Soil Classification Methods Research Articles

Case Study of a Full-Scale Evapotranspiration Cover

The design, construction, and performance analyses of a 6.1 ha evapotranspiration (ET) landfill cover at the semiarid U.S. Army Fort Carson site, near Colorado Springs, Colo. are presented. Initial water-balance model simulations, using literature reported soil hydraulic data, aided selection of borrow-source soil type(s) that resulted in predictions of negligible annual drainage (⩽1 mm∕year) . Final construction design was based on refined water-balance simulations using laboratory determined soil hydraulic values from borrow area natural soil horizons that were described with USDA soil classification methods. Cover design components included a 122 cm thick clay loam (USDA), compaction ⩽80% of the standard Proctor maximum dry density (dry bulk density ∼1.3 Mg∕ m3 ), erosion control measures, top soil amended with biosolids, and seeding with native grasses. Favorable hydrologic performance for a 5 year period was documented by lysimeter-measured and Richards’-based calculations of annual drainage that wer...

Subseafloor stratigraphic profiling and soil classification from piezocone tests: A case study in the Gulf of Lion (NW Mediterranean Sea)

We show the results provided by piezocone tests in determining the stratigraphic profile and the soil classification of two drilling sites in the outer shelf and the upper slope of the Gulf of Lion, PRGL2 and PRGL1, respectively. Correlations with grain‐size data indicate that sleeve friction can be used for profiling fine‐grained sediments (site PRGL1), whereas cone tip resistance is the most adequate for sequences made of alternations of coarse‐ and fine‐grained intervals (site PRGL2). Normalized cone resistance and friction ratio proved to be also appropriate for soil stratigraphy as it depicts trends in the coarse fraction of the tested soil. Silts and clays present in similar proportions at site PRGL1 responded to piezocone testing as pure clays usually do. Consequently, classical soil classification methods resulted in erroneous interpretation of these sediments as clays, whereas classification of the heterogeneous deposits at PRGL2 was consistent with the grain size. When tied to a high‐resolution seismic reflection profile, the stratigraphy interpreted from the piezocone profile matches with the main seismic sequences and discontinuities defined from seismic stratigraphy analysis. Graded bedding also matches with cone tip resistance and sleeve friction data.

Vapor Adsorption Index for Expansive Soil Classification

A laboratory index defined as gravimetric water content at ambient relative humidity of 75% (w75) is proposed for qualitatively classifying the swelling potential of clays and clayey soils (e.g., low, moderate, high). The methodology is calibrated by comparison with existing plasticity-based and suction-based classification methodologies for a series of natural clays from Missouri and Colorado and clay mixtures prepared to represent a wide range of swelling potential. Procedures are described for obtaining w75 by placing samples in the headspace of an environmental chamber maintained under controlled humidity using a saturated NaCl solution. The proposed methodology has potential advantages over existing expansive soil classification methods because a large number of samples may be tested concurrently, no specialized testing equipment is required, and testing procedures may be readily automated. Measurements may be obtained in as little as 3–5days with coefficients of variation ranging from 5 to 25%.

A simple classification of soil types as habitats of biological soil crusts on the Colorado Plateau, USA

AbstractQuestion:Can a simple soil classification method, accessible to non‐experts, be used to infer properties of the biological soil crust (BSC) communities such as species richness, evenness, and structure?Location:Grand Staircase‐Escalante National Monument, an arid region of the Colorado Plateau, USA.Methods:Biological soil crusts are highly functional soil surface communities of mosses, lichens and cyanobacteria that are vulnerable to soil surface disturbances such as grazing. We sampled BSC communities at 114 relatively undisturbed sites. We developed an eight‐tier BSC habitat classification based upon soil properties including texture, carbonate and gypsum content, and presence of shrinking‐swelling clays. We used simple structural equation models to determine how well this classification system predicted the evenness, richness, and community structure of BSC relative to elevation and annual precipitation.Results:We found that our habitat classification system explained at least 3.5 × more variance in BSC richness (R2= 0.57), evenness (R2= 0.59), and community structure (R2= 0.34) than annual precipitation and elevation combined. Gypsiferous soils, non‐calcareous sandy soils, and limestone‐derived soils were all very high in both species richness and evenness. Additionally, we found that gypsiferous soils were the most biologically unique group, harboring eight strong to excellent indicator species.Conclusions:Community properties of BSCs are overwhelmingly influenced by edaphic factors. These factors can be summarized efficiently by land managers and laypeople using a simple soil habitat classification, which will facilitate incorporation of BSCs into assessment and monitoring protocols and help prioritize conservation or restoration efforts.

