Soil Classification Research Articles

Recent innovations in land capability classification for sustainable development: a brief overview.

Land is a finite resource that must be managed wisely to ensure its sustainability. Consequently, land evaluation has become essential. Identifying and utilizing productive capacity of land efficiently and profitably is crucial; otherwise, resource degradation can severely impact natural ecosystems and food production. Over the years, various methodologies have been employed to assess land resources, and one such method is the Land Capability Classification (LCC). LCC is a widely used and fundamental approach to land-use planning, traditionally assessing land based on its intrinsic qualities and climate. This study aims to highlight new approaches and developments in land evaluation techniques. It provides a brief overview of recent technological and scientific advancements integrated into land evaluation. The findings suggest that LCC alone is inadequate for precise land assessment, emphasizing the need to integrate new technologies. Remote Sensing (RS) and Geographic Information System (GIS) technologies are becoming increasingly significant. Integrating various software, decision-making systems, and mathematical models can also enhance the accuracy of land assessment results. The continuous advancement of GIS and remote sensing technologies is paving the way for new tools to facilitate natural resource mapping, appraisal, surveillance, and management. Utilizing these technologies for future projections will be highly beneficial in accurately assessing the long-term impacts of current land management practices.

Soil available phosphorous and potassium stocks related to environmental properties, land uses and soils

Abstract Soil available phosphorus (SAP) and potassium (SAK) are indispensable for crops, and their stocks are important for food production needed for a growing global population. This study analysed 991 soil profiles across a large part of Romania covering forestland, grassland and cropland in almost all ecological regions. This study investigated SAP and SAK stocks for different soil depths and characterized their magnitude and variability within land uses under different environmental ecosystems, soil classes and soil types, for a better soil and land management under a temperate-continental climate. Cropland soils present the highest SAP and SAK stocks. Chernozems, Phaeozems and Vertosols possess the highest SAP and SAK stocks in Romania, representing the largest country's pool. Both SAP stocks and SAK stocks are significantly correlated with basic environmental properties, existing direct correlations between SAP, SAK, soil organic carbon (SOC) and total nitrogen (TN) stocks. For all land uses, SAP and SAK stocks correlated significantly and directly with each other, as well as with annual temperature, clay content, pH and sum of base cations, and inversely with altitude, slope and annual precipitation. The best predictive values using multiple regression models and basic environmental driving factors were found for forestland stocks of SAP and SAK, followed by grassland stocks, while the lowest prediction occurred for cropland stocks, probably due to the long-term additional nutrient input performed by farmers in cropland that changed the natural conditions otherwise present in grassland, and especially in forestland. Based on these results some management measures are discussed.

Biophysical and socioeconomic characterization of Agewmariam experimental watershed in Northern Ethiopia: insights and management options.

Watershed characterization is essential for sustainable watershed management and effective resource utilization, particularly in assessing changes resulting from interventions. This study investigates the biophysical and socio-economic conditions of the Agewmariam watershed, aiming to develop land capability and suitability maps while identifying viable management options. Biophysical data, including land slope, soil properties, erosion severity, stoniness/rockiness cover, and vegetation cover, were collected through field surveys and analyzed using overlay analysis in ArcGIS. Socio-economic data, encompassing population demographics, income, and expenditure, were gathered via individual interviews, complete census, and focus group discussions and summarized using descriptive statistics. The study identified 259 households with an average family size of 4 and a demographic dependency ratio of 96.6%. The major sources of income are crop production and livestock rearing supplemented by casual labor and food aid. The average farmland holding size is 0.5ha, with sorghum, barley, teff, and wheat as the dominant crops. The watershed is characterized by six land capability classes (classes II, III, IV, VI, VII, and VIII). The suitability analysis indicated that the watershed is not currently suitable for wheat and teff crops unless physical, chemical, and biological management improvements are implemented. The study revealed that the major limiting factors for land capability and suitability were slope, erosion severity, stoniness, soil organic matter, and soil texture. The socio-economic characterization enhanced awareness about the local socio-economic condition, informing appropriate planning and management strategies. The study recommends intensive soil and water conservation intervention, afforestation on hillsides, changing the land use system, and the addition of organic matter and fertilizers to enhance land capability and suitability for sustainable agricultural practices.

