Precision Agriculture (PA) can be described as a set of tools and techniques applied to agriculture in order to enable localized production management, considering the spatial and temporal variability of crop fields. Among the numerous existing tools, one of the most important ones is the use of geostatistics, whose main objective is the description of spatial patterns and estimation data in non-sampled places. Nowadays, one of the most limiting factors to the use of PA is the number of samples required to represent the spatial soil attributes. Within this context, multivariate geostatistics emerges as a promising technique for mapping and quantification of soil attributes. One of the techniques, which minimize the number of samples needed, is the use of maps obtained by soil sensors equipment to identify points for sampling. The objective of this study was to map the spatial variability of chemical and physical soil properties, using a reduced number of samples, and applying kriging with external drift (KED) based on maps of apparent soil electrical conductivity (ECa). Samples were taken on a regular grid georeferenced at two depths. ECa soil readings in the whole area were made by means of a direct contact sensor. The results indicate that it is possible to obtain maps with acceptable precision in the spatial distribution of chemical (CEC, BS, SEB, K and pH) and physical attributes (clay) of soil from of 20 sampling points (0.4 samples ha^-1) determined based on the ECa. The methodology used to obtain the maps of spatial variability of chemical and physical soil properties indicate that it is possible to predict, with acceptable accuracy, maps that can be used for fertilizer recommendation at variable rate. This approach opens new possibilities for other important agronomical attributes that can be estimated over large areas from a small number of samples, assisting farmers in crop management.