عنوان مقاله [English]
Introduction: Salinity and sodicity are the most important land degradation problems particularly in arid and semi-arid regions. Due to the depletion of Urmia Lake located in the northwest of Iran during recent years, the proportion of surrounding saline agricultural lands increased at a past pace. In the salt-affected soils, aggregate stability is weak due to the high contents of sodium. The analysis of spatial variability of mean weight diameter of aggregates (MWD) and sodium adsorption ratio (SAR) is necessary to implement a site-specific soil management especially in the salt-affected soils. The main object of this study was evaluating the effects of different land uses (bare and agriculture) on the spatial variability of MWD and SAR in the salt-affected soils around Urmia Lake.
Materials and Methods: This study was conducted in the agricultural and bare lands of Shend Abad region located at the 15 km of Shabestar city, northwest of Iran (45° 36ʹ 34ʺ E and 38° 6ʹ 37ʺ N). Totally, 100 geo-referenced samples were taken from 0-10 cm soil depth with 100×100 m intervals (80 ha) in agricultural (n=49) and bare (n=51) land uses. Sand, silt, clay, organic carbon (OC), CaCO3, pHe, MWD, SAR and electrical conductivity (EC), were measured in the collected soil samples. Thewet sieving method was used to determine MWD of wet aggregates. The sieves were: 2, 1, 0.5, 0.25 and 0.106mm. The EC and SAR were measured in 1:2.5 (soil: distilled water) extra. The SAR was calculated from concentrations of Na+ and Ca+ + Mg+. The best fit semivariogram model (Gaussian, spherical and exponential) was chosen by considering the minimum residual sum of square (RSS) and maximum determination coefficient (R2). Ordinary kriging (OK) and inverse distance weighting (IDW) interpolation methods were used to analyze spatial variability of MWD and SAR. Spatial distribution maps of soil variables were provided by Arc GIS software. The accuracy of OK and IDW methods in estimating MWD and SAR was evaluated by mean error (ME), mean absolute error (MAE), root mean square error (RMSE) and concordance correlation coefficient (CCC) criteria. The CCC indicates the degree to which pairs of the measured and estimated parameter value fall on the 45° line through the origin.
Results and Discussion: According to the results of coefficient of variation (CV) from the study area, the most variable (CV=113.05%) soil indicator was SAR (bare land use), whereas the least variable (CV= 3.52%) was pHe (agricultural land use). The Pearson correlation coefficients (r value) indicated that there are significant (P