Soil science
Sahar Akhavan; Ahmad Jalalian; N. Toomanian; N. Honarjoo
Abstract
IntroductionLand suitability analysis and land use mapping are one of the most practical applications of Geographic Information Systems in land resource management. Complexities in soil have briefly limited studies on how it functions (Karlen, 2008). There are many methods from different centers including ...
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IntroductionLand suitability analysis and land use mapping are one of the most practical applications of Geographic Information Systems in land resource management. Complexities in soil have briefly limited studies on how it functions (Karlen, 2008). There are many methods from different centers including food and agriculture organizations (FAO), to evaluate land suitability. These methods are based on the characteristics of the land and the needs of the plant. Soil quality indicators are a set of measurable soil characteristics that affect crop production or the environment and are sensitive to land use change, management or conservation operations. (Brejda, 2000; Aparico and Costa, 2007). As a result, there is a global need for environmental issues, improvement of soil quality assessment methods for sustainable agricultural development and recognition of the sustainability of soil management and land use systems. Until now, various methods have been used to collect data, measure and evaluate soil quality, and laboratory analysis is the most common method, which has the advantage of being easy to use and characterizing and the quantitative characteristics of the test on different soil quality indicators (and Wang, 1998 Gong). Criteria for soil quality indicators should be a set of physical, chemical, biological characteristics or a combination of them (Doran and Parkin, 1997).Materials and MethodsIn the present study, the qualitative assessment of land suitability was investigated using fuzzy and parametric hierarchical analysis process models for the irrigated wheat and alfalfa crops. Soil characteristics, climatic conditions, topography and accessibility were selected based on the Food and Agriculture Organization framework and expert opinions. The interpolation function was used to plot values to points in terms of quality/ terrain characteristics for the type of operation and the evaluation was performed based on parametric and fuzzy analytical hierarchy process models. The process of evaluation is based on the FAO qualitative land evaluation system (FAO 1976a, b, 1983, 1985), which compares climatic conditions and land qualities/characteristics including topography, erosion hazard, wetness, soil physical properties, soil fertility, and chemical properties, soil salinity and alkalinity with each specific crop requirements developed by Sys et al. (1991a, b, 1993). Based on morphological and physical/chemical properties of soil profiles some 10 land units were identified in the study area.Climate data related to different stages of wheat growth were taken from ten years of meteorological data of the region (2007-2017) and the climatic requirements of the crop were extracted from the Table developed by (Sys et al., 1993). An interpolation technique using the ArcGIS ver 10.3 helped in managing the spatial data and visualizing the land index results in both models for preparing the final land suitability evaluation maps. The FAHP method and (Chang, 1996) method, which is a very simple method for generalizing the hierarchical analysis process to the fuzzy space, was used in order to assign weight to the criteria through. This method is based on computational mean of the experts’ opinion and the time normalization method and the use of triangular fuzzy numbers. A pairwise comparison matrix has been made fuzzy based on the experts’ opinion and using the triangular fuzzy numb. After calculating the weights of the criteria in the present research through the FAHP method, the entire criteria maps were overlaid through the use of the GIS function and the suitability maps were prepared for the main criteria. The main suitability maps went through weight overlaying eventually and the final map of suitability for wheat and alfalfa cultivation was produced. Results and DiscussionThe results of this study showed that the FAHP was an efficient strategy to increase the accuracy of weight allocation to criteria that affect the analysis of ground fit. The inability of conventional decision-making methods to account for uncertainty paves the way for the use of fuzzy decision-making methods. One of the drawbacks of the AHP is its inability to account for the uncertainty of judgments in pairwise comparison matrices. This defect is compensated by the FAHP method. Instead of considering a specific number in a pairwise comparison, a range of values in the FAHP is used for uncertainty for decision makers. The present research method can be useful for prioritizing lands, improving exploitation, conserving resources, and creating sustainable management. The results of this study, considering the main criteria of cultivation in the study area and the opinion of domestic experts, can provide useful insights into choosing the appropriate cultivation pattern in the region. The use of different fuzzy AHP methods as well as comparing the results of different fuzzy AHP methods in future research is recommended.
