Soil Characteristics Research Articles

The Effects of Forest Gaps on the Physical and Ecological Stoichiometric Characteristics of Soil in Pinus densiflora Sieb. and Robinia pseudoacacia L. Forests

Forest gaps alter the environmental conditions of forest microclimates and significantly affect the biogeochemical cycle of forest ecosystems. This study examined how forest gaps and non-gap areas affect soil’s physical properties and eco-stoichiometric characteristics. Relevant theories and methods were employed to analyze the impact of forest gaps on nutrient cycling in Pinus densiflora Sieb. (PDS) and Robinia pseudoacacia L. (RPL) forests located in the Taishan Mountains. The results revealed that (1) forest gaps significantly enhanced the soil physical properties of PDS and RPL forests compared to non-gap areas (NPs). Notably, the bulk density of the soil decreased by 53%–12%, particularly in the surface layer (0–20 cm). Additionally, its non-capillary porosity increased by 44%–65%, while the clay and silt content rose by 39%–152% and 24%–130%, respectively. Conversely, the sand content decreased significantly, by 24%–32% (p < 0.05). (2) The contents of C, N, and P in the gap soil of PDS forests showed a significant increase compared to those in non-gap soil, with increases of 56%–131% for carbon, 107%–1523% for nitrogen, and 100%–155% for phosphorus. There was a significant drop of 10%–33% and 39%–41% in their C:N and C:P ratios, respectively (p < 0.05). The contents of C and P in the gap soil of the Robinia pseudo acacia L. Forest increased significantly, by 14%–22% and 34.4%–71%, respectively. Its C:P and N:P ratios significantly increased, by 14% to 404% and 11% to 41%, respectively (p < 0.05). (3) Compared with NPs, the forest gap significantly reduced the soil electrical conductivity and increased the soil pH. Additionally, compared to the soil at the gap’s edge, the surface soil in the gap’s center had noticeably higher concentrations of C, N, and P. (4) Key variables affecting the soil pH, silt content, bulk density, and overall porosity in forest gaps include the concentrations of carbon (C), nitrogen (N), and phosphorus (P) present and their ecological stoichiometric ratios. The findings showed that forest gaps had a considerable impact on the soil’s physical characteristics and ecological stoichiometry. They also had a high potential for providing nutrients, which might aid in the establishment of plantation plants.

Estimating soil water retention curves over the entire saturation range: A thermal conductivity-based approach

The relationship between soil water content (θ) and suction (h, referring to the absolute value of pressure head), is described by the soil water retention curve (SWRC). Our earlier research (Fu et al. (2021, 2023a) [J. Hydrol.127171]; [J. Hydrol.129898]) recognized underlying correlations between SWRCs and soil thermal conductivity (λ) versus θ curves, and developed methodologies to ascertain the parameters of the van Genuchten (vG) equation using λ(θ) measurements, described by the Ghanbarian & Daigle (GD) equation, and basic soil characteristics. Limitations intrinsic to the van Genuchten equation restrict the GD-vG approach to generate precise estimates only in the wet and medium suction range, specifically h ranging from 0 to 150 mH2O. The validity of these approaches in the dry region remains uncertain. In this study, we associated the Peters-Durner-Iden (PDI) model parameters to those of the GD model. An initial examination was performed on the linearization processes needed to derive the hydraulic continuity water content (θhc) from the capillary water component as characterized by the PDI model and to choose the suction at oven dryness (h0) based on PDI model performance. Subsequently, two piecewise functions and two pedo-transfer functions were formulated to compute the PDI model parameters utilizing soil porosity, particle size distribution, and GD parameters, based on a calibration dataset comprising 25 different soils. The new GD-PDI approach was subsequently assessed with six independent soils and juxtaposed with the previous GD-vG approach. The GD-PDI approach outperformed the GD-vG approach, particularly within the dry range.

