Soil Moisture Content Research Articles

Risk assessment of transmission towers based on variable weight theory and matter-element extension model

Abstract The risk assessment of transmission towers is a critical approach to ensuring the safe and stable operation of power grids. In response to the increasingly frequent and severe impacts of seasonal permafrost on transmission towers in permafrost regions, this paper proposes a novel risk assessment method based on Variable Weight Theory and the Matter-Element Extension Model. By incorporating key influencing factors—such as the number of freeze–thaw cycles, soil moisture content, temperature fluctuations, soil dry density, melting settlement coefficient, and porosity ratio—an indicator system tailored to risk assessment in seasonal permafrost regions is established. Utilizing the Matter-Element Extension Model, this approach applies Variable Weight Theory to integrate both the best-worst method and the entropy weight method for calculating subjective and objective indicator weights. The risk level of the towers is then determined by their proximity to risk thresholds. Finally, a case study involving transmission towers in a specific region of a province in China is presented, where the assessment results are compared with the actual conditions to validate the rationality and effectiveness of the proposed method.

Mapping Soil Surface Moisture of an Agrophytocenosis via a Neural Network Based on Synchronized Radar and Multispectral Optoelectronic Data of SENTINEL-1,2—Case Study on Test Sites in the Lower Volga Region

In this article, the authors developed a novel method for the moisture mapping of the soil surface of agrophytocenosis using a neural network based on synchronized radar and multispectral optoelectronic data from Sentinel-1,2. The significance of this research lies in its potential to enhance precision farming practices, which are increasingly vital in addressing global agricultural challenges such as water scarcity and the need for sustainable resource management. To verify the developed method, data from two experimental plots were utilized. These plots were located on irrigated soybean crops, with the first plot situated on the right bank (plot No. 1) and the second on the left bank (plot No. 2) of the lower Volga River. Two experimental soil moisture geodatasets were created through measurements and geo-referencing points using the gravimetric method (for plot No. 1) and the proximal sensing method (for plot No. 2) employing the Soil Moisture Sensor ML3-KIT (THETAKIT, Delta). The soil moisture retrieval algorithm was based on the use of a neural network to predict the reflection coefficient of an electro-magnetic wave from the soil surface, followed by inversion into soil moisture using a dielectric model that takes into account the soil texture. The input parameter of the neural network was the ratio of the microwave radar vegetation index (calculated based on Sentinel-1 data) to the index (calculated based on the data of multispectral optoelectronic channels 8 and 11 of Sentinel-2). The retrieved soil moisture values were compared with in situ measurements, showing a determination coefficient of 0.44–0.65 and a standard deviation of 2.4–4.2% for plot No. 1 and similar metrics for plot No. 2. The conducted research laid the groundwork for developing a new technology for remote sensing of soil moisture content in agrophytocenosis, serving as a crucial component of precision farming systems and agroecology. The integration of this technology promotes sustainable agricultural practices by minimizing water consumption while maximizing crop productivity. This aligns with broader environmental goals of conserving natural resources and reducing agricultural runoff. On a larger scale, data derived from such studies can inform policy decisions related to water resource management, guiding regulations that promote efficient water use in agriculture.

Improving subseasonal forecast skill in the Norwegian Climate Prediction Model using soil moisture data assimilation

This study shows the importance of soil moisture (SM) in subseasonal-to-seasonal (S2S) predictions at mid-latitudes. We do this through introducing the Norwegian Climate Prediction Model Land (NorCPM-Land), a land reanalysis framework tailored for integration with the Norwegian Climate Prediction Model (NorCPM). NorCPM-Land assimilates blended SM data from the European Space Agency’s Climate Change Initiative into a 30-member offline simulation of the Community Land Model with fluxes from the coupled model. The assimilation of SM data reduces error in SM by 10.5 % when validated against independent SM observations. It also improves latent heat flux estimates, illustrating that the adjustment of underlying SM significantly augments the capacity to model land surface dynamics. We evaluate the added value of land initialisation for subseasonal predictions, by comparing the performance of hindcasts (retrospective prediction) using the standard NorCPM with a version where the land initial condition is taken from NorCPM-Land reanalysis. The hindcast covers the period 2000 to 2019 with four start dates per year. Land initialisation enhances SM predictions, reducing error by up to 2.5-month lead time. Likewise, the error for precipitation and temperature shows improvement up to a lead time of 1.5-month. The largest improvements are observed in regions with significant land-atmospheric coupling, such as the Central United States, the Sahel, and Central India. This method further enhances the prediction of extreme temperature variations, both high and low, with the most notable improvements seen in regions at mid and high latitudes, including parts of Europe, the United States, and Asia. Overall, our study provides further evidence for the significant role of SM content in enhancing the accuracy of subseasonal predictions. This study introduces a technique for improved land initialisation, utilising the same model employed in climate predictions.

