The goal of this research was to develop a procedure to minimize the cost of soil survey optimizing ARP© (Automatic Resistivity Profiling) deployment and selecting the best placement of the sampling sites to employ for soil profile description and analysis.In this respect, devoted tests were conducted in a 3.5 ha vineyard located in Tuscany (central Italy). ARP© produced close-spaced measurements (2335 points) of geo-referenced values of apparent electrical resistivity (ERa) related approximately to 0.5 m depth. A fast soil surface sampling (0.1–0.3 m depth) was contemporarily carried out for analyzing moisture, particle size distribution and electrical conductivity. Relationships between soil properties, elevation and ERa data were analyzed along with a comparative investigation about the cost for soil description, analysis and ARP survey.The best correlated soil property (clay) to ERa was then employed for evaluating its predictability starting from different combinations of reduced ARP measurements and sampling sites chosen by regression-driven method and the ESAP (ECe Sampling, Assessment and Prediction) software.It was noticed that the reduction of the soil sample number affects clay map predictability less than the decrease of ARP survey intensity. The regression approach provided higher clay predictability than ESAP for the densest ARP survey and loosest soil sampling.Such a procedure can be applied to fields once the geoelectrical calibration phase is performed. Given that the study case can be considered representative of many Mediterranean viticulture districts, we are confident that the methodology can be widely used. These findings indicate that ARP on-the-go sensor can fruitfully support traditional soil investigation, allowing the cost reduction for sampling and laboratory analyses.