Soil Type Research Articles

Implications of soil waterlogging for crop quality: A meta-analysis

Soil waterlogging in many arable regions of the world challenge the quantum and quality of crop production. While previous studies have assessed the impact of waterlogging on crop yields, understanding of how waterlogging implicates with crop quality remains in its infancy. Here, we conduct a systematic literature review and meta-analysis to assess how waterlogging influences grain quality. We also explore the role of engineering and agronomic strategies for alleviating adverse effects of waterlogging. We reveal that soil waterlogging has less impacts on grain quality than on yield; the latter decreasing by an average of 23 %, while average grain protein and starch content of waterlogged crops reduced by 6.7 % and 7.3 %, respectively. We attribute these differences to underlying mechanics of yield and grain quality formation, as well as biological processes conferring adaptation, plasticity and recovery. Reduced grain quality under waterlogging is associated with decreased activity of enzymes involved in leaf nitrogen and carbon metabolism. Unlike yields however, grain quality suffers less deterioration with prolonged waterlogging, with ultimate effects realized being a function of species-specific tolerance, timing and duration of waterlogging relative to crop stage, soil type and growing season weather. We underscore the potential in engineering and/or agronomic interventions for alleviating detrimental effects of waterlogging.

Analisis Tingkat Kerawanan Banjir di Sub Daerah Aliran Sungai (DAS) Kampar Kiri di Kecamatan Kampar Kiri

This study aims to describe and analyze the flood vulnerability level in the Kampar Kiri Sub-Watershed, Kampar Kiri District, as well as the extent of flood-prone areas impacting settlements in the region. The primary focus of this research is to understand the flood risk of the area and its effects on the existing settlements. The method applied in this study is Multi-Criteria Evaluation (MCE) using a Geographic Information System (GIS) approach. MCE is used to determine the weights of parameters affecting flood vulnerability and to select appropriate methods for analysis. This approach has proven effective and efficient in flood risk analysis, as demonstrated in previous studies. The parameters analyzed include slope, elevation, rainfall, soil type, land use, and distance from rivers, which are calculated using scoring methods and overlaid to produce a flood vulnerability map. The results indicate that the flood vulnerability level in the Kampar Kiri Sub-Watershed is categorized into three classes: not vulnerable, vulnerable, and highly vulnerable. The areas for each class are: not vulnerable with 16,854 hectares, vulnerable with 58,543 hectares, and highly vulnerable with 11,506 hectares. The extent of settlements in the vulnerability zones includes: vulnerable zone with an area of 277 hectares (approximately 20.83%), and highly vulnerable zone with an area of 1,054 hectares (approximately 79.17%). Based on these findings, it is recommended that the Kampar Kiri District government enhance land use planning to reduce flood risks, collaborate with the Kampar Public Works and Spatial Planning Office, and improve infrastructure such as drainage channels and flood protection embankments.

Bulk Density and pH Relationships on Sandy Soils in Three Florida Fields

Bulk density is an important physical property of soils as it impacts the airflow, water retention, and structure of a soil. pH is a chemical property that affects the availability of nutrients in the soil. Both properties directly affect crop health and production and are often measured as indicators of soil health and crop suitability. Previous studies on this relationship between bulk density and pH focus on silty soils, and there is limited information on relationships between pH and physical soil properties on the sandier type of soils that are prevalent in Florida. This study aimed to investigate potential relationships between bulk density and pH in sandy soils by sampling the soils of three different crop production systems in north-central Florida: a silage corn field in Citra, peanut field in Chiefland, and turfgrass plot in Gainesville. Each of the sites were treated with pH-altering treatments, including ammonium nitrate, gypsum, and lime. While the soils in the turfgrass plot showed no observable relationship between bulk density and pH, the soils in the silage corn and peanut fields did show a slight relationship. In the peanut and silage corn fields, each plot was sampled both within the crop rows and between crop rows. In these fields, samples taken in the root area showed significant differences in pH (p < 0.001) and bulk density (p < 0.01) from the area between rows. Understanding relationships in the soil environment is important to maximize crop health and production, therefore further research is needed in this area.