Pavement performance analysis considering the influence of suction and temperature on subgrade soil modulus

Subgrade soils can experience varying suction (equivalent to moisture) and temperature, which could affect the modulus of subgrade soil and the response of flexible pavements. The influence of these two factors on pavement performance has not been explored on purpose despite their importance in a changing climate. In this study, a simple approach was proposed for analyzing pavement performance with consideration of suction and temperature effects on soil modulus. Modulus values at different conditions of suction and temperature are calculated using a semi-empirical equation. Then, they are utilized in the numerical software KENLAYER to evaluate the stress and strain distributions in pavements composed of an asphalt layer, base layer, and subgrade layer based on linear elastic theory. The computed results are used to predict the rutting and fatigue cracking using some field-calibrated semi-empirical equations. In parametric studies using this method, the suction and temperature-dependent modulus of a clayey soil determined from laboratory experiments was applied. The results reveal that an increase in subgrade soil modulus due to drying and cooling can reduce the total rut depth mainly due to a reduction of the vertical compressive strain in the subgrade layer. For instance, a change in suction from 0 to 150 kPa (equivalent to optimum moisture content of the clayey soil) can lead to a 34 % decrease in the total rut depth. However, the effects of an increase in soil subgrade modulus on fatigue cracking are influenced by the asphalt thickness. A reduced subgrade modulus because of wetting and heating decreases horizontal tensile strain (more susceptible to fatigue cracking) at the lowest section of relatively thinner asphalt but increases horizontal tensile strain for thicker asphalt.

Mammalian herbivory alters structure, composition and edaphic conditions of a grey‐dune community

AbstractQuestionMammalian herbivory affects the structure and composition of plant communities, soil characteristics and intraspecific leaf traits. Understanding the effects of this type of herbivory is particularly relevant in grey dunes, a priority habitat type of the European Union Habitats Directive.LocationSálvora island (NW Spain).MethodsVegetation surveys and sampling were carried out in a coastal grey‐dune community, comparing the structure and composition of plant communities and soil characteristics in plots with herbivory exclusion and plots with herbivore activity, in autumn and spring. Changes in the specific leaf area (SLA), C/N ratio, δ13C and δ15N of two main plant species were also analysed.ResultsThe differentiation between treatments was low in autumn, in contrast to spring results, which demonstrated seasonal variation in the plant community and herbivore behaviour. Spring results showed lower above‐ground dry mass in plots with herbivory due to defoliation, but greater richness and diversity, indicating that intermediate levels of disturbance reduced competition from dominant species. Herbivory treatments were different in terms of species composition, highlighting the positive effects of herbivory on the development of the threatened species Linaria arenaria. Soil temperature and moisture content were higher in herbivory plots because of the suppression of vegetation cover and the effect of trampling, respectively. No differences were detected in the chemical composition of the soil or the SLA, although the variability of these traits was greater in herbivory plots, indicating spatial heterogeneity generated by the activity of herbivores. No differences between treatments were obtained for % C and δ13C, whereas herbivory plots showed lower values of N content and δ15N as an adaptive response to herbivory pressure at the leaf and root level.ConclusionOur findings show that herbivory effects on plant communities vary by season — stronger in spring and weaker in autumn — emphasising the need for seasonal analysis and highlighting disturbance as a driver of spatial heterogeneity.

