Abstract The objective was to evaluate the spatial distribution of chemical and textural soil variables in a multistrata agroforestry system. A total of 73 georeferenced soil samples were collected at depths of 10-20 cm and 20-40 cm. The studied parameters were: pH H2O , potential acidity (H+Al), calcium (Ca 2+ ), magnesium (Mg 2+ ), aluminum (Al 3+ ), sodium (Na + ), potassium (K + ), phosphorus (P), organic carbon (C org ), cation exchange capacity (T-value), base saturation (V-value), total clay, total sand, and silt. Principal component analysis (PCA) was performed in R software using the FactoMineR and Factoextra packages. For variables with spatial dependence, ordinary kriging was performed using the best-fitted model. For variables without spatial dependence, inverse distance weighted (IDW) interpolation was applied (power = 2). The spherical model was the best fit for chemical attributes. IDW interpolation accurately mapped the textural attributes. It was concluded that geostatistics enabled a detailed analysis of chemical and textural attributes.