Land Suitability Assessment and Crop Water Requirements for Twenty Selected Crops in an Arid Land Environment

Expanding projects to reclaim marginal land is the most effective way to reduce land use pressures in densely populated areas, such as Egypt’s Nile Valley and Delta; however, this requires careful, sustainable land use planning. This study assessed the agricultural potential of the El-Dabaa area in the northern region of the Western Desert, Egypt. It focused on assessing land capability, evaluating crop suitability, mapping soil variability, and calculating crop water requirements for twenty different crops. In this work, we evaluated land capability using the modified Storie index model and assessed soil suitability using the land use suitability evaluation tool (LUSET). We also calculated crop water requirements (CWRs) utilizing the FAO-CROPWAT 8.0 model. Additionally, we employed ArcGIS 10.8 to create spatial variability maps of soil properties, land capability classes, and suitability classes. Using a systematic sampling grid, 100 soil profiles were excavated to represent the spatial variability of the soil in the study area, and the physicochemical parameters of the soil samples were analyzed. The results indicated that the study area is primarily characterized by flat to gently sloping surfaces with deep soils. Furthermore, there are no restrictions on soil salinity or alkalinity, no sodicity hazards, and low CaCO3 levels. On the other hand, the soils in the study area are coarse textured and have low levels of CEC and organic matter (OM), which are the major soil limiting factors. As a result, the land with fair capability (Grade 3) accounted for the vast majority of the study area (87.3%), covering 30599.4 ha. Land with poor capability (Grade 4) accounted for 6.5% of the total area, while non-agricultural land (Grade 5) accounted for less than 1%. These findings revealed that S2 and S3 are the dominant soil suitability classes for all the studied crops, indicating moderate and marginal soil suitabilities. Furthermore, there were only a few soil proportions classified as unsuitable (N class) for fruit crops, maize, and groundnuts. Among the crops studied, barley, wheat, sorghum, alfalfa, olives, citrus, potatoes, onions, tomatoes, sunflowers, safflowers, and soybeans are the most suitable for cultivation in the study area. The reference evapotranspiration (ETo) varied between 2.6 and 5.9 mm day−1, with higher rates observed in the summer months and lower rates in the winter months. Therefore, the increase in summer ETo rates and the decrease in winter ones result in higher CWRs during the summer season and lower ones during the winter season. The CWRs for the crops we studied ranged from 183.9 to 1644.8 mm season−1. These research findings suggest that the study area is suitable for cultivating a variety of crops. Crop production in the study area can be improved by adding organic matter to the soil, choosing drought-resistant crop varieties, employing effective irrigation systems, and implementing proper management practices. This study also provides valuable information for land managers to identify physical constraints and management needs for sustainable crop production. Furthermore, it offers valuable insights to aid investors, farmers, and governments in making informed decisions for agricultural development in the study region and similar arid and semiarid regions worldwide.

Design and implementation of a nonlinear observer for a novel Quadrotor UAV: the HELIPLANE

This study presents the design and dynamic modeling of a new type of quadrotor UAV, referred to as the Heliplane, which integrates the vertical take-off and landing (VTOL) capabilities of a multirotor with the horizontal flight efficiency of a fixed-wing aircraft. The modeling process involved creating a dynamic representation of the Heliplane, with special attention to the determination of its aerodynamic coefficients, which were calculated using SolidWorks Flow Simulation. The aerodynamic forces, including lift and drag, were analyzed based on the flow characteristics around the Heliplane's structure. To further enhance the system's performance, a nonlinear observer based on a sliding mode approach was synthesized. This observer was designed in a triangular form to ensure accurate state estimation and robust control, even in the presence of model uncertainties. The observer's key advantage lies in its finite-time convergence, which guarantees quick and precise tracking of the Heliplane's state variables. Simulation results demonstrated the effectiveness of the proposed observer in estimating the rotational and translational velocities with minimal deviation. The sliding mode observer's performance suggests its suitability for control and fault detection tasks in UAV applications, particularly in challenging operational environments.