Effects of freeze–thaw on the detachment capacity of soils with different textures on the Loess Plateau, China

Soil detachment capacity (Dc) is a crucial indicator for quantifying erosion intensity. However, the combined actions of freeze–thaw and water flow complicate the erosion process, leaving the variation mechanism of Dc under this condition systematically unexplored. This study examined five loess soils from a seasonal freeze–thaw area. The mechanism driving changes in Dc was quantified through freeze–thaw simulation combined with flow scouring tests, and a Dc prediction model was established. The results revealed that the shear strength (τm), cohesion (Coh), and internal friction angle (φ) in silt loam were higher than in sandy loam. After freeze–thaw cycles (FTC), τm, Coh, and φ of the five loess soils decreased by 1.02–1.37, 1.07–9.15, and 0.92–1.05 times, respectively. As FTC increased, τm and Coh gradually stabilized, while φ showed minimal fluctuation, indicating that FTC had a cumulative effect on the deterioration of soil mechanical properties. During FTC, Dc in Wuzhong sandy loam was the largest, being 1.14–3.24 times greater than in other soils, suggesting a significant main effect of soil type on Dc variation, with a contribution rate of 19.27 %. Dc eventually stabilized with increasing, indicating a critical FTC of around 10 for its impact on Dc. Compared to unfrozen soils, Dc increased by 33.69 %–102.40 % under the combined effects of freeze–thaw and water flow, clarifying that FTC aggravated soil instability. Effective stream power was the optimum hydraulic parameter, contributing the most to Dc (45.94 %). FTC (6.41 %) and initial soil moisture content (8.59 %) were less influential, as FTC initially degraded soil properties, and then the combined action with water flow intensified soil damage, causing the role of freeze–thaw factors to be obscured by other variables. A Dc prediction model using a general flow intensity index estimated well Dc, with both R2 and NSE at 0.94. Model performance comparison emphasized the need for validation when extending the application range beyond development conditions. These findings provide new insights into the detachment mechanisms of different textured soils under compound freeze–thaw and hydrodynamic influence in freeze–thaw region.

Enhancing field soil moisture content monitoring using laboratory-based soil spectral measurements and radiative transfer models

Enhancing field soil moisture content monitoring using laboratory-based soil spectral measurements and radiative transfer models

Assessing the Soil Infiltration Rate of Alpine Meadows Using the Electrolyte Tracer Method

ABSTRACTGrassland on the Qinghai‐Tibet Plateau is highly susceptible to climate change and human activities, and vegetation degradation can affect biodiversity and soil erosion. Soil infiltration is a crucial water flow process that determines the amount of runoff and water storage capacity, and it is of great importance in maintaining biodiversity. This research investigated the effects of vegetation degradation and soil rates on soil infiltration rate and processes using the electrolyte tracer method. This technique accurately calculated soil infiltration rate by tracking continuous changes in the solute concentration change process throughout the experimental period and did not require calibration. Findings indicate that vegetation type, root mass, soil water content and soil porosity significantly affect soil infiltration rate. In particular, root mass was found to have a negative effect on soil infiltration rate. Soil moisture content initially dominated soil infiltration, but subsequently, soil porosity became increasingly influential in affecting infiltration in degraded meadow. Soil infiltration capacity varied more with vegetation type than with surface runoff. Shrub meadows had the highest infiltration rate followed by normal alpine meadows and degraded meadows, indicating the importance of vegetation on soil infiltration. The research also shows that mixed shrub and meadow can improve the ecological environment by introducing a more complex root system and increasing the infiltration rate. The electrolyte tracer method was used as an alternative to other methods that can be used in different environments than the one studied in this research.

Influence of Trenchless Subsurface Drainage with a Rice Husk Filling System on Soybean Productivity Under a Poorly Drained Paddy Field for Future Applications in Smart Agriculture

In South Korea, paddy fields are increasingly being planted with soybeans to address rice supply and demand issues and increase soybean self-sufficiency. The field crops cannot grow healthily without adequate drainage due to the paddy fields storing water easily. In this study, we identified that Rice Husk Filling Drainage Method (RHDM) technology improved soil permeability and soil aeration. We also found that the soil moisture content was reduced and the water table remained at a lower level in the RHDM plot as compared to the control plot. The soybean moisture stress index showed that in the RHDM plot, the safety standard for stress due to excessive moisture at the 2 m interval was met. The soybean yield was increased by up to 35% in the 2 m RHDM construction interval compared to the control plot. In addition, the high hydraulic conductivity of the rice husk used as the hydrophobic material confirmed sufficient drainage performance and was considered economically advantageous. Therefore, our results show that RHDM is a highly efficient and economical drainage method in poorly drained paddy soils. Drainage management is essential for stable crop production in poorly drained paddy fields. Our research findings suggest that an efficient open field water management method is viable, which we believe will lead to future advances in open field smart agriculture.

