جستجوی مقالات مرتبط با کلیدواژه « Spatial variability » در نشریات گروه « آب و خاک »

Determination of the physico-chemical characteristics of soil for sustainable agriculture on large scales is an important factor in achieving a precision agriculture. Different land use and management practices greatly impact soil properties, and knowledge of the variation in soil properties within different land uses, is essential in determining production constraints related to soil characteristics. Laboratory analyses of the soil properties are usually expensive and time consuming. Surmounting these problems is possible using geostatistics. This study was conducted to assess geostatistical methods for the spatial distribution of some soil properties of Darengan region with different land uses in Fars province. 134 surface soil samples at an interval of 1.0 × 1.0 km on a grid design were taken from pasture and agricultural land uses. Physico-chemical characteristics of the soil samples were analyzed. According to the results, the best spatial structure model with the highest accuracy was exponential model for the variables of sand, EC, CCE, pH, and BD, rational quadratic model for silt, and spherical model for clay, OC, and CEC. The spatial structure was weak for CCE, medium for organic carbon, and strong for the other variables. Among the characteristics studied, the variables of silt, clay and cation exchange capacity have the lowest range, and EC has the highest range. Based on the zoning map of the studied properties, the areas with agricultural land use had greater OC, clay, CEC, EC and lower pH. Understanding soil properties with their spatial dependency is of crucial importance for understanding the behavior of soil and hence providing better soil management.

Lack of awareness of changes in soil content of nutrient elements in different farms and applying the same fertilizer recommendations cause some soils to receive more, and some less, than the required fertilizer. The aim of this study was to investigate the spatial changes and status of macro elements including N, P and K and some important soil properties such as OC, EC, and pH in some paddy fields of Gilan Province. To this end, in 2018, before preparing the land for cultivation, composite soil sampling was performed from 0 to 30 cm depth of paddy fields in Masal, Rudbar, Meqdad (Khoshkbijar, Lashtnashah, Khomam) and Siahkal areas, with spacings of at least 500 m. Using the results of soil analysis, status of the desired characteristics was evaluated based on descriptive statistics. Then, excess and deficiency of nutrients in paddy lands were determined. Data analysis showed that  available phosphorus was one of the major limitations in the study areas, such that more than 80% of the sampled soils in Siahkal, 72% in Rudbar, 65% in Masal, and 54% in Meqdad were deficient in P. Potassium had better conditions than P, such that 51.3% of the soils in Siahkal area, 31% in Rudbar, 66% in Masal, and 26%in Migdad had available K lower than the critical level. Also, 19.4% of soils in Meqdad region,  36.6% in Siahkal, 48.4% in Masal, and 100% in Rudbar had total N lower than the critical level (0.2 %). Examination of the condition of paddy soils at the sampling points showed that the spatial distribution of nutrients was not the same. The findings of this study showed that using the same fertilizer recommendation is not suitable for different paddy lands; and distribution of fertilizer in different areas of the province should be done according to the soil analysis of each region.

Knowledge on the spatial distribution of soil quality is one of the most ‎important issues in recognition, planning, management and optimal ‎exploitation of soil resources. In this study, physical, chemical and biological properties of topsoil ‎‎(0-30 cm) were measured in 50 fields (1650 m × 1650 m) in Savojbolagh region, Alborz ‎province. All of the factors influencing soil quality were selected using ‎the principal component analysis (PCA). The Weighted Additive Soil Quality Index (SQIw), the Additive Soil Quality Index (SQIa) and the Nemero Soil Quality Index (NQI) were quantified using either the total data set (TDS) or the minimum data set (MDS). Spatial variability of these soil quality indices were analyzed using geostatistical technique. In addition, spatial distribution of them were determined using the Ordinary Kriging method‎. The results showed that for the NQI index, the best fitted model was the Gaussian (R2= 0.93) when MDS was used, whereas, the exponential (R2= 0.79-0.99) model was strongly fitted to the other indices obtained from MDS and TDS. Furthermore, the effective range of spatial variability for SQIa, SQIw and NQI indices was 6-6.2, 2.7-3.65 and 4.5-7.2 kilometers, respectively. The NQI obtained from the TDS appeared to have the higher accuracy in the area (R2= 0.85, normalized root mean squares error (NRMSE) = 0.01). A large part of the area has high quality (class II) as well as to some extent areas have medium quality (class III) and very high (class I). Soil quality indices in the region were in moderate spatial dependence (0.31-0.7). So, the soil quality in the region is affected by both natural and external factors. Therefore, regarding the low organic matter as well as the weak structural stability of soil in the study area, one may expect that by implementing appropriate management methods such as adding organic matter, the soil quality will be increased.

Knowledge about the spatial distribution of soil organic carbon (SOC) is one of the practical tools in determining sustainable land management strategies. During the last two decades, the utilization of data mining approaches in spatial modeling of SOC using machine learning algorithms have been widely taken into consideration. The essential step in applying these methods is to determine the environmental predictors of SOC optimally. This research was carried out for modeling and digital mapping of surface SOC aided by soil properties ie., silt, clay, sand, calcium carbonate equivalent percentage, mean weight diameter (MWD) of aggregate, and pH by machine learning methods. In order to evaluate the accuracy of random forest (RF), cubist, partial least squares regression, multivariate linear regression, and ordinary kriging models for predicting surface SOC in 141 selected samples from 0-30 cm in 680 hectares of agricultural land in Khorramabad plain. The sensitivity analysis showed that silt (%), calcium carbonate equivalent, and MWD are the most important driving factors on spatial variability of SOC, respectively. Also, the comparison of different SOC prediction models, demonstrated that the RF model with a coefficient of determination (R2) and root mean square error (RMSE) of 0.75 and 0.25%, respectively, had the best performance rather than other models in the study area. Generally, nonlinear models rather than linear ones showed higher accuracy in modeling the spatial variability of SOC.