Critical levels and fertility classes of soils with high-activity clay in the Brazilian semi-arid region

ABSTRACT Soil fertility evaluation is important for adopting conservation management and adequate nutrient supply. The objective of this study was to identify critical levels and soil fertility classes using the boundary line method for rainfed crops (corn, cowpea and sabiá [Mimosa caesalpiniifolia]) in the Brazilian semi-arid region. A database of 226 soil fertility analyses of samples from the 0.00-0.20 m soil layer, and corn, cowpea and sabiá yields from Ceará State was used to generate interpretation classes (at 80 and 95 % of maximum yield). In a scatter plot, soil nutrient concentrations (x-axis) and relative crop yields (y-axis) were correlated, and the border points fitted to a quadratic model. Proposed interpretation classes were classified as very low, low, adequate, high and very high, except for Na + , whose adequate class was considered tolerable. Generated models showed coefficients of determination (R 2 ) for the chemical properties ranging from 0.54 to 0.92. Based on the interpretation classes, the critical level was determined as 6.3 for pH, 10.8 g dm -3 for OM, 20.9 mg dm -3 for P, 81 mg dm -3 for K, 55 mmol c dm -3 for Ca 2+ , 24 mmol c dm -3 for Mg 2+ and 8 mg dm -3 for S-SO 4 2- . Interpretation classes for soils with high-activity clay in the Brazilian semi-arid region were superior to those in the reference literature. Boundary line method established fertility classes and critical levels for soil chemical properties in more than one crop, using the concept of relative yield.

Nature vs. nurture: Drivers of site productivity in loblolly pine (Pinus taeda L.) forests in the southeastern US

Forest productivity is one of the most important aspects of forest management, landscape planning, and climate change assessment. However, although there are multiple elements known to affect productivity, most of them rely on the “nature” of the edaphic, climatic, and geographic conditions, and only some specific aspects can be modified through forest management or “nurture”. Through evaluation of site resource availability and an understanding of the main drivers of productivity, management can present solutions to overcome site resource limitations to productivity. Therefore, understanding the implications of a specific management regime requires understanding what drives productivity across large spatial extents and among different management regimes. In this study, we used data from over 1 million hectares of industrial forestland, covering over 6000 different soils and several management regimes of Pinus taeda L. plantations, as well as plot-based data from the Forest Inventory and Analysis (FIA) program, facilitating a comparison of planted and natural Pinus taeda stands. Combined with US Geological Survey LiDAR data, we computed site index and generated wall-to-wall productivity maps for planted Pinus taeda stands in the southeastern US, as well as point-based site index estimates for the FIA dataset. We modeled site index using a random forest algorithm considering edaphic, geologic, and physiographic province information based on the Forest Productivity Cooperative “SPOT” system, and also included climate and management history data. Our model predicted site index with an R2 of 0.701 and RMSE of 1.41 m on the industrial data and R2 of 0.417 and RMSE of 1.84 m for the FIA data. We found that year of establishment of the forest, physiographic province, and geology, were the most important drivers of site index. The soil classification modifier indicating root restrictions were the most important soil-specific variable. Additionally, we found an average increase in site index of 3.05 m since the 1950s for all FIA data, and an average increase of 4.73 m for all industrial data since the 1970s. For the latest period analyzed (2000–2012), average site index in planted FIA plots was 1.2 m higher than naturally regenerated FIA plots, and site index in all industrial forestland had a site index almost 3 m greater than planted FIA plots. Overall, we believe this work sets the foundation for better understanding of forest productivity and highlights the importance of intensive silviculture to improve productivity, and as an additional tool to achieve the economic, environmental, and social objectives.

