Apparent Soil Electrical Conductivity: Past, Present, and Future Trends in Application.
Dennis Corwin, USDA-ARS, George E. Brown Jr. Salinity Laboratory, 450 West Big Springs Road, Riverside, CA 92507-4617
Adaptation of geophysical techniques from the measurement of geologic strata to the measurement of surface and near-surface soils for agricultural applications was the next logical step. No geophysical technique has had a greater impact on agriculture than the measurement of apparent soil Electrical Conductivity (ECa) using Electrical Resistivity (ER), ElectroMagnetic Induction (EMI), and Time Domain Reflectometry (TDR). It is the objective to present a historical perspective of the adaptation of ECa to agriculture, the practical and theoretical factors that forged the past and present trends in its application, and anticipated future uses. Measurements of ECa in agriculture first appeared in the early 1970 led by the work of Rhoades and colleagues at the former U.S. Salinity Laboratory (currently, George E. Brown Jr. Salinity Laboratory). The USDA-ARS, in particular the George E. Brown Salinity Laboratory, has played a key role in the adaptation of geophysical techniques to agriculture by introducing the use of EMI to infer soil salinity from ECa measurements in the early 1980s. Historical trends in the use of ECa in agriculture have included (i) observational research through the 1980s and early 1990s that correlated ECa measurements to soil properties; (ii) mapping of soil properties (particularly salinity) correlated with geo-referenced ECa measurements, generally from grid sampling, (iii) directing soil sampling from the variability in geospatial ECa data to minimize grid sampling; and (iv) application of ECa-directed soil sampling to characterize spatial variability for use in landscape-scale solute transport modeling in the vadose zone, soil quality assessment, management-induced change in soil condition, and site-specific crop management. The future of geophysical techniques in agriculture will be the combined use of multiple sensors that can compliment one another to provide spatial information about the myriad of edaphic, anthropogenic, biologic, meteorologic, and topographic factors influencing crop yield variability and soil-related environmental problems.