Determination Of Soil Properties Research Articles

Anthropogenic Impact on Soils of Cities Irkutsk, Angarsk

The morphological field description of soil profiles of the territory of Irkutsk region was carried out. Samples of soils, soil-forming rocks and vegetation were taken for further physical and chemical analyses to determine soil properties in the studied areas. Sample preparation of soils was carried out, phytotoxicity of soils was analyzed. Processing of the obtained data was carried out, as a result of which the percentage of germination of radish seeds in the upper horizons of soils of the key areas of Irkutsk and Angarsk cities was revealed, the length of shoots and the length of roots in the soils of the studied samples were measured, the index of soil toxicity was calculated.

Soil-forming factors controlling Technosol formation in historical mining and metallurgical sites in the high-alpine environment of the Tatra Mountains, southern Poland

The Tatra Mountains are a unique Central European alpine ecosystem where the non-anthropogenic soil cover and soil-forming processes are well recognized. However, the Technosols in the area’s high-mountain environment have not been studied in detail to date. Therefore the aim of this study was to identify the most important soil-forming factors controlling the properties of Technosols developed in historical mining and metallurgical sites in the Tatra Mountains of southern Poland active from the 15th Century until the end of the 19th. The present paper is one of the first attempts to study the genetic aspects of high-mountain Technosols in the temperate climatic zone. The study involved determining soil morphology and classification, soil properties, magnetic susceptibility, mineral composition, optical microscopic observations and total concentration of major elements. The studied Technosols were poorly developed soils with simple soil morphology (mainly A horizon in the topsoil and C horizons in the subsoil). There was a high content of rock fragments. The research has shown that the properties of Technosols in the Tatra Mountains were primarily determined by past human activities like mining and metallurgy as well as the type of anthropogenic parent material, which included mining wastes and metallurgical slags and determined soil properties together with mineral and chemical composition. Soil formation was significantly influenced by vegetation which was conditioned by the relief and climatic conditions dependent on altitudinal zonation. Vegetation and plant-derived soil organic matter shaped topsoil properties. The lengthy soil-forming process acting since a few centuries in some Technosols led to the formation of Bw horizons.

Assessment of Soil Structural Quality Under Area Closure and Open Grazing Land in Hidabu Abote District, North Shewa Zone, Oromia Regional State, Ethiopia

The experiment was conducted at Sire Morise kebele, Hidabu Abote district, to assess soil structural quality of different land management system (area closure and open grazing land). In this study, area closure land management practice was compared with open grazing land management practice in similar slope positions for soil structural quality build up. Soil samples were collected from both land management system at three slope position with three times replication. Totally 36 undisturbed soil blocks were collected from both land management system at 10 and 20 cm sampling depths. The finding of the study showed that the average value of volumetric moisture content and saturated hydraulic conductivity of soil were greater at area closure than open grazing land management system and also higher at bottom slope position than middle and upper slope position. The mean value of air-filled porosity and bulk density were lower at both area closure management practices and bottom slope position. Bulk density, volumetric soil moisture content and saturated hydraulic conductivity at 20 cm sampling depth were shows significantly different with respect to management practices. According to visual assessment in the field, soil physical quality was unfavourable in open grazing land management system and upper part of landscape position soils and the visual assessment scores showed that the soils under area closure had a good soil structural quality. There were strong relations among the three visual field assessment methods and also between visual assessment methods and laboratory determined soil properties of both land management system. In general, the result of the study showed that physical and structural quality of soil in area closure was improved due to good land management system. Based on the result of study it can be conclude that area closure improves soil physical and soil structural qualities and from the technical point of view, open grazing lands in hilly area should be changed to area closure before soil properties and soil nutrient contents are exhausted more.

Bacterial-fungal interactions and response to heavy metal contamination of soil in agricultural areas.

