Predicting Regional-Scale Soil Variability using a Single Calibrated Apparent Soil Electrical Conductivity Model

Multi-field/multi-season approaches used to calibrate apparent soil electrical conductivity (ECa) 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 ECa variability; and (ii) evaluating the feasibility of using a single calibration approach to predict sod variability across different fields. Of the soil properties measured, clay content contributed the greatest to ECa variability. The single calibration approach was used to calibrate an ECa-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.