Morteza Bahmani; jahangard mohammadi; Isa Esfandiarpour Borujeni; Hamidreza Mottaghian; Keramatollah Saeidi
Abstract
Introduction: 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 ...
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Introduction: 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.
Materials and Methods: 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.
Results and Discussion: 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.
Conclusion: 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.
Elham Afzali Moghadam; naser boroumand; vahidreza jalali; saleh sanjari
Abstract
Introduction: 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 ...
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Introduction: 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.
Materials and Methods: 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.
Results and Discussion: 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+C), 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.
Conclusion: 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.
shokrollah asghari; Mahmood Shahabi
Abstract
Introduction: Salinity and sodicity are the most important land degradation problems particularly in arid and semi-arid regions. Due to the depletion of Urmia Lake located in the northwest of Iran during recent years, the proportion of surrounding saline agricultural lands increased at a past pace. In ...
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Introduction: Salinity and sodicity are the most important land degradation problems particularly in arid and semi-arid regions. Due to the depletion of Urmia Lake located in the northwest of Iran during recent years, the proportion of surrounding saline agricultural lands increased at a past pace. In the salt-affected soils, aggregate stability is weak due to the high contents of sodium. The analysis of spatial variability of mean weight diameter of aggregates (MWD) and sodium adsorption ratio (SAR) is necessary to implement a site-specific soil management especially in the salt-affected soils. The main object of this study was evaluating the effects of different land uses (bare and agriculture) on the spatial variability of MWD and SAR in the salt-affected soils around Urmia Lake.
Materials and Methods: This study was conducted in the agricultural and bare lands of Shend Abad region located at the 15 km of Shabestar city, northwest of Iran (45° 36ʹ 34ʺ E and 38° 6ʹ 37ʺ N). Totally, 100 geo-referenced samples were taken from 0-10 cm soil depth with 100×100 m intervals (80 ha) in agricultural (n=49) and bare (n=51) land uses. Sand, silt, clay, organic carbon (OC), CaCO3, pHe, MWD, SAR and electrical conductivity (EC), were measured in the collected soil samples. Thewet sieving method was used to determine MWD of wet aggregates. The sieves were: 2, 1, 0.5, 0.25 and 0.106mm. The EC and SAR were measured in 1:2.5 (soil: distilled water) extra. The SAR was calculated from concentrations of Na+ and Ca+ + Mg+. The best fit semivariogram model (Gaussian, spherical and exponential) was chosen by considering the minimum residual sum of square (RSS) and maximum determination coefficient (R2). Ordinary kriging (OK) and inverse distance weighting (IDW) interpolation methods were used to analyze spatial variability of MWD and SAR. Spatial distribution maps of soil variables were provided by Arc GIS software. The accuracy of OK and IDW methods in estimating MWD and SAR was evaluated by mean error (ME), mean absolute error (MAE), root mean square error (RMSE) and concordance correlation coefficient (CCC) criteria. The CCC indicates the degree to which pairs of the measured and estimated parameter value fall on the 45° line through the origin.
Results and Discussion: According to the results of coefficient of variation (CV) from the study area, the most variable (CV=113.05%) soil indicator was SAR (bare land use), whereas the least variable (CV= 3.52%) was pHe (agricultural land use). The Pearson correlation coefficients (r value) indicated that there are significant (P
Mohammad Ali Mahmoodi; Molood Mirzaie; Mohammad Taaher Hossaini
Abstract
Introduction: 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) ...
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Introduction: 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).
Materials and Methods: 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+1, 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).
Results and Discussion: 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.
Sheyda Kaboodi; farzin shahbazi; Nasser Aliasgharzad; nosratola najafi; naser davatgar
Abstract
Introduction: 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 ...
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Introduction: 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.
Materials and Methods: 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.
Results and Discussion: 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+C)
nargess keyvani; Mohammad hasan Salehi; jahangard mohammadi; Abdolrahman Mohammadkhani
Abstract
Introduction: Soils form from the interplay of five main factors namely parent material, time, climate, relief (topography) and organisms. Topography is one of the local factors that has direct and indirect effects on soil formation, physical and chemical properties of soils. To understand the mutual ...