Accurate and detailed spatial soil information over the landscape is essential for the precision monitoring of land resources, hydrological applications, land use management. The present study aimed to predict the spatial prediction of SOC, CCE, Clay, Silt, and Sand in the Qorve-Dehgolan region, Kurdestan province. Qorve-Dehgolan region, with mean annual temperature and precipitation of 12 C° and 348 mm (20-year statistical period), has soil moisture and temperature regimes xeric and thermic, respectively. A total of 145 samples were collected from the topsoil (0-30 cm) based on a random sampling pattern. Then, all of the soil samples were transferred to a soil laboratory for physicochemical analysis. Random forest (RF) as a nonparametric model and Ordinary kriging (OK) and inverse distance weighting (IDW) as an interpolation method were used for modeling the soil properties and their spatial autocorrelation. All steps of modeling for RF and interpolation methods (OK and IDW) were performed in RStudio, ArcGIS and, GS+ software, respectively. A total of 30 environmental covariates, including the Digital Elevation Model (DEM) derivatives in the SAGA GIS 7.3 software and Landsat 8 satellite reflective band data in the ERDAS IMAGINE software, were developed as environmental variables. All of the environmental covariates were resampled at resolution-30 m. The most appropriate covariates were selected according to the variance inflation factor (VIF). Modeling of soil properties was performed according to 80% and 20% of data sets, respectively for calibration and validation, and two statistics of root mean square error (RMSE) and determination coefficient (R2) was used to determine the accuracy of the models.

Seven variables including SAVI, EVI1, GNDVI, RVI1, DEM, Channel Network, and TPI were selected from the 30 variables prepared as the most appropriate auxiliary variables based on the variance inflation index. Four remote sensing variables include the adjusted soil vegetation index (SAVI), the greenness of the normalized differential vegetative index (GNDVI), the relative vegetation index (RVI) and the Enhanced vegetation index (EVI), and three geomorphometric attributes including, digital elevation model (DEM), Vertical distance to channel network and the topographic position index (TPI) were the most important parameters. The results of modeling showed that RF model for soil organic carbon variable (R2 = 0.5 and %RMSE= 0.4), calcium carbonate equivalent (R2 = 0.4 and %RMSE = 11.61), clay variable (R2= 0.21 and %RMSE=5.65), the Silt variable (R2 = 0.15 and %RMSE= 7.24) and, Ordinary kriging methods for sand variables with (R2 = 0.14 and %RMSE = 10.26) was the most accurate than RF and IDW models. Among the semi-variogram models, the exponential model had the best performance for soil organic carbon, clay, silt, and sand percentage, with the exception of CCE which follows the spherical model. The results of spatial autocorrelation showed that for both variables CCE and Sand had a strong class and, the rest had a moderate class. The highest values of the semi-variogram sill were related to the calcium carbonate equivalent and clay, and the lowest values were related to the soil organic carbon and sand contents. These results indicate that, the existence of a random pattern or weak spatial structure in the samples that used to calculate the experimental semi-variogram. Among the seven environmental covariates were used for spatial modeling of top-soil organic carbon, calcium equivalent carbonate, and clay, the geomorphometric attributes such as digital height model, topographic position index and vertical distance to channel network are of the most important and NDVI, SAVI and RVI covariates were more important in predicting of sand and silt properties.

 Assessing the spatial variability trend of various soil properties is a helpful procedure to access to the efficient site-specific management of this ecosystem vital component. Aimed to investigate the spatial soil variability in lands of Zanjan University, 48 topsoil (0-25) samples were collected based on a regular grid of 250 meter. Soil samples were analyzed for some physical properties, including bulk density, available water capacity, saturated hydraulic conductivity, mean weight diameter and clay, sand, organic carbon and equivalent calcium carbonate percentage. Identifying the spatial structure of selected properties, ordinary kriging was used to estimate the values of soil properties in un-sampled locations and continuous maps were prepared. The results showed that saturated hydraulic conductivity (43.28%) and bulk density (5.53%) had the maximum and minimum coefficients of variations, respectively. The spherical model was performed well to describe the spatial structure of organic carbon and carbonate calcium equivalent; whereas exponential model showed the best fit to the remained soil properties. Saturated hydraulic conductivity showed a strong correlation class; whereas spatial structure of other studied properties was moderately correlated. Interpolated maps revealed that increasing clay percentage from the south to the north of study area led to the decreased contents of saturated hydraulic conductivity and increased values of available water capacity. This observation indicated that considering soil youthfulness and less-developed soil structure, most of the soil physical properties are mainly influenced by soil texture. It is also observed that cultivated crops in the south of studied area and apple trees in the north, resulted in the soil organic carbon to be increased to more than 1%, in turn, led to the significant increase in mean weight diameter of soil aggregates in these areas.

