Soil science
Z. Mosleh Ghahfarokhi; A. Azadi
Abstract
Introduction
Soil properties play a crucial role as they determine the soil's suitability for different types of plant growth, ecosystems, and biota functioning. They have a significant impact on nutrient cycling, carbon sequestration, and soil management. Digital Soil Mapping (DSM) is a process aimed ...
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Introduction
Soil properties play a crucial role as they determine the soil's suitability for different types of plant growth, ecosystems, and biota functioning. They have a significant impact on nutrient cycling, carbon sequestration, and soil management. Digital Soil Mapping (DSM) is a process aimed at delineating soil properties. Soil sampling for DSM serves as a fundamental step in improving prediction accuracy and is crucial for incorporating variability in terms of environmental covariates. Conditioned Latin Hypercube (CLH) sampling is a technique utilized to generate a sample of points from a multivariate distribution conditioned on one or more covariates. Numerous researchers (Ramirez-Lopez et al., 2014; Adhikari et al., 2017; Zhang et al., 2022) have endorsed this approach in their studies, following its inception by Minasny and McBratney in 2006. However, there has been limited research to date on the impact of the Latin hypercube method's random sample selection process on the accuracy of resulting maps. Hence, the central question remains: Is the Latin hypercube sampling method, which is currently widely adopted, always a dependable approach in this field?
Materials and Methods
The study area covers longitudes 50°35'47'' to 51°29'' east and latitudes 31°36''31'' to 32°15'48'' north in Borujen city, Chaharmahal, and Bakhtiari Province. The region, with an average elevation of 2338 meters above sea level, receives an annual rainfall of 250 millimeters and maintains an average temperature of 11.5 degrees centigrade. In this investigation, inherited data from soil studies were utilized, consisting of 250 samples distributed across the study area. In this research, the studied characteristics included percentage of equivalent calcium carbonate, clay, and soil organic carbon at a depth of 0 to 30 cm. Land component variables were extracted using the Alus Palsar digital elevation model with a spatial resolution of 12.5 meters. In the initial stage, digital maps of equivalent calcium carbonate, clay, and soil organic carbon were generated using the support vector machine method. The modeling process proceeded until a highly accurate model was achieved, with the root mean square error percentage (RMSE%) being less than 40. The Latin hypercube approach was utilized for sample design, with 500 repetitions in this study. After selecting sampling points for each run using the Latin hypercube method, these points were mapped onto a detailed map, and the corresponding feature values were retrieved. The final map was created based on the extracted points. Subsequently, the latin hypercube approach was employed to generate soil property maps for each selected dataset. Validation was conducted using criteria such as the coefficient of explanation, root mean square error, and root mean square error in multiple iterations to ensure the accuracy of the generated maps.
Results and Discussion
The results distinctly illustrates the varied selection of sampling positions with each implementation of the Latin hypercube method. It is important to note that there may be some overlaps in different implementations. Consequently, the primary question arises: Is a one-time execution of the Latin hypercube sufficient for selecting study points? The findings indicate that the support vector machine model achieves satisfactory accuracy for all the examined characteristics. In the studied area, the environmental factors such as slope and elevation were identified as a significant predictors for estimating percentage of equivalent calcium carbonate.
Conclusion
In the present study, the accuracy of the latin hypercube method was assessed for selecting sampling location for digital soil mapping endeavors in Chaharmahal and Bakhtiari Province. Given the impracticality of collecting numerous field samples to evaluate the soil sampling method, this research aimed to employ simulation methods based on highly accurate maps for this purpose. The results indicate that the different outputs of the Latin hypercube method influence the accuracy of modeling, although this effect is also influenced by the specific feature under investigation and the extent of its variability within the study area. Considering that the Latin hypercube method is based on the principle that samples are randomly selected in each class of environmental parameters, it is suggested that future studies using this method should account for this principle. Adequate consideration should be given, and the selection of sampling locations should rely on multiple implementations of the Bhattacharya distance method to ensure robustness and reliability.
M. Molaei Arpnahi; M.H. Salehi; M. Karimian Egbal; Z. Mosleh
Abstract
Introduction: The most important factor in environmental degradation and pressure on ecological resources is rapid population growth combined with unsustainable exploitation of resources. Soil is one of the most important and worthful natural resources of environment. Land use change and deforestation ...
