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.
Anahid Salmanpour; Mohammad hasan Salehi; jahangard mohammadi
Abstract
Introduction: The heavy metal concentration in agricultural lands, due to the toxicity, persistence and their accumulation in the environment has become a major concern. Ophiolitic formations extend in southern part of central Iran and parallel to folds of the Zagros Mountains, is located in the north ...
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Introduction: The heavy metal concentration in agricultural lands, due to the toxicity, persistence and their accumulation in the environment has become a major concern. Ophiolitic formations extend in southern part of central Iran and parallel to folds of the Zagros Mountains, is located in the north of Neyriz town and in the west of Bakhtegan Lake. Rock weathering of these complexes forms sediments and soils with a large amount of Mn, Ni, Cr, Co, Mg and Fe. Laboratory analysis of Neyriz ophiolitic rocks indicates that they are a source of heavy metals as well, and may cause problems for the environment. However, there is no investigation in Neyriz area regarding contamination of the soils. The present study was conducted to assess soils pollution in Ghal-e Bahman area, 20 km from Neyriz which derived from ophiolitic formations of this area.
Materials and Methods: The study area located in the Ghal-e Bahman region, eastern part of Bakhtegan Lake. The soils of this region are affected from Neyriz ophiolite. In this region, three physiographic units including a hill, an alluvial fan and a lowland (playa) were separated. In each unit, some pedons were dug and classified according to American Soil Taxonomy. Soil samples were obtained from each genetic horizon and rock samples were also taken from ophiolitic formation. Then, chemical and physical properties were determined. Heavy metals were also extracted by nitric acid and amount of Cr, Ni, Co and Fe were calculated. Enrichment Factor (EF) and Geo-accumulation indices (Igeo) were also calculated and soils were classified according to their pollution level.
Results and Discussion: In general, soils on different landforms had different horizon properties and different classification. They are varied from a shallow, thin layer on hills to relative deep layer on lowland. These soils were classified in three different subgroups according to American Soil Taxonomy. Soils on ophiolitic hills classified as Lithic Torriothents because of a thin surface layer on a weathered bedrock. Soils developed on alluvial fan landform, with several alluvial subsurface horizons with different rock fragments percentage and size, was classified as Typic Torrifluvents; and the soils on lowland (Bakhtegan playa (was Gypsic Aquisalids because of salt and gypsum concentration in all layers and had redox color (chroma of less than 2) affected by high level of groundwater in the soil surface and subsurface layers.
The results showed that the amount of chromium with the average of 2200 mgkg-1, was 10 to 40 times higher than the Iran and Europe threshold levels (100 and 150 mgkg-1, respectively). The amount of nickel, with the average of 300 mgkg-1,were 10 fold higher than the threshold level and cobalt (19 mgkg-1) was lower than criteria defined by soils standards of Iran and Europe (40 mgkg-1).The amount of studied metals were the highest in ophiolitic hills, and playa soils were in second place in this respect. The amount of metals had a significant decrease in alluvial fan but didn’t drop under threshold level. The lowest amount of heavy metals in alluvial fan was probably because of the high percentage of sand, higher permeability and low soil water retention in all horizons. The negative significant correlation between the elements and sand also confirms this hypothesis. In addition, increasing elements at the depth of 70 cm of the soil in alluvial fan showed that land type (orchards) and long period of irrigation may cause leaching heavy metals from topsoil to the soil depth. However, no significance correlation was observed between the elements and soil organic carbon. The correlation coefficients between three elements revealed that all of them had the similar geologic origin and thus their spatial occurrence in soils can be attributed to the weathering of similar parent material.
Igeo showed an almost constant trend from ophiolitic hill (7.7-7.8) to alluvial fan (7.2-7.7) and a significant decrease in playa (3.9-6.2) for all metals. The variation of EF for nickel had an almost constant trend from ophiolitic hill (with the average of 0.6) to alluvial fan (with the average of 0.7) and a significant decrease in playa (with the average of 0.1). Also, a decreasing trend was observed from ophiolite hill (0.9 and 0.6 for chromium and cobalt, respectively) to alluvial fan (0.5 for both) and playa (0.3 and 0.1 for chromium and cobalt, respectively). A decreasing trend observed for indices can be due to the reduction of sediment transport processes and dilution effect of elements from hill to playa during the deposition and their formation .It seems that the EF index and the Igeo provide more useful information about hydrologic processes during formation of landform and development of soils than absolute values of heavy metals.
Conclusions: The present study showed that the amounts of chromium and nickel were higher than the threshold in studied soil. The soils derived from ophiolitic formation showed the highest values and the soils over alluvial fans had the lowest levels of heavy metals. Useful information was obtained from EF index and Igeo about the prominent geomorphic processes during landforms formation
Future studies should be focused on possible transfer of these elements into the groundwater and also trees of the orchards in Ghal-e Bahman region.
Mahboobeh Tayebi; Mahdi Naderi; jahangard mohammadi; Mahdieh Hosseinjani Zadeh
Abstract
Introduction: Soil texture is one of the majorphysical properties of soils thatplays important roles inwater holding capacity, soil fertility, environmental quality and agricultural developments. Measurement of soil texture elements in large scales is time consuming and costly due to the high volume ...
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Introduction: Soil texture is one of the majorphysical properties of soils thatplays important roles inwater holding capacity, soil fertility, environmental quality and agricultural developments. Measurement of soil texture elements in large scales is time consuming and costly due to the high volume of sampling and laboratory analysis. Therefore, assessing and using simple, quick, low-cost and advanced methods such as soil spectroscopy can be useful. The objectives of this study were to examine two statistical models of Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR) to estimate soil texture elements using Visible and Near-Infrared (VNIR) and Short-Wave Infrared (SWIR) reflectance spectroscopy (400-2450nm).
Materials and Methods: A total of 120 composite soil samples (0-10 cm) were collected from the Kafemoor basin (55º 15' - 55º 25' E; 28º 51' - 29º 11' N), Sirjan, Iran. The samples were air dried and passed through a 2 mm sieve and soil texture components were determined by the hydrometer method (Miller and Keeny 1992). Reflectance spectra of all samples were measured using an ASD field-portable spectrometer in the laboratory. Soil samples were divided into two random groups (80% and 20%) for calibration and validation of models. PLSR and PCR models and different pre-processing methods i.e.First (FD) and Second Derivatives (SD), Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) were applied and compared to estimate texture elements. The cross‐validation method was used to evaluate calibration and validation sets in the first part (80%) and coefficient of determination (R2), Root Mean Square Error (RMSE) and Residual Prediction Deviation (RPD) were also calculated. For testing predictive models, the second part of data (20%) was used and R2 and RMSE of predictive accuracy were calculated.
