Farzaneh Bandehelahi; Isa Esfandiarpour-Boroujeni; Alireza Karimi; Mohammad Hady Farpoor; Zohreh Mosleh; Morteza Fattahi
Abstract
Introduction Landscape represents a large portion of land/terrain that is either formed by a repetition of similar or dissimilar relief/molding types or an association of dissimilar relief/molding types (e.g., valley, piedmont, mountain, etc.). It is usually affected by a set of natural (e.g., climate, ...
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Introduction Landscape represents a large portion of land/terrain that is either formed by a repetition of similar or dissimilar relief/molding types or an association of dissimilar relief/molding types (e.g., valley, piedmont, mountain, etc.). It is usually affected by a set of natural (e.g., climate, organisms, parent material, topography, time, erosion, sedimentation, etc.) and/or artificial (e.g., artifacts) factors. Soil is one of the most important components of landscape that is affected by various factors such as water and wind. Aeolian or alluvial sediments (from seasonal rivers) in arid areas cause the formation of different landforms and change the landscapes in these areas. Therefore, the study of geoforms in arid regions can lead to a better understanding of geomorphological processes and soil change in these areas. There are various methods, including soil micromorphology and clay mineralogy, to understand the alteration of landscapes and the soils change on them. The aim of this study was to investigate the physical and chemical properties, clay mineralogy and micromorphology of soils in various geomorphic units of Davaran Region, Rafsanjan.Materials and Methods Seven dominant geomorphic units (geoforms) of the region, including pediment, margin of fan and cultivated clay flat, alluvial fan, desert pavement, margin of pediment and sand sheet, active drainage, margin of fan and uncultivated clay flat were selected using Google Earth images and field studies. Nineteen pedons were excavated and described in the geomorphic units. After selecting a representative pedon in each of the geoforms, their genetic horizons were sampled. Besides, in order to conduct soil micromorphology studies, undisterbed and oriented samples were collected from selected horizons. After transferring the samples to the laboratory, their physical and chemical properties were measured using standard methods. In addition, clay mineralogy studies were performed by X-ray diffraction method and micromorphological studies were done using a petrographic microscope. Finally, soil classification was performed based on both Soil Taxonomy (2014) and WRB (2015) systems.Results and Discussion Results showed that gypsification and calcification are the dominant soil forming processes in the studied region, which have led to the formation of Gypsic and Calcic horizons. This has placed the soils in the Gypsids and Calcids suborders based on the Soil Taxonomy system and the Gypsisols and Calcisols reference soil groups according to WRB system. The representative pedon in the margin of fan and cultivated clay flat (pedon 2) geoform lacks a salic horizon based on the Soil Taxonomy; while it is in the Solonchak reference soil group of the WRB. Also, the presence of argillic horizon in the representative pedon of the margin of fan and uncultivated clay flat geoform (pedon 7) indicates presence of a more humid paleoclimate in the history of the region. The results of clay mineralogy showed that the predominant minerals in the region include chlorite, illite, kaolinite, and smectite. The illite, chlorite, and kaolinite are inherited from papent materials of the soils, and the smectite has a transformation origin (from palygorskite and illite). Addition of this mineral by aeolian or alluvial sediments could not also be neglected. The micromorphological results indicated that the soil pores were mainly chamber. The presence of carbonates and gypsum in the studied soils has caused that the b-fabric in the most horizons to be Calcitic Gypsic Crystallitic. Gypsum was observed in the form of vermicular, lenticular, interlocked gypsum plates and subhedral shapes. Other pedofeatures in the studied soils include calcite nodule and limestone.Conclusion The simultaneous presence of aeolian and alluvial sediments in the different geoforms of Davaran region has caused the formation of stratified soils. Existence of dry climate and lack of significant vegetation in the region from one hand, and the addition of different sedimentary layers at different times (which causes soil rejuvination) on the other hand, has caused that the soils of the region, in general, not to be highly developed. As a result, few differences were observed among soils in different geoforms. Comparing the results of two soil classification systems for the studied soils showed that in general there is a relatively good correlation between them. Totally, the role of climate and parent material in alteration of the studied soils is evident; so that the physical and chemical properties, clay mineralogy and micromorphology of soils in different geoforms have been affected.