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Introduction: Soils form from the interplay of five main factors namely parent material, time, climate, relief (topography) and organisms. Topography is one of the local factors that has direct and indirect effects on soil formation, physical and chemical properties of soils. To understand the mutual relationship between topographic properties, soil properties and plant community (phytocoenosis), it is necessary to decide on the appropriate method for properly managing the soil resources. In addition to the soil properties, topography may affect the soil production indices as well. Soil production index and consequently its productivity will in turn affect the growth and fruiting. Insight about the pattern the spatial variability of soil properties can be used to manage the lands properly. This study was performed to investigate the spatial variability of soil properties regarding aspect and also the relationship of these changes with the quality and quantity of peach production in Saman region in Chaharmahal-Va-Bakhtiari province, Iran.
Materials and Methods: The study area contained 1.5 hectare of 200-hectare peach gerdens belong to BaghGostaran Company located in Saman, Chaharmahal-Va-Bakhtiari Province. The soil moisture and temperature regimes are xeric and mesic, respectively. 136 soil samples were collected from 0-30 and 30-60 cm depths. Two peach trees around the soil samples were also selected. Then, soil physical and chemical properties including soil texture, percentage of calcium carbonate equivalent, organic carbon, plant available potassium, phosphorous, iron and zinc, pH and electrical conductivity were determined and fruit properties including branch length and diameter in the current year, number of fruits, total yield, average of fruit weight, TSS, tissue strength, pH, acid and extract percentage were measured. Finally, the dataset were analyzed using Statistica 6.0 software. Analysis of spatial data was calculated via variogram and performed using Variowin, 2.2 software package. After determination of the best model, kriging maps of the soil and fruit properties were prepared by Surfer 8 software.
Results and Discussion: The statistical results revealed that among the soil properties, pH of the surface and subsurface horizons in both aspects had the lowest CV. Plant available phosphorous and iron showed the highest CV at surface and subsurface horizons of eastern aspect, respectively. Among the soil variables, plant available iron showed the highest CV for both horizons at western aspect. Regarding peach properties, the tissue strength showed the highest and pH showed the lowest variation in both slopes, respectively. The results of mean comparisons revealed that the soil of eastern slope has more clay percentage, silt, organic carbon, plant available potassium, phosphorous, and iron in comparison with western aspect. Peach yield was higher in eastern aspect than the western one. Correlation coefficient among soil and peach propertied did not show a similar trend for two aspects. Amount of clay and plant available potassium in subsurface horizon showed a positive significant correlation with yield in western and eastern aspects, respectivelty. Variography showed that all variables except pH of subsurface horizon have spatial structure. The pattern of spatial variability of the yield and the number of fruits was also approximately the same as that of clay particles and organic carbon, plant available potassium, phosphorous and iron in both depths. The spatial variability of the branch length and diameter in the current year was similar to the spatial variability of clay particles percentage and the plant available potassium. The results suggested the significant effect of soil properties, especially clay particles percentage and the plant available potassium on the performance and vegetative properties of peach. However, the peach qualitative properties showed no significant correlation with the soil properties.
Conclusion: The results suggest that the significant effect of aspect on the soil and fruit properties. It seems that the aspect caused the formation of soil with different properties. Significant differences observed among some soil properties including texture components, the amount of organic carbon and nutrients in both aspects. The trees on the eastern slope had higher yield due to having more organic carbon and nutrients and consequently higher soil quality, while the trees on the western slope had fruits with higher quality which may be due to the climatic factors such as receiving more light, or other soil properties like the mount of available nitrogen. More investigation is needed to understand the effect of NPK and iron fertilizers and climate properties on peach properties in the orchards of the area. The effect of climatic factors on the peach qualitative and quantitative characteristics should be investigated as well.
hojjat ghorbani vaghei; M. Davari
Abstract
Introduction: Soil organic carbon (SOC) has great impacts on soil properties, soil productivity, food security, land degradation and global warming. Similar to other soil properties, SOC has a strong spatial heterogeneity as a result of dynamic interactions between parent material, climate and geological ...