Results and Discussion: The results of applying two statistical models for estimatingLogClay (%) showed that R2of calibration (R2CV) and validation (R2VAL) datasetranged from 0.22 to 0.72 and 0.12 to 0.54, respectively. The lowest RMSE was computed for PLSR model with SD pre-processing. The highest RPD of calibration (RPDCV) and validation (RPDVAL) were obtained for PLSR with SD pre-processing technique which was classified as a very good and good model, respectively. The results indicated possible prediction of soil clay content by using PCR model with SD pre-processing techniques. In addition, the PCR predicted soil texture elements poorly according to RPD values while the PLSR model with SD pre-processing was the best model for predicting soil clay content. The R2CV and R2VAL of PLSR models for LogSilt (%) varied from 0.34 to 0.73 and 0.27 to 0.58, respectively. The RMSECV varied from 0.14 for FD pre-processing to 0.23 for no-preprocessing and the RMSEVAL rangedbetween 0.18 and0.24. The highest RPDCV (2.07) and RPDVAL (1.59) were obtained for PLSR with FD pre-processing which were classified as very good and good models, respectively. The results of PCR model developments for estimating LogSilt (%) indicated that the highest RPDCV and RPDVAL were, respectively, 1.31 and 1.25 for MSC pre-processing techniques which were rated as poor models. On the contrary to PLSR models, PCR models were not reliable for predicting LogSilt (%).Theresultsof PLSR models for estimatingLogSand (%) revealedthat the highest R2CV and R2VAL were 0.56 and 0.47, respectively and the lowest RMSECV and RMSEVAL were 0.14 and 0.16, respectively which were obtained for SD pre-processing. The RPDCV and RPDVAL values for SD pre-processing in PLSR model were 1.59 and 1.39 which were rated as good and poor performance of predictions, respectively. The highest RPDCV and RPDVALfor PCR models were obtained with the MSC pre-processing indicating poor model. Therefore, PLSR model with SD pre-processing techniques was superior model for estimation of LogSand(%).Overall, PLSR model with SD pre-processing techniques performed better in estimatingclay and sand and PLSR model with FD pre-processing gave better estimate of silt content.
Conclusions: Our finding indicated thatclay and silt contentcan be estimated by using electromagnetic spectrum between VNIR-SWIR region. Further, spectroscopy could be considered as a simple, fast and low cost method in predicting soil texture and PLSR model with SD and FD pre-processing seems to be more robust algorithm to estimateLogClay and LogSilt, respectively.
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.
Mina Kiyani; Mohammad hasan Salehi; jahangard mohammadi; Abdolrahman Mohammadkhani
Abstract
Introduction: The spatial variability of soil properties and its importance in production is a matter-of-debate. Insight about the variability of soil properties as well as the yield of orchards is necessary to achieve higher productivity and better management. Orange is one of the most important export ...
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Introduction: The spatial variability of soil properties and its importance in production is a matter-of-debate. Insight about the variability of soil properties as well as the yield of orchards is necessary to achieve higher productivity and better management. Orange is one of the most important export products in our country and to sustainable production of this product, it is necessary to identify the factors affecting its growth. This study was performed to examine the statistical and geo-statistical relationship of some soil properties with the quantitative, qualitative and vegetative properties of Valencia orange in Kazerun area, Fars province.
Materials and methods: The study area contained 1 hectare (Valencia orange crop) of 205-hectare orchards of Rashnabad on the west of Kazerun, Fars province which is 860 meters above the sea level. 120 soil samples were collected from two depths of 0-40 cm and 40-80 cm (according to the root distribution) in order to investigate the statistical and geo-statistical relationship of some soil properties with the properties of Valencia orange. The sampling in the shade and with a minimum distance of one meter from the trunk of the tested tree was performed (It should be noted that orange trees have been planted as row planting with a distance of 5 meters from each other). In addition to the soil samples were collected for statistical studies from the depths 0-40 and 40-80 cm, the combined sampling of two trees that had less distance to the selected points was performed to measure the performance and quality of orange. It should be noted that all the Valencia trees, their age (about eight years) and management approach were similar. Soil samples were then transferred to the laboratory and air dried, the separately packed and passed through 2 mm sieve.Then, different soil and orange properties including soil texture, pH, EC, %OM, %CaCO3, solution potassium and available phosphorous, iron, zinc and manganese, branch length and branch diameter, trunk perimeter, trunk diameter and tree height, total soluble solids, acid percentage, Vitamin C, number of fruits,orange yield,average fruit weight and average fruit size were determined and the data set were analyzed using Statistica 6.0 software.Variograms of the data were drawn using variowin 2.2 and after determining the best fitted model, kriging maps of soil and fruit were prepared using Surfer9 software.
Results and Discussion: The results of correlation coefficient showed the significant and positive relationship between organic matter and available manganese of topsoil with total yield and number of fruits. According to the results of fitness of standard models to the empirical exponent change, all the properties had spatial structure. Soil properties including the percentage of clay, the percentage of organic matter, soluble potassium, phosphorous, available zinc and manganese in both depths in the eastern and south-eastern direction of the study area were higher than that of the others. These maps had the same spatial distribution pattern in terms of orange properties including the diameter and length of the current year branch, performance, number of fruits, average fruit size, acid percentage and total soluble solids.
Conclusion: The variability coefficient of soil and fruit properties did not show a consistent trend in the study. According to the correlation coefficients, in a few cases, a positive significance correlation was observed as an example, it can be referred to the positive significant correlation of orange yield with the organic matter and manganese in the depth of 0-40 cm. All the studied variables have spatial structure. Among the studied variables, the percentage of organic matter, clay particles percentage, soluble potassium, phosphorous, and available manganese in both depths showed the same spatial distribution pattern as that of the vegetative, qualitative and yield properties of orange including the total performance, fruit number, fruit size, the diameter and length of the current year branch. The proximity of the ranges of soil and fruit properties supports this result and is in line with the results of correlation coefficient. The results also showed that the spatial distribution and pattern of soil and crop variables may be different in a short distance with the same management. The study of the effect of NPK fertilizers on vegetative, qualitative and quantitative properties of orange in the region orchards is recommended. It is also suggested to study the effect of climatic factors on the orange qualitative properties.
parvane mohaghegh; Mahdi Naderi; jahangard mohammadi
Abstract
Introduction: The mismanagement of natural resources has led to low soil quality and high vulnerability to soil erosion in most parts of Iran. To have a sustainable soil quality, the assessment of effective soil quality indicators are required. The soil quality is defined as the capacity of a soil to ...