Saleh Sanjari; Mohammad Hady Farpoor; Majid Mahmoodabadi; Saied Barkhori
Abstract
Introduction Soil classification is a process of showing basic differences among soil classes (5). Different soil classification systems are created for soil classification, but Soil Taxonomy and World Reference Base for Soil Resources (WRB) are among the most favoured systems in the world including ...
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Introduction Soil classification is a process of showing basic differences among soil classes (5). Different soil classification systems are created for soil classification, but Soil Taxonomy and World Reference Base for Soil Resources (WRB) are among the most favoured systems in the world including Iran. This system (WRB) is accepted by soil scientists in the world and Soil Taxonomy has also been used in several countries (7). Each of the two mentioned systems has its own strong and/or weak points to show soil characteristics. However, comparing Soil Taxonomy and WRB for calcareous and gypsiferous soils of central Iran, Sarmast et al. (16) reported that according to specifiers used in WRB, this system could be more efficient than Soil Taxonomy. Various environmental conditions and its fluctuations in Kerman Province caused different soils to be formed in the province. Use of soil moisture and temperature regimes by Soil Taxonomy which is totally neglected by WRB system may emphasize that Soil Taxonomy could provide better results for these soils. That is why the present research was performed to compare Soil Taxonomy and WRB systems in the area of the present research with different climates and to show the efficiency of the two systems to describe selected soil characteristics in Kerman Province. Materials and Methods According to climatic variations, four study sites were selected in Kerman Province. Sites 1 (elevation of < 2000 m asl) and 2 (elevation of >2000 m asl) in Baft and Rabor areas were located in the south west of the province. Moreover, sites 3 (around Jiroft and Anbarabad) and 4 (around Roodbar-e-Jonoob and Ghaleganj) were located at the center and south of the province, respectively (Fig. 1). Table 1 shows the soil moisture and temperature regimes of the areas under study (3). Twenty-five pedons on different geomorphic surfaces were described and one representative pedon on each geomorphic surface (total of 11 representative pedons) were selected (Fig 1). Soil description and sampling performed (18) and the collected samples transferred to the laboratory. It is to be noticed that soil moisture regime in site 3 has changed from ustic to aridic during normal years defined in Soil Taxonomy. Ustic/ hypertermic soil moisture/temperature regimes were reported for soils of Jiroft and Anbarabad according to the soil moisture and temperature map of soils of Iran (3). However, according to the latest climatic data (30 years' data and the concept of normal years as defind in Soil Taxonomy, 2014) used in the NSM Software, the soil moisture regime was estimated as weak aridic. Results and Discussion Histic, mollic, argillic, natric, calcic, anhydritic, and cambic horizons were investigated after field work and laboratory analyses. Results of the study show that addition of new Calcixeralfs, Gypsiustalfs, and Gypsicalcids great groups together with newly added Calcic Natrargids, Calcic Natrustalfs, Gypsic Calciustalfs, Typic Petrogypsids, Anhydritic Haplogypsids, and Angydritic Petrogypsids subgroups to the Soil Taxonomy system from one hand, and addition of anhydrite and hypercalcic qualifiers to WRB from the other hand, cause a higher correlation between the two systems. Besides, climatic fluctuations of the recent years in Jiroft and Anbarabad areas caused a change in the soil moisture regime according to normal years defined in Soil Taxonomy. That is why soil name was changed in Soil Taxonomy system. However, WRB system shows no variation because this system is not related to climatic data. Since anhydritic horizon was added to Soil Taxonomy (2014) system, addition of this horizon is recommended to WRB for better correlation of the two systems as was also suggested by Sarmast et al. (16). Meanwhile, soil names in the WRB system provide more information about characteristics of young soil (including yermic qualifier to show desert pavement) compared to Soil Taxonomy.Conclusion Soil classifications showed that WRB system could describe soil characteristics in the area more efficiently compared to Soil Taxonomy. Climate change caused a variation in soil moisture regime of Jiroft and Anbarabad areas according to normal years of Soil Taxonomy system, which in turn changed soil nomenclature in this system. WRB system is not related to climate that is why soil names were not changed in the above mentioned areas. Besides, WRB system is more efficient to classify gypsiferous soils because gypsum content which is an important factor for management of gypsiferous soils is better focused by WRB. However, lack of anhydritic horizon in the WRB system is a weak point, that is why addition of this horizon was suggested by the authors. It is recommended that soil moisture/temperature regimes of study sites be calculated by softwares using climatic data because the climatic variations of the recent years might have changed the soil moisture/temperature regimes reported in the map of 1998 due to the definition of normal years defind in Soil Taxonomy.