Spatial variability of soil organic carbon in different land uses could be effective in interpreting and simulating the behavior of ecosystems in encountering the climate and environmental changes. The hilly lands of the study area are unique ecosystem with particular importance due to the effect of slope in creating microclimates with different vegetation. The spatial variability of soil organic and inorganic carbon contents and storages were studied at various depths. The effects of land use, parent material, topographical properties and some of soil characteristics on soil organic and inorganic carbon contents and storages were investigated in 56 soil pedons. In average, the highest (SOC=196.6 Mg/ha, SIC=88.2 Mg/ha) and lowest (SOC=59.9 Mg/ha, SIC=11.3 Mg/ha) storage contents of soil organic and inorganic carbon were found in Mollisols and Entisols, accordingly. The highest average total carbon storage also was found in Mollisols (284.9 Mg/ha) while the lowest was in Entisols (71.2 Mg/ha). Increasing soil inorganic carbon with increasing soil organic carbon indicates that inorganic carbon originates from soil organic carbon. In forestland, most amounts of soil organic carbon stocks are located in surface horizons, in fresh and semi-decomposed forms and in combination with silt fractions. Therefore, forest soils are more fragile in response to the changes in management rather than rangelands that contain most of their organic carbon stocks in subsurface horizons and in combination with clay fractions.

This research was carried out to compare the methods of determining the spatial distribution of soil organic carbon and nitrogen on 50,000 hectares of Lenjanat area in Isfahan province, central Iran. The spatial distribution of elements was determined by Ordinary Kriging, Inverse Distance Weighting, and Spline Tension interpolation methods. The results indicated that there was an average spatial structure in the variables. Empirical variograms showed that there was no specific anisotropy process for variables. In order to determine the accuracy of the estimation of interpolation methods, the standard data (150 data for each variable) and statistical criteria including Mean Estimation Error (MEE), Mean Square Estimation Error (MSEE), Root Mean Square Error (RMSE), relative Error (Er) and Pearson correlation coefficient (r) were used. Despite the high spatial variability of the parameters, the results indicate the accuracy of the estimated values by means of interpolation methods. For soil organic carbon in the study area, Ordinary Kriging and Spline Tension interpolation methods with lower error (MEE= 0.007, MSEE= 0.09, RMSE= 0.3, Er= 0.32 and r= 0.73) were more accurate compared with the Inverse Distance Weighting interpolation method (MEE= 0.009, MSEE= 0.096, RMSE= 0.31, Er= 0.31 and r= 0.71). Also, Ordinary Kriging and Inverse Distance Weighting interpolation methods with a lower error (MEE= 0.003, MSEE= 0.0006, RMSE= 0.025, Er= 0.296 and r= 0.8), compared with Spline Tension interpolation method (MEE= 0.0017, MSEE= 0.0007, RMSE= 0.026, Er= 0.301 and r= 0.78) had more accurate estimation of soil nitrogen in the region.

In recent decades, site-specific management (SSM) has been specifically considered to achieve to increased input efficiency, improved economic margins of crop production and reduced environmental risks. Short-scale spatial variability of soil properties caused more necessity of SSM techniques. Natural variability of soil results from complex interactions between geology, topography and climatic factors, as well as land use change and land management strategies. Deforesting and vast land use changes are considered as the important land management strategies that have been extensively used in recent decades. Therefore, determining the effects of land use change on soil properties in conjunction with local environmental conditions and on the spatial variability of soil properties may drastically help the land use planners. Therefore, the present study was done aimed to explore the effects of oak trees deforesting in Shah-Mokhtar region in Yasouj and land use changes to dry farming on the spatial variability of nutritional elements using geostatistical techniques. The present study was conducted in a Shah-Mokhtar, north, and northwest of Yasouj, in Kohgiluyeh Province, southern Iran. Spatial variability of seven soil fertility properties, including N, P, K, Fe, Cu, Zn, and Mn concentration in soil, were examined in three land uses, including dense forest, degraded (semi-dense) forest and rain-fed lands. A total of 100 surface (0-30 cm) soil samples were collected and analyzed for the nutritional elements after preparing in the laboratory. Data were analyzed statistically and their normal distribution pattern was examined using the Kolmogrov-Smirnov test. In the geostatistical analyses step, the spatial structure of studied variables was analyzed by fitting the suitable authorized models on calculated experimental semi-variograms. The concentration of studied elements was interpolated using ordinary kriging (OK) and inverse distance weighting (IDW) estimators and finally, the spatial distribution map of each soil nutrient was prepared using ArcGIS 10.3. The results showed that the highest mean soil concentration of almost all of selected nutrients belonged to the dense forest lands and due to the land use changes to the degraded forest and then dry farming, average values of selected elements significantly decreased. Among the studied properties, soil N concentration with an average of 0.34%, 0.17% and 0.08% in dense forest, degraded forest, and rain-fed soil samples, had the most decrease caused by deforesting. Semi-variogram analyses showed that spherical model had the best performance. For interpolating the soil K and Mn concentration, IDW method and for other studied elements, OK method was efficiently used. The spatial correlation class was strong for N, Cu, and Fe; whereas a moderate class was calculated for soil P and Zn concentration. The spatial distribution maps of selected nutrients revealed that the dense forest and dry farming soils had the highest and lowest contents of soil nutritional elements. According to the findings of the present study, it can be stated that degradation of oak forest may lead to the significant decrease of soil nutritional elements, specifically soil N concentration as one of the main soil fertility quality. Therefore, it seems that not only the significant soil quality decline but also the extensive degradation of whole ecosystem and unfavorable climatic consequences will be the unavoidable results of deforesting in Shah-Mokhtar region.

