JWSS - Journal of Water and Soil Science

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Knowledge of the spatial dependency of soil properties, sensitive to grazing systems, is important from an ecosystem protection point of view. In the current study, geostatistical methods and fractal concepts have been used in order to characterize the impact of long-term grazing exclusion on the spatial variability of some soil chemical parameters including organic matter, total nitrogen, available phosphorous, and available potassium in both ungrazed and grazed sites located in Sabzkuh region, Chaharmahal Bakhtiari Province. The spatial pattern of different variables was studied using variogram. The results indicate that spatial variability of soil parameters depends upon grazing history. The variogram of organic matter in grazed areas shows a linear behavior without reaching the sill variance while organic matter in the ungrazed area established a strong spatial structure. Moreover, available phosphorous in the grazed area had pure nugget variation. Variograms of total nitrogen and available potassium in both grazed and ungrazed areas showed spatial structure approximated by spherical model. However, the range of variograms in exclusion sites was twice that of the grazed area. The results of applying the fractal theory show that soil properties have fractional behavior since increasing the scale of study reveals more details. Moreover, calculated fractal dimension values from variograms of different variables were close to 2.0 although this index was usually greater in the grazed area than in the exclusion sites. The calculated fractal dimensions can be used as a proper indicator of describing the pattern of spatial variability and its complexity.

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Pedotransfer functions are the predictive models of a certain soil property from other easily, routinely, or cheaply measured properties. The common approach for fitting the pedotransfer functions is the use of the conventional statistical regression method. Such an approach is heavily based on the crisp obervations and also the crisp relations among variables. In the modeling natural systems, like soil, we are dealing with imprecise observations and the vague relations among the variables. Therefore, we need an appropriate algorithm for modeling such a fuzzy structures. In the present study, the fuzzy regression approach was used in order to fit some chemical and physical pedotransfer functions. The optimum regression models with the fuzzy coefficients were obtained for modeling pedotransfer functions. Sensivity analysis was carried out by using the credibility level. The results indicated that the fuzzy regression might be considered, as a suitable alternative or a complement to the statistical regression, whenever a relationship between variables is imprecise and generally when dealing with the errors due to a vaguness in regression models.

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Over-grazing may induce changes in the dynamics of plant residue carbon and soil organic carbon (SOC). The objective of this study was to evaluate the litter quality of three dominant pasture species, and the relationship between litter quality and C dynamics under different range managements in native rangelands of SabzKou. Aboveground litters from three dominant species including, Agropyron intermedium (AP), Hordeum bulbosum (HB) and Juncus stenophylla (JU) were collected in an area protected from grazing for 15 years. Concentrations of N, P and K in litter samples were determined. Litter decomposition was also studied by using a litter bag experiment. Bagged litters were incubated under field conditions at grazed and ungrazed sites for one year. Concentrations of N in litters were 1.37, 1.36 and 0.98, respectively, for AP, JU and HB. Assuming 50 % C in the litter, C/N ratios of litters were 37.2, 37.3 and 51.1, respectively, for AP, JU and HB. The litters of AP, JU and HB contained, respectively, 0.13,0.12 and 0.21 % of P, and 1.04, 1.01 and 1.72 % of K. Results indicate no significant (p>0.05) and consistent difference in litter decomposition rates between grazed and ungrazed areas. The three pasture species, however, showed significant (p<0.01) differences in litter decomposition rate. It is appearing that the trend in litter decomposition of these plant species correlates initially with litter C/N ratio, but other quality parameters of litter as well as soil environmental conditions would likely affect the litter decomposability in advanced stages of decomposition process.