Computerized Cone Penetration Test for Soil Classification

Computerized MS-Windows Visual Basic software of a cone penetration test (CPT) for soil classification was developed as part of an extensive effort to facilitate the implementation of CPT technology in many geotechnical engineering applications. Five CPT soil engineering classification systems were implemented as a handy, user-friendly, software tool for geotechnical engineers. In the probabilistic region estimation and fuzzy classification methods, a conformal transformation is first applied to determine the profile of soil classification index (U) with depth from cone tip resistance (qc) and friction ratio (Rf). A statistical correlation was established in the probabilistic region estimation method between the U index and the compositional soil type given by the Unified Soil Classification System. Conversely, the CPT fuzzy classification emphasizes the certainty of soil behavior. The Schmertmann and Douglas and Olsen methods provide soil classification charts based on cone tip resistance and friction ratio. However, Robertson et al. proposed a three-dimensional classification system that is presented in two charts: one chart uses corrected tip resistance (qt) and friction ratio (Rf); the other chart uses qt and pore pressure parameter (Bq) as input data. Five sites in Louisiana were selected for this study. For each site, CPT tests and the corresponding soil boring results were correlated. The soil classification results obtained using the five different CPT soil classification methods were compared.

Classification of Soils Based on Double Ring Measured Permeability in Zarrineh-Roud Delta, Western Azarbayejan, Iran

A physical method of soil classification based on soil permeability as a preliminary quick way prior to slow and expensive chemical conventional method was conducted in regional scale using 40 test stations. Double ring method used for permeability measurements in the area. The results clearly differentiated development of various soil types and quality in the area under investigation. The results clearly indicate the physical method of double ring measurement permeability is able to identify various soil types in regional scale and the later chemical methods could be used in local scale for detailed classification. NaCl content of soils was reflected by the amount of infiltration rate. Permeability ranged as high, medium and low, corresponded with loamy sand, loamy and clay soils respectively. Permeability in the vicinity of the higher water flows was high due to leaching of NaCl in the soil. NaCl is considered to be the main factor of impermeability reduction in the area associated with clay and soil textures.

Prediction of Soil Composition from CPT Data Using General Regression Neural Network

Soil type is typically inferred from the information collected during a cone penetration test (CPT) using one of the many available soil classification methods. In this study, a general regression neural network (GRNN) was developed for predicting soil composition from CPT data. Measured values of cone resistance and sleeve friction obtained from CPT soundings, together with grain-size distribution results of soil samples retrieved from adjacent standard penetration test boreholes, were used to train and test the network. The trained GRNN model was tested by presenting it with new, previously unseen CPT data, and the model predictions were compared with the reference particle-size distribution and the results of two existing CPT soil classification methods. The profiles of soil composition estimated by the GRNN generally compare very well with the actual grain-size distribution profiles, and overall the neural network had an 86% success rate at classifying soils as coarse grained or fine grained.

Landscape level variation in soil resources and microbial properties in a no-till corn field