Remediation quantum of organic amendments to immobilize potentially toxic heavy metals in wastewater-contaminated soils through maize cultivation

Wastewater is considered a good reservoir of mineral elements that can be used for agriculture, aquaculture, and some other activities after adopting suitable measures. The gap between supply and demand for water is increasing exponentially because of the abrupt boost to the world’s population. This poses a threat to human life as it has reached alarming levels in some parts of the globe. Normally, wastewater consists of liquid waste produced by commercial or industrial sources for daily use, consumption, and production. It is time to refocus our attention on a kind of circulating water system by reusing municipal wastewater for agricultural purposes, particularly irrigation. The recycled or treated water would be used as an alternative to fresh water. In the current study, the impact of various organic amendments was studied to mitigate the toxic effects of pollutants present in wastewater by cultivating maize as a test crop. The present study comprised five treatments replicated four times with a randomized complete block design under field conditions. In this experiment, the treatments included T1 (treatment 1) = control (wastewater-polluted soil without the application of any amendment), T2 = farmyard manure (FYM) at 2.5 tons ha-1 (hectare-1), T3 = FYM at 5.0 tons ha-1, T4 = compost at 2.5 tons ha-1, and T5 = compost at 5.0 tons ha-1. The application of FYM at 5.0 tons ha-1 (T3) was recorded as being the most effective as the maximum improvement was observed in soil characteristics such as pH, electrical conductivity (EC), sodium adsorption ratio (SAR), and organic matter, and for T3, these were 7.33, 2.22 dS m-1, 8.16, and 0.94%, respectively. T3 remained most superior in reducing the concentration of heavy metals in the soil; for example, lead, cadmium, nickel, and arsenic for T3 were 8.64, 1.34, 10.44, and 2.25 mg kg-1 (milligrams per kg), respectively. Maximum fresh biomass (fodder yield) of 9.98 tons ha-1 was harvested when FYM was applied at 5.0 tons ha-1 to the soil compared to 6.2 tons ha-1 in the control plot. The highest contents of nitrogen (1.20%), phosphorus (0.41%), and potassium (3.97%) were observed in maize plants for T3. In maize plants (T3), the concentration of lead, cadmium, nickel, and arsenic was reduced to levels of 1.92, 0.23, 2.28, and 1.25 mg kg-1, respectively. Therefore, it can be concluded from the findings of the experiment that the application of FYM significantly reduced heavy metal concentrations and improved soil health, along with maize crop growth and productivity.

Runoff Estimation in Kajurli Watershed Using SCS-CN and GIS Techniques

The study focuses on estimating surface runoff using the Soil Conservation Service Curve Number (SCS-CN) method, combined with Remote Sensing (RS) and Geographic Information System (GIS) techniques, for the Kajurli Watershed in Ratnagiri District, Maharashtra. Surface runoff is a key factor influencing agricultural productivity, soil erosion, and water management at the watershed level. The SCS-CN method, a widely used tool for runoff estimation, utilizes rainfall, land use, and soil type data. By integrating RS and GIS, the study achieves a more accurate assessment of land use and soil characteristics, which are critical inputs for runoff calculations. The Kajurli Watershed falls under hydrological soil group B, known for moderate runoff potential. The study uses 33 years of historical rainfall data (1990-2022) to compute annual runoff using the SCS-CN method. Key parameters such as Curve Numbers (CN) are derived based on land use, hydrological soil groups, and antecedent moisture conditions (AMC). The average annual rainfall in the watershed is found to be 3392.8 mm, with 39.69% of this rainfall contributing to surface runoff. The integration of RS and GIS simplifies spatial analysis, enabling accurate and efficient runoff estimation. The study highlights the sensitivity of the SCS-CN method to CN values, emphasizing the importance of precise land use and soil data for reliable hydrological modelling. These findings offer crucial insights for water resource management, flood control, and agricultural planning in the region.

THE INFLUENCE OF SAND AND CEMENT ON THE LATERITIC SOIL COMPACTION WITH IRON ORE TAILINGS

The characteristics of lateritic soils vary significantly, depending on the laterization process they have undergone. Due to this diversity, physicochemical and mechanical tests may be necessary to assess the feasibility of using it. This research aimed to characterize the soil and sedimented iron ore tailings (IOT) and analyze, through compaction tests, the influence of the Portland cement and the sand in the mixtures used for rammed earth (RE) construction. Three groups were proposed for compaction tests: Group 1 (reference – only soil), Group 2 (40% sand + soil), and Group 3 (40% sand + 2.5% Portland cement). Each group consisted of five mixtures with varying content of soil replacement by IOT. The results showed that, although Groups 2 and 3 increased the maximum dry density values in the mixtures without IOT compared to Group 1, the mixtures did not reach the minimum requirements specified by the Brazilian standard for RE. Additionally, the mixtures without IOT presented high optimal moisture content. The use of IOT in mixtures positively reduced the optimum water content and increased the dry density values, which are important parameters to RE production.