Analysis of Land Use and Occupation in a Neighborhood of the City of Manaus-AM Using Geotechnologies

Objective: The objective of this study is to analyze the land use and occupation that occurred between 1999 and 2021 in the Tarumã neighborhood located in the city of Manaus-AM, with the aim of carrying out, through remote sensing techniques, the investigation of the environmental impacts that occurred, considering the classes of soil, vegetation and hydrography. Theoretical Framework: The accelerated population expansion and the lack of environmental planning contribute to the increasing degradation of natural resources. The disorderly distribution of the territory represents one of the main problems in Manaus. Method: Land use and occupation mapping was obtained from the supervised classification of Landsat 7 and Landsat 8 satellite images, RGB bands, through the United States Geological Survey – USGS (Earth Explore). Data comparisons were made based on the temporal analysis of the images and shapefile. Results and Discussion: The results obtained identified the class transformations that occurred in the last 22 years, which corresponds to 41.71% of primary vegetation, 8.98% of secondary vegetation, 17.70% of exposed soil. In the urban area, there was interference of 30.16%, with a 1.74% decrease in the water class, including the Environmental Preservation Area Research Implications: The practical and theoretical implications of this study favor the understanding, measurement and prediction of environmental impacts caused by the ordered and disordered urban occupation of cities. Originality/Value: This study contributes to the monitoring of occupations in the Tarumâ neighborhood, contributing to decision-making in preservation areas.

Nonlinear Static Analysis of RC Buildings: Effects of Soil Type and Seismic Code Differences on Structural Performance

Reinforced concrete (RC) building construction remains predominant in Northern Cyprus, offering resilience against natural disasters when appropriately designed and implemented. The study presents a seismic analysis of RC building models across different soil classes, stories, and configurations, according to multiple seismic design codes: Eurocode 8 (EC8), Northern Cyprus Seismic Code 2015 (NCSC-2015), and Turkish Buildings Earthquake Code 2018 (TBEC-2018). This paper compares regular and irregular forms of Moment Resisting Frame (MRF) and MRF combined with Shear Walls (MRF+SW) systems in various configurations. These configurations include G+3, G+7, and G+11 for regular buildings, and only G+11 for irregular buildings. Pushover analysis using ETABSv18 was employed to assess the base shear, plastic hinge behavior, and displacement. The results indicate that the regularity of the structures enhances resistance and longevity compared to irregular configurations, with shear walls augmenting resistance against earthquake loads in both regular and irregular buildings. Furthermore, soil class emerges as a significant factor influencing results across the codes. While variations among the codes were not consistently observed, EC 8 and TBEC-2018 often appeared more conservative, with TBEC-2018 demonstrating greater adaptability to advanced technologies and a more detailed parameter consideration.

Empowering urban development: geospatial modeling and zonation mapping in New Kabul City, Afghanistan

The main difficulties in urban development, choosing a location, and creating preventative safety precautions are accurately characterizing and valuing subsurface soil information from a geotechnical and geological standpoint. This paper discusses how to define and build geotechnical subsoil soil zonation maps (SZMs) for the new Kabul city, Afghanistan, using traditional ArcGIS software assessing Kriging interpolation approaches. With the city’s expansion plans, including New Kabul City’s development, our research supports informed urban development strategies. Subsoil data from 2,13 locations across the city were collected from geotechnical studies, focusing on soil classification, Standard Penetration Test (SPT-N values), undrained shear strength, and consolidation characteristics up to 15 m depth. SPT-N and soil type were used to create SZMs, and other parameters were used to evaluate bearing capacity and settlement. The results revealed that SPT-N values divided the research region into three main sections: A (8–>50), B (13–>50), and C (14–>50). The subsurface strata consist of low-plasticity clay (CL) and clayey sand (SC) underlain by highly plastic clay (CH) and silt (MH). Linear regression predicted SPT-N values with depth, showing a strong R2 of 0.95. This speeds up sub-soil stiffness and strength assessments during building project planning and feasibility studies. The shallow Kabul foundation has an allowable bearing capacity of over 100 kPa, making it suitable for lightly loaded buildings. Predicting SPT-N levels has an 85% correlation coefficient, while soil type has 94%. Accurate geotechnical data on the soil’s underlying layers will help characterize the site and identify future project risks.