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Introduction: The mismanagement of natural resources has led to low soil quality and high vulnerability to soil erosion in most parts of Iran. To have a sustainable soil quality, the assessment of effective soil quality indicators are required. The soil quality is defined as the capacity of a soil to function within natural and/or managed ecosystem boundaries. Among approaches which are suggested for soil quality assessment like soil card design, test kits, geostatistical methods and soil quality indices (SQIs), SQIs are formed by combination of soil indicators which resulted from integration evaluation of soil physical, chemical and/or biological properties and processes complement by existing/measureable data, sensitive to land use changes, management practices and human activities and could be applied in different ecosystems. As the measurement and monitoring of all soil quality indicators is laborious and costly, many researchers focused on limited soil quality indicators. There are many methods for identification and determination of minimum data set that influence on soil quality such as linear and multiple regression analysis, pedotransfer functions, scoring functions, principle component analysis and discriminant analysis. Among these methods, principle component analysis is commonly used because it is able to group related soil properties into small set of independent factors and to reduce redundant information in original data set. The objective of this research was to investigate the effects of land use change on soil quality indicators and also the determination of minimum effective soil quality indicators for assessment of soil quality in Choghakhor Lake basin, Chaharmahal and Bakhtiari province, Iran.
Materials and Methods: To meet the goal, Latin hypercube sampling method was applied by using slope, land use and geological maps and 125 composite soil samples were collected from soil surface (0-20 cm). After pretreatments, 27 physical and chemical soil properties like clay, sand and silt content, bulk density (ρb), porosity, organic carbon (OC), particulate organic carbon in macro aggregate (POCmac), particulate organic carbon in micro aggregates (POCmic), proportion of particulate organic carbon in macro aggregates to micro aggregates (POCmac/mic), mean weight diameter (MWD), macro porosity (Mac pore), air content, available water content (AWC), relative water content (RWC), effective porosity (Feff), Dexter index (S), porosity, acidity (pH), electrical conductivity (EC), Nitrogen (N), Phosphorous (P), Iron (Fe), manganese (Mn), Zinc (Zn), Cadmium (Cd), lead (Pb), Copper (Cu) and Cobalt (Co) were measured using appropriate methods.
Results and Discussion: The impact of different land use types on soil quality was evaluated by measuring several soil properties that are sensitive to stress or disturbance and comparison of them. The results showed that measured values of OC, POCmac, POCmic, POCmac/mic, P, Fe, Zn, Mn, Cu, ρb, MWD, AWC, air content and S were in order orchards > crop land > good rangelands > dry lands > weak rangelands. In this region, land use changes have different effects on soil quality. The alternation of good pasture lands to orchard and crop lands caused to enhancement of soil quality parameters. The variation of good pasture to dry land and degradation of good pasture in this area led to decreasing of soil quality. The principle component analysis (PCA) was employed as a data reduction tool to select the most appropriate indicators of site potential for the study area from the list of indicators. Based on PCA, 8 components with eigenvalues ≥ 1 were selected that explained 99.96 percent of variance. The prominent eigenvectors in components were selected using Selection Criterion (SC). The results revealed that the most important component, was the first component with the most dominant measured soil property of Cu. 12 soil quality parameters base on SC were determined in the first component. Stepwise discriminate analysis also was applied for determination significant soil quality indicators from 12 soil parameters. Our result showed that the minimum data set influencing on soil quality were Zn followed by POCmac/mic, clay %, Cu, Mn and P, respectively.
Conclusion: The results suggested that the permanent crop management (Orchard and crop land) had generally a positive impact on soil quality, while dry land and degradation of good pasture had a negative impact on soil quality. Our study suggested that the PCA method and stepwise discriminant analysis for determination of minimum data set can be used in Chughakhur lake basin. In this study from27 of physical and chemical soil properties, the fertility factors such as the content of Zn, Cu, Mn and P and the proportion of particle organic carbon in macro aggregate to micro aggregate and also soil texture components can be used to the minimum data set that evaluates soil quality. These parameters mostly depend on soil management system.
shahrokh fatehi; jahangard mohammadi; Mohammad Hassan Salehi; aziz momeni; Norair Toomanian; Azam Jafari
Abstract
Introduction: Spatial scale is a major concept in many sciences concerned with human activities and physical, chemical and biological processes occurring at the earth’s surface. Many environmental problems such as the impact of climate change on ecosystems, food, water and soil security requires not ...
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Introduction: Spatial scale is a major concept in many sciences concerned with human activities and physical, chemical and biological processes occurring at the earth’s surface. Many environmental problems such as the impact of climate change on ecosystems, food, water and soil security requires not only an understanding of how processes operates at different scales and how they can be linked across scales but also gathering more information at finer spatial resolution. This paper presents results of different downscaling techniques taking soil organic matter data as one of the main and basic environmental piece of information in Mereksubcatchment (covered about 24000 ha) located in Kermanshah province. Techniques include direct model and point sampling under generalized linear model, regression tree and artificial neural networks. Model performances with respect to different indices were compared.