Pore Structures in Detritusphere of Soils Under Switchgrass and Restored Prairie Vegetation Community

ABSTRACTRoot detritusphere, that is, the soil in the vicinity of decomposing root residues, plays an important role in soil microbial activity and C sequestration. Pore structure (size distributions and connectivity of soil pores) in the detritusphere serves as a major driver for these processes and, in turn, is influenced by the physical characteristics of both soil and roots. This study compared pore structure characteristics in the root detritusphere of soils of contrasting texture and mineralogy subjected to > 6 years of contrasting vegetation: monoculture switchgrass and polyculture prairie systems. Soil samples were collected from five experimental sites in the US Midwest representing three soil types. Soil texture and mineralogy were measured using a hydrometer and x‐ray powder diffraction, respectively. The intact cores were scanned with x‐ray computed micro‐tomography to identify visible soil pores, biopores, and particulate organic matter (POM). We specifically focused on pore structure within the detritusphere around the POM of root origin. Results showed that the detritusphere of coarser textured soils, characterized by high sand and quartz contents, had lower porosity in the vicinity of POM compared with finer textured soils. POM vicinities in finer textured soils had high proportions of large (> 300 μm diameter) pores, and their pores were better connected than in the coarser soils. Lower porosity in the outer (> 1 mm) parts of the detritusphere of switchgrass than of prairie suggested soil compaction by roots, with the effect especially pronounced in the coarser soils. The results demonstrated that soil texture and mineralogy played a major, while vegetation played a more modest, role in defining the pore structure in the root detritusphere.

An IoT-Enabled Real-Time Crop Prediction System Using Soil Fertility Analysis

Changes in soil fertility have led to a decline in crop production, making it challenging for farmers to select the best crops based on soil conditions. Accurate crop prediction can significantly enhance crop productivity, and machine learning plays a crucial role in this process. Crop forecasting is influenced by soil, geographic, and environmental characteristics, with feature selection being essential for identifying suitable crops. In this study, we developed a real-time soil fertility analyzer to obtain the real-time values of soil parameters such as potassium, phosphorus, nitrogen content, temperature, pH, moisture content, and electrical conductivity. The crops examined were coconut, ginger, plantain, and tapioca. The data collected from this analysis served as the dataset for different training and testing classification algorithms for crop prediction using 100 soil samples. Among the algorithms tested, the k-nearest neighbors (KNN) algorithm demonstrated the highest performance, with an accuracy of 84%, precision of 85%, recall of 88.8%, and specificity of 92.4%. These results indicate that machine learning, combined with real-time soil analysis, can effectively predict suitable crops, enhancing crop productivity and aiding farmers in making informed decisions. This approach can revolutionize traditional farming practices by providing precise, data-driven insights into crop selection, ultimately improving agricultural efficiency and sustainability.

Regulating facility soil microbial community and reducing cadmium enrichment in lettuce by reductive soil disinfestation

The accumulation of cadmium (Cd) in facility soil is attracting increasing attention. Reductive soil disinfestation (RSD) is an effective soil disinfection method, while also having a certain passivating effect on Cd. The application of organic matter is crucial for the success of RSD, but the reduction efficiencies of different organic materials vary. Here, five treatments, namely untreated soil (CK), bean dregs (BD), peanut dregs (PD), sesame dregs (SD), and apple dregs (AD) were applied, and their performances in Cd immobilization in Cd-contaminated soil were assessed. Changes in soil properties and microbial communities were monitored. The results showed that the overall soil pH following RSD treatment decreased significantly, while organic matter, hydrolyzed nitrogen, available phosphorus, available potassium, exchangeable calcium, and exchangeable magnesium increased significantly. Compared to CK, the exchangeable (EX)-Cd contents after treatments SD and AD significantly decreased by 25.4% and 23.7%, respectively. RSD led to significant changes in the composition of soil microbial communities. SD treatment significantly increased the number of soil fungal operational taxonomic units (OTU), while BD, PD, and SD significantly increased the relative abundances of Bacteroidetes, Chloroflexi, Deinococus Thermus, and Basidiomycota in bacterial communities. The Gemmatimonadetes phylum showed a highly significant negative correlation with EX-Cd, indicating that it might have a positive effect on the fixation of Cd in polluted soil. SD significantly reduced the Cd content in the above-ground parts of lettuce by 74.76%, and had the least impact on lettuce biomass. It can be inferred that RSD is an ecologically effective method for the remediation of Cd pollution in facility soil by improving soil characteristics and altering microbial community composition to reduce Cd activity. However, further in-depth research is needed to optimize the types and amounts of organic materials applied.

