H.R. Rafiei; A. Jafari; A. Heidari; Mohammad Hady Farpoor; A. Abbasnejad
Abstract
Introduction: Soil carbon (C) sequestration is recognized as a potentially significant option to off-set the elevation of global atmospheric carbon dioxide (CO2) concentrations. Soils are the main sink/source of carbon and also, an important component of the global C cycle. Total soil carbon (C) comprises ...
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Introduction: Soil carbon (C) sequestration is recognized as a potentially significant option to off-set the elevation of global atmospheric carbon dioxide (CO2) concentrations. Soils are the main sink/source of carbon and also, an important component of the global C cycle. Total soil carbon (C) comprises of the soil organic C (SOC) and the soil inorganic C (SIC) components. The soil inorganic C (SIC) stock mainly consists of carbonates and bicarbonates. Processes governing the dynamics of the soil carbon stock differ among ecoregions and strongly interact with soil properties. Understanding the distribution of organic and inorganic carbon stocks in soil profiles is essential for assessing carbon storage at the regional and global scale. Although global estimates provide a general view of carbon stock levels, accurate local estimates and factors affecting soil carbon dynamics are very important. As a result, there is an essential requirement for accurately estimating the distribution of carbon reserves and their differences with regard to soil properties. Materials and Methods: The study area is located in the Sardooeyeh region, South of Kerman, under semiarid conditions. A total of 5 soil profiles were excavated. Percentage of coarse fragments (> 2 mm) using a 2 mm sieve, total organic C by the K2Cr2O7-H2SO4 oxidation method of Walkley-Black, soil inorganic carbon using the Gravimetric carbonate meter method were determined. Bulk density was measured by drying core samples in an oven overnight and dividing the weight of dry soil by the volume of the core occupied by the soil after correction for coarse fragments. Results and Discussion: Organic carbon in the surface horizons of all profiles is maximum due to vegetation and decreases with increasing soil depth. As the altitude increased, the amount of organic carbon increased in the surface horizons. Lower temperature and higher humidity at higher altitudes lead to the lower organic matter decomposition and consequently higher organic carbon content of the soil. Although the upper soil layers had the maximum soil organic C content, the maximum soil inorganic C content was observed in the sub-surface layers. The soil organic carbon storage was between 5.52 to 9.48 kg m-2 and the storage of soil inorganic carbon in profiles was between 14.41 and 91.34 kg m-2. The total soil carbon storage in the profiles varied between 19.92 to 100.83 kg m-2 and the average was 42.66 kg m-2. The average of soil organic carbon storage in 0-25, 25-60, 60-120 cm depths were 2.6, 1.97 and 1.26 kg m-2, respectively. The amount of soil inorganic carbon storage in 0 -25, 25-60 and 60-120 cm depths were equal to 2.7, 10.40 and 8.26 kg m-2, respectively. Therefore, it seems that more than 50% of the total soil inorganic carbon storage is stored at a depth of 25-60 cm from the soil surface. The portion of inorganic carbon storage of total soil carbon was 77.5%, and about 89% of it was stored in sub-surface horizons (below 25 cm). The portion of organic carbon storage of total soil carbon was 22.4%. It seems that an increase in the partial pressure of CO2 in soils leads to some dissolution of the pedogenic carbonate in the top soil. Dissolved pedogenic carbonate transfers to the deep soil and then re-crystallizes under relatively dry conditions and low CO2. Conclusion: The results showed that soil organic carbon storage was mostly higher in surface horizons, and soil inorganic carbon storage was higher in sub-surface horizons. On average, the ratio of soil inorganic carbon storage to soil organic carbon storage was 4.27. The high percentage of soil inorganic carbon storage in total soil carbon, shows that inorganic carbon plays a very important role in semi-arid regions. Almost 89% of the soil inorganic carbon content and about 80% of the total soil carbon were accumulated in the sub-surface horizon of soil (below 25 cm), indicating the importance of sub-surface soil for storing carbon in semi-arid regions.
E. Mehrabi Gohari; H.R. Matinfar; Ruhollah Taghizadeh-Mehrjardi; A. Jafari
Abstract
Introduction: Soil texture is the most important environmental variable because it plays a very important role in reducing the quality of land and water transfer processes, soil quality control and fertility. On the one hand, soil texture components are the basis of environmental predictive models and ...
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Introduction: Soil texture is the most important environmental variable because it plays a very important role in reducing the quality of land and water transfer processes, soil quality control and fertility. On the one hand, soil texture components are the basis of environmental predictive models and digital mapping of soil and on the other hand, soils are temporally and spatially variable, thus distinguish zoning and their monitoring with traditional sampling methods and laboratory analysis is very costly and time consuming. As a result, the development of methods for analyzing the soil and for required information has become very important. Visible and near infrared spectroscopy (VIS-NIR) is widely used to estimate soil physical properties and estimate soil texture. The present study aims to predict soil texture using spectral measurements and artificial neural network models and partial least squares regression.
Materials and Methods: The study area in southeastern Iran is approximately 70 km from Kerman. In the study area, based on the hypercube technique, 115 profiles were identified and then horizons were sampled. In this way, for each point of study, the necessary information, including the location of the profile on the ground, the type of geomorphic unit and the type of materiel, were recorded and taken from the horizons of each profile. In all soil samples, after drying and passing through 2 mm soil, the soil texture was measured by hypercube. Spectral radiometer was used to measure the spectral reflection of soil samples. The soil samples were air dried and sieved and then placed in a petri dish with an approximate diameter of 10 cm and transferred to the dark room for spectral analysis. Each specimen was tested four times (for each 90 degree sequential rotation) to remove the effects of a change in the radiation geometry. Soil samples were scanned, and absolute reflections at a spectral range of 2500-350 nm yielded 2150 spectral data points (SDPs) per soil sample with a spectral resolution of one nanometer. Finally, to construct a suitable model for forecasting the percentage of clay, sand, and silt, the least squares model was used with the number of factors 1 to 10 by Artificial Neural Network (ANN) modeling using JMP software Work.
