Soil Classification Research Articles

New soil weathering index based on compositional data analyses of silt to sand sized parent mineral assemblages of terra rossa soils

Weathering indices are widely used in soil science and some other environmental disciplines for a variety of purposes (e. g. for soil classification, in soil formation studies, or in wider palaeoclimatic research). Two main categories of weathering indices have been developed: geochemical and mineralogical. In both cases, an approach is developed that takes into account the compositional nature of the geochemical data. To date, however, the most commonly used weathering indices are geochemical indices that do not consider the geochemical data as compositional. Although the compositional approach minimises possible statistical errors, the weathering indices developed in both approaches, i.e., compositional and classical, axiomatically assume two things that are inaccurate in some cases. First, that soil composition is invariant with respect to the preferential translocation of clay-sized particles, i.e. lessivage, and second, that the selective sorting of minerals during transport does not affect the values of weathering indices. As a result, in some cases bulk analyses of mineralogical and geochemical content are an unreliable tool for determining weathering rates of soil. To overcome these difficulties, this study proposes a new weathering index (Wp-min) based on the orthonormal log ratio (olr) transformation of quantitative mineral data derived from silt- to sand-sized allochthonous mineral assemblages. The size fraction used ensures that only the parent mineral assemblages, which are not prone to translocation, are analysed. In this paper, olr transformation is done using the concept of balances. This enabled the construction of variables, which essentially are various mineral log ratios, with the desired properties, i.e. transport invariance and sensitivity to selective dissolution. In this way, undesirable effects, i.e. selective sorting and lessivage, which ultimately affect bulk analyses, are avoided, allowing a more accurate estimation of weathering in the soil profiles studied. The new index was validated by comparison with the geochemical W index, which was modified by consulting the standardized variation matrix prior to element selection. Ultimately, compared to the geochemical index, the new index was better able to characterise weathering in soils where intense lessivage was detected. The study was conducted on terra rossa soils, which have common source, mostly of aeolian origin.

Review of soil form and wetness indicators for wetland delineation in South Africa

Wetland delineation in South Africa incorporates soil form and soil wetness indicators, requiring formal soil classification and description of soil redox morphology. The current wetland definition used administratively in South Africa focuses on saturated (hydric) soil signatures within plant root zones. Saturated soil horizons deeper than plant root zones fall outside the 50 cm criterion in the local approach as well as the accepted zone in USA literature. The field of hydropedology accommodates the classification of the various hydrologically active horizons and provides a tool for the handling of horizons with ephemeral wetness. This approach has been variably accepted by mandated authorities in South Africa. The South African soil classification system has evolved through three editions over the past 50 years while retaining the same redox morphology understanding. However, despite the concepts and context of redox morphology having been thoroughly technically adopted by soil scientists, this is not the case within the wetland research and management environment. This especially because the classification system is structured differently from other international systems, and the South African landscape is geologically ancient with mature soils, introducing challenges to resource assessment specialists who rely on international norms and approaches for wetland assessment. This paper reviews the various components of soil classification and redox morphology based on Fe and Mn minerals within the context of the South African soil classification system, the field of hydropedology and wetland delineation indicators. We provide a qualitative correlation between the various diagnostic horizons and materials in the system and their related redox morphology contexts that are relevant to wetland assessment, delineation, and protection in South Africa. This paper therefore aims to serve as a reference point for the description and correlation of various soil hydrological parameters used in formal assessments.

Characterization and Classifications of Soils in an Experimental Farm in the Tiruvannamalai District, Tamil Nadu, India

It is essential to characterize and classify soil resources in order to plan agricultural land use agroforestry, agri-horticulture and silvipastoral farming systems. In the present study, an attempt was made to characterize and classify the soils occurring on different topography in an experimental farm in Tiruvannamalai District of Tamil Nadu, India. A detailed soil survey was carried out using cadastral map. Physiographically, the area has been characterized into Pediplain shallow weathered and Pediplain moderately weathered which were further subdivided based on slope and erosion categories. Six typical pedons were identified based on landform and their soil morphological properties. The soils were very shallow to moderately deep, dark reddish brown to yellowish red (red soils) in colour with sub-angular blocky in structure, These soils were sandy loam to sandy clay loam in texture, slightly acidic to mildly alkaline (3.28-7.70) in reaction (non-saline soils) and had low to medium (1.2 to 6.4 g/kg) organic carbon content. Calcium and magnesium were the dominant exchangeable cations followed by sodium and potassium. Taxonomically these soils were classified as Typic Rhodustalfs, Typic Haplustalfs, Vertic Haplustepts and Typic Haplustepts at the subgroup level.