Materials and Methods: legacy soil data is used in this research, 320 observation points were randomly selected. Soil samples were collected from 0-30 cm of the soil surface layer in 2008 year. After preliminary data processing and point pattern analysis, spatial structure information of organic carbon determined using variography. Then, the support point data were converted to block support of 50 m by using block ordinary kriging. Covariates obtained from three resources including digital elevation model, TM Landsat imagery and legacy polygon maps. 23 relief parameters were derived from digital elevation model with 10m × 10m grid-cell resolution. Environmental information obtained from Landsat imagery included, clay index, normalized difference vegetation index, grain size index. The image data were re-sampled from its original spatial resolution of 30*30m to resolution of 10m*10m. Geomorphology, lithology and land use maps were also included in modelling process as categorical auxiliary variables. All auxiliary variables aggregated to 50*50 grid resolutions using mean filtering. In this study Direct and point sampling downscaling techniques were used under different statistical and data mining algorithms, including generalized linear models, regression trees and artificial neural networks. The direct approach was implemented here using generalized linear models, regression trees and artificial neural networks in following three steps, (i) creating the spatial resolution of 50m*50m averaged over 10m*10m grid resolution environmental variables within each coarse grid resolution, (ii) establishing relationships between these coarse grid resolutions of 50m*50m environmental variables and soil organic carbon using GLMs, regression tree and neural networks and (iii) using parameter values gained in step 2 in combination with the original 10m*10mgrid resolution environmental variables to produce predictions of soil organic carbon with10m*10m grid resolution. In point sampling approach, within each coarse resolution (50m*50m), a fixed number of fine grid resolution (10m*10m) were randomly selected to calibrate models at high resolution. In this study, 5 fine grid resolutions (20% fine grid cell within each coarse grid cell) randomlywere sampled at. Then, each selected point overlied on an underlying fine-resolution grid and recorded its environmental variables and averaged fine grid resolution (10m*10m) within their corresponding coarse grid resolution (50m*50m). To calibrate model parameters, these averaged environmental variables were used. The calibrated parameters applied to fine-resolution environmental data in order to predict soil organic carbon at spatial resolution of 10m*10m. The prediction accuracy of the resulting soil organic carbon maps was evaluated using a K-fold validation approach. For this purpose, the entire dataset was divided into calibration (n = 240) and validation (n = 80) datasets four times at random. Prediction of soil organic carbon using calibration datasets and their validation was conducted for each split, and the average validation indices are reported here. The obtained values of the observed and predicted SOC were interpreted by calculating Adjusted R2 and the root mean square error (RMSE).
Results and Discussion: Point pattern analysis showed the sampling design is, generally, representative relative to geographical space .A semi-variogram was used to drive the spatial structure information of soil organic carbon. We used an exponential model to map soil organic carbon using block kriging. Grid resolution block kriging map was 50m*50m. Since the distribution of organic carbon variable and covariates were normal or close to normal for run generalized linear models selected Gaussian families and identity link function. The validation results of this model in point sampling was slightly (Adjusted R2=0.57 and RMSE=0.22) better than the direct method (Adjusted R2 =0.47 and RMSE=0.26).The results of modelling using regression tree in point sampling approach (Adjusted R2 =0.57and RMSE=0.22) is very close to the direct method (Adjusted R2 =0.57 and RMSE=0.23).In implementation of neural networks, the combination of the number of neurons and learning rate for direct downscaling method were obtained 10 and 0.10, respectively and for point sampling downscaling method were, 20 and 0.1 The results of validation obtained from the implementation of this model in point sampling approach (Adjusted R2 =0.45 and RMSE=0.27) is very close to the direct method (Adjusted R2 =0.47 and RMSE=0.28).Validation results indicated that in both downscaling approaches, regression tree (Adjusted R2=0.57, root mean square root (RMSE) =0.22-0.23) has higher accuracy and efficiency better than generalized linear models (Adjusted R2=0.49-0.57, RMSE=0.22-0.26) and neural network (Adjusted R2=0.45-0.47, RMSE=0.27-0.28).
Conclusion: In general, the results showed that the efficiency and accuracy of the sampling point approach is slightly better than the direct approach. Validation results indicated that in both downscaling approaches, regression tree has higher accuracy and performed better than neural network and generalized linear models. However, it is required to perform more research on the different ways of downscaling digital soil maps in the future.
Anahid Salmanpour; Mohammad hasan Salehi; jahangard mohammadi
Abstract
Introduction: Soil organic matter is considered as an indicator of soil quality, because of its role on the stability of soil structure, water holding capacity, microbial activity, storage and release of nutrients. Although changes and trends of organic matter are assessed on the basis of organic carbon, ...
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Introduction: Soil organic matter is considered as an indicator of soil quality, because of its role on the stability of soil structure, water holding capacity, microbial activity, storage and release of nutrients. Although changes and trends of organic matter are assessed on the basis of organic carbon, it responds slowly to changes of soil management. Therefore, identifying sensitive components of organic carbon such as carbon labile lead to better understanding of the effect of land use change and soil management on soil quality.
The main components of sustainable agriculture in arid and semi-arid regions are the amount of water; and soil and water salinity. Water deficit and irrigation with saline water are important limiting factors for cropping and result in adverse effects on soil properties and soil quality. Soil carbon changes is a function of addition of plant debris and removal of it from soil by its decomposition. If the amount of organic carbon significantly reduced due to the degradation of the soil physical and chemical properties and soil quality, agricultural production will face serious problems. To this end, this study was done to evaluate soil quality using soil labile carbon and soil carbon management indices in some agricultural lands of Neyriz area, Fars province, Iran.
Materials and Methods: Five fields were selected in two regions, Dehfazel and Tal-e-mahtabi, consisted of irrigated wheat and barley with different amount of irrigation water and water salinity levels. Three farms were located in Dehfazel and two farms in Tal-e-Mahtabi region. In each farm, three points were randomly selected and soil samples were collected from 0-40 cm of the surface layer. Plant samples were taken from a 1x1 square meter and grain crop yield was calculated per hectare. Water samples were obtained in each region from the wells at the last irrigation. Physical and chemical characteristics of the soil and water samples were determined. Soil labile carbon and carbon management indices also were calculated. In carbon management index calculation, a reference farm was chosen at the vicinity of two regions which were abandoned for years. Statistical analysis like analysis of variance and correlation coefficients was done using SPSS 16.0 software.
Results and discussion: Results revealed that the highest crop yield (with the average of 5.7 tonh-1) was related to the farm which was irrigated with saline water (water EC 8.1 dSm-1) with enough water crop requirement. As this farm received the highest amount of water (with thw volume of 1039.5 mm), it seems that much more irrigation water probably provided the leaching fraction and prevented salt accumulation in the the root zone. Therefore, water salinity could not be a limiting factor for crop growth in this farm. This farm also had the highest content of organic carbon but it didn’t have the highest labile organic carbon and carbon management index (the value of 161.5).