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Introduction: Soil organic carbon (SOC) has great impacts on soil properties, soil productivity, food security, land degradation and global warming. Similar to other soil properties, SOC has a strong spatial heterogeneity as a result of dynamic interactions between parent material, climate and geological history, at both regional and continental scales. However, landscape attributes including slope, aspect, altitude, and land use types are dominant factors influencing on SOC in areas with the same parent materials and climate regime. Understanding and identifying the spatial and temporal distribution of SOC is essential to evaluate soil quality, agricultural management, watershed modeling and soil carbon sequestration budgets. Therefore, the objectives of this study was to estimate soil organic carbon content in the Aligodarz watershed, and to investigate the effects of altitude, slope, and land use type on SOC.
Materials and Methods: The research was carried out in the Aligodraz watershed in Lorestan province of Iran. The study area is located between latitudes N 33° 10' 51.72"to N 33° 34' 28.22" and longitudes E 49° 27' 17.99"to E 49° 58' 40.84" 14 that covers an area of 1078.9 km2. It has an altitude between 1866.3 and 3200 m above sea-level. The primary land uses within the watershed include pasture, dryland and irrigated farming. In this study, soil samples were randomly collected from 206 sites at depth of 0– 15 cm during June and August 2003. The mean distance between samples was about 5 km. Soil samples were air-dried in the shade for about 7 days and then passed through a 0.25 mm prior to determination of SOC. Soil organic carbon content was determined in triplicate for each sample using the Walkey-Black method. Basic statistical analyses for frequency distribution, normality tests, Pearson's correlation and analysis of variance were conducted using SPSS (version 18.0). Calculation of experimental variograms and modeling of spatial distribution of SOC were carried out with the geostatistical software GS+ (version 5. 1). Maps were generated by using ILWIS (version 3.3) GIS software.
Results and Discussion: The results revealed that the raw SOC data have a long tail towards higher concentrations, whereas that squareroot transformed data can be satisfactorily modelled by a normal distribution. The probability distribution of SOC appeared to be positively skewed and have a positive kurtosis. The square root transformed data showed small skewness and kurtosis, and passed the K–S normality test at a significance level of higher than 0.05. Therefore, the square root transformed data of SOC was used for analyses. The SOC concentration varied from 0.08 to 2.39%, with an arithmetic mean of 0.81% and geometric mean of 0.73%. The coefficient of variation (CV), as an index of overall variability of SOC, was 44.49%. According to the classification system presented by Nielson and Bouma (1985), a variable is moderately varying if the CV is between 10% and 100%. Therefore, the content of SOC in the Aligodarz watershed can be considered to be in moderate variability. The experimental variogram of SOC was fitted by an exponential model. The values of the range, nugget, sill, and nugget/sill ratio of the best-fitted model were 6.80 km, 0.058, 0.133, and 43.6%, respectively. The positive nugget value can be explained by sampling error, short range variability, and unexplained and inherent variability. The nugget/sill ratio of 43.6% showed a moderate spatial dependence of SOC in the study area. The parameters of the exponential smivariogram model were used for kriging method to produce a spatial distribution map of SOC in the study area. The interpolated values ranged between 0.30 and 1.40%. Southern and central parts of this study area have the highest SOC concentrations, while the northern parts have the lowest concentrations of SOC. Kriging results also showed that the major parts of the Aligodarz watershed (about 87%) have statistically SOC content less than 1%. Lower SOC concentrations were associated with high altitude (r = −0.265**). The results of Pearson correlation analysis showed that soil organic carbon content has a significantly negative correlatiton with slope gradient (r = −0.217**). The results also indicated that the SOC content was variable for the different land use types. The irrigated lands had the highest SOC concentrations, while the pasture lands had the lowest SOC values.
Conclusion: The square-root transformed data of SOC in Aligodarz watershed of Lorestan province, Iran, followed a normal distribution, with an arithmetic mean of 0.81%, and geometric mean of 0.73%. The coefficient of variation and nugget/sill ratio revealed a moderate spatial dependence of SOC in the study area. The results indicated that the major parts of the Aligodarz watershed have SOC content less than 1%. The land use type had a significant effect on the spatial variability of SOC and that lower SOC concentrations were associated with higher altitude and slope gradients. The irrigated and pasture lands had the highest and lowest SOC concentrations, respectively.