Long-term heavy metal contamination of soil affects the structure and function of microbial communities. The aim of our study was to investigate the effect of soil heavy metal contamination on microorganisms and the impact of different heavy metal pollution levels on the microbial interactions. We collected soil samples and determined soil properties. Microbial diversity was analyzed in two groups of samples using high-throughput sequencing technology. Additionally, we constructed microbial networks to analyze microbial interactions. The pollution load index (PLI) < 1 indicates that the area is not polluted. 1 < PLI < 2 represents moderate pollution. PLI was 1.05 and 0.14 for the heavy metal contaminated area and the uncontaminated area, respectively. Cd, Hg, Pb, Zn, and Cu were identified as the major contaminants in the contaminated area, with the contamination factors were 30.35, 11.26, 5.46, 5.19, and 2.46, respectively. The diversities and compositions of the bacterial community varied significantly between the two groups. Compared to the uncontaminated area, the co-occurrence network between bacterial and fungal species in the contaminated area was more complex. The keystone taxa of the co-occurrence network in the contaminated area were more than those in the uncontaminated area and were completely different from it. Heavy metal concentrations played a crucial role in shaping the difference in microbial community compositions. Microorganisms adapt to long-term and moderate levels of heavy metal contamination through enhanced interactions. Bacteria resistant to heavy metal concentrations may play an important role in soils contaminated with moderate levels of heavy metals over long periods of time.

Assessment of remote sensing in measuring soil parameters for precision tillage

Precision tillage (PT) is an innovative method that aims to take mechanical actions in the soil only where it is needed to curb the impact of heavy machinery usage on the soil. This research explores the use of remote sensing to measure relevant soil parameters to implement a PT strategy. This was achieved by combining traditional soil properties measurements and a non-contact approach based on taking hyperspectral camera (HSC) data in the field. Six methods were generated and divided into two sets to determine soil properties to make PT decisions. The first set includes mathematical functions that were generated from the ground true data (GTD). The second set includes functions that were generated from the remotely sensed HSC data and have a relationship with the methods in the first set. It was possible to tune the functions’ parameters to increase the accuracy. In addition, prediction error categories set at 5 % intervals were used to select the best method. The results show that a tuned method based on the GTD has an overall error below 5 %, and a tuned method based on HSC data has an overall error below 10 %.

A Rapid Sampling of Ant Assemblages Diagnoses Soil Physicochemical Properties before Planting Chayote Monoculture

Ants respond fast to changes in soil conditions in agricultural fields, thus they represent a practical, cheap, clean, and accurate tool to diagnose soil fertility before planting a crop. This study assesses the capacity of richness, occurrence, and activity of ant assemblages to indicate soil physicochemical properties in chayote monoculture in Veracruz, Mexico. Sampling was performed in 15 1-ha-plots and was replicated three times before planting chayote from August to October. Ants were collected using pit-fall traps and determined soil properties were texture, density, contents of organic matter (OM), nitrogen (N), and phosphorous (P), pH, and electrical conductivity (EC). In total, 20,385 ant workers belonging to 51 species, 28 genera, 9 tribes and 6 subfamilies were collected. Species richness significantly explained contents of OM, N, and P in a three-weeks sampling. Species occurrence explained clay percentage in the one-week sampling, N, and P contents in the two-weeks sampling and the aforementioned and percentages of sand and silt in the three-weeks sampling. Ant activity predicted more soil properties in the one-week sampling than in those that lasted two and three weeks.Richness, occurrence, and activity of ant assemblages are valuable tools for diagnosing texture, contents of OM, N, and P, and pH of soil before planting chayote monoculture. It contributes to the understanding of the role that ants play in agricultural fields. Therefore, soil management practices should consider indication capacity of ant assemblages to ensure growth and development of morphological structures such roots, stems, leaves, shoots, and fruits of cultivated plants.

Pengaruh Sifat Tanah Terhadap Kejadian dan Keparahan Penyakit pada Kelapa Sawit Main Nursery

Soil properties, both physical, chemical and biological, have a direct impact on the growth and health of oil palm seedlings. Fertile soil means the incidence and severity of plant diseases can be minimized. For this reason, research is needed to examine the influence of soil properties on the incidence and severity of disease in oil palm seedlings in the main nursery phase. The aim is to determine soil properties, both physical and chemical, that influence the incidence and severity of main nursery oil palm diseases. Implementation stages include taking soil samples and observing sick plants. The soil sample taken was the rhizosphere of main nursery oil palm. Data analysis uses Microsoft Excel to describe disease incidence and soil physical and chemical properties. The research results showed that in main nursery-B (MN-B) the incidence of disease attacks had a higher percentage compared to main nursery-A (MN-A). Physical and chemical properties of soil (N-available, P-available, C-organic, CEC) influence the incidence of disease attacks. The high content of available N and available P increases attacks by plant pests, while the high content of organic C and CEC can suppress pest attacks.