Characteristics and classification of the peat at Toba Highland, North Sumatera, Indonesia

Peatland serves as a crucial natural resource with hydrological and other environmental functions essential for all living organisms. In some regions, peat soil isn't limited to lowland areas, it is also found in highland areas. This study is a survey research aiming to examine the characteristics of the highland peatlands of Toba North Sumatera, namely in the Village of Matiti II, Humbang Hasundutan Regency, North Sumatera and lowland peatlands as control of peatlands in general in Sidomulyo Village, Bilah Hilir Subdistrict, Labuhan Batu. The study employed a survey research approach with a descriptive method to determine the differences in the characteristics of highland Toba peat soils. In each area, a representative profile was made, and the soil morphology, characteristics and classification were observed according to the 2014 soil taxonomic classification. Soil samples were taken from each layer in the soil profile for soil analysis in the laboratory. Soil analysis included bulk density, pH H₂O, pH NaF, CEC, base saturation, organic C, total N, C/N, electrical conductivity (EC) and ash content. The results showed that the difference in altitude directly affected the microclimate and the hydrologic conditions, which in turn affected the characteristics of the peat soil. In the context of the Toba highland, the main source of peat soils comes from rainfall. In contrast with the peatland of the lowlands, where the impact of tides is a significant factor, the peat of the Toba highland is affected by the surrounding hills.

Hydropedology of South African soil forms and families

Hydropedology is an interdisciplinary field that studies the interactions between soil and water, recognizing that soils influence hydrological processes through their hydraulic properties, and serve as indicators of hydrological behaviour through their morphological properties that are shaped by water regimes. Given the practical implications of hydropedology and its integration into South Africa's latest soil classification system, an updated categorization of soil forms and 1 657 (1 629 + 28) families was necessary, organizing them into three overarching response groups based on their predominant hydrological responses: recharge, interflow, and responsive. Within these groups, recharge soils are further classified into deep, shallow, and slow subgroups, interflow soils encompass soil/bedrock, shallow, and slow categories, while responsive soils are subdivided into responsive shallow and responsive wet. This paper aims to enhance the reader's comprehension of hydrological responses and simplify the intricacies integrated into South Africa's official soil classification system.

Swelling Potential and Mineralogy of Al-Hartha City Soil in Basrah-Southern Iraq

Mineralogical and geotechnical properties used to analyse the swelling potentials of Al-Hartha City. According to mineralogy, montmorillonite is a dominated clay mineral (33.61%) followed by montmorillonite-chlorite (33.34%), kaolinite (9.18%) and illite (5.38%). While calcite, Quartz, Feldspar, and dolomite are non-clay minerals that are also present. According to geotechnical analysis, the percent of clay range between 9-42% with an average of 25.5%, silt 55-80% with an average of 67.5% and sand 0-17% with an average of 8.5%. While liquid limits and plasticity index are, respectively, 10–25% with an average of 17.5% and 35%–51% with an average of 43%. According to the unified soil classification system (USCS), these soils are mainly low plasticity lean clay soils (CL) and 10% classified as medium stiff elastic silt (MH). According to activity values, soils are primarily inactive-(70%), normally active-(20%), active-(10%), and their consistency is plastic. according to an assessment of soil expansivity based on index properties and activity, a significant amount of the soil is expansive. Additionally, there is an agreement between the results of the mineralogical tests performed on these soils and the results of the measures of soil expansivity obtained from the evaluation of the swelling of the soil based on assessments of its index properties.

