Apparent electrical conductivity (EC_a) of the soil profile can be used as an indirect indicator of a number of soil physical and chemical properties. Commercially available EC_a sensors can efficiently and inexpensively develop the spatially dense datasets desirable for describing within-field spatial soil variability in precision agriculture. The objective of this research was to relate EC_a data to measured soil properties across a wide range of soil types, management practices, and climatic conditions. Data were collected with a non-contact, electromagnetic induction-based EC_a sensor (Geonics EM38) and a coulter-based sensor (Veris 3100) on 12 fields in 6 states of the north-central United States. At 12–20 sampling sites in each field, 120-cm deep soil cores were obtained and used for soil property determination. Within individual fields, EM38 data collected in the vertical dipole orientation (0–150cm depth) and Veris 3100 deep (0–100cm depth) data were most highly correlated. Differences between EC_a sensors were more pronounced on more layered soils, such as the claypan soils of the Missouri fields, due to differences in depth-weighted sensor response curves. Correlations of EC_a with clay content and cation exchange capacity (CEC) were generally highest and most persistent across all fields and EC_a data types. Other soil properties (soil moisture, silt, sand, organic C, and paste EC) were strongly related to EC_a in some study fields but not in others. Regressions estimating clay and CEC as a function of EC_a across all study fields were reasonably accurate (r²≥0.55). Thus, it may be feasible to develop relationships between EC_a and clay and CEC that are applicable across a wide range of soil and climatic conditions.