An integrated dataset of ground hydrothermal regimes and soil nutrients monitored in some previously burned areas in hemiboreal forests in Northeast China during 2016–2022

Abstract. Under a warming climate, the occurrence of wildfires has been becoming increasingly more frequent in boreal forests and Arctic tundra over the last few decades. Wildfires can cause radical changes in forest ecosystems and the permafrost environment, such as the irreversible degradation of permafrost, succession of boreal forests, rapid and massive losses of soil carbon stock, and increased periglacial geohazards. Since 2016, we have gradually and more systematically established a network for studying soil nutrients and monitoring the hydrothermal state of the active layer and near-surface permafrost in the northern Da Xing'anling Mountains in Northeast China. Soil moisture content (depth of 0–9.4 m), soil organic carbon content (0–3.6 m), total nitrogen content (0–3.6 m), and total phosphorus and potassium content (0–3.6 m) datasets were obtained in 2016 via field sampling and subsequent laboratory tests. Ground temperature (0–20 m) and active layer thickness (2017–2022) datasets were obtained using thermistor cables that were permanently installed in boreholes or interpolated with these temperatures. The present data can be used to simulate changes in permafrost features under a changing climate and wildfire disturbances and to explore the changing interactive mechanisms of the fire–permafrost–carbon system in hemiboreal forests. Furthermore, they can provide baseline data for studies and action plans to support the carbon neutralization initiative and assessment of the ecological safety and management of the permafrost environment. These datasets can be easily accessed via the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Cryos.tpdc.300933, Li and Jin, 2024).

Effect of Soil-pH, temperature and moisture content on sorption dynamics of metformin and erythromycin

The rising soil-groundwater quality issues due to pharmaceuticals and personal care products (PPCPs) contamination have spurred significant concern. To understand the sorption characteristics of metformin (MTN) and erythromycin (ETM) in sandy and sandy loam soils with varying organic matter and particle composition, sorption kinetics (single and competitive), isotherms, and thermodynamics were studied. The effects of pH and soil moisture content (SMC) were also investigated at environmentally relevant concentrations. The equilibrium time of MTN and ETM sorption by the three soils in a competitive solute system was about 4 h, and the sorption process was in line with a pseudo-second-order model. The rate-determining step in the process involved both intraparticle diffusion and liquid film diffusion mechanisms for the two PPCPs. The highest pollutant uptake occurred in soils with higher organic matter, driven by enhanced H-bonding, electrostatic interactions, and π-π and n-π interactions facilitated by the organic matter. The equilibrium data in the three soils was well described by the Freundlich model and confirmed favourable adsorption (1/nf = 1.01–1.90). The sorption coefficient (Kd) on the three soils ranged from 2.1 to 332 L/kg for MTN and from 6.25 to 845 L/kg for ETM. The adsorption process was feasible at 293 K and 303 K (ΔG° = - 0.16 to −10.24 kJ/mol), physical and exothermic in nature (ΔH° = −75.21 to −10.30 kJ/mol) for both the contaminants. Observed alterations in Qe with pH confirmed the participation of electrostatic interactions. A low SMC favoured both MTN and ETM sorption onto the sandy soil. Overall, ETM exhibits higher expected sorption, whereas MTN has a greater tendency for migration in the soils and is thus liable to contaminate the groundwater. The study accentuates novel insights into the transport and fate of MTN and ETM in soil-groundwater systems at environmentally relevant concentrations.