Uncertainty reduction in MASW inversion and ground response analysis using a-priori information

Multi-channel analysis of surface waves (MASW) method is commonly used for estimating the shear wave velocity (Vs) profile of soil and subsequent ground response analysis (GRA). However, the uncertainties from inversion non-uniqueness in MASW testing and the absence of soil type information in GRA can undermine their accuracy and affect seismic designs. Incorporating a-priori information from geotechnical investigations can be a potential solution, but its effectiveness in MASW testing is not well understood. This study aims to quantify the uncertainty reduction achieved by incorporating a-priori information in MASW testing and GRA. Extensive numerical simulations of MASW and GRA for nine idealized soil profiles were carried out. Subsequently, the findings were validated by carrying out field testing with and without a-priori information. The a-priori information included number and thickness of soil layers, soil type and index properties. The findings demonstrate that incorporating a-priori information significantly reduces inversion uncertainty in MASW and GRA, enhancing the reliability and accuracy of the analyses. This work provides a quantitative assessment of uncertainty reduction using a-priori information for different possible site and earthquake scenarios. The findings have important implications for design of earthquake-resistant structures and for improving the safety of people and infrastructure in earthquake-prone areas.

Flood risk assessment using modern models integrated with GIS for susceptibility mapping of the M'zab Valley watershed (Algeria)

ABSTRACT Comprehensive flood control is needed because flooding can be catastrophic for people's lives and ways of living. This management must provide information on the hydrologic, geotechnical, environmental, social, and economic elements of floods. This work created maps for the flood susceptibility of the M'zab Valley using logistic regression (LR) and frequency ratio (FR). The main objective of the research was to strengthen the bivariate probability capabilities of FR and LR. A flood inventory map was created by extracting flood sites from multiple sources. The flood inventory was divided randomly into two parts: 30% was used for validation and the remaining 70% was used to train the models. Independent variable datasets included the distance from the river, rainfall, soil type, land use/land cover (LULC), elevation, drainage density, flow accumulation, and slope. The effect of each variable on flooding was assessed by comparing each independent variable with the dependent flood layer. The flood susceptibility map was assessed using the prediction rate approach on the validation dataset, which was not used for model construction. The accuracy assessment's findings revealed a prediction rate of FR 0.86 and LR 0.881, as well as a success rate of FR 0.924 and LR 0.924.

Investigating the Potential Use of End-of-Life Fire Extinguisher Powder as a Soil Amendment in Different Soil Types: A New Approach Following a Circular Economy Model

A first attempt to assess the potential alternative use of fire extinguisher filler powder after its exhaustion has been investigated in the present research. The chemical composition of fire extinguisher filler powder, specifically type ABC 40%, consists of monoammonium phosphate and ammonium sulfate. As its nitrogen and phosphorus content is particularly high, the thought of its possible use as a fertilizer and/or a soil amendment is a challenge. For this purpose, a pot experiment was carried out and two leafy vegetables (spinach and lettuce) were used as biomarkers. Two soil samples from rural areas, one acidic (pH = 5.8 ± 0.1) and one alkaline (pH = 8.2 ± 0.7), were selected for the experiments. Filler powder from a used fire extinguisher was added to the soil samples in two levels (1 and 2% v/v). It was found that the addition of fire extinguisher filler powder caused no toxicity to either of the two plants studied. On the contrary, an increase in their above-ground biomass was observed, proportional to the amount of powder added. It was established that in the pots where the powder was added, in both plant species observed, the plant height, root length, and chlorophyll content of leaves increased, the total antioxidant capacity was enhanced, and the concentrations of nitrate and phosphate in the leaves and roots of plants also increased, compared to the soil without the addition of fire extinguisher powder. The early signs appear to be encouraging, as an increase was observed in almost all aspects. The mandatory end of the life cycle of the powder as a fire-extinguishing agent and its disposal is also a challenge in the context of the circular economy, as reducing the energy requirements for fertilizer production is one of the objectives of sustainable development.