Near-saturated soil hydraulic conductivity (Ks) is a key property for several purposes including simulation of hydrological processes, determination of unsaturated soil hydraulic conductivity function and modelling water flow and solute transport. Since it is soil structure-dependent, field measurement techniques should be used to minimize disturbance of the sampled soil volume. Because of high spatiotemporal variability of Ks, replicated measurements need to be carried out, so that, the method to be applied should be simple and rapid enough as the simplified falling head (SFH) technique is. Alternatively, pedotransfer functions (PTFs) could also be utilized for indirect prediction of Ks through the surrounding soil attributes. The objective of this study was to test some new inputs to predict the Ks of salt-affected soils adjacent to Lake Urmia. A 32 cm diameter ring was used to determine Ks by the SFH technique in soils with different salinity (electrical conductivity of saturated extract, ECe = 0.1 – 95.3 dS/m) and sodicity (exchangeable sodium percentage, ESP = 4 – 70.9 %) levels. A total of 190 SFH runs were carried out, adjacent to each of which, some undisturbed soil cores (5 cm in height by 5 cm in diameter) and disturbed soil samples were randomly collected from the surface soil and were analyzed for their physicochemical properties. The studied soils mainly had moderate (≈ 40%) to moderately rapid (≈ 48%) conductivities. Correlation and stepwise regression analysis showed that Ks was correlated negatively with bulk density (ρb) (r = -0.205), index of crusting (Ic) (r = -0.180), and degree of compaction (SDC) (r = -0.206) (P ≤ 0.01) and positively with structural stability index (SSI) (r = 0.184) and geometric mean diameter of soil aggregates (GMD) (r = 0.157) (P ≤ 0.05). Furthermore, the Ks had negative correlation with both soil ECe and ESP.

The importance and the presence of spatial variability in soil properties is inevitable, however, the understanding of causes and sources of the variability is not complete. Spatial variation of soil attributes can affect the quality and quantity of plants. Investigation of the soil variability at the small scale can be evaluated by classic statistics and geospatial statistics. The present study was conducted to investigate the spatial variability of yield characteristics of rose (Rosa Damasceneea Mill) and soil characteristics in two main cultivated fields of rose (Negar- Golzar) with different climatic and topographic characteristics located in Bardsir city, Kerman Province. In order to achieve the objectives of the present study, 100 soil and plant samples were collected from each farm. The soil samples were taken from 0 to 25 cm depth and analyzed. The measured soil properties at each location were including fragment, clay, silt, sand, and organic matter contents, CEC, calcium carbonate equivalent, EC, pH, total nitrogen, available phosphorus, and available potassium. Moreover, some plant characteristics (yield, plant height, and plant crown diameter) were measured at each point. Then, maps of soil properties and plant induces were prepared using Geoeas, Variowin, and surfer software. Descriptive statistics were applied using Statistica software (version 20). Kolmogorov-Smirnov test was also used to test the tolerance of variables distribution. The results of Kolmogorov-Smirnov test showed that all characteristics of the plant and soil in both farms follow the normal distribution. Statistical analysis showed that coefficient of variation of soil properties was as follows: total nitrogen (54.47%) and pH (3.16%) in Negar farm, and EC (46.09%) and pH (35.3%) in Golzar farm. The variability of nutrients in both farms had similar trends, so that total nitrogen, phosphorus and potassium have the highest to lowest coefficients of variation, respectively. Analysis of variograms indicated that all of the variables in both fields have a strong and moderate spatial variability. Ranges for variograms were from 122.16m (for yield) to 218.46 m (for silt) in Negar farm and from 115.1m (for available K) to 228 m for (total nitrogen) in Golzar unit. The distribution conditions and spatial variations of the soil properties in the study area were not uniform due to variation of the range of the variograms. The results also showed that the yield characteristics of the rose with some soil characteristics have a closer spatial relationship. About this, in the Negar farm, the range of the rose flower yield was close to the clay, available potassium and calcium carbonate contents. In the Golzar farm, the range of rose flower yield was close to the range of clay, silt, fragments and available phosphorus contents. The spatial correlation ratio showed that all plant characteristics including plant yield, plant height and plant diameter had a strong spatial correlation in the Golzar farm, and all characteristics of the soil were in the medium spatial correlation. Also, in the Negar farm, the product yield characteristics were in a strong spatial correlation class, and all other characteristics were in the medium spatial correlation. Kriging maps showed that soil characteristics and product yield in the study area had spatial distribution. The similarity of the spatial distribution pattern of some variables was one of the important features that these maps showed. The results of this study showed the characteristics of plant yield and soil characteristics have a moderate to strong spatial dependency even in small scales. Kriging maps illustrated that the pattern and distribution of soil properties even within a farm can be varied. However, the spatial pattern of some soil characteristics such as organic matter and total nitrogen with the spatial pattern of plant characteristics and the dimensions of the farms showed conformity. This indicates that the variability of these characteristics is mainly under the management of farmers, and in order to optimize the use of nutrients, inputs should be re-evaluated by farm managers. In general, the results of this study indicated geostatistical method can be used to recognize of control factors of plant production and use its information in order to improve management.

