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Title: Past, present and future trends of soil soil electrical conductivity measurement using geophysical methods

Author
item Corwin, Dennis

Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: 7/1/2007
Publication Date: 3/1/2008
Citation: Corwin, D.L. 2008. Past, present and future trends of soil soil electrical conductivity measurement using geophysical methods. In: B.J. Allred, J.J. Daniels and M.R. Ehsani (editors) Handbook of Agricultural Geophysics. CRC Press. Boca Raton, FL. Chapter 2 pp: 17-44.

Interpretive Summary: The USDA-ARS, in particular the George E. Brown Salinity Laboratory, has played a key role in the adaptation of geophysical techniques to agriculture. These techniques have been invaluable tools for measuring and monitoring soil properties to help understand the processes that affect the condition of soil, which influence crop quality and yield. It is the objective of this book chapter in Handbook of Agricultural Geophysics to present a historical perspective of the adaptation of geophysical techniques for use in agriculture by USDA-ARS and other scientist with a primary focus on trends in the adaptation of soil electrical conductivity (EC) to agriculture and the factors that forged these trends. From the pioneering work in agricultural geophysics at the Salinity Laboratory to current uses and future trends and needs, the history of agricultural geophysics is examined. Historical trends have included (i) early observational research relating measurements to soil properties, (ii) mapping of soil properties, such as salinity, (iii) directing soil sampling from the variability in maps of EC to minimize grid sampling; and (iv) application of EC-directed soil sampling to characterize spatial variability for use in modeling the movement of chemicals in soil, soil quality assessment, monitoring changes in soil condition due to management, and precision agriculture. The future will bring a combined and integrated use of multiple geophysical tools to provide the complex set of information needed for precision agriculture.

Technical Abstract: 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 ER, EMI, and TDR. It is the objective of this book chapter in Handbook of Agricultural Geophysics to present a historical perspective of the adaptation of geophysical techniques for use in agriculture with a primary focus on trends in the adaptation of ECa to agriculture as well as the practical and theoretical factors that forged these trends. Measurements of ECa in agriculture first appeared in the early 1970 led by the work of Rhoades and colleagues at the U.S. Salinity Laboratory. The Salinity Laboratory continued to provide leadership in the application 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.