Soil microbial properties are known to exhibit high spatial and temporal variability, which can hinder our understanding of the effects of agricultural management on soil microbial activities, populations and communities. However, if this variability is explicitly considered in soil sampling schemes, experimental results can help us better understand soil microbial properties. In this initial assessment of soil resources and microbial properties in a 16-ha landscape planted to corn, we hypothesized that soil microbial properties will vary by soil type. We defined soil type based on drainage class (4 levels), series (7 levels), map unit (11 levels), and texture of the Ap horizon (3 levels). We took soil samples from 16 map units in April, June, August, and October and measured 12 physical and chemical properties, and 27 microbial properties on at least one of these dates. Soil physical and chemical properties generally varied with soil type and canonical discriminant analyses showed that soil drainage classes, series, and map units were delineated by a similar set of soil properties (total C, total N, moisture, pH, equivalent CEC (CEC e), and available P). Ap texture classes were delineated by a different set of soil properties (available K, Mg, and Ca and CEC e). A number of soil microbial properties varied by soil type, but, in general, a smaller proportion of measured soil microbial properties showed soil type effects compared to the proportion of soil physical and chemical properties that showed soil type effects. Method of soil classification strongly influenced which soil microbial properties varied by soil type. However, soil microbial activities, population sizes, and community structures were generally greater or most unique in mid-range soils regardless of how soils were classified, possibly because soil moisture was optimal for microbiological activity and growth in these soils. Except at the level of drainage class, there was little consistency between patterns of variation in soil physical and chemical properties and patterns of variation in soil microbial properties among soil types. Thus, different soil sampling strategies may be required when accounting for variation in soil physical and chemical properties than when accounting for soil microbial properties at the landscape level. While sampling soils according to soil survey-defined map units indicated that some soil microbial properties vary by soil type, a number of limitations to this approach are discussed. To improve upon results presented here, future efforts to quantify landscape level variation of soil microbial properties should probably be sampled at a finer scale that explicitly accounts for soil properties that vary at the sub-map unit level, including terrain attributes, microtopography, soil texture, soil bulk density, and other factors that impact soil temperature and moisture dynamics.

Substrate, climate, and land use controls over soil N dynamics and N-oxide emissions in Borneo

Nitrogen (N) enrichment of tropical ecosystems is likely to increase with rapid industrial and agricultural development, but the ecological consequences of N additions in these systems are not well understood. We measured soil N- oxide emissions and N transformations in primary rain forest ecosystems at four elevations and across two substrate types on Mt. Kinabalu, Borneo, before and after short-term experimental N additions. We also measured N pools and fluxes across a land use gradient of primary forest, burned secondary forest, and fertilized agriculture. Back- ground soil N20 and NO emissions in primary forest decreased with elevation, and soils derived from sedimentary substrates had larger pools of inorganic N, rates of nitrification, and N-oxide fluxes than ultrabasic soils when there were significant differences between substrate types. N-oxide emissions after N additions and background rates of nitrification were low in all soils derived from ultrabasic substrates compared to sedimentary substrates, even at lowland sites supporting, diverse Dipterocarp forests growing on morphologically similar Oxisols. Rates of potential nitrification were good predictors of N-oxide emissions after N additions. N20 and NO fluxes were largest at low elevations and on sedimentary-derived soils compared to ultrabasic-derived soils, even at the smallest addition of N, 15 kg N ha-1. Because current methods of soil classification do not ex- plicitly characterize a number of soil chemical properties important to nutrient cycling, the use of soil maps to extrapolate biogeochemical processes to the region or globe may be limited in its accuracy and usefulness. In agricultural systems, management practices were more important than substrate type in controlling N-oxide emissions and soil N cycling. N-oxide fluxes from agricultural fields were more than an order of magnitude greater than from primary forests on the same substrate type and at the same elevation. As primary forests are cleared for intensive agriculture, soil N20 and NO emissions are likely to far exceed those from the most N-saturated tropical forest ecosystems. This study highlights the inter-dependence of climate, substrate age, N deposition, and land-use practices determining N cycling and N-oxide emissions in humid tropical regions.

Visible–NIR reflectance: a new approach on soil evaluation

The aim of this work was to develop a spectral reflectance (SR)-based methodology to evaluate soil types and soil tillage systems. Soil samples, located along nine toposequences with basalt and shale, were collected in São Paulo State, Brazil. SR of soil samples was measured with a laboratory spectroradiometer between 450 and 2500 nm. SR curves were evaluated at different depths to determine soil classes, as for conventional methods of soil classification. Soil survey maps were developed by descriptive interpretation of the spectral curves and statistical analysis as well. These maps were compared with detailed and semidetailed soil maps based on chemical, physical and mineralogical soil attributes (conventional method). Organic matter, total iron, silt, sand and mineralogy (quartz, magnetite, kaolinite and smectite) were the most important attributes influencing reflectance intensity and spectral features and allowed characterization and discrimination of soils. Strong absorption features centered at 1900 nm were related to OH molecules in free water of 2:1 minerals. Identification of soil classes was most precise when the entire spectral curve of different depths was analysed simultaneously, including features and intensities. Parent material variation along toposequences and weathering variability modified soil characteristics and its respective SR data. The detection of these alterations was essential to determine soil line demarcation. Soil line demarcation and number of soil classes detected by SR analyses were similar to the detailed soil map determined by conventional methods. SR laboratory measurements can be used as a methodology to assist soil surveys. Spectral data can also group soils under similar tillage systems.