Prediction of seismic performance of steel frame structures: A machine learning approach

The study of seismic performance in structural engineering is deemed crucial due to the intricate and unpredictable nature of earthquakes. This study explores advanced seismic performance prediction in structural engineering using machine learning techniques. Non-Linear Dynamic Analysis (NDA) was conducted on Steel Moment-Resisting Frames (SMRFs) situated on soil type D in seismic zone II with varying configurations using ETABS software. A substantial dataset comprising 29,200 data points from 292 models was generated to train machine learning models aimed at predicting the Maximum Inter-Story Drift Ratio (M-IDR), a critical parameter for assessing seismic limit-state capacity. The machine learning models, including Random Forest (RF), Extreme Gradient Boosting Machine (XGBoost), and Artificial Neural Networks (ANN), demonstrated high accuracy with R2 of 0.9625, 0.95327, and 0.94247 respectively, indicating a robust correlation between predicted and actual values. These results imply that the trained models can effectively predict seismic performance with high precision. A user-friendly Graphical User Interface (GUI) was developed using the trained models to facilitate the practical application of these models, significantly reducing computational costs and analytical efforts for researchers and engineers. The findings underscore the potential of integrating machine learning with structural engineering to enhance seismic performance predictions, contributing to the development of safer and more resilient structures.

Dispersal of microbes from grassland fire smoke to soils.

Wildland fire is increasingly recognized as a driver of bioaerosol emissions, but the effects that smoke-emitted microbes have on the diversity and community assembly patterns of the habitats where they are deposited remain unknown. In this study, we examined whether microbes aerosolized by biomass burning smoke detectably impact the composition and function of soil sinks using lab-based mesocosm experiments. Soils either containing the native microbial community or presterilized by γ-irradiation were inundated with various doses of smoke from native tallgrass prairie grasses. Smoke-inundated, γ-irradiated soils exhibited significantly higher respiration rates than both smoke-inundated, native soils and γ-irradiated soils exposed to ambient air only. Microbial communities in γ-irradiated soils were significantly different between smoke-treated and control soils, which supports the hypothesis that wildland fire smoke can act as a dispersal agent. Community compositions differed based on smoke dose, incubation time, and soil type. Concentrations of phosphate and microbial biomass carbon and nitrogen together with pH were significant predictors of community composition. Source tracking analysis attributed smoke as contributing nearly 30% of the taxa found in smoke-inundated, γ-irradiated soils, suggesting smoke may play a role in the recovery of microbial communities in similar damaged soils. Our findings demonstrate that short-distance microbial dispersal by biomass burning smoke can influence the assembly processes of microbial communities in soils and has implications for a broad range of fields including agriculture, restoration, plant disease, and biodiversity.

Automated prediction of phosphorus concentration in soils using reflectance spectroscopy and machine learning algorithms

A method is presented for predicting total phosphorus concentration in soils from Santander de Quilichao, Colombia, using a UV-VIS V-750 Spectrophotometer and machine learning techniques. A total of 152 soil samples, prepared with varying proportions of P2O5 fertilizer and soil, were analyzed, obtaining reflectance spectra in the 200 to 900 nm range with 3501 wavelengths. Additionally, 152 laboratory results of total phosphorus concentration were used to train the prediction model. The spectra were filtered using a Savitzky-Golay filter. Key wavelengths were identified using Variable Importance in Projection - Partial Least Squares (VIP-PLS) and Random Forest (RF), reducing the spectral bands to 1085. Principal Component Analysis (PCA) further reduced data dimensionality. A feedforward artificial neural network was then trained to predict phosphorus concentration. This method is faster than traditional lab tests by leveraging advanced data analysis and machine learning, offering results in less time. While sample preparation remains consistent with standard spectroscopic analysis, the value added by the proposed method lies in its data processing and interpretation. Currently applied to a single soil type, future improvements will include more soil types and other macronutrients, enhancing nutrient management in agriculture. Accurate macronutrient measurements aid in better fertilizer uses planning.• Filtering spectra and determining relevant wavelengths using VIP-PLS and RF.• Dimensionality reduction with PCA.• Training feedforward artificial neural networks.