Nonlinear Static Analysis of RC Buildings: Effects of Soil Type and Seismic Code Differences on Structural Performance

Reinforced concrete (RC) building construction remains predominant in Northern Cyprus, offering resilience against natural disasters when appropriately designed and implemented. The study presents a seismic analysis of RC building models across different soil classes, stories, and configurations, according to multiple seismic design codes: Eurocode 8 (EC8), Northern Cyprus Seismic Code 2015 (NCSC-2015), and Turkish Buildings Earthquake Code 2018 (TBEC-2018). This paper compares regular and irregular forms of Moment Resisting Frame (MRF) and MRF combined with Shear Walls (MRF+SW) systems in various configurations. These configurations include G+3, G+7, and G+11 for regular buildings, and only G+11 for irregular buildings. Pushover analysis using ETABSv18 was employed to assess the base shear, plastic hinge behavior, and displacement. The results indicate that the regularity of the structures enhances resistance and longevity compared to irregular configurations, with shear walls augmenting resistance against earthquake loads in both regular and irregular buildings. Furthermore, soil class emerges as a significant factor influencing results across the codes. While variations among the codes were not consistently observed, EC 8 and TBEC-2018 often appeared more conservative, with TBEC-2018 demonstrating greater adaptability to advanced technologies and a more detailed parameter consideration.

How Can Land Use Management in Traditional Cultural Landscapes Become a Policy Instrument for Soil Organic Carbon Sequestration and Climate Change Mitigation? A Transylvanian Case Study

Changes in land use from high-nature-value grasslands to arable fields reduce the organic carbon stock in soil, increasing atmospheric carbon concentrations. Maintaining grasslands through traditional agricultural techniques can mitigate climate change by transferring atmospheric carbon to the soil. Benefits of soil organic carbon sequestration include improved soil properties and enhanced ecosystem services and biodiversity. With Romania’s ratification of the Paris Agreement, it is crucial to review climate-related agricultural policies and incentivize carbon sequestration practices in organic soils. This paper presents a soil carbon study in Transylvania’s Târnava Mare region, Romania, known for its preserved cultural landscapes. Soil samples were taken at a depth of 60 cm to assess organic carbon pools under grassland and arable land management across three soil classes: Cernisoils, Hidrisoils, and Luvisoils. Several statistical tests were applied to evaluate the most significant drivers of soil organic carbon sequestration including land use, soil class, and soil depth. The results indicate that land management has the largest impact, with grasslands storing 45% more carbon than arable land on average. This finding should be integrated into national climate action plans, prioritizing the preservation of grasslands and sustainable agricultural practices to support soil organic carbon sequestration.

Effect of Chloride Solution on Liquid Limit, and Plasticity Index of Soil in Selected Sites in Erbil /North Iraq

This study is an attempt to investigate the effect of NaCl, CaCl2, KCl, and NH4Cl solutions with different concentrations (2.5%, 5%, 7.5%) on the liquid limit, plastic limit, and plasticity index of Sarbasty and Mamwastayan soil samples /Erbil Governorate. The soil samples' liquid and plastic limits were found by using the cone penetrometer method. The Results showed that the soil's liquid, plastic limits, and Plasticity index decreased with increasing salt solution concentration. Maximum reduction of liquid limit and plastic limit in Sarbasty was from 53.6 to 34.3 and in Mamwastayan from 35.9 to 27.2, both accomplished by adding a concentration of 7.5% of KCl and CaCl2 respectively. According to the Unified Soil Classification System, the Sarbasty soils were classified as high plasticity clay (CH), and Mamwastayan low plasticity clay (CL). After adding different concentrations of salt solution to the soil, the Sarbasty soil classification changed from CH to CL. Mineralogically these soils are composed of non-clay minerals of calcite and quartz and clay minerals consisting of Montmorillonite, Chlorite, swelling Chlorite, Kaolinite, Illite, Palygorskite, and Illite- Chlorite. Sarbasty soil was the most effective salt chloride solution than the Mamwastayan soil sample swelling.