Results and Discussion: The reflectance spectrum of the visible range - near infrared - was measured for specimens. Since preprocessing of spectral data has an effective role in improving the calibration, in order to perform spectral preprocessing, two first nodes of the first and the end of the spectra were first removed in the range of 350-400 and 2450-2500 nm. In addition, the interruption due to the change in the detector in the range of 900 to 1000 nm was also eliminated. Types of preprocessing methods were performed on spectral data. Then, using partial least squares regression analysis, the best model was produced when the first derivative was fitted to reflection values. The explanation coefficients for this low and unacceptable model were obtained. Therefore, using partial least squares regression analysis, the best wavelengths were selected to predict the percentage of clay, sand, soil, and extracted from the model. Then it was used as input in the neural network model. To determine the best combination, root error index and error coefficient were used. The results of artificial neural network showed that the number of neurons 9.8 and 10 had the best composition for predicting clay, sand and soil silt. The root-squared error results for clay, sand, and soil silt were 3.42, 6.94, and 4.383 respectively. Also, the results of the explanatory factor were 0.84, 0.83 and 0.81, respectively. After obtaining the optimal structure in the artificial neural network training phase described above, the trained network has been tested on the test data to determine the accuracy of this model to predict clay, sand and silt of surface soil. The root-squared error results for clay, sand and silt components were obtained at 5.54.9.14 and 7.01. Also, the results of the explanatory factor were 0.76.0.70 and 0.73 respectively. The best result of the prediction for partial least squares regression was obtained for the sand sample. The results indicate that the neural network performance is better than partial least squares regression, which is consistent with Mouazenet. al (2010) and also ViscarraRossel R. et. al (2009). Acceptable performance of the artificial-neural network can be attributed to the ability of this model for non-linear behavior of soil texture in visible spectroscopy. In this study, specific wavelengths, which Ben Finder et al. (2003) obtained in the study on the soils of Israel, were used. This conclusion confirms that various types of soil can be modeled using specific wavelengths. The advantage of this study is that, when using the artificial neural network, no pre-processing of reflection data is required before applying the model. Since the relationship between the percentage of soil particles (clay and gravel) and the reflection of the soil is not linear, the neural network method is very useful for analyzing the relationship between soils. Finally, the map of clay, sand and silt and map of soil texture was prepared by artificial neural network method in GIS environment.
Conclusion: The results of this study showed that the neural-dynamic network has a better performance than partial least squares regression. Calibration models designed and used in this study can be transported for use with other soils. When the partial least squares regression model was implemented, it had a very low accuracy (R2 ~ 0.1-0.3); on the contrary, the neural network-based method had high accuracy and less error. Note that although neural-dynamic modeling estimates higher precision results from soil texture, both approaches depend on wavelength selections, and so wavelengths should be selected before using any of the two models. To be finally, a meaningful relationship between the selected wavelengths and the percentage of clay, sand and silt in the present study indicates that soil texture is not only possible but also reliable by reflection spectroscopy.
Maryam Yousefifard; A. Jafari
Abstract
Introduction: In recent decades, industrial and technological advancements have led to the gradual increase of heavy metal concentrations. As such, this phenomenon of heavy metals being present in the environment at high concentrations causes deleterious effects on various terrestrial creatures and human ...
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Introduction: In recent decades, industrial and technological advancements have led to the gradual increase of heavy metal concentrations. As such, this phenomenon of heavy metals being present in the environment at high concentrations causes deleterious effects on various terrestrial creatures and human beings. Mercury (Hg) is one of the most toxic elements and can cause renal and neurotoxicity to humans and wildlife. It has been identified as a priority toxic substance in many countries. It is, however, rare to find information on Hg in soils from industrialized areas of Iran in literature. In order to ascertain the distribution of Hg, as well as the extent of contamination with Hg, and to provide policymakers with remediation measures for the affected soils, a study of surface soils was conducted in areas around of Kerman cement plant.
Materials and Methods: Soil samples were collected from the depth of 0 to 20 cm. 103 samples were taken and analyzed. Mercury concentration in soil samples were determined by atomic adsorption method coupled Graphite furnace. Statistical analysis and indices calculation were performed by SPSS and EXCEL, respectively, and distribution maps were prepared by kriging method in ArcGIS software. For evaluating pollution, Geoaccumulation index, enrichment factor and contamination factor were also calculated and interpreted.
Results and Discussion: The mercury concentration in soil samples ranged from 6.70 to 340.96 μg/kg, with a mean value of 164.06 μg/ kg. Mercury is naturally present in very low concentrations in the soil. The concentration of this element in soils ranges from 0.01 to 0.5 mg/kg around the world. The average Hg concentration in the earth crust is reported to be 80 μg / kg. In soils of the study area, the Hg concentration was higher than most of the reported values for soils worldwide and earth crust. This indicates that industrial activities have increased the concentration of mercury in the soil. In fact, the concentration of mercury more than the amount of earth crust indicates the onset of contamination due to various anthropogenic activities. The coefficient of variation of mercury concentration in the soil was 55%, which shows a high variability (CV≥ 35%) according to the classification proposed by Wilding et al. (19). The high variability coefficient shows the heterogeneous and non-uniform distribution of the property. Therefore, there is a high concentration of mercury in some areas of the study region. In other words, soil was affected by external factors in some areas. Based on the cleaning standards of soil for mercury in soils used for industrial purposes in some countries, all soil samples in the studied area have a much lower concentration of mercury than standard values. In other words, although the activity of the cement plant has increased the concentration of mercury in the soil, it can continue its industrial activity. The plant’s managers should, however, take a close look at the release of this metal and other pollutant. According to the results derived from Igeo, Hg was graded as unpolluted to moderately polluted. Low levels of contamination (CF <1) to significant contamination (3.00 ≤ CF <6.00) of mercury were observed based on the contamination factor. The results suggest that anthropogenic sources control the concentration of mercury in the soil. The average contamination factor more than one (CF> 1) indicates that the soils of this region have been exposed to mercury contamination. Spatial distribution map indicates that the highest concentration of mercury in the soil is between 200 and 341 μg/kg, which was observed around the factory and south-east of the region. Release of mercury in the environment is related to natural processes and human activities. Mercury release due to human activities is mainly due to combustion of fossil fuels, iron ore processing, steel industry and cement plants. Considering the high concentrations of mercury in the southeastern part of the region, the lower part of the plant, it seems that environmental factors such as the topography of the area may affect its distribution. The high concentrations of Hg were observed at low elevations, on the south side, and over the areas with relatively low slope gradients.
