A simplified regional-scale electromagnetic induction - Salinity calibration model using ANOCOVA modeling techniques

Directed soil sampling based on geospatial measurements of apparent soil electrical conductivity (ECa) is a potential means of characterizing the spatial variability of any soil property that influences ECa including soil salinity, water content, texture, bulk density, organic matter, and cation exchange capacity. Multi-field ECa survey data often exhibit abrupt changes in magnitude across field boundaries that complicate the calibration of ECa to soil salinity ( i.e., ECe, electrical conductivity of the saturation extract) over large spatial extents. The primary objective of this study is to evaluate three regression techniques for calibrating ECa to ECe over spatial scales ranging from a few thousand to a hundred thousand hectares, where ECa was measured using electromagnetic induction equipment. The regression techniques include analysis of covariance (ANOCOVA), field specific regression (FSR), and common coefficient regression (CCR). An evaluation was made by comparing jack-knifed mean square prediction errors (MSPE) of ECe for two case studies: 2400 ha of the Broadview Water District in California's San Joaquin Valley and roughly 100,000 ha of the west side of Kittson County in the Red River Valley of Minnesota. The ANOCOVA model outperformed the FSR and CCR regression models on a prediction accuracy basis with the smallest MSPE estimates for depth predictions of soil salinity. The implication of this evaluation is that once ANOCOVA models for each depth are established for a representative set of fields within a regional-scale study area, then the slope coefficients can be used at all future fields, thereby significantly reducing the need for ground-truth soil samples at future fields, which substantially reduces labor and cost. Land resource managers, producers, agriculture consultants, extension specialists, and Natural Resource Conservation Service field staff are the beneficiaries of regional-scale maps of soil salinity. Published by Elsevier B.V.