An Advanced Empirical NRCS-CN Model Estimation for Ungauged Catchment Insufficient Data

The calculation of runoff is continuously considered a difficult analysis to define specific prediction methods in ungauged catchments. This leads to the implementation of an observed model to determine curve number (CN) and estimate peak discharge from ungauged basins. Therefore, this study aimed to modify an experimental NRCS model through fieldwork and conduct sensitivity analysis for relevant modeling procedures. In the analysis, the assessment of CN required land use, soil, infiltration in situ measurement, and Hydrological Soil Group (HSG) parameters for the catchment area. The determination of infiltration rate was also initially carried out using the Horton method (double-ring infiltrometer), accompanied by the evaluation of CN through soil classes and land use parameters. Based on infiltration rate and soil classification, HSG was significantly defined for the catchment area. The results showed that the analytical parameters in rainfall-runoff modeling included the Composite Curve Number (SCS-CN ( ⁄S) with a ratio value of 0.2. This was accompanied by the potential retention maximum (S) of 264.37 mm, with the initial proportional abstraction ( ) being 52.87 at an assumed preliminary coefficient of 0.2. Therefore, the CN composite estimation was 65.5, and the correlation between P and Q was evaluated using graph analysis. The trial CN ranging from 39-74 were also significantly considered to optimize the development models of HEC HMS for the best performance, proving that the improvement of CN was interrelated with discharge.

Seismic Hazard Microzonation Map for the Central Plain of Thailand

A study was conducted to investigate the seismic hazard microzonation of the central plain of Thailand, which is situated in an area with a thick quaternary basin. Unconsolidated sediments can lead to amplification of earthquake ground shaking at fundamental frequency and can cause a significant increase in damage to buildings. The research yielded significant results, including the development of a fundamental frequency map through the analysis of HVSR for 149 microtremor measurement sites. Subsequently, a Vs30 map was derived utilizing HVSR inversion techniques, and a soil classification map was constructed based on the NEHRP classification. The upper central plain along the Yom River and the Nan River had a low fundamental frequency of 0.3-0.5 Hz and low Vs30, which can be classified as soil type Class E. In the southern areas of Ayutthaya, Pathum Thani, and central Bangkok, an extremely low Vs30 of less than 100 m/s was observed, indicating soil class F or special soft soil. A comprehensive investigation was conducted on probabilistic seismic hazard analysis by considering the Vs30 sites condition for PGA, SA0.2s, and SA1.0s with a 2475-year return period. The northern region of the upper central plain and the western side of the central basin exhibit relatively high seismic hazards. Furthermore, the site effect significantly amplifies ground motion at the 1.0 second period, surpassing the earthquake-resistant design standard for buildings in Thailand by more than 5 times, particularly in the central region of the lower central plain.

Microbial community structure and carbon transformation characteristics of different aggregates in black soil.

Previous research on whole-soil measurements has failed to explain the spatial distribution of soil carbon transformations, which is essential for a precise understanding of the microorganisms responsible for carbon transformations. The microorganisms involved in the transformation of soil carbon were investigated at the microscopic scale by combining 16S rDNA sequencing technology with particle-level soil classification. In this experiment,16S rDNA sequencing analysis was used to evaluate the variations in the microbial community structure of different aggregates in no-tillage black soil. The prokaryotic microorganisms involved in carbon transformation were measured before and after the freezing and thawing of various aggregates in no-tillage black soil. Each sample was divided into six categories based on aggregate grain size: >5, 2-5, 1-2, 0.5-1, 0.25-0.5, <0.25 mm, and bulk soil. The relative abundance of Actinobacteria phylum in <0.25 mm aggregates was significantly higher compared to that in other aggregates. The Chao1 index, Shannon index, and phylogenetic diversity (PD) whole tree index of <0.25 mm aggregates were significantly smaller than those of in bulk soil and >5 mm aggregates. Orthogonal partial least-squares discrimination analysis showed that the microbial community composition of black soil aggregates was significantly different between <1 and >1 mm. The redundancy analysis (RDA) showed that the organic carbon conversion rate of 0.25-0.5 mm agglomerates had a significantly greater effect on their bacterial community structure. Moreover, humic acid conversion rates on aggregates <0.5 mm had a greater impact on community structure. The linear discriminant analysis effect size (LEfSe) analysis and RDA analysis were combined. Bradyrhizobium, Actinoplane, Streptomyces, Dactylosporangium, Yonghaparkia, Fleivirga, and Xiangella in <0.25 mm aggregates were positively correlated with soil organic carbon conversion rates. Blastococcus and Pseudarthrobacter were positively correlated with soil organic carbon conversion rates in 0.25-0.5 mm aggregates. In aggregates smaller than 1 mm, the higher the abundance of functional bacteria that contributed to the soil's ability to fix carbon and nitrogen. There were large differences in prokaryotic microbial community composition between <1 and >1 mm aggregates. The <1 mm aggregates play an important role in soil carbon transformation and carbon fixation. The 0.25-0.5 mm aggregates had the fastest organic carbon conversion rate and increased significantly more than the other aggregates. Some genus or species of Actinobacteria and Proteobacteria play a positive role in the carbon transformation of <1 mm aggregates. Such analyses may help to identify microbial partners that play an important role in carbon transformation at the micro scale of no-till black soils.

