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Traditional sampling methods are inadequate for assessing the interrelated physical, chemical, and biological soil properties responsible for variations in agronomic yield and ecological potentials across a landscape. Recent advances in computers, global positioning systems, and large-scale sensors offer new opportunities for mapping heterogeneous patterns in soil condition. We evaluated field-scale apparent electrical conductivity (ECa) mapping for delineating soil properties correlated with productivity and ecological properties. A contiguous section of farmland (250 ha), managed as eight fields in a no-till winter wheat (Triticum aestivum L.)–corn (Zea mays L.)–millet (Panicum miliaceum L.)–fallow rotation, was ECa mapped (≈0- to 30-cm depth). A geo-referenced soil-sampling scheme separated each field into four ECa classes that were sampled (0- to 7.5- and 7.5- to 30-cm depths) in triplicate. Soil physical parameters (bulk density, moisture content, and percentage clay), chemical parameters (total and particulate organic matter [POM], total C and N, extract- able P, laboratory-measured electrical conductivity [EC1:1], and pH), biological parameters (microbial biomass C [MBC] and N [MBN], and potentially mineralizable N), and surface residue mass were significantly different among ECa classes (P ≤ 0.06) at one or both depths (0–7.5 and 0–30 cm). Bulk density, percentage clay, EC1:1, and pH were positively correlated with ECa; all other soil parameters and surface residue mass were negatively correlated. Field-scale ECa classification delimits distinct zones of soil condition, providing an effective basis for soil sampling. Potential uses include assessing temporal impacts of management on soil condition and managing spatial variation in soil-condition and yield-potential through precision agriculture and site-specific management.