Maryam Izadi Bidani; A Jafari; Mohammad Hadi Farpoor; Mojtaba Zeraatpisheh
Abstract
Introduction: Soil digital mapping represents a set of mathematical computations to predict the distribution of soil classes in the landscape. . The digital identification of soils as a tool for creating soil spatial data provides ways to address the growing need for high-resolution soil maps. The use ...
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Introduction: Soil digital mapping represents a set of mathematical computations to predict the distribution of soil classes in the landscape. . The digital identification of soils as a tool for creating soil spatial data provides ways to address the growing need for high-resolution soil maps. The use of digital soil mapping technique has been expanded considerably; therefore, new methods of mapping and preparing digital maps have been developed by researchers to eliminate the limitations of traditional methods. This approach relies on statistical relationships between measured soil observations and environmental covariates at the sampling locations. Digital soil data is increasing based on new processing tools and various digital data. The present study was conducted with the purpose of digital soil mapping in Kouhbanan region of Kerman based on a Multinomial logistic regression model. Materials and methods: The study area is located in southeastern Iran, northwest of Kerman city, in Kouhbanan distinct. This study covers a 2000 ha area. In this study, a Latin hypercube sampling design was applied and the sampling was done according to the difference in landforms (geomorphology map), topography (including digital elevation map) and geology. Finally, the geographic locations of 70 profiles were identified. Soil profiles were described according to U.S. Soil Taxonomy (Soil Survey Staff, 2014) and finally, the soil samples were taken from their diagnostic horizons. The collected soil samples were transferred to the laboratory, and some physical and chemical analyzes were performed based on routine standard methods. Environmental data include the parameters derived from the digital elevation model, Landsat satellite images (remote sensing indexes), geology map, geomorphic units (geomorphology map) and legacy soil map of the study area. All environmental variables were derived using ENVI and SAGA software. In this research, a multinomial logistic regression model was used to predict soil classes and the modeling was done in R software using nnet package. It is worth noting that leave-one-out cross validation was used for validation. Estimation of predictive accuracy of soil classes was also done using the overall accuracy index and Kappa coefficient.Results and discussion: The results showed that the soils of the study area were mainly classified in the Aridisols and Entisols orders. The modeling results showed that the terrain attributes were recognized as the effective auxiliary variables in the prediction process of soil classes. This confirms topographic importance on soil genesis in the studied area. After that, geomorphology map was an important tool in soil mapping that helps to increase predictive accuracy. Among the soil classes, the prediction of Haplocambids was accompanied with low accuracy, while Haplosalids great groups were predicted with high accuracy. The low estimation accuracy of the great group of Haplocambids is probably due to the low sample size of this class of soil in the study area. A good identification of the relationships between the predictor variables and the target variable depends primarily on the size and distribution of the sample in the layers. There were only two examples of Haplocambids in the area. Therefore, low accuracy is expected because the model has failed to establish a relationship between this class with environmental variables and makes it difficult to identify threshold values for classifying soil classes and, consequently, a poorly trained model. It is also possible that low prediction accuracy is the result of the conceptual model being incomplete, since there is no characteristic feature that can help model training and ultimately prediction. Among the soil great groups, the best predictions were obtained for the great group of Haplosalids, which demonstrates high values of user accuracy and reliability. Accurate prediction of the class of Haplosalids is highly correlated with the spatial distribution of indices such as wetness index and NDVI. Kappa index and purity map were calculated 0.45 and 0.65 for digital soil map derived from multinomial logistic regression. In the predicted map, six major groups of Haplosalids, Haplocambids, Haplocalcids, Haplogypsids, Calcigypsids and Torrifluvents were identified. The great groups of