Geotechnical assessment of seismicity and liquefaction potential at the Banarli landfill in Tekirdag, Turkey

This study, conducted in the Banarli district of Tekirdag Province, Turkey, focuses on soil reaction analysis and liquefaction potential in an area where a landfill and an associated power plant are planned. The research was examined with a one-dimensional ground response analysis to understand seismic behaviour, revealing the varied responses of soil categories to seismic waves. Soft soil layers were found to lengthen the dominant period of oscillation rather than significantly enhancing peak acceleration during seismic events. Earthquake records specific to the region were crucial for accurate assessments. The study comprehensively assessed liquefaction potential using various estimation methods, including the probability of liquefaction (PL ), liquefaction potential index (LPI), and liquefaction risk index (IR ). Moreover, this study examined liquefaction potential based on Standard Penetration Test (SPT) results, identifying susceptibility in the clayey sand layer at a depth of 3 to 12 meters. Recommendations for soil improvement measures are made to mitigate the risk. This research highlights the need for “customized seismic protocols” and emphasizes the importance of “site-specific assessments” in construction projects. By addressing susceptibility to liquefaction and proposing targeted ground improvement strategies, we contribute to safer and more resilient infrastructure development.

LANDFILL STABILITY ANALYSIS USING CORRUGATED CONCRETE SHEET PILE (CCSP) WITH PLAXIS 2D V22 SOFTWARE

Landfill in road projects requires a stable condition before it can be continued on further work. The stability factor of the landfill depends on the bearing capacity of the subgrade, the shear strength of the soil, the planned elevation, and the angle of inclination of the landfill. This research is located on one side of the Jagorawi Toll road and the Bogor Outer Ring Road, near the South Sentul Toll exit gate. The results of the soil investigation obtained a clay type soil classification at a depth of 0 – 13 m (N-SPT value 3-5). Initial analysis shows the need for landfill height, varies along the road (ranging 4.9 m - 2.4 m). The purpose of this research is to compare the stability of the landfill in the following 2 conditions, 1st condition from STA 1+789 – STA 1+850 (4.9 m height of landfill) using Corrugated Concrete Sheet Pile (CCSP) reinforcement on the left and right of the road side with a 2 m deep geotextile layer, and 2nd condition from STA 1+850 – STA 1 +950 (2.4 m height of landfill) does not use any reinforce. The research approach uses quantitative descriptive, research data including other soil classifications, bor logs, N-SPT values, soil properties from samples (C, Ø, γ, etc.) taken from the site. Apart from that, data on the geotextile and CCSP parameters that will be used is also needed. Analysis was carried out using the Finite Element approach and Plaxis 2D V22 software. The results show that in 1st condition, the safety factor value is 3.7, where the reinforcement system provided is able to withstand 3.7x the working load (vehicles and active soil). Meanwhile, on the 2nd condition produces a safety factor value of 5.2. The analysis results show that these 2 areas have a safety factor that exceeds the existing literature requirements, namely 1.5.

Assessment of Land Capability for Urban Building Height Planning in Bukittinggi, Indonesia

With urban development and rapid growth, the challenge of land capability to accommodate urbanization is becoming increasingly complex. Therefore, the concept of a "vertical city" has surfaced, indicating the importance of integrating sustainable development with spatial and building height restrictions. This study aimed to analyze the land capability of Bukittinggi City in relation to building height control. The methodology adopted adhered to the Ministry of Public Works Regulation Number 20/PRT/M.200 and relevant literature, using the scoring and overlay method with ArcGIS tools. The analysis results showed that the majority of Bukittinggi's area had a relatively high level of land capability, with a significant portion covering 71.3% of the total territory. Moreover, most buildings adhered to the land capability, even though seven unsuitable buildings were identified. These results served as a crucial basis for making informed decisions regarding land use and fostering sustainable urban development.

