JWSS - Journal of Water and Soil Science

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Industrial, agricultural and urban activities have contaminated soil by heavy metals that can also increase concentration of the metals in food chains. This study was carried out in Isfahan province where lots of such activities are in progress. The purpose of this study was to determine spatial variability of Arsenic )As) in Isfahan soils. In this research, the soil samples )0-20 cm) were collected in a stratified random sampling system at about 4 Km intervals in a study area of 6800 Km2. The positions of samples were recorded using a GPS. After laboratory preparation, soil samples were measured for total As. Spatial structures of total As were determined by directional variograms. Spherical model was the best model to describe spatial variability of As. Mean-square error )MSE) and correlation coefficient were used to validate variograms. Distribution map for Arsenic was prepared using the obtained information from element by point kriging method and by using Surfer software. Interpolation in blocks by dimensions of 1000×1000 m was made. The mine effective factors with high concentration of As are parent material, and direction of dominant wind has affected the spread of As in north-west of the study area.

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This study is aimed to investigate the spatial variation of soil macronutrients such as phosphorus, potassium and organic matter using different methods of Geostatistics and Geostatistical method combined with Fuzzy logic to estimate the values of this element to provide a spatial distribution map for the proper distribution of fertilizer in the plain of Uremia. Spatial variations in soil nutrients are natural but knowing these changes for careful planning and management particularly in the agricultural lands is simply inevitable. This information is necessary to increase the profitability and sustainable agricultural management. Therefore, to estimate the changes in the elements of places not sampled, the Kriging, Fuzzy Kriging, Cokriging and Inverse Distance Weighting  methods have been used in GS +. In this study, Matlab 9.1 software was used to fuzzification of the data and GIS was used for the final mapping. The parameters MAE, MBE and RMSE were used to compare these methods. The results showed that the combined method of Fuzzy Geostatistic with the mean absolute error values for the elements phosphorus, potassium and organic matter i.e. 0.17, 0.18 and 0.18, respectively, is recognized as the preferred method based on which zoning maps have been prepared for P, K and OC in GIS.

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Salinity and alkalinity decreases physical, chemical and biological quality of soils and as a result reduces crop yield. This study aims to evaluate spatial variability of soil salinity in Ahvaz using geostatistical approaches. Accordingly, 69 surface soil samples (0-10 cm) were collected and their electrical conductivities (EC) were measured in 1:1 soil: water extracts. The data were then analyzed using ordinary kriging (OK), log-normal kriging (LOK) and indicator kriging (IK) interpolation techniques to produce soil salinity maps. Finally, the quality control of soil maps was performed by calculation of root mean square error (RMSE) and coefficient of determination (R²). The results indicated that due to the lowest RMSE and the highest R² values, the LOK interpolation method is the best approach in mapping soil salinity in Ahvaz. The results also illustrated that based on defined threshold values (4, 8, 16, and 32 dS m^-1) the indicator kriging methods have been able to show risk of soil salinity in the area. Based on this, most of the area is covered by soils with salinity higher than 4 dS m^-1. Evaluation of final soil maps showed that the highest concentrations of salts are related to the western and southwestern parts of Ahvaz city. In contrast, the lowest amounts of salinity were found in Eastern and Northern parts of the city.