Geospatial analysis and AHP for flood risk mapping in Quetta, Pakistan: a tool for disaster management and mitigation

The 2022 flood events in Quetta, Pakistan, caused severe damage to the economy, properties, and lives. Therefore, flood risk mapping to identify flood-prone areas is essential for planners and decision-makers to take critical protective measures to control the effects of flooding. This study focuses on mapping flood-prone regions in the Quetta district of Pakistan using an analytical hierarchy process (AHP) and a geographic information system (GIS). The factors influencing flood used in the present study were topographic witness index (TWI), elevation, slope, land use, land cover, precipitation, stream distance, drainage density, and soil type. Weights and ranks were allocated separately to all factors through AHP and were interpreted in a GIS environment. The produced flood hazard model of the study area depicted four zones. These zones ranged from low (19.49%), moderate (43.34%), high (28.30%), to very high (8.87%). The model was further validated through previous flood events in the study area. Around 90% of flood hazard events in the past took place mainly in the produced model's very high and high zones, which is why the current model is reliable. Finally, integrating geospatial approaches with AHP in flood hazard mapping is a quick, reliable, and affordable method that may be utilized in the area.

Soil density specification ranges to optimise plant water use and root growth for phytocapping and urban greening projects

Soil density or compaction is one of the key variables that need to be specified for designed soil profiles as part of urban greening. Examples include land rehabilitation (mining), waste containment (phytocapping), stormwater infrastructure (bioretention basins) and green infrastructure (green roofs and street trees). This study investigates the impact of a range of specified soil densities on plant water use and root development. Native Australian plant species selected for the study include: the C4 and C3 grasses, Themeda triandra and Microlaena stipoides respectively; the Eucalyptus trees, E. camaldulensis and E. cladocalyx; and the nitrogen-fixing pioneer trees, Acacia mearnsii and Allocasuarina verticillata. The plants were established at four soil density (compaction) levels: 72, 77, 82, and 87 % MDD (maximum dry density) in tall cylinders with weekly plant water use measurement over 8 months. Root growth was analysed using WinRhizo image analysis. The experimental results were used to create generalisable models for root length density (RLD), root diameter and plant water use. The models for RLD and plant water use were parabolic in nature, revealing clear optimum ranges that could be used to guide soil density specification. Root diameter provided additional insight into the allocation of resources to root thickening above a threshold soil density of 87 % MDD, indicating plant allocation of resources towards penetrating highly compacted soils. There were correlations between plant water use and RLD that were moderate and significant, particularly for grasses. Notably, T. triandra had the greatest mean RLD, thickest roots and plant water use at 16.8cm/cm3, 0.18 mm and 10mm/week, respectively. Findings demonstrate that RLD and plant water use can be optimised together within practically achievable soil density specification ranges that are sensitive to the pitfalls of both excessively low and excessively high soil densities. Recommended soil density specification ranges include: 75–82 % MDD with plant performance within 5 % of optimum (considered excellent), 74–84 % MDD with plant performance within 10 % of optimum (considered good) and 73–85 % MDD with plant performance within 15 % of optimum (considered fair). Within these ranges, plant water use and root growth performance is well balanced with practical achievement of the soil density ranges. Due to the use of %MDD, the modelled results can be usefully generalised for any soil type. Implementation of these specifications for urban greening and phytocap projects will optimise plant growth, transpiration and hydrological function while maintaining root networks essential for establishing and maintaining resilient living infrastructure.