Conclusion: The results demonstrated that the concentration of Hg was higher than most of the reported values for soils worldwide and earth crust. This indicates that industrial activities have increased the concentration of mercury in the soil. According to the results derived from Igeo, Hg was graded as unpolluted to moderately polluted. In addition, the level of contamination was identified to be low to high, based on the contamination factor (CF). The spatial distribution map of the total concentration of mercury shows that the highest concentration of mercury was observed around the factory and to the south and southeast of the region. The high concentrations of this metal were at low elevations and on the south side of the catchment and in areas with relatively low slope gradients. It is concluded that although the concentration of this pollutant is not critical in the study area, due to the close proximity of the industrial area to the residential area, planning to control the release of this metal and other pollutants should be seriously considered.
zohreh mosleh; Mohammad hasan Salehi; azam jafari; Abdolmohammad Mehnatkesh; Isa Esfandiarpoor Borujeni
Abstract
Introduction: There is a concern with assessment of land performance when used for specific purposes. Land evaluation analysis is considered as an interface between land resources and land use planning and management. However, the conventional soil surveys are usually not useful for providing quantitative ...
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Introduction: There is a concern with assessment of land performance when used for specific purposes. Land evaluation analysis is considered as an interface between land resources and land use planning and management. However, the conventional soil surveys are usually not useful for providing quantitative information about the spatial distribution of soil properties that are used in many environmental studies. Development of the computers and technology lead to digital and quantitative approaches have been developed. These new techniques rely on finding the relationships between soil and the auxiliary information that explain the soil forming factors or processes and finally predict soil patterns on the landscape. Different types of the machine learning approaches have been applied for digital soil mapping of soil classes, such as the logistic and multinomial logistic regressions, neural networks and classification trees. To our knowledge, most of the previous studiesapplied land suitability evaluation based on the conventional approach. Therefore, the main objective of this study was to assess the performance of digital mapping approaches for the qualitative land suitability evaluation in the Shahrekord plain of Chaharmahal-Va- Bakhtiari province.
Materials and Methods: An area in the Shahrekord plain of Chaharmahal-Va-Bakhtiari Province, Iran, across 32º13′ and 32º 23′N, and 50º 47′ and 51º 00′E was chosen. The soils in the study area have been formed on Quaternary shale and foliated clayey limestone deposits. Irrigated crops such as wheat, potato, maize and alfalfa are the main land uses in the area. According to the semi-detailed soil survey, 120 pedons with approximate distance of 750 m were excavated and soil samples were taken from different soil horizons. Soil physicochemical properties were determined. The average of soil properties was determined by considering the depth weighted coefficient up to 100 and 150 centimeters for annual and perennial crops, respectively. Qualitative land suitability evaluation for main crops of the area including wheat, maize, alfalfa and potato was determined by matching the site conditions (climatic, hydrology, vegetation and soil properties) with studied crop requirement tables presented by Givi (5). Land suitability classes were determined using parametric method. Land suitability classes reflect degree of suitability as S1 (suitable), S2 (moderately suitable), S3 (marginally suitable) and N (unsuitable). Different machine learning techniques, namely artificial neural networks (ANNs), boosted regression tree (BRT), random forest (RF) and multinomial logistic regression (MLR) were used to test the predictive power for mapping the land suitability evaluation. Terrain attributes, normalized difference vegetation index (NDVI), clay index, carbonate index, perpendicular vegetation index (PVI), geology map, existing soil map (1:50000 scale) and geomorphology map were used as auxiliary information. Finally, all of the environmental covariates were projected onto the same reference system (WGS 84 UTM 39 N) and resampled to 50 * 50 m since the soil samples were collected with approximate distance of 750 m (1:50,000 scale). According to the suggested resolutions for digital soil maps, the pixel size 50 *50 m fits to a 1:50,000 cartographic scale. Training the models was done with 80% of the data (i.e., 96 pedons) and their validation was tested by the remaining 20% of the dataset (i.e., 24 pedons) that were split randomly. The accuracy of the predicted soil classes was determined using error matrices and overall accuracy.
Results and Discussion: The results showed that climatic conditions are suitable (S1) for wheat and potato whereas the most important limiting factors for maize and alfalfa were the average of minimum temperature and average temperature, respectively. Results demonstratedthat among the studied models, random forest showed the highest performance to predict the land suitability classes and subclasses. However, different models had the same ability for prediction. In addition, the overall accuracy decreased from class to subclass for all of the crops. The terrain attributes and remote sensing indices (normalized difference vegetation index and perpendicular vegetation index) were the most important auxiliary information to predict the land suitability classes and subclasses.
Conclusion: Results suggest that the DSM approaches have enough accuracy for prediction of the land suitability classes that affecting land use management. Although digital mapping approaches increase our knowledgeabout the variation of soil properties, integrating the management of the sparse lands with different owners should be considered as the first step for optimum soil and land use management.