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In order to achieve a sustainable management of land resources and to improve land quality, quantitative assessment of effective factors and soil quality indicators are required. The aim of this study was to evaluate variability of selected soil quality attributes in central Zagros affected by such factors as region, land use and management practices. Twelve sites were selected in three provinces including Chahar Mahal va Backtiari (Sabzku, Broujen), Isfahan (Semirum), and Kohkeloyeh va Boyerahmad (Yasodje). Different management practices were considered such as: protected pasture, intensive grazing, controlled grazing, dryland farming, irrigated wheat cultivation, legume-farming practice, protected forest, and degraded forest. Systematic sampling with taking 50 samples of surface soil in each site was carried out. The results of univariate and multivariate analysis revealed that all factors significantly influenced the spatial variability of selected soil quality attributes namely phosphatase activity, microbial respiration, soil organic matter, and total nitrogen. The results obtained from discriminant analysis indicated that all selected soil quality parameters could significantly be used as soil quality indicators in order to recognize and discriminate sustainable agricultural and forestry ecosystems and/or optimal management practices.

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Uprooting trees and pit – mound micro topographies are due to creation of heterogeneity condition in soil ecosystem. Pay attention to mountainous condition of hyrcanian forests in Iran and presence of trees with high diameters (old trees), therefore, it is imagined that many of trees are prone to uprooting. Thus, the aim of this research is studying variability some of soil physico - chemical characters in uprooted trees location and pit – mound position. For this purpose, twenty hectare areas of Tarbiat Modares University Experimental Forest Station studied that is located in Mazandaran province, northern Iran. Numbers of thirty four uprooted trees were found. Five microsites were distinguished including mound top, mound wall, pit bottom, pit wall and closed canopy. Soil samples were taken at 0 - 15, 15 - 30 and 30 - 45cm depths from all microsites using core soil sampler (81cm2 cross section). Soil acidity, water content, organic matter, total nitrogen and carbon to nitrogen ratio measured in the laboratory. A statistical result is indicating that the most water content, organic matter and nitrogen devoted in pit bottom. Mound top and wall included maximum of acidity. The most value of carbon to nitrogen ratio found in mound wall, as well. Soil acidity showed no significantly differences among different depths. The results of this research can be considered in forest soils survey and forest management.

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Saturated hydraulic conductivity (Kfs) and macroscopic capillary length of soil pores are important hydraulic properties for water flow and solute transport modeling. Measuring these parameters is tedious, time consuming and expensive. One way is using indirect methods such as Pedotransfer functions (PTFs). The objective of this research was to develop some PTFs for estimating saturated hydraulic conductivity and inverse of macroscopic capillary length parameters (*). Therefore, the coefficients, Kfs and * from 60 points of Azadegan plain in Shahrekord were measured using single ring and multiple constant head method. Also, some of the readily available soil parameters from the two first pedogenic layers of the soils were obtained. Then, the desired PTFs were developed using stepwise multiple linear regression. The accuracy and reliability of the derived PTFs were evaluated using root mean square error (RMSE), mean error (ME), relative error (RE) and Pearson correlation coefficient (r). The highest correlation coefficients of 0.92 and 0.72 were found between Kfs-bulk density and *-bulk density, respectively. There was no significant correlation between soil particle size distribution and Kfs and *. This can be related to the fact that most of the soil samples were similar in texture and macro pores. The most efficient PTFs in predicting Kfs and * could explain 85 and 66 percent of the variability of these parameters, respectively. All the derived PTFs underestimated the Kfs and * parameters.

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To investigate spatial variability of soil characteristics in the most valuble forest stands in the northern Iran using geostatistical approach, a twenty hectare area in Experimental Forest Station of Tarbiat Modares University was considered. Soil samples were taken from pits, mounds, canopy gaps, under single trees and closed canopy positions at 0 - 15, 15 - 30 and 30 - 45 cm depths using core soil sampler (81cm2 cross section). pH and organic matter were measured in the laboratory, and then carbons to nitrogen ratio and carbon sequestration were calculated. Spatial variability for soil characteristics revealed anisotropic variogram due to the same variability of surface variogram in different depths. pH in all the depths and carbon sequestration in 30 - 45cm depth showed exponential model whereas the other soil characteristics resulted in a linear model in different layers. The results of spatial structure showed pH with medium structure in all the depths, organic matter and carbon to nitrogen ratio with pure nugget effect (non structure), carbon sequestration in 0 - 30cm depth with weak structure and 30 - 45cm depth with medium spatial structure.

