Factors influencing spatial variability of soil apparent electrical conductivity

Soil apparent electrical conductivity (ECa) can be used as a precision farming diagnostic tool more efficiently if the factors influencing ECa spatial variability are understood. The objective of this study was to ascertain the causes of ECa spatial variability in soils developed in an environment with between 50 and 65 cm of annual rainfall. Soils at the research sites were formed on calcareous glacial till parent materials deposited approximately 10,000 years ago. Soil samples (0 - 15 cm) collected from at least a 60 by 60 m grid in four fields were analyzed for Olsen phosphorus (P) and potassium (K). Elevation was measured by a carrier phase single frequency DGPS and ECa was measured with an EM 38 (Geonics Ltd., ON, Canada) multiple times between 1995 and 1999. Apparent electrical conductivity contained spatial structure in all fields. Generally, the well drained soils in the summit areas and the poorly drained soil in the toeslope areas had low and high ECa values, respectively. The landscape differences in EC,, were attributed to: (i) water leaching salts out of summit areas and capillary flow combined with seepage transporting water and salts from subsurface to surface soils in toeslope areas; (ii) lower water contents in summit than toeslope soils; and (iii) water erosion which transported surface soil from summit/shoulder areas to lower backslope/footslope areas. A conceptual model based on these findings was developed. In this model, topography followed a sine curve and ECa followed a cosine curve. Field areas that did not fit the conceptual model were: (i) areas containing old animal confinement areas; (ii) areas where high manure rates had been applied; and (iii) areas where soils were outside the boundary conditions of the model, i.e., soils not developed under relatively low rainfall conditions in calcareous glacial till with temperatures ranging between mesic and frigid. This research showed that the soil forming processes as well as agricultural management influenced ECa and that by understanding how landscape position influences salt loss and accumulation, water redistributions following precipitation, and erosion areas that do not fit the conceptual model can be identified. This information can be used to improve soil sampling strategies.