One important tool within the technological precision agriculture (PA) package are the apparent electrical conductivity (ECa) sensors. This kind of sensor shows the ability in mapping soil physicochemical variability quickly, with high resolution and at low cost. However, the adoption of this technology in Brazil is not usual, particularly on sugarcane fields. A major issue for farmers is the applicability of ECa, how to convert ECa data in knowledge that may assist the producer in decision-making for crop management. The objective of this study was to map the ECa by three different sensors, commercially available, to assess their relations with soil properties in order to help farmers in selecting the proper device for soil characterization. Soil ECa data was collected in a sugarcane field (100 ha) with three sensors, two based on resistivity principle (ARP^® and Veris 3100^®) and other based in electromagnetic induction (EM38-MK2^®). Thirty-four soil samples were collected (≈1 sample each 3.0 ha) at two depths. This approach sough to determine the correlation between sensors and soil properties in strategic places of the field, aiming to determine which sensor brings more reliable information about soil fertility. Results show the ECa present significant correlations with many soil properties, where the electromagnetic induction (EMI) sensor presented the highest correlation with clay content (r = 0.83), Organic Matter (r = 0.73), Cu (r = 0.87) and Mn (r = 0.77). The variables that represent main soil properties by principal component analysis (PCA) showed that the EMI sensor showed the greatest potential for physicochemical characterization of soil spatial variability. One reason for the difference between correlations can be explained by the sensors sensibility depth, but further investigations should be carry on seeking to explain with more details the difference among the information provided by the sensors. Through ECa using of commercial available sensors, it is possible to assess soil spatial variability, making it a powerful tool for farmers in a decision-making.