Integrated Geospatial and Analytical Hierarchy Process Approach for Assessing Sustainable Management of Groundwater Recharge Potential in Barind Tract

Groundwater depletion in Bangladesh’s Barind tract poses significant challenges for sustainable water management. This study aims to delineate groundwater recharge potential zones in this region using an integrated geospatial and Analytical Hierarchy Process (AHP) approach. The methodology combines remote-sensing data with GIS analysis, considering seven factors influencing groundwater recharge: rainfall, soil type, geology, slope, lineament density, land use/land cover, and drainage density. The AHP method was employed to assess the variability of groundwater recharge potential within the 7586 km2 study area. Thematic maps of relevant factors were processed using ArcGIS software. Results indicate that 9.23% (700.22 km2), 47.68% (3617.13 km2), 37.12% (2816.13 km2), and 5.97% (452.70 km2) of the study area exhibit poor, moderate, good, and very good recharge potential, respectively. The annual recharge volume is estimated at 2554 × 106 m3/year, constituting 22.7% of the total precipitation volume (11,227 × 106 m3/year). Analysis of individual factors revealed that geology has the highest influence (33.57%) on recharge potential, followed by land use/land cover (17.74%), soil type (17.25%), and rainfall (12.25%). The consistency ratio of the pairwise comparison matrix was 0.0904, indicating acceptable reliability of the AHP results. The spatial distribution of recharge zones shows a concentration of poor recharge potential in areas with low rainfall (1200–1400 mm/year) and high slope (6–40%). Conversely, very good recharge potential is associated with high rainfall zones (1800–2200 mm/year) and areas with favorable geology (sedimentary deposits). This study provides a quantitative framework for assessing groundwater recharge potential in the Barind tract. The resulting maps and data offer valuable insights for policymakers and water resource managers to develop targeted groundwater management strategies. These findings have significant implications for sustainable water resource management in the region, particularly in addressing challenges related to agricultural water demand and climate change adaptation.

Quantitative Analysis about the Spatial Heterogeneity of Water Conservation Services Function Using a Space–Time Cube Constructed Based on Ecosystem and Soil Types

Precisely delineating the spatiotemporal heterogeneity of water conservation services function (WCF) holds paramount importance for watershed management. However, the existing assessment techniques exhibit common limitations, such as utilizing only multi-year average values for spatial changes and relying solely on the spatial average values for temporal changes. Moreover, traditional research does not encompass all WCF values at each time step and spatial grid, hindering quantitative analysis of spatial heterogeneity in WCF. This study addresses these limitations by utilizing an improved water balance model based on ecosystem type and soil type (ESM-WBM) and employing the EFAST and Sobol’ method for parameter sensitivity analysis. Furthermore, a space–time cube of WCF, constructed using remote-sensing data, is further explored by Emerging Hot Spot Analysis for the expression of WCF spatial heterogeneity. Additionally, this study investigates the impact of two core parameters: neighborhood distance and spatial relationship conceptualization type. The results reveal that (1) the ESM-WBM model demonstrates high sensitivity toward ecosystem types and soil data, facilitating the accurate assessment of the impacts of ecosystem and soil pattern alterations on WCF; (2) the EHSA categorizes WCF into 17 patterns, which in turn allows for adjustments to ecological compensation policies in related areas based on each pattern; and (3) neighborhood distance and the type of spatial relationships conceptualization significantly impacts the results of EHSA. In conclusion, this study offers references for analyzing the spatial heterogeneity of WCF, providing a theoretical foundation for regional water resource management and ecological restoration policies with tailored strategies.

Preference for water sources and agricultural utilisation among rural households: a case of Akoko district of Ondo State, Nigeria

The increasing challenge of freshwater scarcity amid rising demand and diminishing availability has led to a critical issue threatening sustainable development, especially in rural areas of developing countries like Nigeria. Despite the abundance of studies on rural water use, gaps remain in understanding the interplay between socio-economic factors, and agricultural practices in shaping these preferences and utilisation. Thus, the study investigates the preference for water sources and their utilisation in agricultural activities among rural households in the Akoko district of Ondo State, Nigeria. Cross-sectional data from randomly sampled 240 rural households were collected with the aid of a questionnaire, and employed for the data analysis. Descriptive statistics, logit, and multinomial regressions were used for the data analysis. The findings revealed a dominant preference for rainwater and well water. Factors such as access to safe water sources, time spent, extension service, technology adoption, soil type, experience, and proximity to water sources significantly influence the choice of water source utilization in the area using multinomial logit regression. Again, the results from logit regression revealed that water source, technology adoption, soil type, fish farming, crop farming, farming experience, and distance to water sources significantly affect the use of water for agricultural purposes. Therefore, the policy should enhance water access, consider soil characteristics in agricultural planning, and facilitate knowledge transfer among farmers for optimal water use, recognizing the symbiotic relationship between water access and agricultural sustainability.

