This study attempted to characterize the spatial distributions of soil pH and electrical conductivity (EC_e) of coastal fields in the Miyandoroud region, northern Iran, for three soil layer depths by assessing spatial variability and comparing different interpolation techniques such as inverse distance weighting (IDW), ordinary kriging (OK), and conditional simulations (CS). Three soil composite samples were collected from 0–50, 50–100, and 100–150 cm depths at 105 sampling sites. At all three soil depths, pH and EC_e were best fitted by exponential and spherical models, respectively. Nugget effects were higher for soil EC_e data sets compared with soil pH at all three soil depths showing soil EC_e had a spatial variability in small distances. The prediction accuracy of the interpolation methods indicated that the minimum error for all data sets was achieved with the OK method, except for pH at 50–100 cm depth, and the CS technique revealed the largest error. The effect of different numbers of simulations (100, 500 and 1000) in the CS interpolation method resulted not in a realistic mapping for the soil EC_e and pH. Considering the high importance of irrigated agriculture in the Caspian Sea coastal areas, more subsoil salinity build-up and groundwater salinity monitoring plans are needed as a prerequisite for sustainable agricultural production systems of the future.

Vulnerability of soil separates to detachment by water is described as soil erodibility by Universal Soil Loss Equation which can be affected by land use change. In this study it was attempted to quantify the changes of Universal Soil Loss Equation K-factor and its soil driving factors in three land uses including rangeland, rainfed farming, and orchards in Babolrood watershed, northern Iran. Soil composite samples were obtained from two layers in three land uses, and the related soil physico-chemical properties were measured. The rainfed farming land use showed the highest clay contents, but the highest amounts of soil organic matter and sand particles were found in orchard land use. The high intensity of tillage led to the significant decrease of soil aggregate stability and permeability in the rainfed farming land use. The Universal Soil Loss Equation K-factor was negatively correlated with soil permeability (r = -0.77**). In rangeland, the K-factor (0.045 Mg h/MJ/mm) was significantly higher and the particle size distribution had a great impact on the K-factor. The orchard land use, converted from the rangeland, did not show any increase of soils erodibility and can potentially be introduced as a good alternative land use in sloping areas. However, more detailed studies on environmental, social and economic aspects of this land use are needed.

Due to the time and expenses, essential for sampling, and the inaccessibility of different parts of the land in the world, the use of geostatistical methods and artificial neural network (ANN), which can predict the spatial variability of SOM and soil organic carbon (SOC) is of outmost significance. The present research investigated the effects of different land use type including cultivated and uncultivated land, rangeland, forestry, and orchard at different altitudes (-20 to >500 m) on the spatial variability of SOM and SOC using the above-mentioned methods. A total of 249 combined samples from the depth of 0-15 cm (using GPS) on the basis of land use type and topography were collected from different parts of the research area (9545 km²). Using cross validation, the methods were compared and the best fitted one was selected on the basis of mean error (ME) and root mean square error (RMSE). The best-fitted semivariogram models for SOM (R²= 0.894) and SOC (R²= 0.761) were spherical. The cross-validation method indicated ANN as the most accurate method for the prediction of SOM and SOC with the ME and RMSE of 0.37 and 0.36 for SOM, and 0.37 and 0.35 for SOC, respectively. The comparison of different land types indicated that uncultivated land (1.37%) and forestry (3.94%) had the least and the highest SOC, respectively. Altitude also significantly affected SOC, and A500 and A0 resulted in the highest and least SOC, respectively. The use of ANN for the estimation of SOM and SOC is recommendable.