Objective Site Characterization Using Clustering of Piezocone Data

Cluster analysis is a statistical method for grouping similar mathematical data sets and is used herein for delineating geostratigraphy from piezocone penetration test data. In terms of site characterization, clustering is an improvement over other statistical methods because no preliminary estimation of the inherent groups within the analyzed data is needed, and no overlapping is permitted between identified clusters. Clustering can accommodate single or multivariables and no data filtering is required. Its application to defining stratigraphic interfaces is illustrated using five case studies with layered profiles. Clustering is able to detect major changes within the stratigraphy not apparent by visually examining the trends of piezocone data or by available cone soil classification methods.

75 years The International Society of Soil Science

This paper describes the history of the International Society of Soil Science (ISSS), including a regional analysis of memberships and an overview of the ISSS main activities. The ISSS was founded in 1924 by, mainly, European agro-pedologists who were interested in establishing standardised methods of soil analysis and soil classification. In total, 16 international congresses were organized by the ISSS in the past 75 years and eight of these congresses were held in Europe. Commissions, Subcommissions, Working Groups and Standing Committees have been established, which deal with virtually all aspects of soil research and management. Number of members increased from around 550 after WWII to over 7000 in 1998. Almost 60% of the members are from North America and Western Europe. Large differences were found in the number of soil scientists and ISSS/International Union of Soil Sciences (IUSS) members in relation to the total population and agricultural land area. One of the important contributions of the ISSS has been the Soil Map of the World, which was prepared in the 1960s and 1970s. The ISSS has also been active in dissemination and publication of scientific results through periodicals and cooperating journals. In 1998, the ISSS was restructured into the IUSS following upon its admission as a Union member of the International Council for Science (ICSU) in 1993.

Applying case-based reasoning techniques in GIS

This paper outlines an approach in applying artificial intelligence techniques to solve spatial problems. The approach consists of combining casebased reasoning with geographical information systems to form a hybrid system to solve spatial problems. In this paper, the hybrid is applied to the problem of soil classification which is solved by searching the case base for other spatial cases similar to the problem case. The knowledge from the retrieved case is used to formulate an answer to the problem case. The paper compares the results from this new approach with a traditional method of soil classification.

Soil Identification Using Piezocone Data by Fuzzy Method

Several charts have been proposed so far for soil classification using either cone penetration test (CPT) or CPT with pore pressure measurement (CPTU) data. In the present paper, a soil classification method has been proposed based on the fuzzy theoretical method using CPTU data. The proposed method has been applied at different field sites where a soil boring was carried out at an adjacent site in order to obtain data on the soil profile. Reasonable agreement was observed between the boring soil profile and the soil profile estimated from the proposed method. It has also been shown that the proposed method is applicable to reasonably estimate a very thin sand layer overlying a soft clay stratum.

A fuzzy allocation scheme for the Australian Great Soil Groups Classification system

SummaryConventional methods of soil classification are based largely on hierarchies and treat soil attribute value ranges as exactly specifiable quantities. Misclassification may result from ignoring the continuous complex nature of soil variation, and the inability to sample and measure every aspect of soil. The Australian Great Soil Groups (GSG) system of classification is essentially a fuzzy classification, and for the most part central concepts have not been defined explicitly.Centroids were generated, together with fuzzy group membership, using data from the 147 soil profile descriptions in The Handbook of Australian Soils. Some of the GSG, such as Siliceous Sands and Red and Brown Hardpan Soils, were divided into their component parts for better and easier quantification and allocation. The centroids of GSG were examined, and the method of fuzzy κ‐means was then used to allocate unknown profiles to the GSG. The results show that the system is intuitively reasonable.