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Introduction: The most important factor in environmental degradation and pressure on ecological resources is rapid population growth combined with unsustainable exploitation of resources. Soil is one of the most important and worthful natural resources of environment. Land use change and deforestation decrease soil quality. Land use change also causes destruction of the evolved soils and decrease soil quality which result in permanent destruction of land fertility. Therefore, studying land use management effects on the soil quality has got an attention in recent years. Destroying the vegetation especially in the last 50 years resulted in important problems like soil erosion, land slide as well as increasing flood in the Bazoft area. In this area, degradation of the forests and their convert to other land uses like pasture, agriculture and urban or rural land use, occurs annually at high extent, in which make high damages to natural resources. In this study, the effect of land use change on soil quality indices in this area located at Chaharmahal-Va-Bakhtiari province was investigated.
Materials and Methods: In this research, four different managements with relatively similar conditions in terms of the influence of soil producing processes were chosen. Then, 10 composite samples from 0-30 cm depth of each land use (40 samples in total) were taken and different soil properties including soil texture, mean weight diameter of aggregates (MWD), porosity, bulk density, soil acidity, electrical conductivity and calcium carbonate equivalent were determined. One-way ANOVA was used to analyze the dataset. Tukey HSD test was applied to compare the means at the probability level of 5%. The first land use includes the natural forest with predominant cover of Iranian oak and the highest density and cover with the least human interference. Another land use is the degraded forest, caused by deforestation over the last 50 years. The third land use is the agricultural land which transformed from forest land use by deforestation in the last 50 years. The fourth land use is the walnut garden which established from agricultural land about 20 years ago.
Results and Discussion: The results showed that land use change from natural forest to other uses had a significant effect on most of the studied parameters. The percentage of particle size distribution was affected by different land uses, so that the percentage of clay was significantly higher in the land use of natural forest and walnut orchard than other land uses. The results also showed that the mean weight diameter of aggregates was influenced by the land use change (P <0.001). Factors like soil compaction due to livestock grazing and machinery traffic, agricultural operations and reduced biological activity increased the bulk density in all land uses compared with the forest land use. Deforestation also resulted in 6.92%, 12.05% and 14.16% porosity reduction in walnut orchard, agricultural land and deforestation, respectively. Changing management from farmland to walnut orchards also improved soil porosity by 6 percent. In the study area, the problem of changing vegetation, grazing, planting and other mismanagement increased soil pH in other land uses compared with the forest land use. The comparison of means showed that degraded forest and agriculture land uses had the highest rate of electrical conductivity which showed significant difference with natural forest land use and walnut orchard. Analysis of variance indicated that the land use had a significant effect on calcium carbonate equivalent at the probability level of 0.001. The comparisons also showed that the equivalent calcium carbonate content in agricultural land was higher than the other land uses, and there was no significant difference between walnut orchard and natural forest.
Conclusion: The results of the present study showed that the soil physical and chemical properties were significantly affected by land use change. Overall, it can be stated that the rate of changes in soil quality under human management and different utilization systems indicates failure in sustainable management of soil resources in the study area. Some characteristics such as soil particle size distribution percentage, soil porosity and calcium carbonate equivalent shows that there is no significant difference between walnut orchard and natural forest. However, the walnut orchards can be selected as the best management in areas where it is impossible to restore natural forests. Also, the need for stopping deforestation in Zagros ecosystem is highly recommended.
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.
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.
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.
R. Karimi; M.H. Salehi; Z. Mosleh
Abstract
Nowadays, changing the rangelands to agriculture and garden is common. To investigate the impact of land use change on the soils type and clay mineralogy, four land uses including rangeland with poor vegetation, agricultural land, new and old apple orchards were selected in Safashahr area, Fars province. ...
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Nowadays, changing the rangelands to agriculture and garden is common. To investigate the impact of land use change on the soils type and clay mineralogy, four land uses including rangeland with poor vegetation, agricultural land, new and old apple orchards were selected in Safashahr area, Fars province. In each land use, three soil profiles were excavated and described and one profile was considered as representative. After required physical and chemical analyses, they were classified according to Soil Taxonomy (ST) and the World Reference Base for Soil Resources (WRB). Selected surface and subsurface samples were also collected for clay mineralogy studies. Results showed that changing land use did not have significant effect on soil type and clay minerals and all soils consist of mica, chlorite, smectite, kaolinite and mixed layer minerals. Results demonstrated that ST is more efficient compared to WRB to classify the studied soils.