Precision Agriculture using ML for Soil and Weather Prediction

Agriculture is the backbone of many economies and plays a critical role in global food security. However, with growing challenges posed by climate change, erratic weather patterns, and soil degradation, the need for precise and predictive techniques in agriculture has never been more urgent. This paper focuses on the application of machine learning (ML) techniques in soil fertility prediction and weather forecasting, two critical components that can significantly impact agricultural productivity. By analysing soil properties, such as moisture, pH, and nutrient levels, and combining them with accurate weather predictions, our system aims to help farmers make informed decisions regarding crop selection, irrigation scheduling, and pest control. Leveraging algorithms like Naive Bayes for soil classification and Long Short-Term Memory (LSTM) networks for weather prediction, we provide a comprehensive solution for precision agriculture. This system not only enhances productivity but also promotes sustainable agricultural practices by optimizing resource use and reducing wastage

Providing quality-assessed and standardised soil data to support global mapping and modelling (WoSIS snapshot 2023)

Abstract. Snapshots derived from the World Soil Information Service (WoSIS) are served freely to the international community. These static datasets provide quality-assessed and standardised soil profile data that can be used to support digital soil mapping and environmental applications at broad scale levels. Since the release of the preceding snapshot in 2019, refactored ETL (extract, transform and load) procedures for screening, ingesting and standardising disparate source data have been developed. In conjunction with this, the WoSIS data model was overhauled, making it compatible with the ISO 28258 and Observations and Measurements (O&M) domain models. Additional procedures for querying, serving and downloading the publicly available standardised data have been implemented using open software (e.g. GraphQL API). Following up on a short discussion of these methodological developments we discuss the structure and content of the “WoSIS 2023 snapshot”. A range of new soil datasets was shared with us, registered in the ISRIC World Data Centre for Soils (WDC-Soils) data repository and subsequently processed in accordance with the licences specified by the data providers. An important effort has been the processing of forest soil data collated in the framework of the EU-HoliSoils project. We paid special attention to the standardisation of soil property definitions, description of the soil analytical procedures and standardisation of the units of measurement. The 2023 snapshot considers soil chemical properties (total carbon, organic carbon, inorganic carbon (total carbonate equivalent), total nitrogen, phosphorus (extractable P, total P and P retention), soil pH, cation exchange capacity and electrical conductivity) and physical properties (soil texture (sand, silt and clay), bulk density, coarse fragments and water retention), grouped according to analytical procedures that are operationally comparable. Method options are defined for each analytical procedure (e.g. pH measured in water, KCl or CaCl2 solution, molarity of the solution, and soil / solution ratio). For each profile we also provide the original soil classification (i.e. FAO, WRB and USDA system with their version) and pedological horizon designations as far as these have been specified in the source databases. Three measures for “fitness for intended use” are provided to facilitate informed data use: (a) positional uncertainty of the profile's site location, (b) possible uncertainty associated with the operationally defined analytical procedures and (c) date of sampling. The most recent (i.e. dynamic) dataset, called wosis_latest, is freely accessible via various web services. To permit consistent referencing and citation, we also provide a static snapshot (in this case, December 2023). This snapshot comprises quality-assessed and standardised data for 228 000 geo-referenced profiles. The data come from 174 countries and represent more than 900 000 soil layers (or horizons) and over 6 million records. The number of measurements for each soil property varies (greatly) between profiles and with depth, this generally depending on the objectives of the initial soil sampling programmes. In the coming years, we aim to gradually fill gaps in the geographic distribution of the profiles, as well as in the soil observations themselves, this subject to the sharing of a wider selection of “public” soil data by prospective data contributors; possible solutions for this are discussed. The WoSIS 2023 snapshot is archived and freely available at https://doi.org/10.17027/isric-wdcsoils-20231130 (Calisto et al., 2023).