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Discrete Models of Spatial Variability (DMSV) have limitations for soil identification in traditional soil maps. New approaches, generally called digital soil mapping (DSM), using continuous methods (CMSV), try to predict soil classes or soil properties based on easily-available environmental variables. The objective of this study was to map the soil classes of the Borujen area, Chaharmahal-va-Bakhtiari province, using digital elevation model (DEM) and its attributes and Soil-Land Inference Model (SoLIM). To do this, eighteen terrain attributes were derived from the DEM of the area. The primary analysis showed seven attributes are the most important derivatives. These derivatives as well as three dominant soil subgroups and seven soil families of the region (41 profiles from 125 profiles) were used to construct the input data matrix of the model. Then, output fuzzy soil maps of SoLIM were converted to polygonal soil map, using ArcGIS. Results showed that different combinations of DEM attributes have different accuracy rates for soil prediction. The accuracy of the interpolation was twice that of the extrapolation. Although SoLIM had an acceptable accuracy for soil nomination, and identification of soil map units’ types, it did not have enough accuracy for the location of soil classes. It seems that using other data like parent material and geomorphic surface maps will increase the accuracy of the model prediction.

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This research was carried out to determine spatial distribution of heavy metals concentration in soil particle size classes using Landsat ETM+ reflectance in Southern Isfahan city in the vicinity of Bama mine. To fulfill this goal, 100 compound soil surface samples were collected randomly from the area. The samples were air dried and soil particle size classes 250-500, 125-250, 75-125, 50-75 and <50 μm were determined using appropriate sieves after dispersion of the bulk samples of soil using ultrasonic apparatus. Total Zn, Pb and Cd concentrations were measured using Atomic Absorption Spectrophotometer after wet digestion of samples in acid nitric. The results indicated significant negative correlation coefficients between heavy metals concentrations of soil particle size classes and soil spectral reflectance in the visible, near infrared and panchromatic bands of Landsat ETM+ satellite. Stepwise multiple regression models were used for estimating heavy metals concentration in soil particle classes through satellite data. Furthermore, spatial distributions of heavy metals were mapped using stepwise multiple regression equations. Results also showed heavy metals concentrations in all soil particle size classes were maximum close to the mines and decreased by increasing the distance from these sources.

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Rhizosphere is a small zone and has quite different chemical, physical, and biological properties from bulk soil. This research was performed to study the available Zn and its fractions in the wheat rhizosphere and bulk soils by using rhizobox at greenhouse conditions. Total organic carbon (TOC), dissolved organic carbon (DOC), microbial biomass carbon (MBC) and available Zn using 7 chemical procedures and Zn-fractions were determined in the rhizosphere and bulk soils. The results indicated that TOC, DOC and MBC in the rhizosphere were increased significantly (P<0.01). Zn extracted using several chemical extractants (except 0.01 M CaCl2) in the rhizosphere were significantly (p<0.05) higher than in the bulk soils. Also, concentrations of Zn-fractions (except carbonate-associated) in the rhizosphere were significantly (p<0.05) different from concentrations of Zn-fractions in the bulk soils. Results indicated that correlation between uptake index and extracted Zn using DTPA-TEA and Mehlich 3 were significant (p<0.05). Moreover, significant correlation (p<0.05) between uptake index and exchangeable Zn and Zn associated with iron-manganese in the wheat rhizosphere and bulk soils were found. The results of this research illustrated that available Zn and its fractions in the rhizosphere soils are different from the bulk soils. Therefore, application of the rhizosphere soil would be recommended in the study on these properties after planting.