Design and analysis of land-use management scenarios requires detailed soil data. When such data are needed on a large scale, pedotransfer functions (PTFs) could be used to estimate different soil properties. Because existing regression-based PTFs for estimating cation exchange capacity (CEC) do not, in general, apply well to arid areas, this study was conducted (i) to evaluate the existing models and (ii) to develop neural network-based PTFs for predicting CEC in Aridisols of Isfahan in central Iran. As most researches have found a significant correlation between CEC and soil organic matter content (OM) and clay content, we also used these two variables for modelling of CEC. We tested several published PTFs and developed two neural network algorithms using multilayer perceptron and general regression neural networks based on a set of 170 soil samples. The data set was divided into two subsets for calibration and testing of the models. In general, the neural network-based models provided more reliable predictions than the regression-based PTFs.

Arid soils in central Iran that evolved from the weathering of post-Tethyan sediments contain palygorskite. Clay fractions of gypsiferous soils and their associated sediments from different landforms from central Iran were investigated. Palygorskite was the dominant silicate clay mineral in clay fractions of the soils, and of Oligo-Miocene limestone, a less common parent rock. The Jurassic shale and Cretaceous limestone contain illite and chlorite with a trace amount of palygorskite. Association of large amounts of palygorskite bundles with gypsum in the gypsiferous soils studied, supports the hypothesis that palygorskite was probably formed after the initial precipitation of gypsum, that created a high pH and Mg/Ca ratio. The major portion of the palygorskite present in colluvial and plateau soils was probably formed authigenically when central Iran was covered by post Tethyan shallow hyper-saline lagoons. Palygorskite in alluvial soils appeared to be essentially detrital.

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