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In the present study, spatial distribution model of iron (Fe), zinc (Zn), copper (Cu), and manganese (Mn) in soil and leaves of Dezful orange trees was simulated using Gaussian model and geographic information system (GIS) in 2019. Totally, 130 points were determined using global positioning system (GPS) and soil and leaf sampling was performed. Concentration of elements was measured by dry digestion and read by atomic absorption spectrophotometer. After initial statistical analysis of the data, level of correlation between variables measured in soil and leaves was calculated using Pearson's correlation coefficient test. Location of sampling points in Gaussian model was simulated by R software (Ricker’s Model). Data interpolation was performed using simple Kriging and Kernel methods, and interpolation accuracy was evaluated using baseline analysis of variance. Results of the study showed that mean squared error was equal to 0.153, 0.203, 0.145, and 0.161, respectively, for Fe, Zn, Cu, and Mn in soil using simple Kriging; and it was equal to 0.166, 0.153, 0.129, and 0.16, respectively, using Kernel method; and also mean squared error was obtained as 0.131, 0.158, 0.167, and 0.129, respectively, for leaf samples of orange trees using simple Kriging; and it was obtained as 0.229, 0.160, 0.14, and 0.17, respectively, using Kernel method. Finally, the values simulated by the model were implemented by GIS software. Results of semi-variogram obtained using Gaussian model showed that Gaussian statistics were suitable for modeling these elements in soil and leaf samples of orange trees. Our findings revealed that the model has good accuracy in predicting spatial distribution of these elements, and the current research has practical implications since, the model enables identification of areas with deficiencies and can be used in recommending fertilizer for the study area and similar areas.

Simulation of Spatial Distribution Model of Some Micronutrients in Soil and Orange Tree Leaves Using Gaussian Model and GIS