Estimation of Geotechnical Parameters for Coal Exploration from Quasi-3D Electrical Resistivity Measurements

Geotechnical parameters are crucial for mine planning and operation at different stages of development. However, estimating these parameters requires a large number of boreholes and subsequent detailed analysis of the samples, making it a cumbersome exercise. Moreover, even after conducting these studies, it is not possible to cover the entire operational area. To address this issue, this study presents an indirect method of estimating geotechnical parameters through mathematical relations using resistivity data. The present study incorporated 2D and 3D subsurface imaging techniques for exploring coal reserves and analyzing geotechnical parameters that define subsurface soil properties. Electrical resistivity tomography (ERT) was utilized for data acquisition, employing a Dipole–dipole array with a multielectrode ABEM Terrameter LS instrument. Six parallel profiles were conducted, each 400 m in length, with an inter-electrode spacing of 10 m and a spacing of 50 m between profiles. These profiles were combined into a 3D dataset referred to as quasi-3D ERT. The inversion process for both 2D and 3D data was performed using the Res2dinv and Res3dinv programs, respectively. This study overcame the challenges of 2D resistivity sections by evaluating horizontal depth slices in the x-z plane from layers 1 to 10, reaching a depth of 81.2 m. The geotechnical parameters, including cohesion, friction angle, moisture content, and plastic index, were derived from the resistivity data. The ERT method proved to be cost-effective and efficient in determining soil properties over a large area compared with traditional laboratory analysis of borehole samples. Additionally, the variation of geotechnical parameters with resistivity values exhibited unique characteristics. The results from both the 2D and quasi-3D ERT were well correlated with the borehole data. Such studies are valuable for resource exploration and mine planning purposes.

A machine learning based approach for prediction and interpretation of soil properties from soil spectral data

Aim: An active agricultural sector depends on good soil quality, essential for sustained food cultivation. However, intensive farming and rising demand can lead to soil deterioration, affecting crop yields. Smart soil prediction driven by machine learning is crucial for precision farming and efficient nutrient distribution. Methodology: Visible-near infrared Spectroscopy (vis-NIRS) is used to capture the soil's spectral data.Then, the spectral data is preprocessed with Savitzky-Golay Smoothing.The data that has been preprocessed is then used to train the machine learning model.The preprocessed data enhances model performance compared to spectral reflectance data in its unprocessed state.The machine learning model acquires data-based knowledge, identifies patterns, and predicts soil quality parameters. The Random Forest and Gradient Boosted Regression Tree are two algorithms employed in this study. Results: The spectral reflectance data is used to train, validate, and evaluate the machine learning model.In determining soil properties, both algorithms demonstrated a high degree of prediction accuracy, as demonstrated by the results.Gradient Boosted Regression Tree out performs Random Forest, but is expensive and requires sequential data. Random forest algorithm works well with large datasets, but over-fitting issues arise in some instances. Interpretation: The findings of the study indicate that machine learning can automate the current soil testing procedure in laboratories, thereby making it more efficient, affordable, and environmentally friendly. Key words: Gradient Boosted Regression Tree, Machine learning, Random forest, Soil fertility, Soil moisture

Properties of soils on fluvio-marin landform and their land management strategy for paddy field in Bataguh swampy area, Kapuas Regency, Central Kalimantan Province, Indonesia