Comprehensive analysis of thermo-mechanical responses in collapsible soil under varied water content conditions

AbstractIn this research, the effect of both temperature gradients and varied water content on heat transfer in collapsible soil is investigated. The study based on one-dimensional laboratory setup, soil temperature distribution in proximity to a heat source, was examined across four distinct temperatures (ranging from 50 to 200 °C) under varying water content (0%, 10%, 15%, and 20%). Through steady-state conditions and extended measurements over days, data were collected to compare soil thermal conductivity at 10% water content using two different methods. The first method required some of soil characteristics, such as dry density and optimum water content, while the second method relied on heating parameters and supplied heating content. A robust agreement between thermal conductivity values obtained through these two methods was observed. Correlations from experimental data were analyzed to enrich understanding, and multivariate linear regression was employed to predict the thermal conductivity and resistivity of collapsible soils. Results indicated that the higher soil density, the increasing the thermal conductivity, whereas greater soil porosity exhibited the opposite trend. Elevated temperatures were found to enhance soil density, influencing the spatiotemporal distribution of heat within the soil. This research contributes valuable insights into the dynamic behavior of heat transfer in collapsible soil, emphasizing the complex interaction of temperature gradients and water content variations. The findings of this study can advance the development of efficient and sustainable geothermal systems in regions with collapsible soils, potentially enhancing the design and management of structures built on such soils, especially in arid and semi-arid areas.

Interpretable machine learning for predicting heavy metal removal efficiency in electrokinetic soil remediation

Electrokinetic remediation (EKR) presents a promising approach for polluted soil remediation, leveraging electric fields to mobilize contaminants and facilitate their removal. Accurate efficiency prediction is crucial for optimizing EKR processes, reducing costs, and minimizing environmental impact. However, the EKR efficiency is influenced by numerous complex factors, making it challenging to predict outcomes reliably. This study introduces a novel approach for developing interpretable machine learning (ML) models tailored for efficient and unbiased EKR efficiency prediction based on material properties and experimental conditions. A large experimental dataset comprising 185 tests was compiled, encompassing various EKR parameters, heavy metal concentration, soil characteristics. A structured ML workflow was devised, evaluating diverse ML algorithms and employing model-agnostic interpretation methodologies to uncover intrinsic correlations between removal efficiency and pertinent attributes. Among the investigated ML models, Extreme Gradient Boosting (XGB) exhibited the best performance. Bayesian optimization was applied to further enhance its capabilities. The optimized XGB model demonstrated superior predictive accuracy on the test set, with an RMSE of 0.255, an MAE of 0.158, and an R² of 0.82. Moreover, SHapley Additive exPlanations analysis was employed to interpret the contributions of the input variables. Among inputs, electrode distance emerged as the most significant factor influencing EKR efficiency, followed by area, electrolyte species, and remediation duration. This research not only advances the predictive capabilities for EKR efficiency but also provides crucial insights into the underlying factors influencing remediation success, paving the way for more efficient and scalable applications in environmental management.

Impact of coupled input data source-resolution and aggregation on contributions of high-yielding traits to simulated wheat yield

High-yielding traits can potentially improve yield performance under climate change. However, data for these traits are limited to specific field sites. Despite this limitation, field-scale calibrated crop models for high-yielding traits are being applied over large scales using gridded weather and soil datasets. This study investigates the implications of this practice. The SIMPLACE modeling platform was applied using field, 1 km, 25 km, and 50 km input data resolution and sources, with 1881 combinations of three traits [radiation use efficiency (RUE), light extinction coefficient (K), and fruiting efficiency (FE)] for the period 2001–2010 across Germany. Simulations at the grid level were aggregated to the administrative units, enabling the quantification of the aggregation effect. The simulated yield increased by between 1.4 and 3.1 t ha− 1 with a maximum RUE trait value, compared to a control cultivar. No significant yield improvement (< 0.4 t ha− 1) was observed with increases in K and FE alone. Utilizing field-scale input data showed the greatest yield improvement per unit increment in RUE. Resolution of water related inputs (soil characteristics and precipitation) had a notably higher impact on simulated yield than of temperature. However, it did not alter the effects of high-yielding traits on yield. Simulated yields were only slightly affected by data aggregation for the different trait combinations. Warm-dry conditions diminished the benefits of high-yielding traits, suggesting that benefits from high-yielding traits depend on environments. The current findings emphasize the critical role of input data resolution and source in quantifying a large-scale impact of high-yielding traits.