In-Vivo and In-Vitro Investigation of Germination Rate of Buried Sclerotia, and Variability in Carpogenic Germination Among Sclerotinia sclerotiorum Isolates

The sclerotia of Sclerotinia sclerotiorum serve as a primary inoculum source for initiating infections. This study aimed to evaluate the effects of environmental factors on sclerotial germination under field conditions by establishing sclerotia depots to monitor apothecia appearance over four consecutive years. Additionally, the effects of soil moisture content (25%, 50%, 75%, and 95%), air temperature (10 °C/10 °C, 14 °C/10 °C, and 18 °C/10 °C), and light conditions (white and UV light) on sclerotial germination and apothecial formation were investigated under controlled conditions with a 17 h day/7 h night regime. Furthermore, variability in carpogenic germination among S. sclerotiorum isolates was examined. From 2021 to 2024, significant differences were observed in both the sclerotia germination rate and timing of germination within the season. High soil moisture, particularly prolonged wetness, and soil temperatures between 10 and 14 °C were key factors for apothecial formation under field conditions. Under controlled conditions, higher soil moisture levels (75% and 95%) accelerated sclerotial germination, with sclerotia incubated at 14 °C/10 °C germinating earlier after 38 days than those at 10 °C/10 °C or 18 °C/10 °C. Additionally, the type of light significantly affected apothecial formation, which was observed only in treatments exposed to a combination of white and UV light. Furthermore, significant variations were also found in the duration until sclerotia of different S. sclerotiorum isolates produced the first stipe and the first apothecium, indicating that the genetic characteristics of each isolate affect carpogenic germination.

Differential effects of pH on cadmium accumulation in Artemisia argyi growing in low and moderately cadmium-contaminated paddy soils

BackgroundPhytoremediation is affected by physical and chemical properties of the soil such as soil pH, moisture, and nutrient content. Soil pH is a key element influencing Cd bioavailability and can be easily adjusted in agricultural practices. The soil pH level may relate to the effectiveness of phytoremediation; however, this has not been extensively investigated. In the current study, we evaluated the effect of Cd contamination level (0.56 and 0.92 mg/kg) and soil pH (5, 6, and 7) on Cd accumulation and allocation in Artemisia argyi, a fast-growing perennial crop.ResultsOur results indicated that higher soil Cd concentrations reduce A. argyi biomass, and the loss of the root mass was particularly significant. Higher soil pH decreased Cd content in stems and roots of A. argyi cultivated in moderately Cd-polluted soil, and increased Cd content in stems and roots of the plant grown in low Cd-polluted soil. Higher soil pH decreased the percentage of Cd distributed in the soluble fraction and cell walls and increased the percentage of Cd in the organelles of leaf cells for moderate soil Cd levels. The bioconcentration and translocation factor exceeded 4.0 and 1.0, respectively, across all tested treatments, indicating that A. argyi is a promising candidate for phytoremediation. Notably, the effects of soil pH on Cd accumulation and subcellular distribution in A. argyi differed between low and moderately Cd-contaminated soils.ConclusionAdjustments to soil pH based on the degree of Cd contamination can enhance Cd extraction by A. argyi, thereby reducing the overall remediation cycle of cadmium-polluted paddy fields of South China.Graphical

Effect of layered fertilizer strategies on rapeseed (Brassica napus L.) productivity and soil macropore characteristics under mechanical direct-sowing

Layered fertilization parameters affects crop root system configuration and growth distribution, which in turn affects soil pore properties and aggregate structure. Therefore, understanding the spatial distribution of the root system and soil pore space is helpful in choosing a reasonable fertilization ratio in production practice, which ensures high and stable crop yields and at the same time improves fertilizer utilization and soil health. Albeit the impact of layered fertilization ratio at varied rates on soil pore characteristics in the soil profile at a microscale level remains limited. This study quantifies the impacts of layered fertilization ratio under on rapeseed root distribution and soil pore characteristics using the root analysis system and X-ray computed tomography (XCT). A new fertilization pattern that shallow-deep layer synchronized mechanical sowing was using, rotary tillage mixed fertilization in the shallow layer and 10 cm side-deep band fertilization in the deeper layer. It set five ratios of fertilizer amounts between shallow and deep layers as 0:4 (FD), 1:3 (FL), 2:2 (FM), 3:1 (FH), and 4:0 (F0), with non-fertilization (CK) as the control treatment. The results indicated that layered fertilization effectively improved the rape root conformation and spatial distribution, significantly increased total soil porosity and moisture content, and reduced soil bulk density and resistance (P < 0.05). Additionally, root configuration is closely related to soil pore structure and crop yield. Notably, compared with other treatments, the macroscopic porosity, equivalent pore diameter, hydraulic radius and roundness of FM treatment are significantly improved, and it effectively improves the yield and quality of rapeseed. Correlation analysis showed that the root system configuration parameters were negatively correlated with soil bulk density and resistance but positively correlated with soil moisture content, and macropore morphology parameters and rapeseed yield. Our study demonstrate that layered fertilization can improved rape growth and soil macropore porosity, but its effect depends on good tillage macropore characteristics and distribution created by reasonable fertilization ratio, which provided a theoretical basis for high-yield, efficient, green, and sustainable mechanized direct-sowing production of rapeseed.