Shaghayegh Havaee; Ardavan Kamali; Norair toomanian
Abstract
Introduction: Sustainable management of natural resources is one of the main goals of land use planning and is quite complicated due to various interactions in any given ecosystem. Therefore soil as the bed for interactions of main ecosystem components can be a good indicator candidate as the main requirements ...
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Introduction: Sustainable management of natural resources is one of the main goals of land use planning and is quite complicated due to various interactions in any given ecosystem. Therefore soil as the bed for interactions of main ecosystem components can be a good indicator candidate as the main requirements of sustainable land use management. Soil classification is a valuable technique for transferring a large set of data along with soil history and is a necessary tool for zoning and soil management plans. This indicates efficiency and potential of classification for showing inner and outer soil properties and stimulating for achieving the best possible soil classification system. Among various existing soil classifications, the World Reference Base for Soil Resources (WRB) as an international soil classification system and USDA Soil Taxonomy (STus) are more globally accepted and applied. In both systems soil orders are similar and their classifications are based on acceptable rules. However each one of them has its own characteristics and reflects its potentials. Also the relationship between soil and landscape and the necessity of sustainable nature management convert soil classification to a tool which is essential for appropriate management decisions about utilization and conservation of natural resources. This study was conducted to investigate the efficiency of Soil Taxonomy and WRB for classification of developed soils of Zayandeh-rud River’s upper terrace.
Materials and Methods: This study applied on the current pathway of Zayandeh-rud River. Several pedons were studied in a semi-detailed scale study. Finally, four different pedons were selected. Routine physical and chemical analyses Selected physicochemical properties of the soil samples were determined according to the Soil Survey Laboratory Manual and soils were classified according to Soil Taxonomy (2014) and WRB (2015) systems. Argillic (Argic), and Cambic diagnostic horizons were investigated after field and laboratory work.
Results: Based on both field and lab studies, for these soil pedons due to lithologic discontinuity, presence or absence of Cambic horizon and accumulated clay horizon, four different sequence of horizons are realizable. Calceric matrix of soil pedons is also another prominent property of them. Due to aridity condition of the region and presence of Argilic horizon based on STus, all soil pedons were classified in great group of Haplargids. According to WRB, all pedons considered as reference group of Luvisols. As the results show, the difference between these two systems of classification was originated at family level for STus and qualifiers for WRB. In fact the difference is due to environmental qualifiers and intrinsic soil profile propertie. STus is performing better than WRB indefining the environmental conditions. Such pattern reflects the climate conditions in any given soil name. Moisture regime (Aridic) at order level (Argids) and temperature conditions (Thermic) at family level have been realized for all soil pedons. However WRB not only at first level but also for second level of classification (qualifiers) was not able to indicate. On the other hand this study results showed that main qualifiers have enhanced WRB efficiency compared to STus and was also able to define the variety of horizon sequences. STus did not show such potential even at family level and all four soils were classified under one name.
Based on the findings of the research, using qualifiers and little laboratory data requirement by WRB caused this system to be more successful than Soil Taxonomy to describe internal attributes in the pedons. WRB was more flexible in reflecting described properties in soil nomenclature. But WRB was not able to reflect soil variability in this semi-detailed study, completely. Also results showed that Soil Taxonomy couldcharacterize environmental properties of soils using soil moisture and temperature regimes. On the other hand, the presence or absence of Cambic diagnostic horizon in the four pedons can indicate a difference in their evolutionary pathway, but the presence or absence of Cambic horizon has been disregard in the names of both systems.