Tidal swamp land has strategic and prospective value in sustaining national food resiliency and reserves. This land has the potential for the development of commodity paddy fields. However, this type of tidal swamp has a unique character and influences land management. This research aims to determine soil properties on landform fluvio-marine and its potential for developing lowland rice in Bataguh District, Kapuas Regency, Central Kalimantan. A total of 4 representative soil profiles were identified and characterized in the field and laboratory. Research results show that the lands under study have fine texture. Soils in the surface layer are classified as very acidic to acidic (soil pH 3.3-4.7), has very high organic C levels (5.46-9.37%), potential P moderate (21-40 mg.100g-1), K potential low until moderate (16-21 mg.100g-1), exchangeable Ca low (2.24-3.17 cmolckg-1), exchangeable Mg medium (5.11-8.30 cmolckg-1), soil CEC medium until high (19.63-38.09 cmolckg-1), and base saturation low until high (37-63%). The depth of sulfidic materials or pyrite layer is shallow until deep within 50-85 cm. The sand mineral fraction is dominated by quartz, while the mineral fraction of clay is dominated by kaolinite, followed by vermiculite and illite. The land studied is classified as marginally suitable (S3) for the development of tidal rice fields with a limiting factor for acid to very acid soil reactions (soil pH &lt;5). Balanced fertilization, liming, and micro water management can be done to increase the productivity of this land.

Evaluation of Physical Properties of Soil Determinants Glucomannan Content of Porang Tuber (Amorphophallus muelleri) in Entisols of North Lombok

Porang (Amorphophallus muelleri) is a tuber plant that contains the compound of glucomannan which has high economic value with a number of benefits, including as an industrial raw material. The glucomannan content of porang tubers has received special attention in research, including field research to determine soil properties that influence growth and content of gucomannan in porang tubers. A field survey has been carried out to determine the physical properties of soils that influence the glucomannan content of porang tubers grown in four clusters of agroforestry in the dry land of North Lombok, Eastern Indonesia. The results of the research showed that the physical properties of the soil that had a significant effect on the glucomannan content of porang tubers were soil bulk density (BD) with an R2 value of 53.15%, soil porosity with an R2 value of 60.23%, and silt particle fraction with an R2 value of 23. 47%. Thus, managing soil physical properties to facilitate the better quality of porang tubers is necessary.

INVESTIGATION DYNAMIC PROPERTIES OF SOIL (THE DAMPING RATIO AND SHEAR MODULUS) FOR RESISTANCE DESIGN AT THE LNG BAC LIEU GAS PIPELINE PROJECT BY CYCLIC TRIAXIAL TEST

The LNG Bac Lieu gas pipeline project is part of the 3.200MW LNG power plant complex in Bac Lieu province. The entire pipeline will be designed underground at the sea floor to be directly subjected to dynamic loads (earthquakes, waves ...). Soil response to dynamic loads is measured by the soil's dynamic properties such as dynamic strength, shear modulus and damping ratio. These soil parameters are very significant to study the movement of the ground as well as the local reaction of soil sediments under the effect of loads and cyclic soil structure. Therefore, it is very important to investigate the earth's dynamic properties (dynamic strength, damping ratio and shear modulus) to design seismic resistance for this project. From the relevant literature, it is known that the dynamic properties of the soil are influenced by the soil type and the position of the soil structure. 6 soil samples were examined randomly at different depths to determine soil properties. When studying dynamic strength, people often use direct test methods such as torsion cutting, cyclic flat cutting and cyclic triaxial test to determine the dynamic strength (dynamic resistance) of the soil. In which, the cyclic triaxial test method allows the simulation of real-world conditions to be easily simulated. The results of applying this method to soft clay and clay phases show that the damping ratio is 0.117-0.162D, the average is 0.136D, the shear module is 1650-2632kPa, the average is 2141kPa.

Environmental impacts of industrial activities on floral coverage with special emphasis on detoxification enzyme activities in Cataglyphis savignyi as pollution biomarker