Haplocalcids, Haplosalids, and Calcigypsids cover most of the area and the great groups Haplocambids and Haplogypsids occupy lowest of the area. The great group of Haplosalids is located in the north of the region and in the piedmont plain landform. Haplocalcids great groups were most commonly found in alluvial fan landform, while Calcigypsids are located in pediments, alluvial fans, and piedmont plain landforms. Haplocambids and Haplogypsids great groups are located more in the geomorphic surface of the alluvial fan and the piedmont plain, respectively. The parts of the region with the most variations or diversity of soil classes are exactly where the geomorphological map has the most segmentation. Therefore, the presence of different soil classes in the least-differentiated and most similar regions is resulted to an inefficient conceptual model and poor prediction results. Conclusions: The results showed that topographic parameters were the most important and powerful variable in modeling, and confirms that topography or relief is the most important soil forming factor in the study area. Predictive results of soil classes in Kuhbanan area of Kerman province showed that geomorphological map in the study area is very useful and necessary and also is effective in understanding and communicating between soil and landscape. Using this map as a qualitative auxiliary variable can explain much of the variability of soils in the study area. Careful field observation, satellite imagery consideration, study and interpretation of data obtained from soil profiles indicate that the study area has been evolved by geological, geomorphological, and hydrological processes that lead to the formation of various landforms including rock outcrops, hills, pediment , alluvial fan and plain. For the multinomial logistic regression model in the study area, terrain attributes have the most influence on the prediction of soil classes and soil properties than the remote sensing indices. The strong relationship between soil data and environmental parameters is one of the factors influencing model accuracy. Logistic regression models will have great potential in predicting soil classes if a complete understanding of the study area and proper selection of auxiliary variables are carried out.
Zahra Masoudi; A. Jafari; Mohammad Hady Farpoor
Abstract
Introduction: Soil maps are a common source of information for land suitability studies. Land suitability studies are to compare land characteristics with the needs of land-use types and to select the best land-use productivity types for cultivation. Land evaluation analysis is considered as an interface ...
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Introduction: Soil maps are a common source of information for land suitability studies. Land suitability studies are to compare land characteristics with the needs of land-use types and to select the best land-use productivity types for cultivation. 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 develop the digital and quantitative approaches in soil studies. These new techniques rely on the relationships between soil and the environmental variables 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. To our knowledge, most of the previous studies applied 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 Jiroft plain of Kerman province. Materials and Methods: An area in the Jiroft plain of Kerman Province, Iran, across 28º14′ and 28º 26′N, and 57º 30′ and 57º 46′E was chosen. The study area is placed on alluvial plain, gravelly alluvial fans, eroded hills. Based on Google Earth image, geomorphology and topography maps and also field survey, 62 pedons were selected and excavated, and soil samples were taken from different soil horizons. Then, soil physicochemical properties were determined. To assess the climate, the climate information obtained from the Jiroft Synoptic Station. The average of soil properties was determined by considering the depth weighted coefficient up to 100 centimeters for potato. Qualitative land suitability evaluation for 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). For digital approach, multinomial logistic regression (MLR) was used to test the predictive power for mapping the land suitability evaluation. Terrain attributes (elevation, slope, aspect, wetness index and multiresolution valley bottom flatness (MrVBF)), remote sensing indices (normalized difference vegetation index (NDVI), perpendicular vegetation index (PVI), and ratio vegetation index (RVI)), geology map, and geomorphology map were used as auxiliary information. Finally, all of the environmental covariates were projected onto the same reference system (WGS 84 UTM 40 N). Training and validating the model was done by leave-one-out cross validation. 