Conclusion: The efficiency of each system is vary according to the aims of soil survey, and both systems have advantages and disadvantages in relation to displaying the internal features and the soil environment. The WRB was more succeed than STUS in displaying variety of the characteristics of developed soils in this study, due to its advantages such as Clayic, Cutanic, Ochric, Ruptic, Endocalcaric and also requires less laboratory data. On the other hands, WRB is facing a serious challenge to management objectives related to climatic conditions and vegetation. Therefore, it can be concluded that purpose and scale of a study affects the efficiency of Soil Taxonomy and WRB classification systems to describe soil properties.
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.
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
M. Bagheri-Bodaghabadi; M.H. Salehi; J. Mohammadi; N. Toomanian; I. Esfandiarpour Borujeni
Abstract
Abstract
Limitations of traditional (conventional) soil surveys and improvement of information technology have lead soil surveyors to invent new methods which are generally called digital soil mapping (DSM). The aim of these methods is the prediction of soil classes or soil properties based on easily-available ...
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Abstract
Limitations of traditional (conventional) soil surveys and improvement of information technology have lead soil surveyors to invent new methods which are generally called digital soil mapping (DSM). The aim of these methods is the prediction of soil classes or soil properties based on easily-available or measuring environmental variables. The objective of this investigation is to study the efficiency of digital elevation model and its derivates for soil mapping using Soli-Land Inference Model (SoLIM) and credibility of its results in the Borujen area, Chaharmahal-va-Bakhtiari province. Eighteen terrain attributes including height, slope (angle), aspect, curvature, minimum curvature, maximum curvature, tangent curvature, profile curvature, planform curvature, flow direction, flow accumulation, direct radiation, diffuse duration, diffuse radiation, area solar radiation, power index, sediment index and wetness index, were derived from the DEM. These derivates as well as three dominant soil subgroups and seven soil families of the region were used to construct the input data matrix of the model. Results showed an accuracy of 65% and 40% for interpolation and extrapolation of the soils at subgroup level, respectively. The accuracy decreased to half when soil families were considered for credibility of the model. Because of using crisp limitations in American Soil Taxonomy system, assessing soil survey results can be miss-leading partially, whereas using SoLIM model shows well the reality of the soils in the field.
Keywords: SoLIM, Fuzzy logic, Digital soil mapping, Digital elevation model
I. Esfandiarpour Borujeni; N. Toomanian; M.H. Salehi; J. Mohammadi
Abstract
Abstract
Geopedology is a systematic approach of geomorphic analysis for soil mapping which focuses the field operation mainly on sample area. The purpose of this study is to determine the credibility of generalization of the results of geopedological approach for similar landforms in the Borujen region, ...
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Abstract
Geopedology is a systematic approach of geomorphic analysis for soil mapping which focuses the field operation mainly on sample area. The purpose of this study is to determine the credibility of generalization of the results of geopedological approach for similar landforms in the Borujen region, using diversity and similarity indices in a soil taxonomic hierarchical structure. After a primary interpretation of the study area on air photos (1:20000 scale), the largest delineation of Pi111 geomorphic unit was selected and 19 pedons with an approximate 125 m interval were excavated, described and sampled. The credibility of generalizing the results of the geopedological approach for the studied unit was tested by comparison with 15 pedons in a similar unit outside the sample area, named the validation area. Results showed that as the category decreases from order to soil family, the Shannon's diversity index increases in both the sample and validation areas. A significant difference at 95% confidence level was observed for pedodiversity mean values of two areas at family level. Soil diversity also remains high through the soil taxonomic hierarchy when we change the understanding level and consider the horizon/genetic diversity in both the sample and validation areas. Jaccard index and proportional similarity also indicated that up to subgroup level, the geopedological approach can be used for generalization of the similar geomorphic unit results and it does not have a good efficiency for lower soil taxonomic levels (family and series). Therefore, the use of landform phases and also phases of soil families and/or series for each of landform phases is recommended to increase the accuracy of geopedological results.
Key words: Geopedology, Pedodiversity, Similarity index, Sample area, Validation area