Digital soil mapping for soil types using machine learning approaches at the landscape scale in the arid regions of Iran

The aims of this research are (i) to compare random forest (RF), boosted regression tree (BRT), and multinomial logistic regression (MnLR) models to prepare the prediction maps of soil great group and subgroup levels, (ii) determination of the most important environmental covariates influencing the production of digital soil mapping (DSM) in an arid climate, (iii) to evaluate the efficiency of spectra indices extracted from Sentinel-2A digital images and data capability of ALOS-PALSAR radar data, and (iv) investigating the effect of sub-surface genetic horizons in the modeling of different types of soil map classes distribution. The principal component analysis method was employed to select the best set from the pool of environmental covariates (n = 46) such as geomorphometric parameters (GPs), RS indices, and diagnostic soil properties (DSP). The relative importance results indicate that Gypsic (GYP) subsurface horizon, standardized height (StH), slope length (SL), and normalized different vegetation index (NDVI) had an important role in the prediction of soil classes compared to the other selected covariates. DSM methodology was used in this research by incorporating of RF model and representative soil-forming factors that can be used for preparing the maps of soil classes in low-relief areas with a similar soil-landscape relationship. Totally, in this study places a spotlight on the profound impact of sub-surface genetic horizons, shedding light on their pivotal role in accurately modeling soil class distributions. These findings not only advance our comprehension of soil variability in arid regions but also hold immense implications for the burgeoning field of pedometrics.

Development of Seismic Fragility Function for Underground Railway Station Structures in Korea

This study describes the methodology employed to construct a seismic fragility function based on a pre-existing numerical model tailored for underground stations. Employing a dynamic numerical model, a comprehensive analysis encompassing 110 distinct cases was conducted, each varying in soil depth and classification. Seismic waves, conforming to the standard design spectrum, were utilized within these numerical analyses. The formulation of the fragility function within the constructed model follows a structured approach, segmented by damage indices and severity levels. This systematic breakdown serves to outline the fundamental framework for establishing the fragility function, providing insights into its development process. Subsequently, the derived fragility function underwent a rigorous comparative analysis against established seismic fragility functions from prior studies. This comparative assessment serves as a critical evaluation tool, allowing for an appraisal of the suitability and robustness of the newly developed fragility function in relation to existing benchmarks.

Assessing leachate contamination and groundwater vulnerability in urban dumpsites: a case study of the Ipata Area, Ilorin, Nigeria

This study explores the extent of leachate contamination and groundwater vulnerability in urban dumpsites, with a specific focus on the Ipata area in Ilorin, Nigeria. The study employs a combination of 2D Electrical Resistivity Tomography (ERT), soil classification, and physicochemical analyses to investigate the percolation of leachate into groundwater and its potential environmental and health implications. The ERT data unveiled subsurface layers, highlighting the presence of decomposed topsoil down to approximately 1.2m. Beneath this layer, a low-resistivity zone (6.53 to 10.7 ?m) indicated the potential risk of leachate percolation into groundwater. Soil classification revealed a shallow topsoil layer with insufficient clay content to hinder leachate penetration, emphasizing the need for enhanced containment measures. Physicochemical analysis of leachate, well water, and soil displayed variations in key parameters such as pH, electrical conductivity, total dissolved solids, and anion concentrations. Leachate exhibited high pH and electrical conductivity, suggesting elevated total dissolved solids, while well water remained within acceptable pH limits for drinking water. Heavy metal concentrations exceeded permissible WHO limits in topsoil, leachate, and well water, with cadmium presenting a high ecological risk. The absence of persistent organic pollutants (POPs) in the samples indicates a current focus on heavy metals as a primary concern. In conclusion, this study underscores the urgent need for proactive pollution abatement measures in urban dumpsites like Ipata. Regular monitoring of surface and groundwater quality is essential to safeguard public health and the environment.