Nano Management Techniques for Soil Reclamation

The application of nano-scale management strategies for soil remediation presents a promising avenue for tackling soil salinity issues and bolstering agricultural sustainability. Nano-sized materials like gypsum, biochar, sulphur, and zinc offer novel solutions by capitalizing on their distinct properties to alleviate soil stressors and bolster soil vitality. These nano materials demonstrate heightened reactivity, mobility, and efficacy compared to traditional alternatives, facilitating precise delivery and enduring effects on soil characteristics and crop performance. Through targeted ion displacement, soil structure enhancement, and improved nutrient accessibility, nano management approaches play a pivotal role in rehabilitating saline and sodic soils, thereby fostering agricultural resilience in adverse environmental conditions. Moreover, the integration of nano technologies in farming practices holds considerable promise for advancing precision agriculture, optimizing resource utilization, and mitigating ecological concerns associated with conventional farming methods. As research in this domain advances, ongoing exploration and refinement of nano management techniques are imperative to ensure their effectiveness, safety, and long-term sustainability, thereby contributing to the establishment of robust and productive agricultural systems.

Influence of anthropogenic factors and soil properties on earthworm diversity in southern Mediterranean agroecosystems

This study examines the biodiversity of earthworms in agroecosystems in the Annaba region of northeastern Algeria, focusing on the impact of agricultural practices and soil environmental conditions on these species. Seven earthworm species from the Lumbricidae and Megascolecidae families have been identified, of which five are new records for this region. Areas with intensive human activity exhibited a decrease in earthworm abundance and diversity, whereas areas with less intensive agricultural practices showed higher levels of earthworm diversity. Variations in soil properties related to land use and plant diversity were notable. Intensive agricultural practices resulted in altered soil characteristics, such as higher pH, electrical conductivity (EC), and salinity, while organic amendments increased organic carbon and nutritional diversity. Essential nutrients, such as calcium and magnesium, are crucial for earthworm vitality, while high levels of pH, salinity, and EC can reduce their populations. Canonical Correspondence Analysis supports these findings. In summary, agricultural practices and soil environmental conditions significantly influence earthworm populations, underscoring the need for sustainable methods to preserve underground biodiversity and ecosystem services.

Influence of earthworms on the mobility and bioavailability of metals, metalloids and radionuclides in historically contaminated soil

While soil characteristics such as pH, cation exchange capacity and organic matter are known to influence the mobility of pollutants in soil and the transfer to vegetation, far less attention has been paid to the possible non-trophic influence of organisms on pollutant transfer to other species. When pollutants are present in a bound unavailable form in the mineral or organic fraction, they can be made available through bio-weathering and decomposition of organic material. Within this study, the impact of earthworms on the mobility of metal(loid)s and radionuclides in soil and their transfer to vegetation was evaluated and the pathways through which earthworms work were studied. Soil from a Belgian field, historically contaminated with metal(loid)s and radionuclides, was used and microcosm systems were applied to mimic the natural interactions between soil, earthworms (Lumbricus terrestris) and vegetation (Lolium perenne). Metal and radionuclide concentrations were determined in soil, pore water and grass. In addition, soil pH, (chromophoric) dissolved organic matter, the ionic composition of pore water and the soil microbial activity were analysed. In general, earthworm presence significantly increased the concentration of most pollutants in soil pore water. This could be a direct consequence of decreased soil pH and increased humification. The latter also lead to higher nutrient concentrations in pore water that were depleted in the root zone due to high accumulation by plants. Indications were found for earthworm-induced effects on the soil microbial activity. The results show that earthworm activity can increase leaching of metal(loid)s and radionuclides and enhance dispersion into the environment and transfer to other biota and the food chain. Biologically induced release or remobilization of pollutants in the soil should therefore be considered in the context of risk assessment and site management strategies.

