Preprint Article Version 1 This version is not peer-reviewed

Estimation of Exchangeable Sodium Percentage with the Aid of Sodium Adsorption Ratio in Sedimentary Soils

Version 1 : Received: 5 April 2019 / Approved: 8 April 2019 / Online: 8 April 2019 (13:14:09 CEST)

How to cite: Niknamian, S. Estimation of Exchangeable Sodium Percentage with the Aid of Sodium Adsorption Ratio in Sedimentary Soils. Preprints 2019, 2019040101 (doi: 10.20944/preprints201904.0101.v1). Niknamian, S. Estimation of Exchangeable Sodium Percentage with the Aid of Sodium Adsorption Ratio in Sedimentary Soils. Preprints 2019, 2019040101 (doi: 10.20944/preprints201904.0101.v1).

Abstract

Soil salinity and sodicity are two main factors limiting plant growth in irrigated agricultural land. Sodium adsorption ratio (SAR) and exchangeable sodium percentage (ESP) are two different criteria as an index of soil sodicity and salinity. Various approximate relationships between ESP and SAR have been reported for soils in different regions of the world. Since there is possibility that these relationships change substantially with clay content, mineralogy, salinity of equilibrium solution, and saturation percentage of soils, it seems essential doing specific studies for different regions.  The purpose of this research was to i) find the relationship between ESP and SAR, and ii) estimate the ESP from SAR in alluvial soils of Sistan, the dry plain in east of Iran. Thus, 301 soil samples were collected from study area and  analyzed. The best linear and logarithmic equations found between ESP and SAR using Datafit software were ESP = 8.89 × ln(SAR1:1) + 14.04 and ESP = 8.73 × ln(SAR1:5) + 14.59, that ESP variation was justified 78% and 76%, respectively. Then, the multi-layer perceptron neural network (MLP) and ANFIS system performance were investigated in order to estimate ESP. Results showed superior performance of MLP and ANFIS compared with the regression models. ESP estimation from SAR1:1 using ANFIS was more accurate than other models (coefficient of determination and root mean square error values were 0.99 and 0.014, respectively). These results indicate the superiority of the intelligent models in order to explain the relationship between ESP and SAR over  linear and non-linear regression equations.

Subject Areas

Soil salinity, Soil sodocity, Regression equations, SAR, ESP, MLP, ANFIS

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