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 potato. The most important limiting factors were the gravel content, soil acidity and soil salinity for potato growing in the study area. Land suitability classes S2 to N were determined based on land index in the study area. The modelling results demonstrated overall accuracy 0.47 and 0.25 for class and subclass of land suitability, respectively. It seems that low number of soil samples for training and validating of the model were probably caused to low accuracy as compared to the other researches. In addition, the overall accuracy decreased from class to subclass. The terrain attributes (slope and aspect), remote sensing indices (normalized difference vegetation index) and geomorphology map were the most important auxiliary information to predict the land suitability classes and subclasses. This indicates the importance of geomorphological processes for determining the land suitability class in the study area. Conclusion: Results suggest that the land form, land position and geomorphology processes affect soil properties and then, land suitability classes. Therefore, variability of land suitability classes is function of variability of soil properties. Digital approaches could help to obtain the information with high resolution, provided that the criteria of suitability are associated with variability of soil properties. Although digital mapping approaches increase our knowledge about 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.
Soil Physics, Erosion and Conservation
Ruhollah Rezaei Arshad; M Mahmudabadi; Mohammad Hady Farpoor; Majid Fekri
Abstract
Introduction Under natural conditions, intensive and erosive storms commonly associate with high-speed winds. In fact, wind velocity affects water erosion rate through enforcing falling drops and enhancing rainfall erosivity. Therefore, knowledge of interaction between wind and rain as erosive agents ...
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Introduction Under natural conditions, intensive and erosive storms commonly associate with high-speed winds. In fact, wind velocity affects water erosion rate through enforcing falling drops and enhancing rainfall erosivity. Therefore, knowledge of interaction between wind and rain as erosive agents on interrill erosion is of prime importance. However, no comprehensive study has been done on this topic under controlled laboratory conditions. This study was conducted to investigate interrill erosion affected by different rain intensities and wind velocities on several soils with different aggregate size distributions using the Simultaneous Wind-Rainfall-Runoff Simulator (SWRRS). For this purpose, a multisystem was constructed for the first time in Iran to investigate the simultaneous effects of wind and rain erosivity agents on soil erosion under laboratory conditions. Materials and Methods The simulator was calibrated in two cases. First, the intensity and uniformity of the simulated rains were assessed for each nozzle, separately. Second, the calibration procedure was performed for different combinations of the selected nozzles to achieve the best performance. For each case, different water pressures were generated to introduce several water discharges and make initial raindrop velocities. Afterwards, the interrill erosion experiment was done using four constant wind speeds including 0, 6, 9 and 12 m s-1at the height of 40 cm which were applied in combination with three rain intensities of 30, 50 and 75 mm h-1 on three soil samples with different aggregate size distributions (D2mm, D4.75mm and D8mm). Each treatment was conducted at three replicates under laboratory controlled conditions. By using different wind speeds, rain intensities and soil aggregate sizes, interrill erosion rate was measured under steady state conditions. Results and Discussion Results showed that wind velocity has a significant effect on interrill erosion rate and the interaction between wind and rain on interrill erosion was significant, as well. Although, there was no significant difference between the erosion rate at wind velocity of 0 and 6 m s-1, the wind velocity of 9 and 12 m s-1 showed significant difference with and higher erosion rates than the velocity of 6 m s-1. The mean erosion rate at wind velocities of 0, 6, 9, 12 m s-1 was 0.43 × 10-4, 0.54 × 10-4, 0.97 × 10-4 and 1.46 × 10-4 kg m-2 s-1, respectively. With increasing rain intensity from 30 to 75 mm h-1, the erosion rate increased from 0.52 × 10-4 to 1.16 × 10-4 kg m-2 s-1. On average, the erosion rate of the soil containing larges aggregates i.e. D8mm (0.73 × 10-4 kg m-2 s-1) was less than that with the finest aggregates i.e. D2mm (0.99 × 10-4 kg m-2 s-1). The findings of this study highlighted the importance and