Effect of spatially varying earthquake ground motions on seismic response of a railway viaduct considering multiple site configurations

Currently, it is admitted that extended structures are subjected to spatially varying earthquake ground motions. It has been recognized that the causes of this variability are time delay at each support of the structure, coherency loss effect caused by the propagation of seismic waves and local site effect. The spatial variation of local characteristics of the soil profile defines the site effect, which affects the amplitude and the frequency content of seismic ground motion. The main objective of this work is to provide comparative results and evaluate the variation of the dynamic response of a bridge adopting varying characteristics of soil foundation. Based on the density spectral method, an efficient simulation method of spatially varying earthquake ground motions is developed. The simulation of spatially variable seismic ground motions is performed for different locations on the ground surface with varying site conditions. According to of soil classification described in seismic codes, four different soil configurations were considered for generating sixty-four displacement time series. Several dynamics analyses of a bridge to three cases of spatially variable seismic ground motions, besides the uniform case, are made. The results of this study indicate that depending on soil configurations beneath each support, the seismic response may vary significantly.

Predicting USCS soil texture classes utilizing soil spectra and deep learning

Abstract Purpose Soil texture identification is vital for various agricultural and engineering applications but generally involves rigorous laboratory work, especially for estimating USCS (Unified Soil Classification System) soil texture classes. Soil texture influences soil water storage capacity, soil fertility, compaction characteristics, and soil strength. Soil spectroscopy offers a reliable approach that is non-destructive, rapid, and cost-effective to estimate several soil properties including texture. For engineering applications, the USCS soil texture classes are preferred, but very few studies have focussed on estimating USCS soil texture using soil spectroscopy or remote sensing data in general. Methods Two large soil spectral libraries (SSLs), viz., Kellog Soil Spectral Library (KSSL) and Open-source Soil Spectral Library (OSSL), as well as three deep learning algorithms (VGG-16, ResNet-16, and Swin transformers), were used in this study to predict six USCS soil texture classes and three USCS soil texture groups. The USCS soil texture classes and groups were derived by grouping clay, sand, and silt fractions that are closely associated with the corresponding USCS soil texture classes. Results The results indicate that the Swin transformer model performed the best with an accuracy of 67% for six USCS soil texture class predictions and 81% for three USCS soil texture group predictions. Cohen’s kappa value implies a moderate agreement (0.55) for soil texture class predictions and a substantial agreement (0.64) for soil texture group predictions. Conclusion The proposed methodology offers a novel approach for USCS soil texture class predictions utilizing SSLs and deep learning techniques.

A vertical take-off and landing (VTOL) unmanned aircraft vehicle trajectory control model with air-launched missiles

Purpose In today’s technology, the significance of unmanned aerial vehicles is steadily increasing. Many unmanned aerial vehicles design, especially those used for military purposes, have achieved autonomy from human operators. Undoubtedly, one of the most crucial features of these aircraft is their vertical take-off and landing (VTOL) capability. Inspired by quadrotor methodology, this paper aims to conduct, a modeling of an aircraft with VTOL capabilities. Design/methodology/approach The impact of releasing air-launched missiles, considered as the useful payload carried during the flight of the aircraft, has been taken into account in this modeling. The release of air-launched missiles disrupts both the symmetric structure of the system and alters the mass and inertia parameters. Simulations were conducted to investigate scenarios involving the simultaneous release of all air-launched missiles and their release at different times. Findings The investigation focused on determining how quickly an aircraft, aiming to consecutively hit targets, can return to its desired trajectory. The time interval between the consecutive releases of two air-launched missiles has been identified. Originality/value It is crucial for a VTOL-capable aircraft to possess a unique modeling structure to examine its capability of releasing air-launched missiles in various scenarios. This entails understanding not only the aircraft’s VTOL functionality but also its ability to effectively release missiles in different operational conditions.