Ali reza Karimi; Isa Esfandiarpour Borujeni
Abstract
Soil maps are the common sources of soil information for land evaluation and land use planning. The objective of this study was to evaluate the capability of conventional and geostatistical methods for mapping selected physical (sand, silt and clay) and chemical (carbonate calcium equivalent and pH) ...
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Soil maps are the common sources of soil information for land evaluation and land use planning. The objective of this study was to evaluate the capability of conventional and geostatistical methods for mapping selected physical (sand, silt and clay) and chemical (carbonate calcium equivalent and pH) soil properties. Based on interpretation of aerial photographs, satellite images and field observations, five geopedologic map units were identified in an area of about 12 km2 in southern Jiroft. 100 surface soil samples (0-20 cm) were taken from a regular grid of 500 × 250 m. The results indicated that geopedological map units were significantly different in at least one soil property. Differences of characteristics between units are resulting differences in geomorphic processes. Continuous soil maps prepared by the ordinary kriging also revealed continuous variations of characteristics in accordance with the changes in geomorphic processes. However, variations between units obviously recognizable in the boundary of units were not revealed by the geostatistical method. Based on results of this study, the conventional method is proposed for large areas (small scale maps) and geostatisticals method for small areas (large scale maps) are proposed for soil mapping.
Gh. Rahimi; A. A. Charkhabi
Abstract
The paddy soils in Lenjan area exposed to pollution owing to uncontrolled discharge of sewage sludge, wastewater and unessential fertilizers. Little information exists on Cadmium (Cd) distribution in paddy soils of Isfahan Province, this study was therefore investigated the spatial variability of cadmium ...
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The paddy soils in Lenjan area exposed to pollution owing to uncontrolled discharge of sewage sludge, wastewater and unessential fertilizers. Little information exists on Cadmium (Cd) distribution in paddy soils of Isfahan Province, this study was therefore investigated the spatial variability of cadmium which is considered as the most toxic metals. 90 soil samples (0-20 cm) were collected from study area. Soil properties such as pH, EC, calcium carbonate equivalent, soil texture, organic matter and cation exchange capacity were measured. The total and available Cd concentrations of soil samples analyzed by atomic absorption spectrophotometer. In addition, estimation of spatial data performed via kriging interpolation method (ordinary and blocky kriging) and by GIS. The total and available concentration of Cd in the study area were averagely 1.747 and 0.073 mgkg-1 respectively, which were much higher than the standard limit and classified in high pollution. Geostatistical analysis result was shown that exponential and spherical models for the total and available Cd concentration were best model, respectively. The most effective range of total and available Cd was 1011 and 1050 meter respectively and correlation ratio was weak in this range. Evaluation of correlation coefficient, MEE and RMSE parameters showed that both methods of kriging for data estimation in comparison with real data had acted in an appropriate manner. The result also showed that human activities such as industrial and urban wastewater entering to the water resources and application of excessive fertilizers had an impact on cadmium concentrations significantly.
salman naimi marandi; shamsollah Ayoubi; H. Khademi
Abstract
Soil pollution by heavy metals from the manufacturing process due to metal smelting plants closely related to human health is very important. Given the importance of the province to industrial and agricultural activities, this study was conducted to explore the vertical and horizontal variability of ...
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Soil pollution by heavy metals from the manufacturing process due to metal smelting plants closely related to human health is very important. Given the importance of the province to industrial and agricultural activities, this study was conducted to explore the vertical and horizontal variability of lead and nickcl metals in contaminated soils around the Zobahan melting factory, in nearby of Isfahan city. For this purpose, 202 profiles were dug and described in the green landscapes of Zobahan industrial site by a manner of gird sampling method. Five hundred soil samples were taken from depths of 0–30, 60–90, and 120–150 cm. Conccntration of total lead (Pb) and nickel (Ni) were measured in the soil samples. To explore the vertical distribution of selected metals, the mean values of Ni and Pb were compared statistically. The horizontal variability of selected metals was evaluated by variography analysis and the spatial distributions of them were constructed by kriging method. The overall results of study showed that Pb content in surface horizons is controlled by industrial activity, otherwise the concentration of Ni mainly attributed to parent material.