Respon Tanaman Kedelai Edamame (Glycine Max (L.) Merr.) Terhadap Pemberian Dolomit Dan Pupuk Hayati Majemuk Pada Tanah Gambut

Edamame soybeans are soybeans that are harvested young, so they are known as green soybeans. The high nutritional content and delicious taste make edamame soybeans popular with the public. Extensification is one effort that can be done to increase edamame soybean production. One type of soil that has the potential to expand edamame soybean plants is peat soil. This research aims to examine the response of edamame soybean plants to the application of dolomite and compound biofertilizer on peat soil. The experiment was carried out using a two-factor factorial Completely Randomized Design (CRD) with three replications. The first factor, namely dolomite (0, 15, 30, and 45 g polybag-1) and the second factor, namely biological fertilizer (0, 5, 10, and 15 g kg-1 seed). The results of the study showed that the influence of the interaction between dolomite and compound biofertilizer as well as the single factor of compound biofertilizer had no significant effect on the growth and yield of edamame soybeans. Edamame soybean plants are responsive to dolomite. The highest number of plant seeds was obtained when administering dolomite at a dose of 30 or 45 g polybag-1, namely 29.50-32.25 seeds plant-1.

Physico-chemical characteristics and quality assessment of soil in parts of the Banganga Basin in northeastern Rajasthan, NW India.

Soil is a vital natural resource that has a bearing on any developmental activity. A database comprising primary physicochemical characteristics and soil suitability would assist the planners in devising an appropriate land use policy and any intervention strategy for soil quality improvement. The physicochemical characteristics of soils in parts of the Banganga Basin, located in the semi-arid terrain of NW India were evaluated mainly through surface samples and sub-surface ones wherever sections were exposed in stream banks. The soil in the studied terrain is a Quaternary age, pale brown to dark brown and occasionally red, alluvial soil. Sandy loam is the dominant soil type, while sandy soil and loamy sand types also occur. The soil maintains a depth-wise consistency in physicochemical characteristics. The average soil pH value is 8.55, indicating a slightly alkaline and healthy soil. The soil has a high available nitrogen content (0.53 to 2.24%), adequate available phosphorus (23.58 and 62.18mg/kg), and low potassium (22.5 and 200mg/kg) contents. The organic carbon, Mg, and Ca levels are generally on the lower side. A consistency in overall soil characteristics is evident in the narrow range of the Soil Quality Index, varying between 10 and 14, and soil can be categorized into three categories with overlapping soil qualities.

Study on the effect of soil type and pore structure on radon release from soils in coal mine areas

Study on the effect of soil type and pore structure on radon release from soils in coal mine areas

Glucose addition in natural forest soils has higher biological nitrogen fixation capacity than other types of soils

Land use changes soil microbial and chemical properties, but the mechanism of biological nitrogen fixation under different land use patterns is rarely reported, so we used four types of soil: Natural forest soil (NS), healthy banana soil (HS), diseased banana soil (DS) and paddy soil (PS). Treatments included the control (CK), addition of glucose (G), addition of glucose and ammonium nitrate (GN), addition of banana straw (BS), addition of banana straw and ammonium nitrate (BSN), addition of banana root (BR), and addition of banana root and ammonium nitrate (BRN). The study found that the change of soil utilization types, glucose addition increased carbon dioxide emissions (Compared with the control, increased by 963.11%, 508.39%, 794.77% and 511.34%, respectively) and enhanced the ability of soil microbial nitrogen fixation. Importantly, natural forest soil microorganisms have a higher biological nitrogen fixation capacity compared to other types of soils. Glucose addition caused the accumulation of ammonium nitrogen (Compared with the control, increased by 426.08%, 934.21%, 420% and 1065.95%, respectively), indicating that microorganisms had higher utilization efficiency of soluble carbon and enhanced the biological nitrogen fixation capacity, and nitrogen addition caused the accumulation of ammonium nitrogen, thereby weakening the biological nitrogen fixation capacity. At the same time, glucose significantly increased the Fimicutes phylum (83.73%, 66.38%, 67.18% and 70.36%) and lowered the level of other bacterial phylums, thereby reducing the bacterial network structure, and the stability of the soil environment has decreased. Forest analysis showed that CO2 was an important factor in predicting the bacterial community structure of different soil types, an increase in CO2 content can predict drastic changes in the bacterial community. Bacteria at the Fimicutes phylum level preferred glucose, which may also have a negative effect on bacteria at the level of other phylums.