National-scale digital soil mapping performances are related to covariates and sampling density: Lessons from France

Accurate soil property and class predictions through spatial modelling necessitate a thoughtful selection of explanatory variables and sample size, as their choice greatly impacts model performance. Within the framework of Global Soil Nutrient and Nutrient Budget maps (GSNmap), the FAO Global Soil Partnership (GSP) launched a country-driven digital soil mapping (DSM) approach. The GSP asked the countries if they could implement the DSM prediction of ten soil properties, using their national point data and a set of widely available covariates (GSP_Cov). In this study, we examined the effect of including additional national-based covariates and soil observations on the performance of the prediction models using mainland France as a pilot. The learning soil dataset was based on a systematic 16-to-16 km grid. For a subset of soil properties, we also assessed using repeated k-fold cross-validation the effect of adding to this dataset many other irregularly spread measurements. The GSP_Cov included common widely available covariates that represented information about terrain, climate, and organisms. The second set of covariates consisted of the GSP_Cov, extended to extra covariates available at a national level, such as previously existing soil maps, geological maps, remote sensing products and others. Random Forest approach in combination with the Boruta selection method was employed for mapping ten soil properties: soil organic carbon (SOC), pH (water), total nitrogen (N), available phosphorus (P), available potassium (K), cation exchange capacity (CEC), bulk density (BD), and texture (clay, silt, and sand). The results revealed noteworthy enhancements in prediction performance for more than half of the properties, although, for some of them, the improvements were negligible. The most significant improvements were obtained for pH, CEC and texture, where geological variables and a previous pH map significantly contributed to the increase in accuracy. Adding numerous points (around 25,000) to the learning dataset improved the performance of soil particle-size fractions predictions. By broadening the spectrum of covariates and better covering the feature and geographical spaces considered in soil prediction models, this research underscores the importance of implementing a more diverse range of covariates at a national scale and of densifying soil information to enlarge the feature and geographical spaces of multidimensional soil/covariates combinations. This information should be taken into account in national and continental digital soil mapping endeavours.

Low zinc availability limits the response to phosphorus fertilization in cotton

AbstractBackgroundCotton productivity is commonly limited by the imbalanced nutritional status of phosphorus (P) and zinc (Zn) in Brazil. Evaluating the effect of the P–Zn interaction on nutrient availability in soil is crucial, as this interaction promotes plant adaptations that modify the availability of these nutrients in the rhizosphere. However, the influence of root growth on the P–Zn interaction and its adsorption in rhizosphere soil remains poorly understood.AimWe tested the interaction of P and Zn rates in two classes of soil cultivated with cotton. The aim of this investigation was to evaluate the P and Zn availability and adsorption capacity by rhizosphere soils cultivated with cotton plants subjected to P and Zn rates.MethodsCotton plants were grown under greenhouse conditions in two different soils (Entisol and Oxisol), with different P and Zn rates. Phosphorus fractionation and Zn sequential extraction were assessed in rhizosphere soil, while total P and Zn concentrations were measured in the shoot and roots.ResultsSoil type significantly affected the P–Zn availabilities on rhizosphere soils. Although increased P‐rates raised P‐soil availability on bulk and rhizosphere soils, cotton growth was not increased under low soil‐Zn availability. The labile inorganic P content was influenced by P and Zn rates just in Oxisol. In both soils, the Zn‐ Zn‐exchangeable content was decreased by P rates, while Zn bound to oxides increased.ConclusionsSoil‐type effects on P–Zn interaction could have important implications for increasing cotton productivity. Increased cotton productivity by P application was only achieved with an adequate supply of Zn.

Revegetation of disturbed land reclaimed with suboptimal topsoil replacement depth and organic amendments

AbstractSuccessful reclamation and revegetation of disturbed sites depends on the availability of sufficient topsoil. Current regulations in western Canada require the replacement of ≥80% of the original topsoil depth (TRD) to restore pre‐disturbance productivity. However, salvaged topsoil at many legacy and other disturbed sites is often insufficient to achieve the 80% TRD (TRD80). This 5‐year study examined vegetation responses to 50% TRD without organic amendment (TRD50) or amended with either peat (PTRD50) or biochar (BTRD50), relative to the TRD80 treatment, following borrow site reclamation in northeastern Alberta, Canada. Amendments were applied once at rates calculated to bring organic carbon in the topsoil to concentrations approximating those in the TRD80 treatment. Tree and shrub seedling mixes were transplanted following amendment application and vegetation attributes were measured annually thereafter. Results showed that PTRD50 performed as well as TRD80 and outperformed BTRD50 with respect to native species' canopy cover and tree seedling height. Canopy covers of forb and non‐native species decreased significantly whereas those of graminoid and native species increased over time for all treatments. Across treatments, native plant species richness increased by 5% per year while non‐native species richness decreased by 19% per year. Overall, peat showed a satisfactory rate of recovery and a trajectory toward the natural boreal forest, indicating its potential to improve reclamation success at disturbed boreal sites where salvaged soil is insufficient to attain equivalent land capability. This finding will provide an additional tool for regulators seeking to refine land reclamation guidelines for such sites in boreal forest regions.