Stabilization strategy of normal and chrome tanning effluent mixed subsoil: a novelty to enhance the soil characteristics

Soil stabilisation is a unique method to effectively and accurately find a solution to the issues caused by loose subsoil. This research investigates the problems of uncontrolled industrial effluent disposal, mainly from tannery enterprises, which presents severe environmental problems in emerging nations due to soil properties changing and large regions being unfit for cultivation and human habitation. The study uses Atterberg's limits, sieve analysis, and direct shear testing to examine the effects of untreated tannery effluents on soil parameters. The findings show that the soil's shear strength, moisture content, and flexibility have all significantly decreased. Lime and waste stone powder were added to lessen these impacts, and this improved soil stability was shown by an increase in the values of all the variables. For efficient soil remediation, the study suggests using waste stone powder and lime in the following proportions: 0%, 3%, 6%, and 9%. Combining lime stabilisation methods with industrial by-products like slag and fly ash opens up new possibilities for enhancing the geotechnical characteristics of polluted soils. This study emphasises how important it is to use customised geotechnical design techniques to handle soil contamination issues in infrastructure and foundation projects, especially in areas where the leather industry predominates.

Comparison of transformer, LSTM and coupled algorithms for soil moisture prediction in shallow-groundwater-level areas with interpretability analysis

Accurate quantification of soil moisture is essential for understanding water and energy exchanges between the atmosphere and the Earth’s surface, as well as for agricultural applications. Predicting soil moisture content is vital for efficient water management, irrigation scheduling, and drought monitoring. Traditional forecasting methods, such as numerical regression models, often struggle due to various influencing factors and poor observation data quality. In contrast, deep learning algorithms, particularly recurrent and convolutional neural networks, show promise in predicting nonlinear data like soil moisture. This study focuses on shallow groundwater regions, using groundwater levels and meteorological data as features while coupling the Transformer model with other neural network structures. We investigate the potential of attention-based neural networks for soil moisture time series prediction. Our findings demonstrate that the Transformer model achieves an average R2 of 0.523 across different time lags, outperforming the LSTM model with an R2 of 0.485. The introduction of LSTM enhances the Transformer’s stability in handling temporal changes. Additionally, we verified the importance of groundwater for soil moisture prediction. This study introduces new methods for soil moisture prediction and offers new insights and recommendations for the development of artificial intelligence technology for soil moisture prediction.

Effect of Some Ecological Factors on Earthworms (Lumbercus terrestris) in Wasit Governorate

Monthly earthworm samples were collected from two locations in Suwairah City‎ of Wasit Governorate. where the first location was northwest of the district and is characterized by an abundance of citrus trees such as orange, lemon and Punica granatum as well as many palm trees of different types, the second area is characterized by an abundance of vegetable plants like Cauliflower, Allium cepa, and Brassica oleracea. Soil temperature, moisture, pH, texture and organic matter levels were measured at all sample sites. Soil temperature ranged between 11C° to 50C° as recorded during February and August respectively. The values of PH have ranged between 6.8 to 8.0. Soil moisture and Organic matter content were highest in Region (B) samples, 40% and 6.6% respectively. However, lowest organic matter and soil moisture percentage were 17% and 3.13% recorded at in Region (A) samples respectively. All sites were characterized by loamy acidic reactive soils.