Leili Neghadzamani; Mohammad Hady Farpoor; Azam Jafari
Abstract
Introduction: Genesis and development of soils are highly affected by soil forming factors and processes. Climate and topography (landform) are among the factors affecting weathering of parent material and genesis and development of soils in an area. Besides, various morphological, physical, and chemical ...
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Introduction: Genesis and development of soils are highly affected by soil forming factors and processes. Climate and topography (landform) are among the factors affecting weathering of parent material and genesis and development of soils in an area. Besides, various morphological, physical, and chemical properties, micromorphology, and clay mineralogy of soils at different geomorphic positions are usually affected by different soil forming factors including parent material and climate. The objectives of the present research were to study the effect of climate and geomorphology on physicochemical properties, micromorphology, and clay mineralogy of the soils in Rayen area, Kerman Province.
Materials and Methods: The study area starts from Hezar mountain elevations close to Rayen city (south east of Kerman Province) and extends to plateaus surfaces around Bam city. Quaternary and Neogene formations were found from geology point of view. Mean annual precipitation is in the range of 200-300 mm. Five landforms including rock pediment, mantled pediment, piedmont plain, plateaus, and valley were investigated during field work followed by topography, geology, and Google map studies in the area. According to 1:2500000 map provided by Soil and Water Research Institute, xeric and aridic soil moisture regimes together with mesic soil temperature regime were found in the area. Nine representative pedons were studied based on climatic regimes and different geomorphic surfaces. Pedons 1 and 2 were located on rock pediment with an aridic soil moisture regime. On the other hand, pedon 3 was located on the same surface, but with xeric moisture regime. Pedons 4 and 5 were also located on mantled pediment with aridic and xeric moisture regimes, respectively. Pedon 6 was located on piedmont plain and in the aridic moisture zone. Pedons 7, 8 (Plateaus), and 9 (Valley) were all in the aridic moisture zone. Physical and chemical properties, micromorphology, and clay mineralogy of soils were investigated and the soils were classified using USDA Soil Taxonomy (12th edition) and latest edition of World Reference Base for Soil Resources (WRB) systems.
Results and Discussion: Cambic, gypsic, argillic (or argic), calcic, and petrocalcic horizons were investigated during field and laboratory studies. Typic Haplocambids (pedons 1 and 2), Typic Calcixerepts (pedon 3), Typic Torriorthents (pedon 8), Calcic Petrocalcids (pedon 7), Typic Calcigypsids (pedon 6), Typic Xerorthents (pedon 5), Typic Haplocalcids (pedon 4), and Typic Calciargids (pedon 9) were classified using Soil Taxonomy (2014) and Gypsisols (pedon 6), Calcisols (pedons 3, 4, 7, and 9), Cambisols (pedons 1 and 2), and Regosols (pedons 5 and 8) Reference Soil Groups were determined using WRB (2015) system. Electrical conductivity increased from rock pediment toward valley and decreased from aridic toward xeric soil moisture regimes. Formation of argillic horizon in pedon 5 (Xeric moisture regime) was attributed to the climate of present time, but pedons 8 and 9 with aridic moisture regime could probably have experienced a climate with more available humidity for argillic horizon to be formed. Besides, petrocalcic horizon formation in pedon 7 was also attributed to a climate with more available humidity in the past. A buried soil (Btkb horizon) was determined in pedons 5 and 8 under the modern soil. Soil moisture regime change from aridic to xeric in rock pediment surface caused change of Aridisol to Inceptisol, but classification of soils in WRB system, was not affected. Secondary forms of calcium carbonate including powdery pocket, soft masses, and mycelium and secondary gypsum such as fine and coarse pendants were found during field studies. Calcite, gypsum, and clay coatings and infillings together with isolated gypsum crystals and gypsum interlocked plates were among dominant micromorphological pedofeatures investigated. Calcite coatings on aggregates and soil particles associated with clay coating prove the role of paleoclimate in soil formation. On the other hand, presence of manganaze nodules is an evidence of oxidation/reduction condition taken place in the xeric moisture conditions of pedon 5 (rock pediment). Illite, chlorite, kaolinite, and smectite were investigated in both rock and mantled pediments, but palygorskite was only found in mantled pediments. Climate also played a significant role in determining the source (pedogenic or geogenic) of clay minerals.
Conclusions: Results of this study clearly showed the close relationship among soil formation, topography (geomorphic surface) and climate. Soil physicochemical properties, micromorphology, clay mineralogy, and soil classification were highly affected by climate and geomorphology.
zohreh mosleh; mohammad hassan salehi; azam jafari; Isa Esfandiarpoor Borujeni
Abstract
Introduction: Effective and sustainable soil management requires knowledge about the spatial patterns of soil variation and soil surveys are important and useful sources of data that can be used. Prior knowledge about the spatial distribution of the soils is the first essential step for this aim but ...
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Introduction: Effective and sustainable soil management requires knowledge about the spatial patterns of soil variation and soil surveys are important and useful sources of data that can be used. Prior knowledge about the spatial distribution of the soils is the first essential step for this aim but this requires the collection of large amounts of soil information. However, the conventional soil surveys are usually not useful for providing quantitative information about the spatial distribution of soil properties that are used in many environmental studies. Recently, by the rapid development of the computers and technology together with the availability of new types of remote sensing data and digital elevation models (DEMs), digital and quantitative approaches have been developed. These new techniques relies on finding the relationships between soil properties or classes and the auxiliary information that explain the soil forming factors or processes and finally predict soil patterns on the landscape. Different types of the machine learning approaches have been applied for digital soil mapping of soil classes, such as the logistic and multinomial logistic regressions, neural networks and classification trees. In reality, soils are physical outcomes of the interactions happening among the geology, climate, hydrology and geomorphic processes. Diversity is a way of measuring soil variation. Ibanez (9) first introduced ecological diversity indices as measures of diversity. Application of the diversity indices in soil science have considerably increased in recent years. Taxonomic diversity has been evaluated in the most previous researches whereas comparing the ability of different soil mapping approaches based on these indices was rarely considered. Therefore, the main objective of this study was to compare the ability of the conventional and digital soil maps to explain the soil variability using diversity indices in the Shahrekord plain of Chaharmahal-Va- Bakhtiari province.
