Agricultural Research,Education and Extension Organization Iranian Journal of Soil Research 2228-7124 28 2 2014 08 23 Use of Fractal Parameters of Particles and Micro-Aggregate Size Distributions for Estimation of Saturated Hydraulic Conductivity in Soils of Guilan Province Use of Fractal Parameters of Particles and Micro-Aggregate Size Distributions for Estimation of Saturated Hydraulic Conductivity in Soils of Guilan Province 447 458 126211 10.22092/ijsr.2014.126211 FA H. Bayat Azadeh Sedaghat M. Sc. student of Bu Ali Sina University Naser Davatgar Assistant Professor, Rice Research Institute of Iran Ali Akbar Safari Sinegani Professor, Bu Ali Sina University Journal Article 2012 09 09 Soil saturated hydraulic conductivity (K_s) is one of the most important soil physical properties. Its direct measurements is difficult, expensive, and time consuming because of spatial and temporal variability. Therefore, pedotransfer functions have been used to estimate K_s. The purpose of this study was to improve the estimation of <em>K_s</em> using fractal parameters of particle and micro-aggregate size distributions and to compare their efficiency with the structural parameters at six stages in the estimation of K_s. As a matter of fact, one pedotransfer function was developed in each stage with different input variables. In this study, 260 soil samples were taken from different parts of Guilan province, Iran. Particle and micro-aggregate size distributions (0-2 mm) were measured and fractal model of Bird and Perrier (2003) was fitted to them and their parameters were calculated. Significant correlations (P < 0.01) were found between K_s and fractal parameters of particles and micro-aggregates. Estimation of K_s was improved and root mean square error (RMSE) decreased significantly by using fractal parameters of soil particles and micro-aggregates as predictors. Using geometric mean diameters of soil aggregates at the stage four improved K_s estimations significantly, but, using geometric mean and standard deviation of soil particles at the stage five did not improve <em>K_s</em> estimations significantly. Using fractal parameters of particles and micro-aggregates, simultaneously, at the stage six, decreased RMSE considerably and had the highest effect on the estimation of K_s. Generally, fractal parameters may be successfully used as input parameters to improve the estimates of K_s by the pedotransfer functions. Soil saturated hydraulic conductivity (K_s) is one of the most important soil physical properties. Its direct measurements is difficult, expensive, and time consuming because of spatial and temporal variability. Therefore, pedotransfer functions have been used to estimate K_s. The purpose of this study was to improve the estimation of <em>K_s</em> using fractal parameters of particle and micro-aggregate size distributions and to compare their efficiency with the structural parameters at six stages in the estimation of K_s. As a matter of fact, one pedotransfer function was developed in each stage with different input variables. In this study, 260 soil samples were taken from different parts of Guilan province, Iran. Particle and micro-aggregate size distributions (0-2 mm) were measured and fractal model of Bird and Perrier (2003) was fitted to them and their parameters were calculated. Significant correlations (P < 0.01) were found between K_s and fractal parameters of particles and micro-aggregates. Estimation of K_s was improved and root mean square error (RMSE) decreased significantly by using fractal parameters of soil particles and micro-aggregates as predictors. Using geometric mean diameters of soil aggregates at the stage four improved K_s estimations significantly, but, using geometric mean and standard deviation of soil particles at the stage five did not improve <em>K_s</em> estimations significantly. Using fractal parameters of particles and micro-aggregates, simultaneously, at the stage six, decreased RMSE considerably and had the highest effect on the estimation of K_s. Generally, fractal parameters may be successfully used as input parameters to improve the estimates of K_s by the pedotransfer functions. https://srjournal.areeo.ac.ir/article_126211_7eee2d0a686d63b4cdda7d18a1dfd250.pdf