necessity of more attention to wind speed particularly those velocities faster than a threshold velocity in the study of interrill erosion. Conclusion In arid and semi-arid regions such as most parts of Iran, rainstorms are usually accompanied by strong winds. Despite the undeniable influence of wind on the erosive power of rain, a host of research has investigated water and wind erosion processes, separately. Therefore, this study was done to investigate the simultaneous effect of wind velocity and rain intensity on interrill erosion rate in three soil samples. The results indicated that wind velocity has a remarkable influence on interrill erosion rate due to wind-driven rain. Wind velocities faster than 6 m s-1 increased interrill soil erosion rate, particularly those combined with higher rain intensities. This is due to an increase in the velocity of falling raindrops on the soil surface which results in greater kinetic energy. Also, the findings showed that the soil containing coarser aggregates due to greater random roughness exhibited less sensitivity and interrill erosion rates as compared with the soil having finer aggregates, especially at faster wind velocities. The rate of interrill erosion in soil D2mm was 1.35 times higher than soil D8mm indicating the importance of random roughness. In addition, there was no significant difference between the measured erosion rates at wind speeds of 0 and 6 m s-1, in all cases. However, with increasing wind speed from 6 to 9 and also to 12 m s-1, significant increases in soil erosion rates were observed. Accordingly, a threshold wind velocity can be considered in wind-driven interrill erosion. The findings of the present study can be applied for better understanding and modeling of water and wind erosion mechanisms and dominant processes.
Soil Genesis and Classification
Mansooreh Khaleghi; Azam Jafari; Mohammad Hadi Farpour
Abstract
Introduction Soil digital mapping represents a set of mathematical computations to predict the distribution of soil classes in the landscape. This approach relies on statistical relationships between measured soil observations and environmental covariates at the sampling locations. The need for digital ...
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Introduction Soil digital mapping represents a set of mathematical computations to predict the distribution of soil classes in the landscape. This approach relies on statistical relationships between measured soil observations and environmental covariates at the sampling locations. The need for digital soil mapping as an addition to conventional soil surveys results from a worldwide growing demand for high- resolution digital soil maps for environmental protection and management as well as projects of the public authorities. Digital soil data is increasing based on new processing tools and various digital data. The digital identification of soils as a tool for creating soil spatial data provides ways to address the growing need for high-resolution soil maps. The main objective of this study is to generate the digital soil map based on the legacy soil data. Materials and methods The study area is located in southeastern Iran, 330 km from Kerman city, in Faryab distinct. In this study, a Latin hypercube sampling design was applied and the sampling was done according to the difference in landforms (geomorphology map), topography (including digital elevation map) and geology. The geographic locations of 70 profiles were identified. Soil profiles were described according to U.S. Soil Taxonomy (Soil Survey Staff, 2014) and finally, the soil samples were taken from their diagnostic horizons. The collected soil samples were transferred to the laboratory, and some physical and chemical analyzes were performed based on routine standard methods. Environmental data include the parameters derived from the digital elevation model, Landsat satellite images (remote sensing indexes), geology map, geomorphic units (geomorphology map) and legacy soil map of the study area. All environmental variables were derived using ENVI and SAGA software. In this research, a multinomial logistic regression model was used to predict soil classes and the modeling was done in two scenarios: 1- modeling without the legacy soil map and 2- modeling with the legacy soil map. Estimation of predictive accuracy of soil classes was also done using the overall accuracy index and Kappa coefficient. Results and discussion The result of the modeling with the multinomial logistic regression method in two sets of input variables showed that the topographic position index is the most effective variable in predicting soil classes. This confirms topographic importance on soil genesis in the studied area. After topographic variables, the legacy