Predicting and mapping soil organic carbon (SOC) contents and stocks are important for C sequestration, greenhouse gas emissions and national carbon balance inventories. The SOC plays a vital role in sustaining agricultural productions in arid ecosystems. It shows very quick and direct changes with atmosphere through the photosynthesis and the SOC decomposition. The depletion of C storage not only exacerbates the risk of soil erosion but also reduces agricultural production. An accurate knowledge of regional SOC contents and stocks and their spatial distribution are essential to optimize the soil management and land-use policy for SOC sequestration. Today, digital soil mapping methods such as geostatistics and artificial neural network (ANN) have focused more on SOC contents and stocks mapping. Geostatistics is a robust tool widely applied to model and quantify soil variation and analyze the spatial variability of SOC in large scale. The ANN as a nonlinear technique has been received much less attention for modeling SOC contents and stocks. Therefore, in this study, we aimed to develop and compare the performance of ordinary Kriging, co-kriging, inverse distance weighting (IDW) and artificial neural network models in predicting and mapping the SOC contents and stocks in East and Southeast of the Kohgiluyeh and Boyer-Ahmad province, southern Iran. The composite soil samples were collected randomly from the 0-15 cm soil depths at 204 sampling sites at different land uses in east and southeast of the Kohgiluyeh and Boyer-Ahmad province. The collected soil samples were air-dried, ground, and sieved to pass through a 2 mm mesh. Soil properties such as organic carbon contents and stocks, pH, electrical conductivity (EC), bulk density (BD) and soil texture were determined according to the standard analysis protocols. The normality tests were done according to the Kolmogrov–Smirnov method, and the variability of SOC contents and stocks were analyzed by the classical statistics (mean, maximum, minimum, standard deviation, skewness, and coefficient of variations). The digital elevation model (DEM), slope gradient, precipitation and temperature and Normalized Difference Vegetation Index (NDVI) were used as co-variables (auxiliary data). The NDVI was obtained by the remotely sensed data of LANDSAT 8. The geostatistical parameters were calculated for each soil property as a result of corresponding semivariogram analysis. The spatial prediction maps of soil properties were generated by ordinary kriging (OK), cokriging (Co-K) inverse distance weighting (IDW) with powers of 1, 2, 3, 4 and 5 as well as the Artificial Neural Network (Multilayer Perception model, MLP) methods. The mentioned interpolation methods were used to prepare the SOC spatial distribution maps by using the 80 % of data as the training datasets. The prediction results were then evaluated by the validation data set (20 % of all data). The differences between the observation and prediction values were evaluated by Mean Error (ME), Root Mean Square Error (RMSE), Correlation Coefficient (R2) and Concordance Correlation Coefficient (CCC). The spatial distribution maps of the SOC contents and stocks in the study area were finally developed by ArcGIS 10 software. The SOC content for all samples largely varied from 0.20 to 3.96 % .The high coefficient of variation of 53.38 % demonstrates the strong spatial variation of SOC content in the study area. The SOC stocks had also a relatively high variability compared with other soil properties. Such strong variation could be attributed to the diverse soil types, land covers and other environmental conditions across the study area. The average SOC content for forest land use was significantly higher than the other land uses. The intensive tillage in cropland soils appears to have induced the acceleration of organic carbon oxidations leading to the lowest SOC contents and stocks. By increasing the mean precipitation within our study area (in eastern and northeastern regions), the SOC contents and stocks increased significantly. The inverse trend was, however, observed for temperature implying the fact that the higher the temperature, the lower the SOC. Gaussian model was found to be the best model for parameters such as SOC contents and stocks due to the lowest RSS and R2.Overall, the results denoted the higher ability of ANN compared to geostatistical techniques (cokriging, kriging and IDW methods) in estimating both soil organic carbon contents and stocks. According to the results, ANN (MLP) method with one hidden layers with 50 neurons performed better in estimating soil organic carbon contents and stocks atunsampled points, whereas the largest errors were obtained for IDW method. The good performance of ANN method can be attributed to the division of the study area and the capability of ANN to capture the nonlinear relationships between SOC and environmental factors i.e. slope, DEM, precipitation, temperature and NDVI. The results suggest that the proposed structural method for ANN can play a vital role in improving the prediction accuracy of SOC spatial variability in large scale.