The present study investigates the environmental impact of industrial activities on floral coverage within the major industrial district of Borg El-Arab City, Egypt. Additionally, it aims to evaluate the detoxification enzymatic activity of Cataglyphis savignyi as a pollution biomarker. To achieve this objective, seasonal soil samples were collected from the studied sites to determine soil properties and heavy metal concentrations. Furthermore, a seasonal specimen of C. savignyi was collected to study the enzymatic activity of acetylcholinesterase (AChE) and glutathione-S-transferase (GST). Heavy metal contamination pollution indices were calculated, and fourteen plant species were identified at the investigated sites for four successive seasons from 2020 to 2021. The soil physicochemical parameters significantly varied in the industrial sites compared to the control site. The accumulation of heavy metal contamination in soil for investigated sites followed the order Ni > As > Pb > Hg. Calculated Cdeg and PLI for industrial 3 revealed a very high degree of contamination, attributed to increased industrial activity from the chemical and silicate factories that characterize this region. The current results highlight the inhibition of GST levels in C. savignyi at the industrial site compared to the control site. In contrast, AChE increases, which might be due to heavy metals enhancing acetylcholine activity at synapses. Consequently, the antioxidant enzymatic activities are useful as biomarkers for assessing and monitoring environmental contamination. In conclusion, this study underscores insects as potent biomarkers for heavy metal contamination, marking a significant advancement in environmental monitoring. These bioindicators offer crucial insights into the impacts of climate change and industrial pollution. The research reveals distinct plant diversity variations and higher heavy metal content in industrial sites, indicating pronounced contamination. Additionally, the study highlights altered enzyme activities in insects, emphasizing their utility as biomarkers for assessing environmental contamination. This work represents a substantial leap forward in comprehending the complex dynamics between contamination and ecological balance.

Quantifying Soil Particle Settlement Characteristics through Machine Vision Analysis Utilizing an RGB Camera

Soil particle size distribution is a crucial factor in determining soil properties and classifying soil types. Traditional methods, such as hydrometer tests, have limitations in terms of time required, labor, and operator dependency. In this paper, we propose a novel approach to quantify soil particle size analysis using machine vision analysis with an RGB camera. The method aims to overcome the limitations of traditional techniques by providing an efficient and automated analysis of fine-grained soils. It utilizes a digital camera to capture the settling properties of soil particles, eliminating the need for a hydrometer. Experimental results demonstrate the effectiveness of the machine vision-based approach in accurately determining soil particle size distribution. The comparison between the proposed method and traditional hydrometer tests reveals strong agreement, with an average deviation of only 2.3% in particle size measurements. This validates the reliability and accuracy of the machine vision-based approach. The proposed machine vision-based analysis offers a promising alternative to traditional techniques for assessing soil particle size distribution. The experimental results highlight its potential to revolutionize soil particle size analysis, providing precise, efficient, and cost-effective analysis for fine-grained soils.

Effect of sample pretreatment on pelletization and performance of laser-induced breakdown spectroscopy for predicting key soil properties

Laser-induced breakdown spectroscopy (LIBS) is a promising analytical technique for determining soil properties in a rapid, cost-effective, and environmentally friendly manner. However, an optimal sample pretreatment method that enhances sample homogeneity for improved prediction accuracy is needed. In this study, the effect of different sample pretreatments on pelletization of soil samples, and prediction of texture and soil organic carbon (SOC) content, was assessed. Four pretreatments, namely 2 mm, 200 μm, and 100 μm sieved, and milled pretreatment, were applied to 133 soil samples representing a wide variability in texture and SOC content range. Visual assessment of soil pellet surface quality was done where poor surfaces, deemed unsuitable for LIBS measurement, were separated from the good-surface pellets. Partial least square regression (PLSR) models for the pretreatments across the investigated soil properties were compared, followed by interval partial least square regression (iPLSR), to evaluate potential improvements in the accuracy of the prediction models. The ratio of performance to interquartile distance (RPIQ) was used to rank the PLSR models for each pretreatment across the soil properties. The 100 μm pretreatment had the highest number of good-surface pellets followed by the 2 mm pretreatment, and lastly the 200 μm and milled pretreatments, which were comparable. The milled pretreatment achieved the best model for sand prediction (RPIQ = 7), followed by the 2 mm pretreatment for clay prediction (RPIQ = 2.5), 100 μm pretreatment for silt prediction (RPIQ = 2), and the milled pretreatment for SOC prediction (RPIQ = 1). A reduction in RMSEP of 31%, 23%, and 11% for the prediction of sand, SOC, and silt content, respectively was achieved by the milled pretreatment while the 100 μm pretreatment recorded a 15% reduction in RMSEP for the prediction of silt content. Overall, PLSR performed better than iPLSR for the prediction of texture (apart from clay) and SOC. These results show that sample pretreatment that involves sieving, or milling, influences the pellet surface quality and the prediction accuracy of texture and SOC content.

