. Articles by Corwin, D. L. Search for Related Content PubMed Articles by Lesch, S. M. Articles by Corwin, D. L. Agricola Articles by Lesch, S. M. Articles by Corwin, D. L. Related Collections Soil Salinity Electromagnetic Induction, EMI Soil Models Soil Classification and Mapping

SOIL MANAGEMENT

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

USDA-ARS George E. Brown, Jr., Salinity Lab., 450 West Big Springs Rd., Riverside, CA 92507-4617

^* Corresponding author (slesch{at}ussl.ars.usda.gov)

Received for publication March 29, 2002. The correlation structure between apparent soil electrical conductivity (EC_a) 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 EC_a survey data and various soil properties, given information about the soil properties themselves. In this paper, we review an existing model for EC_a and hypothesize that this model can be used to accurately predict the expected correlation structure between EC_a 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 EC_a 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 EC_a 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.

Abbreviations: CalcEC_a, calculated soil electrical conductivity • DPPC, dual-pathway parallel conductance (model) • Dy-DPPC, dynamic water content partitioning (model) • EC_a, apparent soil electrical conductivity • EC_e, electrical conductivity of the saturated soil extract • EC_wc, specific electrical conductivity of the continuous soil water phase • EC_ws, specific electrical conductivity of the series-coupled soil water phase • EM, electromagnetic induction • EM_avg, average electromagnetic induction • SP, saturation percentage • _g, gravimetric soil water content • _w, volumetric soil water content • _wc, volumetric soil water content in the continuous liquid pathway • _ws, volumetric soil water content in the series-coupled pathway • _b, bulk density • _wc, adjusted volumetric soil water content of continuous liquid pathway • _ws, adjusted volumetric soil water content of series-coupled pathway

This article has been cited by other articles:

				D. B. Lobell, J. I. Ortiz-Monasterio, F. C. Gurrola, and L. Valenzuela Identification of Saline Soils with Multiyear Remote Sensing of Crop Yields Soil Sci. Soc. Am. J., April 5, 2007; 71(3): 777 - 783. [Abstract] [Full Text] [PDF]

				L. M. Hall, J. R. Brainard, R. S. Bowman, and J. M. H. Hendrickx Determination of Solute Distributions in the Vadose Zone Using Downhole Electromagnetic Induction Vadose Zone J., November 1, 2004; 3(4): 1207 - 1214. [Abstract] [Full Text] [PDF]