No abstract

The concentration and dynamic of soil trace metals in natural ecosystems, in particularly, is dependent on the lithology of parent rock as well as topography and geopedological processes. To ascertain more knowledge for this dependency, soils on three parent rocks involving peridotite, pegmatite, and dolerite in two contrasting topography aspects were investigated. The total values of Fe, Mn, Zn, Cu, and Ni were determined and compared for different soil pedons. ) [ dolerite (28 mg kg -1 ), respectively. For most of the studied pedons, profile metals distribution differed among the soils: The values of Fe, Cu, and Ni were enriched in the cambic horizons mainly as result of release, mobilization, and redistribution of the studied metals during geopedological processes, whereas those of Zn and Mn were concentrated in the surface horizons. Probably due to greater weathering rate of trace metal-bearing rocks on north-facing slope, the content of the trace metals along with the geoaccumulation index (I geo ) and the degree of soil contamination (C d ) were higher than on south-facing slope. Based on assessment of soil pollution indices, the soils were categorized as unpolluted [I geo B 0 (class 0)], unpolluted to moderately polluted levels [0 \ I geo \ 1 (class 1)], and very low [C d \ 1.5 (class 0)] to low degree of contamination [1.5 \ C d \ 2 (class 1)].

Multiple Linear Regression (MLR), Artificial Neural Network (ANN) and Rosetta model were employed to develop pedotransfers functions (PTFs) for soil moisture prediction using available soil properties for northern soils of Iran. The Rosetta model is based on ANN works in a hierarchical approach to predict water retention curves. For this purpose, 240 soil samples were selected from the south of Guilan province, Gilevan region, northern Iran. The data set was divided into two subsets for calibration and testing of the models. The general performance of PTFs was evaluated using coefficient of determination (R 2 ), root mean square error (RMSE) and mean biased error between the observed and predicted values. Results showed that ANN with two hidden layers, Tan-sigmoid and linear functions for hidden and output layers respectively, performed better than the others in predicting soil moisture. In the other hand, ANN can model non-linear functions and showed to perform better than MLR. After ANN, MLR had better accuracy than Rosetta. The developed PTFs resulted in more accurate estimation at matric potentials of 100, 300, 500, 1000, 1500 kPa. Whereas, Rosetta model resulted in slightly better estimation than derived PTFs at matric potentials of 33 kPa. This research can provide the scientific basis for the study of soil hydraulic properties and be helpful for the estimation of soil water retention in other places with similar conditions, too. Additional keywords: multiple linear regression; neural networks; pedotransfer function; Rosetta; soil moisture curve.

Optimum irrigation management is an important factor in precise agriculture. The main objective of this research is to compare different irrigation methods based upon a parametric evaluation system in an area of 41,200 ha in the Rasht region, north Iran. Once the soil properties were analyzed and evaluated, suitability maps were generated for surface, sprinkler and drip irrigation methods using a geographic information system and remote sensing. Parametric methods including Storie and Khidir, were used for land suitability evaluation to propose suitable irrigation system. Khidir method was more accurate than Storie method. The obtained results of khidir method showed that for 40,487.3 ha (98.3 %) of the study area the drip irrigation method was suitable. Capability ratings were the highest in all soil series for drip irrigation. The results demonstrated that by applying drip irrigation method instead of sprinkler and surface irrigation methods, the arability of 40,487.3 ha in the Rasht region would improve. The comparison of the different types of irrigation revealed that drip irrigation system was more effective and efficient than the surface and sprinkler irrigation methods and improved land suitability for irrigation purposes. It is of note, however, that the main limiting factors in using surface, sprinkler and drip irrigation methods in this area were drainage and soil texture.

Analysis and design of land-use management scenarios requires detailed soil data. The cation exchange capacity (CEC) of soil is a basic chemical property, as it has been approved that the spatial distribution of CEC is important for decisions concerning pollution prevention, crop and farming management. Since laboratory procedures for measuring CEC are cumbersome and time-consuming, it is essential to develop an indirect approach such as pedotransfer functions to predict this parameter from more readily available soil data. The aim of this study was to compare multiple linear regression, multiple non-linear regression, adaptive neuro-fuzzy inference system and artificial neural network including feed-forward back propagation (FFBP) model to develop PTFs for predicting paddy soils CEC in Guilan province, northern Iran. Two soil parameters including organic carbon and clay were considered as input variables for proposed models. 171 soil samples were used. The data set was divided into two subsets for calibration and testing of the models. The models prediction capability was evaluated by comparison with observed data through various descriptive statistical indicators include root mean square error, determination coefficient, mean bias error and relative improvement values. Results showed that the FFBP model had the most reliable prediction when compared with other models and that provide a new methodology with acceptable accuracy to estimate the CEC of soil that diminished the engineering effort, time and funds and can provide the scientific basis for the study of soil CEC and be helpful for the estimation of soil CEC in other places with similar conditions, too.