On the other hand, the farm containing the highest labile carbon and carbon management indices (the value of 284), didn’t have the highest crop yield (with the average of 2.6 tonha-1) although it has recieved enough amount of water as well as non-saline irrigation water (water EC 2.28 dSm-1). The more carbon management index represents the higher soil carbon lability and soil quality and it demonstrates that soil have better condition for living microorganisms. Therefore, it can be concluded from the results that the higher soil quality not necessarily resulted in higher crop yield. Many researchers reported that better soil properties are not always resulted in the higher productivity.
Taking everything into account, carbon management index is not related to crop yield, but since it indirectly is related to microbial activity and calculated easily, it could be a useful indicator for rapid assessment of soil quality. Meanwhile, this indicator may be associated with qualitative properties of the crops such as grain protein, which is recommended for future investigations.
Conclusion: Results showed that labile organic carbon is more sensitive to crop management than total organic carbon. Amount of irrigation water and its salinity can influence the labile organic carbon content and thus the soil quality even in the fields with the same crop yield and management. Although, a higher amount of carbon management index does not result in higher yield production, it may be associated with crop quality attributes. More investigation is needed to give better idea in this regard.
N. Namazi; M.H. Salehi; jahangard mohammadi
Abstract
Introduction: Heavy metals released from stationaryand mobile origins can be transported in water, air and soil and can be even absorbed by plants, animals and human bodies. Trace elements are currently of great environmental concern. Nowadays, one of the most important environmental problems is pollution ...
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Introduction: Heavy metals released from stationaryand mobile origins can be transported in water, air and soil and can be even absorbed by plants, animals and human bodies. Trace elements are currently of great environmental concern. Nowadays, one of the most important environmental problems is pollution of agricultural soils occurs by heavy metals due to human activities. Atmospheric subsidence is one of the main sources of these elements which can result from industrial activities, fertilizers, sewage sludge, compost and pesticides. Heavy metals mapping of the atmosphere dusts indicates the status of pollution and its intensity in industrial regions. This information can also be used as a guideline for better management and pollution control. This study was performed to investigate the spatial and temporal availability of heavy metals in atmospheric dusts of Lenjanat region, Isfahan where agricultural land is extensively surrounded by industrial activities like steel making factory (Esfahan), cement making factory (Sepahan and Esfahan) and Bamalead mine.
Materials and Methods: Sampling was done from 60 points with the same altitude(three to six meters from the ground)and their location was recorded by GPS. Glass traps (1×1 m2) covered by plastic mesh (2 × 2 cmvents) were used to trap the dusts for four seasons of the year. Collected dust samples were passed through a 200 mm mesh screen size and the total weight of the dusts and the heavy metals content of Cd, Zn, Cu, Ni and Pbwere determined(with HNO3 60%). Data analysis was performed using Statistical 6.0 software. Analysis of spatial data via variogram was calculated and performed using Variowin, 2.2 software packages. After determination of the best fitting model, kriged maps of the total concentration of heavy metals were prepared by Surfer 8 software.
Results and Discussion: The average concentrations of Zn, Pb and Cd in dust in most parts of the study area were much higher than the soil standard values and the maximum value was around the Zn and Pb mines. However, the concentrations of Cu and Ni were higher than the standard values only in some parts of the area. Comparison of the averages for different seasons showed that in most cases there were significant differences between the concentrations of the various elements. A significant correlation was observed among Pb, Zn and Cd concentrations in all seasons indicating similar origin of these elements. The average dust deposition rate in the summer was more than the other seasons. Moreover, in all seasons except the spring and fall, there was a significant difference between the average dust deposition rates. Kriged maps of Zn, Cd, and Pb showed that the maximum concentrations of these elements occurred near the Pb and Zn Bama mine and the concentrations of these elements decreased with increasingthe distance from the mine. The contamination was lower in the spring and higher in the summer. Based on the kriged maps, samplingfrom one seasononly can be used to assess the trend of element contamination but if the objective focuses on absolute heavy metals values, season strongly influences the results and interpretation from one season can be misleading. The determination of the amounts of dust and their heavy metal contents in different wind directions is recommended to identify the source of dusts and heavy metals.
Conclusion: Results showed a significant difference among the mean values of dusts for the different seasons except for the spring and fall. The mean values of Pb and Cd in all seasons and Zn except for the spring were higher than the threshold values reported for the soils. A significant correlation was observed among the concentrations of some elements which may suggest their same origin. Interpretation of kriged maps showed that zinc and lead Bamamine in the region could be the main source of the contamination of Zn, Pb and Cd. According to quantitative calculations, a low accordance was observed for the pattern and the values of each element in different seasons. This can be related to the wind velocity and its direction, intensity of industrial and mining activities and also the amount of humidity of soil and air during the year. Interpretation of atmospheric data based on one season may be considerably misleading.
Keywords: Atmosphericdusts, Heavy metals, Season
nargess hoseinzadeh; Mohammad hasan Salehi; jahangard mohammadi
Abstract
Now a day, performing soil surveys by minimum time and budget as well as enough accuracy is one of the soil scientists' interests. The present study was investigated to study the effect of sampling density on statistical and geostatistical accuracy of estimation for some of soil properties in Shahrekord ...
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Now a day, performing soil surveys by minimum time and budget as well as enough accuracy is one of the soil scientists' interests. The present study was investigated to study the effect of sampling density on statistical and geostatistical accuracy of estimation for some of soil properties in Shahrekord plain. For this purpose, 240 soil samples (horizon A) with 125 m interval were collected. Then, the physical and chemical analysis included bulk density, soil texture, volume percent gravel, percentage of calcium carbonate equivalent, organic matter, pH and electrical conductivity were determined. The second (120 samples) and third (60 samples) sampling densities were randomly selected the first samplings (240 samples) by considering uniform distribution throughout the region. Results showed that there are no significant differences among the studied soil properties for three sampling intervals. Also, by reducing the number of samples, no regular increase or decrease was observed for coefficient of variation, mean estimation error and root mean square error. The cross validation showed that mean estimation error was close to zero and the root mean square error was low which indicate the acceptable accuracy of estimations. Visual and quantitative interpretation of kriging maps showed high accordance for three sampling densities. Therefore, semi-detailed soil surveys can be replaced for detailed ones which allow saving time and budget.