Quantitative description of the spatial variability of soil hydraulic characteristics is crucial for planning, management and the optimum application. Field measurement of infiltration is very expensive, time-consuming and laborious. Soil structure also important effects on water infiltration in the soil. The objectives of this study were to determine the spatial variability of water infiltration, to select the most appropriate infiltration model, to calculate the parameters of relevant models, and to quantify the soil structure by using the fractal geometry. Infiltration parameters were estimated by using some physical soil properties, as well as fractal parameters, in this research. To achieve these purposes, 161 sites were selected and their infiltration was measured by using the constant head double-ring infiltrometers method in a systematic array of 500*500 m. The observed infiltration data from all examined sites were fitted to three selected infiltration models. Soil bulk density (BD), soil water content, soil particle size distribution, soil aggregate size distribution (ASD), organic carbon content (OC), saturation percentage (SP), soil pH and electrical conductivity (EC) were also measured in all 161 sites. For the quantitative assessment of soil structure, the aggregate size distribution, fractal parameters of the Rieu and Sposito model as well as the mean weight diameters (MWD) and geometric mean diameter (GMD) were also obtained. The obtained results indicated that the infiltration rates of the studied areas had generally low basic infiltration rates (1.1-31.1 cm hr-1) for most sites with the average of 6.69 cm hr-1. According to all obtained results and based on the least-square method, the Philip model was selected as the best performing model to account for infiltration. The aggregate size distribution demonstrated a fractal behavior, and the infiltration parameters could be significantly correlated with the fractal parameters and other soil physical properties.

Soil organic matter (SOM) is an important soil quality factor that affects physical, chemical and biological properties of the soil. Accurate estimation of SOM spatial variabilities provides critical information especially in precision agriculture. The objective of this study was to estimate SOM spatial variabilities and to assess its status using regression kriging (RK) in Ghorveh plain in Kurdistan province (Iran). Therefore, 150 soil samples from a depth of 0-15 cm were taken systematically in a grid spaced 2 Km × 2 Km. Particle size distribution and SOM content of the soil samples were measured in the laboratory. Stepwise multiple linear regressions (MLR) was used to estimate SOM variabilities based on the soil texture data (percentages of sand, silt and clay) and vegetation indices obtained from Landsat Enhanced Thematic Mapper (ETM) imagery. The MLR model was used to provide an initial map of SOM content. Furthermore, the residuals of MLR model were interpolated using ordinary kriging (OK) and they were combined with the initial map of SOM to produce the final map of RK SOM. The SOM status map was derived from overlaying of soil texture and SOM maps in four different levels (very low, low, medium and high). The results of MLR indicated that both clay content and soil adjusted vegetation index (SAVI) variables have a significant effect on SOM content (p <0.05). The cross-validation results indicated that the RK method was able to explain about 84% of the spatial variabilities of SOM. The SOM status map indicated that more than 96% of the soil in the proposed region is in a low condition in terms of organic matter.

Soil organic matter (SOM) is an important soil quality factor that affects physical, chemical and biological properties of soil. Accurate estimation of SOM variability provides critical information especially in precision agriculture. Geostatistics and geographic information system (GIS) are powerful tools for characterizing and mapping the spatial distribution and variability of soil properties. Kriging is a basic geostatistical technique that provides the best linear unbiased estimation for a spatially dependent variable. This method will produce satisfying results if enough sample points are available. Unfortunately, laboratory measurements of the SOM are costly and time-consuming. Artificial neural network-kriging (ANNK) is another geostatistical method that extends kriging of a primary variable to the readily available auxiliary variables based on their relationship with the primary variable. This relationship is captured using an artificial neural network (ANN) model. The residuals of the model were then interpolated using kriging, and added to the prediction obtained from the ANN model. Terrain attributes, derived from digital elevation models (DEMs), are useful for estimating SOM at landscape scale. Topographic indicators including slope, aspect, elevation, and topographic wetness index may be the dominant factors affecting SOM variability in an area with same parent material and climate. Hence, these factors can be used as auxiliary variables for estimating spatial variability of SOM using ANNK. The objective of this study was to estimate SOM spatial variability using ANNK and topographic indices and assess its status in hilly areas of Ghorveh in Kurdistan province (Iran).
A total of 150 soil samples from a depth of 0-15 cm were systematically collected in a grid spaced 2 Km × 2 Km. The SOM content of soil samples was measured in the laboratory. Topographic indicators including slope, aspect, elevation, and topographic wetness index were derived from the DEM. ANN was used to predict SOM variability based on topographic index combinations. The feed-forward network consisted of an input layer, one hidden layer with sigmoid neurons, and an output layer with linear neurons. The network was trained with Levenberg-Marquardt backpropagation algorithm. According to the Kolomogrov’s theorem, the number of nodes in the hidden layer was 2n, in which n is the number of input neurons. The optimal subset of topographic index combinations correlating best with the SOM was selected as the best ANN model. This model was used to generate an initial SOM surface. The residuals of ANN model were interpolated using ordinary kriging (OK) and combined with the initial SOM surface to produce the final ANNK SOM surface. The SOM status map was derived from overlaying of soil texture and SOM maps in four different levels (very low, low, medium and high).
The results of ANN suggested that elevation was the most important variable determining the distribution of SOM across the landscape. Further, aspect was the other variable which had a significant influence on SOM distribution. The selected two inputs ANN model (elevation and aspect) can explain about 33% of total variance of SOM. The cross-validation results indicated that the OK and ANNK techniques can explain about 37 and 89% of total variance of SOM, respectively. The ANNK technique performed better than the OK and ANN techniques since it was able to capture most of the small variations of SOM. The resulting SOM status map indicated a low and very low SOM content in relation with soil texture in most regions surveyed (79%). Low SOM level can be attributed to the erosive processes under Mediterranean climate on hills coupled with intensive and/or inappropriate agricultural practices. Based on the results of this study, proper agronomical and environmental planning such as soil conservation strategy is highly required in this area to restore and increase the SOM content in agricultural soils, combat soil erosion and maintain soil ecological functions and productivity. The SOM replenishment can be achieved in the degraded areas (i.e., low SOM content) by adopting conservative practices such as conservation tillage or no-tillage (e.g., direct seeding), improving land use rotations with forage crops, returning crop residues to soil, growing green manure crops, and supplying the soil with proper exogenous organic matter (compost, manure, sewage sludge, etc.). Furthermore, the results highlighted the potential of ANNK in combination with GIS to provide improved distribution patterns of SOM.