Application of Laser-Induced Breakdown Spectroscopy to quantify the changes in plasma characteristics in calcite-precipitated soils

The changes in calcium carbonate precipitation of modified soil by Enzyme Induced Calcite Precipitation (EICP) were investigated using Laser-Induced Breakdown Spectroscopy (LIBS). This experiment enabled elemental composition analysis of the soil samples. The plasma electron density and temperature were also extracted from the collected spectra of distinct soil samples i.e., W1 and W2 treated with EICP. The type of the crystal of CaCO3 deposited during the treatment process was investigated using scanning electron microscopy (SEM) and X-ray diffraction (XRD) tests. This research was conducted by utilizing an Nd: YAG laser with a wavelength of 532 nm, a pulse duration of 6 ns, and an energy of 25 mJ, from where the plasma temperature and electron density of modified soil throughout several treatment cycles were analyzed. The spectra were collected from different locations within the sample and the spectral range considered for the study was 370 nm-800 nm. The plasma electron density and plasma temperature determined using LIBS spectral data increase with the increase in the treatment cycle. With the increase in the treatment cycle, the deposition of CaCO3 was found to increase and correspondingly plasma temperature increased by 220 % for soil W1 and 117.3 % for soil W2. A similar rise in plasma electron density was noticed at the highest cycle of the treatment process; plasma electron density is augmented by 106.4 % for W1 and 21.50 % for W2. This investigation of the LIBS spectral data information indicates a positive correlation between the number of treatment cycles and the plasma temperature, meaning that as the treatment cycles rise, the plasma temperature also increases indicating a larger deposition of CaCO3. The findings are valuable in the domain of geotechnical engineering, where lasers are extensively employed for the examination of the soil profile and can prove to be a useful method for the characterization of the modified soils.

Smart hotspot detection using geospatial artificial intelligence: A machine learning approach to reduce flood risk

This study employs Geospatial Artificial Intelligence (GeoAI) and the Random Forest Machine Learning (ML) algorithm to enhance flood hazard assessments in Portugal. It utilizes NASA's LP DAAC (2023) Digital Elevation Model (DEM) and slope data from EPIC WEBGIS PORTUGAL DATA, offering detailed topographical insights for environmental planning. Additionally, it incorporates data on proximity to water bodies from the Portuguese Environment Agency and the European Environment Agency, and soil characteristics from EPIC WEBGIS PORTUGAL DATA, facilitating a thorough examination of flood risks. This approach prioritizes long-term land features over short-term weather patterns, providing a comprehensive understanding of flood vulnerability. The study processes data at a 1 km x 1 km resolution, adapting TIFF maps for compatibility with the Random Forest model. The produced flood hazard maps identify potential flood hotspots at both national and city levels, crucial for urban planning. These maps aid in assessing the vulnerability of key infrastructure and assets, such as transport networks and buildings. The research highlights the importance of integrating additional data on assets and socioeconomic factors to enhance urban resilience. It sets the stage for future research aimed at improving predictive accuracy and underscores the necessity of extensive geospatial analytics in managing infrastructure risks.

Evapotranspiration and Rainfall Effects on Post‐Storm Salinization of Coastal Forests: Soil Characteristics as Important Factor for Salt‐Intolerant Tree Survival

AbstractFlooding and salinization triggered by storm surges threaten the survival of coastal forests. After a storm surge event, soil salinity can increase by evapotranspiration or decrease by rainfall dilution. Here we used a 1D hydrological model to study the combined effect of evapotranspiration and rainfall on coastal vegetated areas. Our results shed light on tree root uptake and salinity infiltration feedback as a function of soil characteristics. As evaporation increases from 0 to 2.5 mm/day, soil salinity reaches 80 ppt in both sandy and clay loam soils in the first 5 cm of soil depth. Transpiration instead involves the root zone located in the first 40 cm of depth, affecting salinization in a complex way. In sandy loam soils, storm surge events homogeneously salinize the root zone, while in clay loam soils salinization is stratified, partially affecting tree roots. Soil salinity stratification combined with low permeability maintain root uptakes in clay loam soils 4/5‐time higher with respect to sandy loam ones. When cumulative rainfall is larger than potential evapotranspiration ETp (ETp/Rainfall ratios lower than 1), dilution promotes fast recovery to pre‐storm soil salinity conditions, especially in sandy loam soils. Field data collected after two storm surge events support the results obtained. Electrical conductivity (a proxy for salinity) increases when the ratio ETp/Rainfall is around 1.76, while recovery occurs when the ratio is around 0.92. In future climate change scenarios with higher temperatures and storm‐surge frequency, coastal vegetation will be compromised, because of soil salinity values much higher than tolerable thresholds.