H.R. Motaghian; A. Hosseinpour; jahangard mohammadi; Fayez Raiesi
Abstract
Rhizosphere is a small zone and has quite different chemical, physical and biological properties from bulk soil. This research was conducted to investigate the availability and fractionation of copper in the wheat rhizosphere and bulk soils by using rhizobox at greenhouse conditions. Three seeds of wheat ...
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Rhizosphere is a small zone and has quite different chemical, physical and biological properties from bulk soil. This research was conducted to investigate the availability and fractionation of copper in the wheat rhizosphere and bulk soils by using rhizobox at greenhouse conditions. Three seeds of wheat were plant in the rhizobox. After 8 weeks, plants were harvested and rhizosphere and bulk soils were separated. Total organic carbon (TOC), dissolved organic carbon (DOC), microbial biomass carbon (MBC) and available Cu (by using 7 chemical procedures) and Cu-fractions were determined in the rhizosphere and bulk soils. The results indicated that TOC, DOC and MBC in the rhizosphere were increased significantly (p
M. Bagheri-Bodaghabadi; M.H. Salehi; J. Mohammadi; N. Toomanian; I. Esfandiarpour Borujeni
Abstract
Abstract
Limitations of traditional (conventional) soil surveys and improvement of information technology have lead soil surveyors to invent new methods which are generally called digital soil mapping (DSM). The aim of these methods is the prediction of soil classes or soil properties based on easily-available ...
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Abstract
Limitations of traditional (conventional) soil surveys and improvement of information technology have lead soil surveyors to invent new methods which are generally called digital soil mapping (DSM). The aim of these methods is the prediction of soil classes or soil properties based on easily-available or measuring environmental variables. The objective of this investigation is to study the efficiency of digital elevation model and its derivates for soil mapping using Soli-Land Inference Model (SoLIM) and credibility of its results in the Borujen area, Chaharmahal-va-Bakhtiari province. Eighteen terrain attributes including height, slope (angle), aspect, curvature, minimum curvature, maximum curvature, tangent curvature, profile curvature, planform curvature, flow direction, flow accumulation, direct radiation, diffuse duration, diffuse radiation, area solar radiation, power index, sediment index and wetness index, were derived from the DEM. These derivates as well as three dominant soil subgroups and seven soil families of the region were used to construct the input data matrix of the model. Results showed an accuracy of 65% and 40% for interpolation and extrapolation of the soils at subgroup level, respectively. The accuracy decreased to half when soil families were considered for credibility of the model. Because of using crisp limitations in American Soil Taxonomy system, assessing soil survey results can be miss-leading partially, whereas using SoLIM model shows well the reality of the soils in the field.
Keywords: SoLIM, Fuzzy logic, Digital soil mapping, Digital elevation model
B. Ghahraman; M. Sadeghi; J. Mohammadi
Abstract
Abstract
Spatial variability of soils makes difficult analysis of soil water flow phenomena especially in a large area such as a watershed. Using scaling methods is a solution in variability problems. The objective of this study was to investigate the effect of the non-linear variability on performance ...
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Abstract
Spatial variability of soils makes difficult analysis of soil water flow phenomena especially in a large area such as a watershed. Using scaling methods is a solution in variability problems. The objective of this study was to investigate the effect of the non-linear variability on performance of the scaling methods of Richards’ equation for modeling infiltration in a watershed. The method of Warrick et al. by adopting van Genuchten hydraulic functions was used and variability of n values (power of van Genuchten hydraulic functions) was considered as the nonlinear variability. Marghmalek watershed, a sub watershed of Zayanderoud, with 97 Sq. kilometers was studied. In addition, ten virtual watersheds with various degrees of variability of n were evaluated which were generated by stochastic method of Monte Carlo. Using HYDRUS-1D model, original and scaled Richards’ equations were solved for infiltration condition with constant hydraulic head and uniform initial soil water content. The results indicated that coefficient of variations of n values in the Marghmalek watershed (equal to 2.57%) is small enough that the scaling method can be used efficiently in modeling infiltration. Therefore, in this watershed, generalized solutions of Richards’ equation can be adequately used instead of individual solutions for every points of the watershed. Evaluations in the virtual watersheds indicated that variability of n values considerably affect the error between the generalized and individual solutions. Based on the result of this study, it can be concluded that scaling methods of Richards’ equation can be adequately applied in the watersheds in which coefficient of variations of n values does not exceed 3%.
Keywords: Scaling, Richards’ equation, Infiltration, Nonlinear variability, Marghmalek watershed
Sh. Gholami; S.M. Hosseini; J. Mohammadi; A. Salman Mahiny
Abstract
Abstract
Soil invertebrate and their spatial pattern are affected strongly by environmental factors. Spatial variability of soil properties is one of the most important reasons of the macrofauna variability. This study was conducted to investigate the spatial variability of soil properties and soil ...
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Abstract
Soil invertebrate and their spatial pattern are affected strongly by environmental factors. Spatial variability of soil properties is one of the most important reasons of the macrofauna variability. This study was conducted to investigate the spatial variability of soil properties and soil macrofauna biomass in the riparian forest landscape of Karkhe River. Soil macro fauna were sampled using 200 sampling point along parallel transects (perpendicular to the river). The sampling procedure was hierarchically, maximum distance between samples was 0.5 km, but the samples were taken at shorter distance at different location of sampling. Soil macro fauna were extracted from 50 cm×50 cm×25 cm soil monolith by hand-sorting procedure. At each transect point, approximately 1m distance from the macro fauna sample location, three 50 cm×50 cm×25 cm samples were taken from 0-25 depth of soil. Soil macro fauna biomass, pH, EC and soil texture were measured. Then the spatial continuity, using geostatistics (variogram) were described. The maps obtained by block kriging. The variograms of variable revealed the presence of spatial autocorrelation. The range of influence was 1728 m for macro fauna biomass, 1800 m for pH, 1536 m for EC, 2964 for sand (%), 2100 for silt (%) and 3264 for clay (%).The kriging maps showed that the soil macro fauna biomass and soil properties have spatial variability. In this research, the spatial pattern of soil macro fauna biomass is similar with the spatial pattern of silt and soil pH, that was shown in correlation.