In Situ Seedling Establishment and Performance of Cyperus esculentus Seedlings

Cyperus esculentus seeds are often considered irrelevant for C. esculentus spread as their fragile seedlings would not establish or survive in agricultural soils. However, the ever-increasing spread and upsurge of genetically different clonal populations in NW-Europe raises questions about the establishment of C. esculentus seeds and the reproductive performance of seedlings. Indeed, little is known about the potential of C. esculentus seedlings to grow and propagate under outdoor conditions relative to plants grown from tubers. Seeds from different clonal populations were sown outdoors in various soil types and under different irrigation levels (rainfed, irrigated) to assess seed germination and seedling establishment. Additionally, two pot experiments were conducted with three different plant types (plants originating from mother tubers and from seeds harvested on open- or self-pollinated plants) obtained from eight clonal populations. Plant performance was investigated by measuring vegetative and generative parameters. Germination under outdoor conditions was significantly affected by clonal population and was highest in irrigated sand (5.3%). Germination in sand was 4.1 times higher in irrigated plots than in rainfed plots. In irrigated plots, germination was 3.8 and 4.7 times higher in sand than in sandy loam and clay, respectively. Depending on the year, three out of five to five out of six clonal populations produced more tubers when grown from mother tubers than from seeds. Maximal tuber reproduction factors of 1:965, 1:752, and 1:618 were achieved for plants from mother tubers and seeds from open- and self-pollinated flowers, respectively. Plants originating from open-pollinated seedlings have the potential to equal or exceed the vegetative reproductive capacity of plants originating from mother tubers. As a result of their ability to establish in situ and their substantial vegetative reproductive capacity, C. esculentus seedlings are highly relevant for agriculture and merit appropriate attention in any integrated weed management system targeting C. esculentus.

Enhancing Load-Bearing Capacity of Weak Soils Using Geosynthetics: A Finite Element Analysis

In the context of mining applications and the increasing demand for high load-bearing soils, utilizing weak soils poses a significant challenge. This study investigates the effectiveness of geosynthetics in stabilizing weak soils through numerical modeling using Abaqus software (R2016X)and validation via laboratory model testing. We examined the impact of various geosynthetic lengths and embedment depths across three soil types: clay loam (ML), sand (SM), and well-graded sand (SW). Our results reveal that ML and SM soil types exhibit local shear failure, while SW soil types demonstrate general shear failure. Notably, the bearing capacity of soils increases with coarser particle sizes due to higher Meyerhof parameters, leading to soil failure at lower settlements. Optimal geotextile embedment depths were determined as H/B = 0.125 for ML soil, H/B = 0.250 for SM soil, and H/B = 0.5 for SW soil. Additionally, the effect of geotextile length on bearing capacity is more pronounced in ML soil, suggesting greater effectiveness in fine-grained soils. The optimal geotextile lengths for installation are approximately 1.5 times the width for ML soil, 1.0 times for SM soil, and 1.0 times for SW soil. We also found that SW soil typically fails at lower settlements compared to ML and SM soils. Consequently, geotextile placement at shallower depths is recommended for SW soil, where the soil experiences higher tension and pressure. These findings contribute to enhance soil stabilization and load management in mining geotechnics.