In recent years, interpolation techniques have become a commonly used method in soil science. Inverse distance weighting (IDW), radial basis function (RBF), and Kriging techniques are widely used to determining the spatial distribution of particularly labor and effort-intensive analysis results. This present study was carried out in 256 ha of Atabey Plain where has been intensively used as agricultural activity. A total of 113 soil samples (0-20 cm) were collected on a 200 m-spaced grid, and it was determined for basic Physico-chemical properties in all soil samples. In order to determine the spatial distribution of soil moisture constant (Field capacity-FC, Wilting point-WP, and Available water capacity-AWC), deterministic and scholastic (Ordinary kriging-Spherical, Exponential, Gaussian and Cokriging) models were used. In addition, mean absolute error (MAE) and root-mean-square error (RMSE) were used to select and validate the best methods. Texture class of soils was determined as clay, clay loam, sandy clay loam, silty clay, silty clay loam in the study area. Soil organic matter content was generally found at low levels and lime content was at high levels. Soils have a slightly alkaline reaction and salinity problem is not observed. Field capacity, WP and AWC varied between 23.30-47.57, 12.09-29.50, 9.98-21.87 %, respectively. According to obtained results, it was found the Gaussian model of OK (RMSE: % 4.289; MAE: % 3.267) for FC as the best model while the most suitable model was Cokriging (RMSE: 3.187%; MAE: 2.450%) for WP. The lowest RMSE (1.421%) and MAE (1.115%) values were determined in IDW-1 for the available water contents of soils. As a result of the study, it was found that there are differences in the predictive accuracy of interpolation methods according to different soil properties.