Materials and Methods: The soils in the study area have been formed on Quaternary shale and foliated clayey limestone deposits. Irrigated crops such as wheat, barley and alfalfa are the main land uses in the area. According to the semi-detailed soil survey, 120 pedons with approximate distance of 750 m were excavated and described according to the “field book for describing and sampling soils”. Soil samples were taken from different genetic horizons and soil physicochemical properties were determined. Based on the pedons description and soil analytical data, pedons were classified according to the Soil Taxonomy (ST) up to subgroup level. Using aerial photo interpretation, geology map, google earth image and field observations primary soil map was created. With considering the taxonomic level, the representative pedons were determined and soil map was prepared. Multinomial logistic regression was used to predict soil classes at great group and subgroup levels. The map units that have the highest frequency were selected as indicator to calculate diversity indices in the conventional soil map at each taxonomic level. The selected map units were overlay to digital soil map and further diversity indices were calculated. Diversity indices including the Shannon’s diversity, evenness and richness index. In order to know whether the means of Shannon’s diversity for two approaches are significantly different, means comparison was done.
Results and Discussion: The results confirmed that the Shannon's diversity index was higher in the digital soil map than the conventional soil map for most soil map units. At great group and subgroup levels, a significant difference was observed for the Shannon's diversity index at 0.05 and 0.001 probability levels, respectively. Comparing the conventional and the digital soil maps showed the numbers of soil map units with significant difference regarding the Shannon's diversity index decreased from great group to the subgroup level. Although the conventional soil map did not show a good efficiency to explain the soil variability in this region considering more soil information to select the representative pedons at subgroup level in the conventional soil mapping could increase the ability of this approach.
Conclusion: A significant difference for the Shannon's diversity index between the conventional and the digital soil maps demonstrated that conventional soil mapping has not enough ability to explain the soil variability. It is recommended to test the effect of soil mapping approaches on explanation of the soil variability in other areas. Despite the deficiencies of traditional soil survey, it is still difficult to state about their replacement by digital methods.
A. Bayat; M. H. Farpoor; A. Jafari
Abstract
Introduction: Soil genesis and development in arid and semi-arid areas are strongly affected by geological formations and geomorphic surfaces. Various morphological, physical, and geochemical soil properties at different geomorphic positions are usually attributed to different soil forming factors including ...
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Introduction: Soil genesis and development in arid and semi-arid areas are strongly affected by geological formations and geomorphic surfaces. Various morphological, physical, and geochemical soil properties at different geomorphic positions are usually attributed to different soil forming factors including parent material and climate. Due to variations in climate, geological formations (Quaternary, Neogene and Cretaceous) and geomorphology, the aim of the present research was the study of genesis, development, clay mineralogy, and micromorphology of soils affected by climate, geology and geomorphology in Bardsir area, Kerman Province.
Materials and Methods: The study area, 25000 ha, starts from Bardsir and extends to Khanesorkh elevations close to Sirjan city. The climate of the area is warm and semi-arid with mean annual temperature and precipitation of 14.9 °C and 199 mm, respectively. Soil moisture and temperature regimes of the area are aridic and mesic due to 1:2500000 map, provided by Soil and Water Research Institute. Moving to west and southwest, soil moisture regime of the area changes to xeric with increasing elevation. Using topography and geology maps (1:100000) together with Google Earth images, geomorphic surfaces and geologic formations of the area were investigated. Mantled pediment (pedons 1, 3, 7, and 8), rock pediment (pedon 2), semi-stable alluvial plain (pedon 6), unstable alluvial plain (pedon 5), piedmont plain (pedons 9 and 11), intermediate surface of alluvial plain and pediment (pedon 4), and old river terrace (pedon 10) are among geomorphic surfaces investigated in the area. Mantled pediment is composed of young Quaternary sediments and Cretaceous marls. Rock pediments are mainly formed by Cretaceous marls. Quaternary formations are dominant in alluvial plains. Alluvial terraces and intermediate surface of alluvial plain and pediment are dominated by Neogene conglomerates. Siltstone, sandstone, and Neogene marls together with Neogene conglomerates are among dominant geological formations of piedmont plain. Eleven pedons affected by young Quaternary sediments, Neogene and Cretaceous marls in aridic, aridic border to xeric, and xeric moisture regimes on above-mentioned geomorphic surfaces were described and sampled using Natural Resources Conservation Service (2012) guideline. Physicochemical properties, clay mineralogy, and micromorphology of soil samples investigated and soils were classified by Soil Taxonomy (2014) and WRB (2015) systems.