Pedotransfer Functions in the Assessment of Fragile Mountainous Areas: Carbon and Nitrogen Stocks in Mountains in Southeastern Brazil

Objective: This study aimed to obtain the Bd of organic constitution horizons through PTFs and evaluate the C and N stocks of different soil classes found in the upper part of the INP. Theoretical framework: The evaluation of bulk density (Bd) depends on the method used to obtain the sample and presents great spatial variability, especially in mountainous regions such as the upper part of Itatiaia National Park (INP), Brazil. Techniques can be used for this purpose, including those of pedotransfer functions (PTFs) allowing the quantification of nutrients such as C and N in soils. Methodology: The research was conducted within the Itatiaia National Park (INP), situated in the Mantiqueira Mountains of southeastern Brazil. Soil samples were collected from the upper portion of the INP, specifically targeting soil profiles containing hystic horizons (O or H). The study employed pedotransfer functions (PTFs) established by Beutler et al. (2017) to estimate the soil bulk density (Bd) of organic horizons. Based on these assessments, stocks of carbon (C) and nitrogen (N) were calculated. The standard depth for these calculations was 30 cm. In total, 47 profiles were selected, 26 of which belonged to the order Histosols, 15 to Cambisols, and 6 Leptosols &amp; Regosols. Results and conclusion: In general, a high C content, between 5 and 15 g kg-1, and N contents greater than 1.0 g kg-1 were observed. For Bd an increase in depth occurred, with values close to 0.50 Mg m-3 in the superficial horizons and &gt; 0.75 Mg m-3 in depths greater than 30 cm. The area occupied by soil profiles presenting hystic horizons corresponds to 16403.69 ha (hectares) with 315645.7 Mg of C and 20043.0 Mg of N. Of this total, 10254.44 ha were Cambisols that store approximately 205282.1 Mg of C and 13121.8 Mg of N. Histosols occupied an area of 4148.38 ha and stored 90698.4 Mg of C and 5576.0 Mg of N. The area occupied by Leptosols &amp; Regosols was 1347.74 ha and they stored 19665.1 Mg of C and 1345.1 Mg of N. For the Cambisols, the average values of C and N stocks were lower than those quantified in the Histosols, but considering the area occupied, the total C stock was more than twice the amount quantified in the Histosols. Research implications: Soils with organic horizons complicate Bd determination due to costly and unrepresentative collection methods. Additionally, converting soil masses to volume for nutrient stock measurements (e.g., C and N) is problematic. Due to limited accessibility in certain areas, obtaining undisturbed soil samples for evaluation can be challenging. In such cases, pedotransfer functions (PTFs) play a crucial role in assessing and modeling soil phenomena. Indirect quantification of Bd using PTFs has become essential for addressing these issues. Originality/value: This study utilizes pedotransfer functions (PTFs) to estimate soil bulk density (Bd) and, consequently, nutrient stocks in fragile mountainous soils, enabling monitoring of these environments.

A comparative study of soil classification machine learning models for construction management

Prior to the construction of buildings and roads, soil classification plays a vital role to ensure proper project management and durability of the structure. Civil engineers need to possess knowledge about soil behavior and properties of soil classification to construct reliable and long-lasting infrastructures. However, traditional soil classification methods are both expensive and time-consuming. Recently, machine learning (ML) has become increasingly popular in solving complex problems in Geotechnical Engineering, leading to novel approaches for automating soil classification. This research evaluates the effectiveness of various machine learning algorithms, including Multinomial Logistic Regression (MLR), Gaussian Naive Bayes (GNB), Extreme Gradient Boosting (XGBoost), Random Forest, and Artificial Neural Network-Multilayer Layer Perceptron (ANN-MLP), in classifying soils. The paper also implemented Hard and Soft Voting Ensemble models. Each model was quantitatively evaluated and compared using various metrics. The models achieved high scores in multiple performance metrics across the soil classes with accuracies ranging between 87% and 93%, except the Naive Bayes model with a balanced accuracy of 77.6% while the hard-voting model outperformed the others with a balanced accuracy of 93%. The study’s results benefit industry practitioners and academic researchers for integrating and assessing ML algorithms in soil classification.