soil data is an effective parameter in modeling. The legacy data of soil is a strong and valuable database for predicting soil characteristics. The old soil map consists of the salt surfaces and Inceptisols order. Unlike the hot and arid climate of the study area, Inceptisols order was identified in the old soil map. Soil survey with very small scale was probably led to generalization of the studied soils and hiding the main soils of the study area. However, the small-scale mapping and the presentation of different soils in the region do not prevent the presence of the old soil map as an important predictor. It seems that there is a high concordance between the borders of old soil map and the described soils diversity in the study area. The matching and concordance between the boundaries of the old map and the described soil profiles help the model to differentiate different soils, although the correspondence between the soils type of the old soil map and the observed soils can play a more effective role in predicting by the model. Soil legacy information is a powerful and valuable database for predicting any feature of the soil. In both predicted maps, four major groups of Haplosalids, Haplocambids, Haplocalcids and Torriorthents were identified. The great group of Torriorthents is located in the north of the region and in the alluvial fan landform. Haplosalids great groups were most commonly found in clayey surfaces. Haplocambids and Haplocalcids great groups are located more in the geomorphic surface of the cultivated fan and the piedmont plain, respectively. The results of the predictive quality of the logistic regression model showed that the number of well-estimated soils in the presence of the old soil map is more than when there is no old soil map in the modeling. In addition, the results of the validation of the models showed that the map accuracy and kappa index increased in presence of the legacy soil map. As a result, the model's validation indices including the map purity and Kappa index increased from 0.47 and 0.16 to 0.63 and 0.43, respectively. In both models, the highest accuracy of the estimation was obtained for Haplocambids great group. Conclusions The results showed that topographic position index was the most important and powerful variable for forecasting in both models, and confirms that topography or relief is the most important soil forming factor in the study area. Using the legacy soil map as one of the environmental variables in modeling, efficiency and accuracy are more accurate than modeling without the legacy soil map. If the old soil maps as legacy information are used in digital soil mapping, the similarity and matching of the soils of the studied area shoud be cheched even with the very small scale because the high concordance leads to rational prediction, and random and chance predictions do not occur.
Atefeh Esmaili Dastjerdipour; Mohammad Farpoor; Mehdi Sarcheshmehpour
Volume 36, Issue 2 , March 2014, , Pages 17-35
Abstract
Cyanobacteria play an important role in providing biological soil crusts in sandy soil of desert areas. The aims of the present research are to investigate the possibility of crust formation under three cyanobacteria (Nostoc. sp (N), Phormidium.sp (Ph) and combination of two genus (Ph + N), two polymer ...
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Cyanobacteria play an important role in providing biological soil crusts in sandy soil of desert areas. The aims of the present research are to investigate the possibility of crust formation under three cyanobacteria (Nostoc. sp (N), Phormidium.sp (Ph) and combination of two genus (Ph + N), two polymer treatments [blank (S0) and 0.3 g per 250 g soil (S1)] and two moisture levels [FC (M1) and 80% FC (M2)] in three separated parts. Crust thickness, increase in soil organic carbon and resistance to penetration of crusts after complete coating (60 days) were measured in the first part of the experiment. Then the effects of time (15, 30, 45 and 60 days) on crust thickness and micromorphological investigations of crusts were performed in the second and third parts of the experiment respectively. Results of the study showed that simultaneous application of two genus at FC level with polymer creates the thickest (6.83 mm) and the most resistant crusts (0.27 MPa) to penetration. Both cyanobacteria genus with polymer and FC level caused the highest organic carbon contents (1.89% and 1.66% respectively). Also the thickest crust (6.8 mm) was formed by simultaneous application of two genus during 60 days. Micromorphological observation showed decrease in macro pores in treated samples compared to control and this decrease of pore space size with application of both cyanobacteria genus was higher than each of them alone.