Results and Discussion: Calcic, gypsic, argillic, and cambic diagnostic horizons investigated after field and laboratory studies. Typic Calcigypsids, Lithic Torriorthents, Typic Haplogypsids, Typic Haplocalcids, Typic Torrifluvents, Sodic Haplocambids, Typic Calciargids, and Xeric Haplocalcids subgroups were found using Soil Taxonomy (2014) system. Gypsisols, Calcisols, Luvisols, Cambisols, and Regosols reference soil groups identified by WRB (2015) classification system. Developed Alfisols, formed on piedmont plain geomorphic surface in xeric moisture regime. On the other hand, Entisols formed on rock pediments with aridic moisture regime. Soils in aridic moisture regimes were little developed with gypsic horizon, and where calcic horizon was formed, it was near the surface. Moving to the west with increasing humidity, gypsum was leached from the pedon and clay illuviation caused argillic horizon to be formed. Formation of Btk horizon in pedon 9 was attributed to a more paleoclimate. The maximum gypsum content of 44.7 % (gypsiferous soils) was found in soils affected by Quaternary formations and Cretaceous marls, but the maximum calcium carbonate (44 %, calcareous soils) was investigated in soils formed on Neogene conglomerate formations. Moreover, the maximum sodium adsorption ratio (SAR) content (29.2 (mmol(±) L-1)0.5) was determined for soils on unstable surface of alluvial plain. Smectite, vermiculite, illite, kaolinite, and chlorite clay minerals were investigated and smectite to illite ratio increased moving from aridic to xeric moisture regimes that prove the pedogenic source of smectite from weathering of illite. Coating and infilling of calcium carbonate, lenticular and interlocked plates and infillings of gypsum, and clay coatings were observed during micromorphological investigations. Micromorphological observations also showed that gypsum crystals decreased and calcite crystals and thickness of clay coatings increased from aridic to xeric moisture regimes. The minimum amount of gypsum crystals was found in Neogene formations. The results also showed that gypsum pedofeatures are dominant in Cretaceous formations, but calcium carbonate pedofeatures are the main features of Neogene formations. Due to presence of animal voids (channel, regular and star-shaped vughs, chamber, and vesicles), spongy microstructure was formed in agricultural lands.
Conclusion: Results of the research showed the important role of parent material, climate, and geomorphic surface on genesis and development of soils in Bardsir area.
zohreh mosleh; mohammad hassan salehi; azam jafari; Isa Esfandiarpoor Borujeni
Abstract
Introduction: Soil classification generally aims to establish a taxonomy based on breaking the soil continuum into homogeneous groups that can highlight the essential differences in soil properties and functions between classes.The two most widely used modern soil classification schemes are Soil Taxonomy ...
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Introduction: Soil classification generally aims to establish a taxonomy based on breaking the soil continuum into homogeneous groups that can highlight the essential differences in soil properties and functions between classes.The two most widely used modern soil classification schemes are Soil Taxonomy (ST) and World Reference Base for Soil Resources (WRB).With the development of computers and technology, digital and quantitative approaches have been developed. These new techniques that include the spatial prediction of soil properties or classes, relies on finding the relationships between soil and the auxiliary information that explain the soil forming factors or processes and finally predict soil patterns on the landscape. These approaches are commonly referred to as digital soil mapping (DSM) (14). A key component of any DSM mapping activity is the method used to define the relationship between soil observation and auxiliary information (4). Several types of machine learning approaches have been applied for digital soil mapping of soil classes, such as logistic and multinomial logistic regressions (10,12), random forests (15), neural networks (3,13) and classification trees (22,4). Many decisions about the soil use and management are based on the soil differences that cannot be captured by higher taxonomic levels (i.e., order, suborder and great group) (4). In low relief areas such as plains, it is expected that the soil forming factors are more homogenous and auxiliary information explaining soil forming factors may have low variation and cannot show the soil variability.
Materials and Methods: The study area is located in the Shahrekord plain of Chaharmahal-Va-Bakhtiari province. According tothe semi-detailed soil survey (16), 120 pedons with approximate distance of 750 m were excavated and described according to the “field book for describing and sampling soils” (19). Soil samples were taken from different genetic horizons, air dried and grounded. Soil physicochemical properties were determined. Based on the pedon description and soil analytical data, pedons were classified according to the ST (20) and WRB (11). Terrain attributes, remote sensing indices, geology, soil and geomorphology map were considered as auxiliary information. All of the auxiliary information were projected onto the same reference system (WGS 84 UTM 39N) and resampled to 50×50 m according to the suggested resolution for digital soil maps (14). Four modeling techniques (multinomial logistic regression (MLR), artificial neural networks (ANNs), boosted regression tree (BRT) and random forest (RF)) were used for each taxonomic level to identify the relationship between soil classes and auxiliary information in each classification system. The models were trained with 80 percent of the data (i.e., 96 pedons) and their validation was tested by remaining 20 percent of the dataset (i.e., 24 pedons) that split randomly. The accuracy of the predicted soil classes was determined by using overall accuracy and Brier score.For each classification system, the model with the highest OA and the lowest BS values were considered as the most accurate model for each taxonomic level.
Results and Discussion: The results confirmed that ST showedmore accessory soil properties compared to WRB. The ST described the cation-exchange activity, soil depth classes, temperature and moisture regime. The different models had the same ability for prediction of soil classes across all taxonomic levels based on ST. Among the studied models, MLR had the highest performance to predict soil classes based on WRB. For all the studied models and both classification system, OA values showed a decreasing trend with increasing the taxonomic levels. Predicted soil classes based on the ST had the higher accuracy. Different models selected different auxiliary information to predict soil classes. For most of the models and both classification systems, the terrain attributes were the most important auxiliary information at each taxonomic level.
Conclusion: Results demonstrated that although ST showed more accessory soil properties compared to WRB, the DSM approaches have not enough accuracy for prediction of the soil classes at lower taxonomic levels. More investigations are needed in this issue to make a firm conclusion whether DSM approaches are appropriate for prediction of soil classes at the levels that are important for soil management. Prediction accuracy of soil classes can be influenced by the target taxonomic level and classification system, soil spatial variability in the study area, soil diversity, sampling density and the type of auxiliary information.
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.
Mohammad Akbar Bahoorzahi; Mohammad Hady Farpoor; Azam Jafari
Abstract
Introduction: The optimum and sustainable use of soil is only possible with correct and complete understanding of its properties. The objectives of the present research were to study 1) genesis and development of soils related to different geomorphic surfaces in Kouh Birk Area (Mehrestan City), 2) Soil ...
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Introduction: The optimum and sustainable use of soil is only possible with correct and complete understanding of its properties. The objectives of the present research were to study 1) genesis and development of soils related to different geomorphic surfaces in Kouh Birk Area (Mehrestan City), 2) Soil classification according to Soil Taxonomy (2014) and WRB (2014) systems, and 3) physicochemical properties, clay mineralogy and micromorphology of soils.