Soil acidification in a tailing area of ionic rare earth in Southeast China

Ionic rare earth ores are now commonly mined using the ammonium sulfate in situ leaching method, causing soil acidification in tailings. To evaluate the degree of soil acidification in tailings and the influence of mining activities on acidification, we selected an ionic rare earth tailing and a nearby unmined area in Southeast China. This tailing had been closed for 12 years. We sampled the soil from the surface to the bedrock in layers and determined soil properties related to soil acidification. The results showed that the average soil pH was 5.0 in the unmined area and 4.5 in the mined area (tailing area). Rare earth mining led to a decrease in soil pH of 0.47 units per 10 years, which was 2–5 times higher than that of other land uses. The shallow soil acidification in the mined area is not affected by mining. Deep soils were significantly acidified and the H+ concentration in the soil solution was approximately nine times that of the unmined area soil. Deep soil acidification is influenced very little by natural factors. The average soil ammonium‑nitrogen (NH4+-N) and nitrate‑nitrogen contents in the mined area were 58.34 mg kg−1 and 8.19 mg kg−1, respectively, 84 times and 21 times that of the unmined area. There were large amounts of NH4+, NO3−, and H+ in the soil of the mined area, indicating that soil acidification is closely related to exogenous NH4+-N input and nitrogen transformation. Nitrification is the most important driver of soil acidification in mining areas. Continued nitrification of excess NH4+-N will continue to produce H+ and migrate with water, which will cause long-term harm to the soil and surrounding environment in the mining area. Therefore, it is necessary to remove the enriched NH4+-N in tailings soil to avoid further soil acidification.

Integration Vis-NIR Spectroscopy and Artificial Intelligence to Predict Some Soil Parameters in Arid Region: A Case Study of Wadi Elkobaneyya, South Egypt

Understanding and determining soil properties is reflected in improving farm management and crop production. Soil salinity, pH and calcium carbonate are among the factors affecting the soil’s physical and chemical properties. Hence, their estimation is very important for agricultural management, especially in arid regions (Wadi Elkobaneyya valley, located in the northwest of Aswan Governorate, Upper Egypt). The study objectives were to characterize and develop prediction models for soil salinity, pH and calcium carbonate (CaCO3) using integration soil analysis and spectral reflectance vis-NIR spectroscopy. To achieve the study objectives, three multivariate regression models: Partial Least Squares Regression (PLSR), Multivariate Adaptive Regression Splines (MARS) and Least Square-Support Vector Regression (LS-SVR)); and two machine learning algorithms, Random Forest (RF) and Artificial Neural Networks (ANN) were used. Ninety-six surface soil samples were collected from the study area at depths 0–5 cm. The data were divided into a calibration dataset (70% of the total) and a validation dataset (30% of the total dataset). The obtained results represent that the PLSR model was the best model for soil pH parameters where R2 of calibration and validation predictability = 0.68 and 0.52, respectively. The LS-SVR model was the best model to predict soil Electrical Conductivity (EC) and soil Calcium Carbonate (CaCO3) content, with R2 0.70 and 0.74 for calibration and R2 0.26 and 0.47 for validation, respectively. On the other hand, the results of the implemented machine learning algorithm model showed that RF was the best model to predict soil pH and CaCO3, as the R2 was 0.82 for calibration and 0.57 for validation, respectively. Nevertheless, the best model for predicting soil EC was ANN, with an R2 of 0.96 for calibration and 64 for validation. The results show the advantages of machine learning models for predicting soil EC, pH and CaCO3 by Vis-NIR spectroscopy. Therefore, Vis-NIR spectroscopy is considered faster and more cost-efficient and can be further used in environmental monitoring and precision farming.