Keywords: Spatial pattern, Soil macrofauna biomass, Soil properties, Variogram, Block kriging
H.R. Motaghian; J. Mohammadi
Abstract
Abstract
The effect of land use type on soil functioning within an ecosystem can be assessed and monitored using soil quality attributes. Such studies, which are carried out to create a balance between the biological production and the maintenance and improvement of land resource quality, provide a ...
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Abstract
The effect of land use type on soil functioning within an ecosystem can be assessed and monitored using soil quality attributes. Such studies, which are carried out to create a balance between the biological production and the maintenance and improvement of land resource quality, provide a framework for land degradation control and also for identification of sustainable management. In this research investigated the effect of different land uses on soil physical quality indices. Three land uses including a natural pasture, dryland farming and irrigated farming were selected. From natural pasture 54 samples, dryland farming 40 samples and irrigated farming 17 samples were collected in the surface soil (0-15cm). Saturated hydraulic conductivity, infiltration rate, bulk density, soil distribution size, soil erodibility index, organic carbon and water aggregate stability (three classes) were determined for each land use. The results showed that mean, minimum and maximum of saturated hydraulic conductivity in irrigated farming land use is more than others land uses. Water aggregate stability index the macroaggregates (>2 mm) in irrigated farming land use is lower than others land uses. In the among studies variables, saturated hydraulic conductivity, clay percentage, soil erodibility index and water aggregate stability in macroaggregates in different land uses are significant differences in 5% level. Soil erodibility index and water aggregate stability for macroaggregates seems to be the most reliable soil quality indices for the area.
Keywords: Water aggregate stability, Soil quality index, Soil erodibility index
M. Dayani; M. Naderi; J. Mohammadi
Abstract
Abstract
Mine excavation, concentration and transportation of minerals make Southern Esfahan municipality and suburb of Sepahanshahr vulnerable to pollution and endanger their people by heavy metals. Nowadays, spectrophotometers and spectral reflectance of soils in different parts of spectrum are applied ...
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Abstract
Mine excavation, concentration and transportation of minerals make Southern Esfahan municipality and suburb of Sepahanshahr vulnerable to pollution and endanger their people by heavy metals. Nowadays, spectrophotometers and spectral reflectance of soils in different parts of spectrum are applied for estimating soil characteristics and pollutants. This research was conducted to study the potential of Landsat ETM+ data for estimating and mapping spatial distribution of heavy elements in the Southern Isfahan municipality. During a field survey 100 surface soil samples were collected randomly. Samples were air dried and fine earth was treated by 4 M HNO3 (at 80 °C) and total Pb, Zn and Cd concentration was measured by Atomic Absorption Spectrophotometer and ICP. Statistical analysis reveals negative and significant correlation coefficient between concentration of heavy metals and visible and NIR data and consequently, possible delineation of heavy metals. Spatial distribution of Pb, Zn and Cd concentration mapped using several stepwise multiple regression equations. Results indicate that concentration of Pb, and Zn are above the thresholds and that of Cd is not serious at the moment in proximity of Sepahanshahr.
Keywords: Soil pollution, Heavy metal, Landsat ETM+, Mapping
Sh. Gholami; S.M. Hosseini; J. Mohammadi; A.R. Salman Mahiny
Abstract
Abstract
Information about the spatial patterns of soil biodiversity is limited though required, e.g. for understanding effects of biodiversity on ecosystem processes. This study was conducted to determine whether soil macrofauna biodiversity parameters display spatial patterns in the riparian forest ...
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Abstract
Information about the spatial patterns of soil biodiversity is limited though required, e.g. for understanding effects of biodiversity on ecosystem processes. This study was conducted to determine whether soil macrofauna biodiversity parameters display spatial patterns in the riparian forest landscape of Karkhe. Soil macrofauna were sampled using 200 sampling point along parallel transects (perpendicular to the river). The sampling procedure was hierarchically, maximum distance between samples was 0.5 km, but the samples were taken at shorter distance at different location of sampling. Soil macrofauna were extracted from 50 cm×50 cm×25 cm soil monolith by hand-sorting procedure. Abundance (Number of animals), diversity (Shannon H’ index), richness (Menhinick index) and evenness (Sheldon index) were analyzed using geostatistics (variogram) in order to describe and quantify the spatial continuity. The variograms of indices were spherical and revealed the presence of spatial autocorrelation. The range of influence was 1724 m for abundance, 1326 m for diversity, 1825 m for richness and 1450 for evenness. The variograms featured high ratio of nugget variance to sill (abundance (52%), diversity (55%), richness (53%) and evenness (35%)). This showed that there was the small-scale variability and proportion of unexplained variance. The kriging maps showed that the soil macrofauna have spatial variability.
Key words: Spatial pattern, Soil macrofauna, Geostatistics, Variogram, Kriging
M. Boyrahmadi; F. Raiesi; J. Mohammadi
Abstract
Abstract
Soil microbiological criteria are a complex reflection of interactive metabolic processes that may not be evaluated only by measuring a single parameter but rather it requires the simultaneous determination of more parameters and combining them. The objective of this research was to study enzyme ...
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Abstract
Soil microbiological criteria are a complex reflection of interactive metabolic processes that may not be evaluated only by measuring a single parameter but rather it requires the simultaneous determination of more parameters and combining them. The objective of this research was to study enzyme activities: microbial biomass carbon ratios in salinized and none-saline soils in the presence and absence of plant's rooting system. This ratio indicates the amount of enzyme activity per unit of microbial biomass. In this study, five levels of salinity using NaCl, CaCl2, MgCl2 and KCl; with 2:1:1:1 ratio and three soils (unplanted soil, soil planted with wheat and clover) replicated three times consisted our factorial experiment arranged in a completely randomized design. Results showed that salinity caused significant reduction in enzyme activities: microbial biomass ratio in all three soils. Furthermore, at all salinity levels, unplanted and planted treatments had a significant effect on urease activity: microbial biomass carbon ratio and arylsulphatase activity: microbial biomass carbon ratio. However, there were no significant differences in ß-glucosidase: microbial biomass carbon ratio and alkaline phosphatase: microbial biomass carbon ratio among the three soils at all salinity levels. In the other words, the presence of plant did not have any substantial effect in increasing or reducing microbial ability to produce and synthesize these enzymes. The effect of planted and un-planted treatments on the ratio of L-glutaminase, saccharase and acid phosphatase to microbial biomass carbon in different salinity treatments were variable. In summary, results showed that the presence of plants may support the synthesis of some enzymes by soil microorganisms. But the synthesis of some other enzymes is not affected by the presence and absence of plants living roots. In other words, the effect of roots and its exudates on moderating the effect of salinity on the amount of the enzymes synthesized by soil microbes depends on the salinity level, plant type and enzyme.