Materials and Methods: Mean annual rainfall and soil temperature in the selected location are 153.46 mm and 19.6 oC, respectively. From geological point of view, the studied area is a part of west and south west zones and Flysch zone of east Iran. Soil temperature and moisture regimes of this part are thermic and aridic, respectively. Eight representative pedons on different surfaces including rock pediment, mantled pediment, Alluvial fan and Upper terraces were selected, sampled, and described. Routine physicochemical analyses, clay mineralogy, and micromorphological observations performed on soil samples. Soil reaction, texture, electrical conductivity, calcium carbonate, and gypsum were identified. Four samples including Bt horizon of pedon 1, Bk1 horizon of pedon 4, By2 horizon of pedon 5 and Bk1 horizon of pedon 7 were selected for clay mineralogy investigations. Four slides including Mg saturated, Mg saturated treated with ethylene glycol, K saturated, and K saturated heated up to 550 oC were analyzed. A Brucker X-Ray diffractometer at 40 kV and 30 mA was used for XRD analyses. Undisturbed soil samples from Bt horizon of pedon 1, Bk2 horizon of pedon 2, Btn horizon of pedon 3, By2 horizon of pedon 5, Bk1 horizon of pedon 7, and By1 horizon of pedon 8 were selected for micromorphological observations. A vestapol resin with stearic acid and cobalt as hardener was used for soil impregnation. Bk-Pol petrographic microscope was used for micromorphology investigations.
Results and Discussion: Due to the presence of argillic and petrocalcic horizons in rock pediment, soils of this surface were more developed compared to other landforms. High amount of CaCO3 (39.5%) was observed in pedon 4 on rock pediment geomorphic surface which is attributed to calcareous parent material. The presence of argillic horizon in this geomorphic position is due to the more available water of the past climate. The maximum salinity was observed in the mantled pediments. Calcic over gypsic horizons formed in pedon 7 on alluvial fan surface due to higher solubility of gypsum than calcium carbonate. Kaolinite, illite, chlorite, and palygorskite clay minerals were found in pedons 1 and 4 on rock pediment. Palygorskite in this position seems to be pedogenic, but kaolinite, illite, and chlorite are inherited from parent material. Mantled pediment and alluvial fan showed smectite, kaolinite, illite, chlorite, and palygorskite clay minerals. Pedogenic smectite in this position is probably formed from weathering of illite and chlorite. On the other hand, palygorskite stability decreased in mantled pediment surface. This is the reason why smectite was the dominant clay mineral in this landform. Clay and calcite coatings were investigated in Bt horizon of pedon 1 (rock pediment). Coatings and infillings of calcite in Bk2 horizon of the same geomorphic position caused a calcic crystallitic b fabric. A diffused clay coating due to the presence of Na in Btn horizon of pedon 3 in rock pediment was observed. Micromorphological observations of By2 horizon in pedon 5 (mantled pediment) showed gypsum interlocked plates and gypsum infillings. Interlocked plates formed due to re-solubility of gypsum crystals. Micro spars and infillings of calcite are among dominant pedofeatures found in Bk1 horizon of pedon 7 (alluvial fan geomorphic surface). A calcic crystallitic b fabric and Primary calcite mineral were also observed in this pedon. Release of Ca from calcareous parent material caused Ca+2 to SO4-2 ratio to be increased which could be a probable source of gypsum formation. Results of the study showed that more and less developed soils formed on rock pediment and upper terrace geomorphic surfaces, respectively. Illuviation of clay, gypsum, and CaCO3 together with formation of cambic, calcic, petrocalcic, gypsic, argillic, and natric horizons were among the dominant pedogenic processes in studied soils. Paleosols containing Bt horizons were only observed on rock pediment geomorphic surface. Kaolinite, illite, chlorite, and palygorskite clay minerals were observed in almost all surfaces. Smectite was not discovered in rock pediment, but was only investigated in mantled pediment and alluvial fan which could be attributed to higher available moisture of formation time in these surfaces. Secondary calcite and gypsum caused stability of pedogenic palygorskite in soils under study. Micromorphological observations proved the presence of clay and calcite coatings, calcite and gypsum infillings, and gypsum interlocked plates. Gypsum pedofeatures were not observed in rock pediment, but clay and calcite pedofeatures were only found. On the other hand, clay and calcite pedofeatures were not observed in upper terraces and gypsum pedofeatures were the only features determinded in this position.
Conclusion Results of the present research showed that difference in soil characteristics is highly affected by geomorphology.
A. Jafari; Norair Toomanian; R. Taghizadeh Mehrjerdi
Abstract
Introduction: Methods of soil survey are generally empirical and based on the mental development of the surveyor, correlating soil with underlying geology, landforms, vegetation and air-photo interpretation. Since there are no statistical criteria for traditional soil sampling; this may lead to bias ...
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Introduction: Methods of soil survey are generally empirical and based on the mental development of the surveyor, correlating soil with underlying geology, landforms, vegetation and air-photo interpretation. Since there are no statistical criteria for traditional soil sampling; this may lead to bias in the areas being sampled. In digital soil mapping, soil samples may be used to elaborate quantitative relationships or models between soil attributes and soil covariates. Because the relationships are based on the soil observations, the quality of the resulting soil map depends also on the soil observation quality. An appropriate sampling design for digital soil mapping depends on how much data is available and where the data is located. Some statistical methods have been developed for optimizing data sampling for soil surveys. Some of these methods deal with the use of ancillary information. The purpose of this study was to evaluate the quality of sampling of existing data.
