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Predicting Regional‐Scale Soil Variability using a Single Calibrated Apparent Soil Electrical Conductivity Model
Author(s) -
Harvey Omar R.,
Morgan Cristine L. S.
Publication year - 2009
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj2008.0074
Subject(s) - calibration , soil science , environmental science , watershed , spatial variability , soil test , hydrology (agriculture) , pedotransfer function , field (mathematics) , digital soil mapping , remote sensing , soil water , soil map , hydraulic conductivity , geology , mathematics , statistics , geotechnical engineering , computer science , machine learning , pure mathematics
Multi‐field/multi‐season approaches used to calibrate apparent soil electrical conductivity (EC a ) models for predicting soil spatial variability across large landscapes are time‐consuming. In this study an alternative calibration approach was evaluated. The study was conducted on an agricultural watershed in Texas with the objectives of (i) assessing the contribution of different soil properties to EC a variability; and (ii) evaluating the feasibility of using a single calibration approach to predict soil variability across different fields. Of the soil properties measured, clay content contributed the greatest to EC a variability. The single calibration approach was used to calibrate an EC a –clay model using data from a designated calibration area (CA). When the calibrated model was used to predict clay content in four validation fields, prediction accuracies were between 2 and 4% clay. Accuracies were comparable with other methods indicating that the single‐calibration approach was a suitable alternative to multi‐field/multi‐season calibration approaches.