Understanding soil biology and ecology is increasingly important for renewing and sustainability of ecosystems. In all ecosystems, soil microbes play an important role in organic matter turnover, nutrient cycling and availability of nutrients for plants. Different scenarios of land use may affect soil biological properties. Advanced information technologies in modern software tools such as spatial geostatistics and geographical information system (GIS) enable the integration of large and complex databases, models, tools and techniques, and are proposed to improve the process of soil quality and sustainability. Spatial distribution of chemical and biological properties under three scenarios of land use was assessed.
This study was carried out in Mirabad area located in the western part of Souldoz plain surrounded by Urmieh, Miandoab, Piranshahr and Naghadeh cities in the west Azerbaijan province with latitude and longitude of 36°59′N and 45°18′E, respectively. The altitude varies from 1310 to 1345 with average of 1325 m above sea level. The monthly average temperature ranges from -1.4 °C in January to 24.6 °C in July and monthly precipitation ranges from 0.9 mm in July to 106.6 mm in March. Apple orchard, crop production field and rich pasture are three selected scenarios in this research work. Soil samples were systematically collected at 65 sampling points (0-30 cm) on mid July 2010. Soil chemical and biological properties i.e. microbial community, organic carbon and calcium carbonate equivalent were determined. The ArcGIS Geostatistical Analyst tool was applied for assessing and mapping the spatial variability of measured properties. The experimental design was randomized complete blocks design (RCBD) with five replications. Two widely applied methods i.e. Kriging and Inverse Distance Weighed (IDW) were employed for interpolation. According to the ratio of nugget variance to sill of the best variogram model three following spatial dependence conditions for the soil properties can be considered: (I) if this ratio is less than 25%, then the variable has strong spatial dependence; (II) if the ratio is between 25% and 75%, the variable has moderate spatial dependence; and (III) otherwise, the variable has weak spatial dependence. Data were also integrated with GIS for creating digital soil biological maps after testing analysis and interpolating the mentioned properties.
Spherical model was the best isotropic model fitted to variograms of all examined properties. The value of statistics (R2 and reduced sum of squares (RSS)) revealed that IDW method estimated calcium carbonate equivalent more reliably while organic carbon and microbial community was estimated more accurately by Kriging method. The minimum effective range (6110 m) was found for microbial community which had the strong spatial dependence [(Co/Co)

Soil is a diverse, dynamic and heterogeneous system and investigation of its spatial and temporal changes is essential for systematic management of natural resources. In this study the spatial variabilities of some physical and chemical properties of the soil affected with aspect in Kiasar rangelands were investigated. Two sampling sites were selected with the same climatic level, slope, elevation and lithology but with different slope aspect. A sampling grid with 400 m2 cells was established in each site and 50 soil samples were collected from 0-30 cm soil depth. Soil properties including pH, caco3, total nitrogen, organic carbon, percent of silt, clay, sand and moisture were measured in the laboratory. Then data were normalized and applied for the statistical and geostatistical analyses. Results of the t test showed that organic carbon in northern slope did not have significant difference (p

Phosphorus (P) has been one of the major nutrients limiting agricultural production in many rice growing regions of the world, and certainly this is the case in north of Iran, where soils deficient in proper and balanced P management in the cultivated paddy fields. Phosphorus (P) chemistry in paddy soils is complicated due to various chemical and biological processes involve in its availability through various P fraction pools. Therefore, P fractions play a crucial role on soil available P supplying capacity especially in paddy fields. Proper and effective managerial practices of P and assessment of their effects on rice production requires an understanding of their variability in concentration across the fields. The spatial variability of soil properties and P content is the outcome of the interaction of several soil characters and processes and in agricultural fields involves also effects of management practices. Thus, the objective of this study was to conduct a large-scale analysis of the spatial variability of available and organic P, and also inorganic P fractions, and the important physical and chemical soil characters on P factions’ distribution for better fertilizers managerial practices.
This experiment was conducted at Some-e-Sara city in Guilan province. Soil samples were collected from 103 paddy fields with uniform geographical distribution in grids of 2x2 km. Soil samples were analyzed to determine available P, organic P, their effective physical and chemical properties (Texture, pH, CEC, T.N.V, Clay and O.C), and also sequentially extracted to determine inorganic P fractions(soluble P, Al-P, Fe-P and Ca-P). Descriptive and spatial variability analysis, and mapping was done by using SPSS (ver. 16), GS (ver. 5.1) and Arc GIS (Ver. 9.1), respectively.
The highest coefficients of variation belonged to available P and its controlling P fractions, Al and Fe bounded P, about 132 and 194, respectively, whereas, pH showed the lowest CV, 10.6. The nugget to sill ratio ranged from 12 to 60% for almost all the studied data sets, showing moderate to strong pattern of spatial dependence. The small to moderate nugget effects also indicate that the sampling grid used was proper to reflect the spatial dependence of the studied soil properties and P fractions. Analysis of semivariogram corresponding to experimental data sets showed that the values for the range of spatial dependence of the semivariogram models varied from 2700 to 6000 m. Based on the results obtained from linear correlation analyses and spatial distribution maps, available P strongly correlated with both Al and Fe-P fractions and OC, about 0.84, 0.94 and 0.40, respectively.
Based on the results obtained from linear correlation analyses and spatial distribution maps, available P strongly correlated with both Al and Fe-P fractions and OC. Therefore, we concluded that determination of spatial correlation’s area of these soil chemical properties can effectively improve P fertilizers management in paddy fields. In this case, incorporation of remained rice straw with soil through shallow ploughing two weeks after grain harvesting enhance the oxidative condition of soil and consequent better straw decomposition increase the soil available P balance. These two mechanisms considerably decrease application of P fertilizers. Moreover, due to lack of, and/ or, insufficient P application in studied area, continuous flooding influence on available P condition by altering soil oxidative status to reduced, naturalizing the pH, and increasing P minerals stability.