Materials and Methods: The study area is located in the central basin of the Iranian plateau (Figure 1). The geologic infrastructure of the area is mainly Cretaceous limestone, Mesozoic shale and sandstone. Air photo interpretation (API) was used to differentiate geomorphic patterns based on their formation processes, general structure and morphometry. The patterns were differentiated through a nested geomorphic hierarchy (Fig. 2). A four-level geomorphic hierarchy is used to breakdown the complexity of different landscapes of the study area. In the lower level of the hierarchy, the geomorphic surfaces, which were formed by a unique process during a specific geologic time, were defined. A stratified sampling scheme was designed based on geomorphic mapping. In the stratified simple random sampling, the area was divided into sub-areas referred to as strata based on geomorphic surfaces, and within each stratum, sampling locations were randomly selected (Figure 2). This resulted in 191 profiles, which were then described, sampled, analyzed and classified according to the USDA soil classification system (16). The basic rationale is to set up a hypercube, the axes of which are the quantiles of rasters of environmental covariates, e.g., digital elevation model. Sampling evaluation was made using the HELS algorithm. This algorithm was written based on the study of Carre et al., 2007 (3) and run in R.
Results and Discussion: The covariate dataset is represented by elevation, slope and wetness index (Table 2). All data layers were interpolated to a common grid of 30 m resolution. The size of the raster layer is 421 by 711 grid cells. Each of the three covariates is divided into four quantiles (Table 2). The hypercube character space has 43, i.e. 64 strata (Figure 5). The average number of grid cells within each stratum is therefore 4677 grid cells. The map of the covariate index (Figure 6) shows some patterns representative of the covariate variability. The values of the covariate index range between 0.0045 and 5.95. This means that some strata are very dense compared to others. This index allows us to explain if high or low relative weight of the sampling units (see below) is due to soil sampling or covariate density. The strata with the highest density are in the areas with high geomorphology diversity. It means that geomorphology processes can cause the diversity and variability and it is in line with the geomorphology map (Figure 2). Of the 64 strata, 30.4% represent under-sampling, 60.2% represent adequate sampling and 9.4% represent over-sampling. Regarding the covariate index, most of the under-sampling appears in the high covariate index, where soil covariates are then highly variable. Actually, it is difficult to collect field samples in these highly variable areas (Figure 7). Also, most of the over-sampling was observed in areas with alow covariate index (Figure 7). We calculated the weights of all the sampling units and showed the results in Figure 8. One 64 strata out of 16 were empty of legacy sample units. Therefore, if we are going to increase the number of samples, it is better to take samples from the empty strata.
Conclusion: Since, we assume that soil attributes to be mapped can be predicted by the environmental covariates, our estimation of the sample units is based on the covariates. Then, the results are very dependent on the covariates (number and spatial resolution of the covariates and the quality of their measurement or description). Hypercube sampling provides the means to evaluate adequacy of sampling units according to the soil covariates. The main advantage of such a method is that all the sample units can be estimated according to their density in the feature space that represents soil variability. From the results, it is possible to add new sampling units in order to cover the whole feature space. Thus, in case some parts are missing, we can enhance some parts of the feature space that appear to be under-sampled.
Keywords: Environmental variables, Latin hypercube, Soil sampling, Soil survey
A. Jafari; Sh. Ayoubi; H. Khademi
Abstract
چکیده
شناسایی رقومی خاک ها بهعنوان ابزاری برای ایجاد اطلاعات مکانی خاک، راه حل هایی برای نیاز رو به افزایش نقشه های خاک با تفکیک مکانی بالا را تأمین می کند. بنابراین، ...
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چکیده
شناسایی رقومی خاک ها بهعنوان ابزاری برای ایجاد اطلاعات مکانی خاک، راه حل هایی برای نیاز رو به افزایش نقشه های خاک با تفکیک مکانی بالا را تأمین می کند. بنابراین، باید روش های جدید بهمنظور بهدست آوردن اطلاعات مکانی خاک با تفکیک مکانی بالا توسعه پیدا کند. به همین منظور مطالعه ای جهت پیش بینی کلاس های خاک با استفاده از مدل های رگرسیونی در منطقه زرند کرمان طراحی گردید. در این مطالعه، مدل های رگرسیونی شامل رگرسیون لاجیستیک چندجمله ای و رگرسیون درختی توسعه یافته چندکلاسه برای پیش بینی گروه بزرگ خاک به کمک داده های سنجش از دور، پارامترهای سرزمین و نقشه ژئومرفولوژی استفاده گردید. کیفیت پیش بینی مدل ها با شاخص های حاصل از آرایه خطا بررسی گردید. نتایج نشان داد در پیش بینی همه گروه های بزرگ خاک، سطوح ژئومرفیک بهعنوان یک پیش بینی کننده مؤثر محسوب می شود. بعد از سطوح ژئومرفیک، پارامترهای سرزمین و شاخص های سنجش از دور در پیش بینی وارد شدند. در هر دو مدل خلوص نقشه برای همه گروه های بزرگ خاک در موقعیت های اعتبارسنجی و واسنجی بیشتر از 6/0 بود. نتایج نشان داد عملکرد پیش بینی برای گروه های بزرگ هاپلوجیپسید و هاپلوسالید بهتر از گروه های بزرگ کلسی جیپسید و هاپلوکمبید بود. در بین گروه های بزرگ خاک، مقادیر بالای دقت کاربر و قابلیت اطمینان تولیدکننده برای گروه بزرگ هاپلوسالید بهدست آمد. خاک های با قابلیت اطمینان بهتر خاک هایی هستند که به شدت تحت تأثیر مشخصات توپوگرافی و ژئومرفولوژی قرار گرفتند (گروه های بزرگ هاپلوسالید، تری سامنت و هاپلوجیپسید) و خاک های با قابلیت اطمینان و دقت پیش بینی کمتر خاک هایی هستند که به سختی تحت تأثیر مشخصات توپوگرافی و ژئومرفولوژی (گروه های بزرگ هاپلوکمبید و کلسی جیپسید) قرار گرفتند.
واژه های کلیدی: نقشه برداری رقومی خاک، رگرسیون لاجیستیک چندجمله ای، رگرسیون درختی توسعه یافته