Calculating soil titratable acidity from routine soil analyses of cultivated South African soils using various model structures

Soil titratable acidity (TA) is determined through extraction with an acetate-buffered salt solution (potassium sulphate, K2SO4) and is routinely used to predict the soil lime requirement using the Eksteen method. Soil TA is not always reported by commercial soil testing laboratories, but rather exchangeable acidity (1M KCl) is determined. Therefore, a need exists to use other routinely determined soil properties in a model to derive TA. In this study, routine soil-analysis results from a commercial laboratory (n = 5 915 measurements) were used to generate various model structures to calculate a theoretical value of TA and to evaluate the accuracy thereof. Measured TA was found to be significantly correlated to the calculated TA using the Eksteen R-value, soil pH, organic carbon percentage, exchangeable Ca2+ and Mg2+, and a function of the interaction between pH and organic carbon. Soil TA could be calculated most accurately using these factors in multivariate adaptive regression splines model (r 2 = 0.69, mean absolute error = 0.16) at TA values of < 2 cmolc kg−1. However, the exponential model calculations remained stable over a larger range of actual TA values up to 4 cmolc kg−1. Given the ease of use and interpretability, it is recommended that an exponential function model is used to calculate TA.

C:N:P stoichiometric variations of herbs and its relationships with soil properties and species relative abundance along the Xiaokai River irrigation in the Yellow River Delta, China.

Soil salinity is known to affect plant performance and nutrient stoichiometry by altering their ecophysiology, and thus playing a crucial role in determining plant distribution patterns and nutrient cycles in salinized ecosystems. However, there was little consensus on the effects of salinity stress on plant C, N, and P stoichiometries. Moreover, determining the relationships between species relative species abundance and plant C, N, and P stoichiometries can help to understand the different adaptive strategies between the common and rare species as well as the community assembly process. We determined the plant C, N, P stoichiometries at the community and species levels and the relative abundance of species as well as the corresponding soil properties from five sampling sites along a soil salinity gradient in the Yellow River Delta, China. We found that the C concentration of belowground part increased with soil salinity. Meanwhile, plant community N concentration and C:N ratio tended to decrease with soil salinity, whereas the P concentration, C:P, and N:P ratios exhibited the opposite trends. This indicated that N use efficiency increased, while P use efficiency decreased with soil salinity. Moreover, the decreased N:P ratio indicated that N limitation was gradually aggravated along the soil salinity gradient. The soil C:P ratio and P concentration were the major factors of plant C, N, and P stoichiometries in the early growth stage, whereas the soil pH and P concentration were the major factors of plant C, N, and P stoichiometries in the late growth stage. Compared with that of the rare species, the C:N:P stoichiometry of the most common species was medium. Moreover, the intraspecific variations in the aboveground part N:P ratio and belowground part C concentration showed a significant correlation with species' relative abundance, which indicated that higher intraspecific trait variation might facilitate greater fitness and survival opportunities in environments with high heterogeneity. Our results revealed that the plant community C:N:P stoichiometry and its determining soil properties varied with plant tissues as well as sampling seasons, and emphasized the importance of intraspecific variation in determining the functional response of plant communities to salinity stress.

Subgrade reaction for closely spaced raft and isolated foundations on sand: case study

This paper investigates the interference effect of closely spaced foundations on the modulus of subgrade reaction (k) and provides an insight into some of the factors affecting the k-value of a raft foundation when other foundations (isolated footings or other identical rafts) are closely placed on both sides. The investigation adopted a three-dimensional, non-linear, finite-element numerical analysis based on a case study of a residential project with several multi-storey buildings constructed on sand underlain by weathered limestone. A site-specific ground investigation was conducted to determine soil properties, which were used to construct three-dimensional numerical models to simulate different arrangements of foundations and estimate the corresponding k-values. The results show that the k-value starts to decrease when the spacing between the foundations becomes less than three times the raft width, and the percentage of reduction in k-value increases in a non-linear way as the spacing decreases further. In addition to spacing, the study revealed that the effect of adjacent foundations depends on their size and the magnitude of their applied pressure. The case study provides a framework for developing a correction factor that can be applied to k-value for improving the structural and geotechnical design of closely spaced shallow foundations.