Characterizing soils for hazardous waste site assessments

This paper provides a review and justification of the minimum data needed to characterize soils for hazardous waste site assessments and to comply with the Comprehensive Environmental Response, Compensation and Liability Act (CERCLA). Scientists and managers within the regulatory agency and the liable party need to know what are the important soil characteristics needed to make decisions about risk assessment, what areas need remediation and what remediation options are available. If all parties involved in characterizing a hazardous waste site can agree on the required soils data set prior to starting a site investigation, data can be collected in a more efficient and less costly manner. Having the proper data will aid in reaching decisions on how to address concerns at, and close-out, hazardous waste sites.This paper was prepared to address two specific concerns related to soil characterization for CERCLA remedial response. The first concern is the applicability of traditional soil classification methods to CERCLA soil characterization. The second is the identification of soil characterization data type required for CERCLA risk assessment and analysis of remedial alternatives. These concerns are related, in that the Data Quality Objective (DQO) process addresses both. The DQO process was developed in part to assist CERCLA decision-makers in identifying the data types, data quality, and data quantity required to support decisions that must be made during the remedial investigation/feasibility study (RI/FS) process. Data Quality Objectives for Remedial Response Activities: Development Process (US EPA, 1987a) is a guidebook on developing DQOs. This process as it relates to CERCLA soil characterization is discussed in the Data Quality Objective Section of this paper.

T.E.A.M. Soil Judging-An Experiment

T.E.A.M. Soil Judging-An Experiment

Soil classification method using all three components of CPTU data : Tsuchiya, H; Muromachi, T; Sakai, Y; Iwasaki, K Proc 1st International Symposium on Penetration Testing, ISOPT-1, Orlando, 20–24 March 1988V2, P1021–1026. Publ Rotterdam: A A Balkema, 1988

Soil classification method using all three components of CPTU data : Tsuchiya, H; Muromachi, T; Sakai, Y; Iwasaki, K Proc 1st International Symposium on Penetration Testing, ISOPT-1, Orlando, 20–24 March 1988V2, P1021–1026. Publ Rotterdam: A A Balkema, 1988

Fertilizer experiments on maincrop potatoes 1955–61

Results are given of 124 factorial experiments testing N, P and K fertilizers for maincrop potatoes; the experiments were done by N.A.A.S. Soil Chemists in the years 1955–61. The experimental sites were in sixteen groups, corresponding to particular soil series or complexes.There were substantial differences in mean yield and in response to the lower levels of P and K between soil series; the influence of soil was less at higher rates of application. Alternative methods of soil classification which appeared to be associated with response were texture, experiments on the lighter-textured soils showing higher responses to K; and depth of freely drained soil, soils with impeded drainage being associated with larger P responses. Responses to P and K were particularly large on soils in Wales and N.W. Cumberland.

The Development of Soil Mechanics in Sweden, 1900–1925

Synopsis Prior to 1925, soil mechanics in Sweden developed along two different lines. The fundamental study of soils contributed to a better understanding of their nature and behaviour and, among other things, led to the methods of soil classification now generally used. The practical development resulted in the construction of field and laboratory equipment for soil investigations, and in the working out of new design methods, such as the use of circular-cylindrical sliding surfaces for stability computations. Thus, by 1925 soil mechanics was a well-established science of everyday application in the solution of difficult foundation problems. Mécaniques des sols qui, en Suède, avant 1925, se développaient suivant deux voies différentes. L'étude fondamentale des sols a contribué à une meilleure compréhension de leur nature, ainsi que de leur comportement, et entre autres chases, a conduit aux méthodes de classification des sols généralement employées à présent. Le développement pratique a mené à la construction d'équipement de terrain et de laboratoire pour les recherches sur les sols, tel que l'emploi de surfaces de glissement circulaires-cylindriques pour calculs de stabilité. Ainsi en 1925 les mécaniques des sols étaient une science bien établie d'application quotidienne pour la solution de difficiles problèmes de fondation.