A Semi-Detailed Survey of Soils in the City of Jaboatão dos Guararapes – PE

The economic development of the city of Jaboatão dos Guararapes was generated spontaneously without considering the land use capacity or its position in the landscape. Due to the occupation and intensive disordered use of the city pedological coverage, there have been mass movements that have caused economic and social losses. These can be resized based on the understanding of the pedological aspects inherent to the city. The objective of the present paper was to map the soils of the city area at a semi-detailed level. For this purpose, different toposequences were used, with soil examinations at a rate of 1 for every 2.75 km², totaling 94 points. The mapping and taxonomic classification of soils were made in accordance with standards, procedures and criteria adopted by Embrapa Soils. Based on the morphological data, locations were defined for soil description and sampling, totaling 14 soil profiles. 15 soil classes were identified, and 23 mapping units were defined, which highlighted the notable role of the source material in the distribution of the city's pedological properties. The mapping units that require greater care for use are: a) in hill environments - PAdr1, PVAd1 and PVd; and b) in the fluvial-marine plains – EKu1, EKu2 and GXbd2. The expansion of urban areas must consider the type of soil and the behavior of the material, especially during the rainy season, in order to minimize risks and adverse losses.

Pokrywa glebowa miasta Lublina (wschodnia Polska)

Lublin is a voivodeship city located in the Lublin Upland (Eastern Poland). The study aimed to present the spatial differentiation of soils of the city of Lublin and some of their properties. The knowledge of soil cover is very important for spatial planning and for protection and proper use of soil resources of each city. The environmental-genetic map of Lublin’s soils included in this work was made on the basis of soil-agricultural map in the scale 1:25 000 of Lublin and the soil-habitat map of the Świdnik Forest District. The soil units distinguished on the map were classified according to the Systematics of Soils of Poland (2019) and the international soil classification (World Reference Base for Soil Resources, WRB). The spatial variability of Lublin’s soils is determined by the occurrence of different parent rocks, relief, water relations, vegetation cover and human activities. The main soil units occurring within the city of Lublin include: brown soils (WRB: Eutric/Dystric Cambisols), rusty soils (Brunic Arenosols), podzolic soils (Albic Podzols), clay-illuvial soils/lessive soils (WRB: Albic/Haplic Luvisols), black earths (WRB: Gleyic Phaeozems), chernozemic rendzinas (WRB: Rendzic Phaeozems) and brown rendzinas (WRB: Calcaric Cambisols), chernozemic/black alluvial soils (WRB: Fluvic Phaeozems) and ordinary alluvial soils (WRB: Dystric/Eutric Fluvisols), peat soils (WRB: Hemic/Sapric Histosols) and murshic soils (WRB: Murshic Histosols), and technogenic soils (WRB: Urbic/Spolic/Ekranic Technosols). The grain size composition of Lublin’s soils is quite diverse, with the most common being silts, loams and sands with varying proportions of skeletal parts. The reaction of the Lublin soils studied also varies. Very acidic and acidic reaction is present mainly in podzolic and rusty soils and typical acid brown soils. In contrast, proper brown soils are slightly acidic to neutral in the upper genetic horizons. Lessive soils also have an acid reaction in the upper genetic horizons. Fluvisols have a slightly acidic to alkaline reaction, and black earths have a neutral to alkaline reaction. The alkaline reaction is found in rendzinas and technogenic soils, in which significant amounts of calcium carbonate are present. Soil organic matter is mainly accumulated in organic soils (peat and muck soils), as well as in the surface organic horizons (O) of forest soils. In the mineral soils of Lublin, the highest content of organic carbon (C-org) was found in such soils as black earths, chernozemic rendzinas, chernozemic alluvial soils and in some technogenic soils. The usable quality of Lublin’s soils is high. There is a phenomenon of management of land with high bonitation classes for non-agricultural purposes, despite the high fees for excluding such land from agricultural production.