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Using the Dual‐Pathway Parallel Conductance Model to Determine How Different Soil Properties Influence Conductivity Survey Data
Author(s) -
Lesch S. M.,
Corwin D. L.
Publication year - 2003
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2003.3650
Subject(s) - soil science , soil survey , environmental science , pedotransfer function , soil salinity , soil water , soil structure , water content , geology , hydraulic conductivity , geotechnical engineering
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.