In precision agriculture, knowledge of soil variability is essential to the optimal management of on-farm nitrogen applications to grain corn. Measuring the apparent electrical conductivity (EC_a) of soils makes it possible to characterize and map specific soil properties such as soil texture and drainage. Our research into post-emergence application of nitrogenous (N) fertilizers to grain corn crops revealed that N dosages modulated by surface EC_a classes take into account only part of the soil variability that must be considered. The purpose of this study is to confirm deep (granulometry of parent materials and soil drainage) and shallow EC_a measurements in order to characterize and map maximum soil properties with a view to optimizing N in a variable rate application perspective. About 20 fields were mapped using a Veris 3100 EC (Veris Technologies Inc., Salina, KS). Hundreds of soil profiles in these fields were described and identified in soil series; some were sampled for horizons A, B and C to determine granulometry and texture. On several other sites in these fields, topsoil samples were taken to determine the granulometry, texture class and organic matter percentage of soils. The electrical conductivity data gathered with the Veris mapping machine were interpolated by kriging and mapped at a one-square-metre resolution with ArcGIS (ESRI, Redlands, CA). The databases were scrubbed of inconsistent data beforehand, and the spatial pattern was verified with the aid of semi-variogram modelling. Shallow (0-30 cm) and deep (0-90 cm) values were processed separately.  Deep apparent electrical conductivity (EC_a d) maps were sorted into six classes according to the granulometry of parent materials and drainage, ranging from 10 for well-drained skeletal materials to 60 for very fine clays with very-poor-to-poor drainage; shallow apparent electrical conductivity (EC_a s) maps were separated into five classes according to the texture of topsoil, ranging from 1 for coarse to 5 for fine to very fine textures. Both maps were then combined to create a new map (EC_a ds) with values ranging from 11 for skeletal materials with coarse surface texture to 65 for very fine clayey materials with a fine-to-very-fine surface texture. By studying the correlations between deep and shallow EC_a values and the data for the granulometry of parent materials, topsoil and soil drainage, it was possible to determine specific EC_a ds classes by soil type and surface texture (soil series and surface texture phases). It will thus be possible, using available soil maps for the Montérégie region of Quebec, to estimate EC_a classes and adjust the doses of post-emergence applications of N fertilizers to grain corn crops to match the variability of soils.