Keywords: Salinity, Soil enzyme activity, Microbial biomass carbon ratio, Uncultivated soil, Planted soil, Wheat, Clover
M. Dayani; J. Mohammadi; M. Naderi
Abstract
Generally heavy metals exist in all soils, but soil pollution is rising by time due to human activities. Soils in the proximity of mines are more pruning to pollution of heavy metals due to mine exploring and excavation. This research was carried out to evaluate the soil pollution of Sepahanshahr Suburb ...
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Generally heavy metals exist in all soils, but soil pollution is rising by time due to human activities. Soils in the proximity of mines are more pruning to pollution of heavy metals due to mine exploring and excavation. This research was carried out to evaluate the soil pollution of Sepahanshahr Suburb with Pb, Zn and Cd. During a field work campaign 100 soil samples were selected randomly from 9000 ha area. The soil samples were treated with 4 M HNO3. Total amounts of Pb, Zn and Cd were measured using Atomic Absorption Spectrometer. The results indicated that concentration of Pb and Zn were beyond the defined soil pollution thresholds (
I. Esfandiarpour Borujeni; N. Toomanian; M.H. Salehi; J. Mohammadi
Abstract
Abstract
Geopedology is a systematic approach of geomorphic analysis for soil mapping which focuses the field operation mainly on sample area. The purpose of this study is to determine the credibility of generalization of the results of geopedological approach for similar landforms in the Borujen region, ...
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Abstract
Geopedology is a systematic approach of geomorphic analysis for soil mapping which focuses the field operation mainly on sample area. The purpose of this study is to determine the credibility of generalization of the results of geopedological approach for similar landforms in the Borujen region, using diversity and similarity indices in a soil taxonomic hierarchical structure. After a primary interpretation of the study area on air photos (1:20000 scale), the largest delineation of Pi111 geomorphic unit was selected and 19 pedons with an approximate 125 m interval were excavated, described and sampled. The credibility of generalizing the results of the geopedological approach for the studied unit was tested by comparison with 15 pedons in a similar unit outside the sample area, named the validation area. Results showed that as the category decreases from order to soil family, the Shannon's diversity index increases in both the sample and validation areas. A significant difference at 95% confidence level was observed for pedodiversity mean values of two areas at family level. Soil diversity also remains high through the soil taxonomic hierarchy when we change the understanding level and consider the horizon/genetic diversity in both the sample and validation areas. Jaccard index and proportional similarity also indicated that up to subgroup level, the geopedological approach can be used for generalization of the similar geomorphic unit results and it does not have a good efficiency for lower soil taxonomic levels (family and series). Therefore, the use of landform phases and also phases of soil families and/or series for each of landform phases is recommended to increase the accuracy of geopedological results.
Key words: Geopedology, Pedodiversity, Similarity index, Sample area, Validation area
H. Afshar; M.H. Salehi; J. Mohammadi; A. Mehnatkesh
Abstract
Abstract
The quality of soil maps depends upon their ability to show the soils variability. Thus, the accuracy of the maps used for crop recommendations is due to the accuracy of soil maps. This study was performed to investigate the amount of soil properties and crop yield spatial variability in S ...
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Abstract
The quality of soil maps depends upon their ability to show the soils variability. Thus, the accuracy of the maps used for crop recommendations is due to the accuracy of soil maps. This study was performed to investigate the amount of soil properties and crop yield spatial variability in S 2 and S3 units of a semi-detailed quantitative suitability map (1:50000 scale) for irrigated wheat in Shahr-e-Kian area, Chaharmahal-Va-Bakhtiari province. Eighty soil samples were collected in each land unit at 0-30 cm depth using multi-scale sampling method to determine available P, K, total N, %O.M., %CaCO3 equivalent, soil texture and particle size distribution, EC and pH. A 0.5×0.5 m plot of wheat was harvested at each of 160 sites previously sampled to determine crop biomass, 1000 seeds weight and harvest index. The highest CV was related to available potassium (47.43 for S2 and 46.46 for S3 units, respectively) and the lowest one was related to pH (1.07 for S2 and 0.925 for S3 units, respectively). Variography showed a good spatial structure for all variables in both land units. Ranges for variograms were from 17.75 for N to 61.06 m for EC in S2 unit and from 17.47 for P to 62.93 m for 1000 seeds weight in S3 unit. Kriging maps showed high spatial variability of soil properties as well as biomass, wheat yield and harvest index within two land units. This indicates that suitability maps have not enough credibility for precision agriculture. Using information of all pedons as well as representative pedons in land units and combining the information of suitability maps with geostatistical data can be a choice way to improve the accuracy and quality of land suitability maps.
Keywords: Kriging, Precision agriculture, Soil properties, Spatial variability, Suitability map, Wheat yield
H.R. Motaghian; A. Karimi; J. Mohammadi
Abstract
Abstract
Analysis and interpreting spatial variability of soil hydraulic and physical properties on a catchment scale is important in hydrological modeling and decision making. This study was conducted to analyze and interpret spatial distribution of selected soil hydraulic and physical properties including ...
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Abstract
Analysis and interpreting spatial variability of soil hydraulic and physical properties on a catchment scale is important in hydrological modeling and decision making. This study was conducted to analyze and interpret spatial distribution of selected soil hydraulic and physical properties including clay, silt, and sand content, bulk density (BD), infiltration rate (IR) and saturated hydraulic conductivity (Ks) in Marghmalek watershed. In this research, 111 soil samples were collected in a regular spaced grid pattern of 1 km from 0-30 cm depth in order to determine the soil size distribution. In addition, at each sampling site undisturbed soil samples were obtained from the topsoil using cylinder method to determine soil bulk density and consequently the saturated hydraulic conductivity. Saturated hydraulic conductivity was determined using the falling head method. Infiltration tests were conducted on all 111 sample sites using double-ring infiltrometers. Maximum coefficient of variation (CV) was found for IR (72%) and Ks (67%). In contrast, the minimum CV value of 8% was found for BD. Statistical analysis illustrated that there was a significant difference (P