Influence of soil properties on electrical conductivity under humid water regimes

Citation
K. Auerswald et al., Influence of soil properties on electrical conductivity under humid water regimes, SOIL SCI, 166(6), 2001, pp. 382-390
Citations number
28
Categorie Soggetti
Environment/Ecology
Journal title
SOIL SCIENCE
ISSN journal
0038075X → ACNP
Volume
166
Issue
6
Year of publication
2001
Pages
382 - 390
Database
ISI
SICI code
0038-075X(200106)166:6<382:IOSPOE>2.0.ZU;2-0
Abstract
Apparent electrical conductivity (ECa) of soils can be measured easily by n ondestructive methods and, therefore, could be useful in the mapping of soi l properties, which influence ECa. The influences of soil structure, bulk d ensity, clay and water content, EC of the soil solution, and cation exchang e capacity (CEC) on ECa were examined in laboratory experiments for 19 soil s at 16 to 18 water contents. Soil structure increased variability in ECa b ut had no other influence. Bulk density also had no influence when the othe r variables were used on a volume basis rather than on a weight basis. This agrees with the concept that an electric current has to pass a given volum e between two electrodes but not a constant mass. Cation exchange capacity, which depends primarily on charged sites of the clay and organic matter fr actions, correlated less with ECa than clay content alone, indicating that the charged sites of organic matter are less important. Despite the humid c limate, the EC of the soil solution varied considerably in the soils, which could be explained by differences in fertilizing history which, in turn, i nfluenced ECa. Water content had a comparably small influence, which increa sed near the wilting point and below. In a multiple regression, volumetric clay content, EC of the soil solution, and logarithmic water content influe nced ECa at a ratio of 1 : 0.8 : 0.4. This regression explained 84% of the variation and performed equally well on a validation data set of 145 soil-m oisture combinations. This is superior to an existing model. Thus, ECa offe rs the opportunity to improve high-resolution mapping of these three proper ties by selecting conditions under which the variation can be assigned main ly to a single factor. Together with the ease of ECa measurement, this is e specially useful for precision agriculture.