Impact of red mud on soil properties and revegetation species growth in bauxite mining land reclamation

Bauxite mining, a key aluminum production process, can cause environmental degradation, soil erosion, and biodiversity loss. Reclamation measures like reforestation and water management can restore balance. Red mud, a by-product of alumina production, can enhance soil fertility and plant growth in post-bauxite mining reclamation areas. Its alkalinity and mineral composition reduce reliance on synthetic fertilizers, promoting sustainable soil management and addressing environmental challenges. This study aimed to examine the impact of red mud on soil characteristics and the growth of plants in areas during bauxite mining land reclamation. This study was conducted in the post-reclamation area of bauxite mining in West Kalimantan. The experiment involved two treatments: red mud application and a species of revegetation plant. Plant species consist of the plants Embeng, Forest Guana, Johar, and Rambutan. The study used a randomized block design with 24 experimental units. The parameters measured in the study included pH, organic carbon, nitrogen, phosphorus, exchangeable cations, cation exchange capacity, and base saturation, while growth parameters included a high percentage of plant growth and percentages of increased stem diameter. The findings showed that adding red mud to the planting hole increased soil pH and base saturation, improved nutrient availability, and enhanced plant growth in the areas post-mining bauxite at PT Antam, UBPB West Kalimantan. The Embeng Plant is highly regarded as a suitable plant species for re-vegetating areas after bauxite mining.

Effects of alpine meadow degradation on the soil physical and chemical properties in Maqu, China

While alpine meadow degradation presents a significant ecological challenge, research on the complex patterns of soil property changes induced by degradation has been somewhat limited. In this study, we investigated the Maqu alpine meadow, meticulously categorizing it into different grassland types exhibiting varying degrees of degradation based on factors such as vegetation coverage and community, soil characteristics, and landscape features. These classifications included typical grassland, degraded grassland, desertified grassland, and sandy land. In August 2018, we established three quadrats at each sample point and collected soil samples at five depths (surface [0–2 ​cm], 2–5 ​cm, 5–10 ​cm, 10–20 ​cm, and 20–40 ​cm) to analyze soil particle size distribution (PSD) and nutrients content. The results revealed a discernible trend: alpine meadow degradation led to selective loss of nutrient-rich soil fine particles, resulting in significant alterations in soil PSD and nutrients, particularly pronounced in grasslands with low degrees of degradation. Moreover, within a specific range of degradation degree, the clay content in the shallow soil of alpine meadow increased with the degradation degree, but it declined when the degradation degree exceeded a certain threshold. Degradation also disrupted the intricate relationship between soil nutrients, with notable variations in their coupling. This difference was also reflected in the influence of soil physical and chemical properties on soil nutrients, with the explanatory power of each environmental indicator on soil nutrients decreasing significantly with increasing degradation. This study systematically analyzed the variation in soil physical and chemical properties throughout the degradation process, revealing the mechanism of soil nutrient imbalance and decline caused by the degradation process. It provides crucial theoretical foundations and reference points for the preservation and rejuvenation of alpine meadows, enriching the methodology for assessing the impact of grassland degradation.

Laboratory assessment of the effect of rice husk, groundnut shells and RHA on the shear, permeability and consolidation of clayey sands

Utilizing waste materials to improve soil properties can result in cost savings and environmental benefits. This study aims to determine the effect of adding agricultural wastes like rice husk ash (RHA), rice husk (RH), and groundnut shells (GS) to clayey sands from Mysore district, India. The study focused on the undrained strength, permeability and volume change behaviour of soils mixed with RHA, RH and GS. The soil samples were mixed with varying amounts of RHA (5%, 10%, 15%, and 20%) and RH-GS (1%, 2%, 3%, 4%, and 5%) and cured for 1, 7, 14 and 21 days. These blended specimens were then used to determine the maximum dry density (MDD), optimum moisture content (OMC) and unconfined compressive strength (UCS). The soil mixture containing 5% RHA and 4% (RH + GS) yielded optimal results in the UCS test. Further tests, including unconsolidated undrained (UU) triaxial, permeability and consolidation tests, were conducted on the parent soil and the soil composite with 5% RHA and 4% (RH + GS). From the undrained test, the strength increased for the soil admixture, with a reduction in the cohesion and an increment in the friction angle, compared to that of the parent soil. The experimental results also showed improvement in the permeability and consolidation characteristics of the blended soil. As RH, GS, and RHA are abundant and considered to be agricultural waste products, using them represents an innovative, sustainable, and cost-effective solution with promising potential for soil improvement.