The hydraulic parameters are very important for perception of water flow in unsaturated soil and using pollutants and nutrient flow modeling in the soil. The effect of soil management and land uses on soil parameters can directly alter soil hydraulic parameters. Because of interactive and tight relationship between soil and plant covering, studying the soil parameters and its changing during different land uses is vital. The main object of this study was evaluating the effects of different land uses on soil saturated hydraulic conductivity.
This study was performed in about 100 hectare fields of Khezrabad region in the 25 km south of the Jiroft county located in south eastern of Kerman province. The region gridded into 1000×1000 meter grids with use of Google earth and Arc GIS software, sampling places has been selected in the center of each grid. Measurement of soil saturated hydraulic conductivity done with the Guelph permeameter in the center of each grid. For the measurement of physical parameters such as bulk density, percent of sand, silt, clay in the laboratory, sampling done from 30cm depth so samples transferred to the laboratory. In this study in order to ensure the normal distribution of variables, the Kolmogorov-Smirnov test has been used with SPSS14 software. The Kriging method was used for interpolation and providing spatial maps.
Agriculture, garden and sterile lands were selected for the object of the present study. The study area includes garden, agriculture and sterile lands at the same time. The study area contains 3 classes of soil texture as: sandy, sandy-loamy and loamy-sand. The results showed that soil saturated hydraulic conductivity (ks) with strong spatial correlation had a high spatial variability. The fluctuation ranges of its values changes from 0.02 to 2325.71 cm per hour. The lowest value of ks was observed in garden land (by having the lowest value of soil bulk density) while the highest value was observed in sterile land (by having the highest value of soil bulk density). The results also showed that semi-variogram of garden, agriculture and sterile land were not the same, and it may gain from different types of agricultural operations, type of land use and various textures so that from garden land to sterile land, the soil texture becomes lighter and level of saturated hydraulic conductivity changes completely different. Several reasons maybe considered including soil different structures due to different type of agricultural operations and type of cultivation for every single land use. The change process of saturated hydraulic conductivity for garden and agricultural land was identical and for both the Gaussian model were fitted. According to the nugget effect ratio to the sill (C0/C0), variability of saturated hydraulic conductivity in agricultural land has a stronger spatial correlation (0.0006) and also has a higher radius of effect range (11740m) compared to garden land in which the ratio of the nugget effect ratio to sill is 0.28 and its radius of effect range is 8030 meters. the radius of effect range in sterile land had the lowest value among studied land uses, though having strong correlation, the effect range of this correlation is low and, compared to other lands, the changes process was more randomly obtained. To mention the reasons of this finding it is possible to refer to area of the sterile land, dispersion of the sampling points and long distance between pair points. The lowest spatial correlation belonged to garden land with middle spatial correlation class and the reason can be explained as due to increase of sand, decrease of clay and silt, bulk density of soil increases as well and leads to increase of coarse pores and consequently increasing saturated hydraulic conductivity of soil.
Results showed that soil saturated hydraulic conductivity (ks) with strong spatial correlation has high spatial variability and these variability consist lowest quantity in the garden lands and highest quantity in the sterile lands. The distribution pattern of Ks was seen similar to the sand and the soils bulk density, this pattern was opposite to the clay distribution pattern, this indicates the effect of soil physical parameters on saturated hydraulic conductivity.
According to the evaluation parameters CRM, MAE and MBA, Gaussian model is the best fitted model to soil saturated hydraulic conductivity data and soil parameters such as saturated hydraulic conductivity consist spatial variability related to sampling scale. The factors of land type and consequently type of land cultivation, lands management system, type of agricultural operations, soil particles size and bulk density of soil have the most impact on variability of Ks.

The aim of this work was to study distribution of some heavy metals in different soil particle-size fractions and to assess their spatial distribution. The study was carried out in Kafe Moor (Kerman, Iran) where the Gol-Gohar Iron Mine is located. One hundred twenty composite soil samples were randomly collected and transferred to the laboratory in bags. After air-drying, the samples were fractionated into six classes including 2- 0.5, 0.5-0.25, 0.25-0.125, 0.125- 0.075, 0.075-0.05 and