Optimized management of water resources, conservation and their quality increase is needful with data existence in basis of situation, amount and distribution of water chemical factors for example; electrical conductivity (EC) in determined geographical region. Accuracy of interpolation appropriate methods and variation map preparation of groundwater quality variables is independent to region conditions and existence of enough data. That is true selection of interpolation methods is basic and important step in management of groundwater resources. EC is one of the important indicators for groundwater quality evaluation. The objective of this research was to determine the most suitable interpolation method and their accuracy for analysis and checking spatial variation of groundwater EC amount in central regions of Guilan province, northern Iran. This investigation evaluated the inverse distance weighting (IDW), global polynomial interpolation (GPI), local polynomial interpolation (LPI), radial basis function (RBF) and ordinary kriging (OK) methods for estimation of groundwater EC in paddy fields. In IDW method, for variable estimation used power value 1-5 that power value equal 1 was exact. Gaussian model was the best one fitted on empirical semivariogram of variable data in OK method. Standard statistical performance evaluation criteria include root mean square error (RMSE), correlation coefficient (R) and mean absolute error (MAE) were used to control the accuracy of the prediction capability of the developed methods. Results showed that the best estimator was OK method which was the most exact with regard to other methods for estimation groundwater electrical conductivity.
In spite of many studies that have been carried out, there is a knowledge-gap as to how different sizes of biochars alter soil properties. Therefore, the main objective of this study was to investigate the effects of different sizes of biochars on soil properties. The biochars were produced at two pyrolysis temperatures (350 and 550°C) from two feedstocks (rice husk and apple wood chips). Produced biochars were prepared at two diameters (1-2 mm and <1 mm) and mixed with soil at a rate of 2% (w/w). Multiple effects of type, temperature and size of biochars were significant, so as the mixture of soil and finer woodchip biochars produced at 550°C had significant effects on all soil properties. Soil aggregation and stabilization of macro-aggregates, values of mean weight diameter and water stable aggregates were improved due to increased soil organic matter as binding agents and microbial biomass. In addition, plant available water capacity, air capacity, S-index, meso-pores and water retention content were significantly increased compared to control. But, saturated hydraulic conductivity (Ks) was reduced due to blockage of pores by biochar particles, reduction of pore throat size and available space for flow and also, high field capacity of biochars. So, application of biochar to soil, especially the finest particles of high-tempered woody biochars, can improve physical and hydrological properties of coarse-textured soils and reduce their water drainage by modification of Ks.
Since particle size distribution (PSD) is a fundamental soil physical property, so determination of its accurate and continuous curve is important. Many models have been introduced to describe PSD curve, but their fitting capability in different textural groups have been rarely investigated. The aim of this study was to evaluate the fitting ability of 15 models on 2653 soil samples from 13 province of Iran, and to determine the best model among them for the PSD of all soil samples as well as for each soil textural group based on evaluation criteria. Results showed that the Weibull model was the most accurate model for all soil samples as well as for the clayey and loamy groups. After the Weibull, Fredlund, Rosin-Rammler and van Genuchten were the most accurate models. However, their differences were not significant (p B 0.05). Also, for the coarse texture group the S-shape model showed the better fit than the others. These results showed the performance of a particular model varies with the soil textural characteristics.
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.
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