Analysis of Soil Viability Monitoring System for In-House Plantation Growth Using an Internet of Things Approach

Houseplant cultivation has become increasingly popular, allowing individuals to bring nature into their homes. However, successful indoor gardening requires careful monitoring of soil parameters to ensure optimal plant growth. To address this need, sensor technology and Internet of Things (IoT) devices are utilized to monitor soil temperature and moisture levels, which play crucial roles in plant growth. Various soil factors are sensed and collected using an IoT-based microcontroller, with data transmission facilitated by a Message Queue Telemetry Transport (MQTT) broker. Visualization of the data is achieved through the Node-RED programming tool, simplifying dashboard creation for easy monitoring. Furthermore, the collected data is stored in a MySQL server, enabling further analysis through SQL queries. The day is divided into four quarters with six-hour intervals, allowing for soil data collection using temperature and moisture sensors. The resulting information on the dashboard facilitates informed decision-making to enhance soil conditions for optimal indoor plant growth. Experimentation has revealed a reduction in soil temperature of 3°C during daytime due to air conditioning operation, while soil moisture content remains consistently between 60 to 65% during early mornings and late evenings. Additionally, emphasis is placed on remote management using IoT systems, enabling monitoring of plant growth even when access is limited. Overall, monitoring soil factors using IoT technology offers a promising approach to optimizing indoor gardening practices and minimizing environmental resource consumption.

Study on Shear Strength of Soil-Root Systems of Different Vegetation Types.

The root systems of vegetation significantly contribute to enhancing slope stability. The shear strength of soil-root systems is a crucial parameter for assessing slope stability. This study focuses on six types of vegetation in the Yellow River Basin of China (woodland: Populus przewalskii and Broussonetia papyrifera; shrubland: Periploca sepium and Ziziphus jujuba; grassland: Artemisia hedinii and Setaria viridis), employing in situ shear tests and the Wu-Waldron model (Wu model) to investigate the shear strength of soil-root systems. The results show that the shear stress-displacement curves for P. przewalskii, B. papyrifera, and Z. jujuba are higher and steeper, with clear inflection points. The tensile strength of the roots from the six vegetation types decreases as the root diameter increases. According to the Wu model, the additional root cohesion is ranked as follows: A. hedinii > B. papyrifera > P. przewalskii > Z. jujuba > P. sepium > S. viridis. Based on the in situ shear tests, the shear strength increments are ranked as follows: Z. jujuba > B. papyrifera > P. przewalskii > A. hedinii > P. sepium > S. viridis. Overall, the additional root cohesion obtained by the Wu model in each soil layer is greater than the shear strength increment measured from the in situ shear tests. In the 0-30 cm soil layers, the soil-root systems of Z. jujuba, B. papyrifera, P. przewalskii, and A. hedinii exhibit a better shear strength, whereas P. sepium and S. viridis perform poorly. A principal component analysis reveals that the shear strength of the soil-root systems of different vegetation types is primarily influenced by the soil moisture content and root mass density. Z. jujuba, B. papyrifera, P. przewalskii, and A. hedinii are recommended for ecological restoration projects in the Yellow River Basin of China.

Robinia pseudoacacia Quickly Adjusts Its Water Uptake after Rainfall in Seasonally Dry Regions

Precipitation is a key factor affecting plant growth and development in seasonally arid regions. However, most of the traditional hydrological methods mainly select typically sunny days for sampling, and the immediate water absorption strategy of plants during and after rainfall is still unclear. This study used stable hydrogen and oxygen isotope technology to study the soil moisture absorption rates of Robinia pseudoacacia and the soil moisture content at different soil layers at different sampling times (0, 6, 12, 18 and 24 h) after rainfall. The results showed that the moisture content of the shallow soil layer decreased, while that of the deep soil layer increased over time after rainfall. R. pseudoacacia mainly utilized water from the 0–20 and 20–40 cm soil layers at 6 h after rainfall, which accounted for 36.52% and 22.25% of the rainfall, respectively. At 24 h, the 40–60, 60–80 and 80–100 cm soil layers contributed 25.25%, 18.44% and 24.45% of the water content, respectively. The shallow soil layer retained more rainfall within 6 h after rain fell, and the water retention ratio of the medium–shallow soil layer (0–60 cm) increased to 48.4%, retaining more water at 14–20 h. At 12 h, the medium–shallow soil layer (0–60 cm), runoff and groundwater constituted 37.1%, 14.4% and 15.7% of the precipitation, respectively, and rainfall retained in the deep soil layer (60–100 cm) accounted for 32.8%. In summary, R. pseudoacacia tends to use a large amount of shallow soil water in seasonally arid regions when precipitation supplements the surface soil moisture content and it utilizes deep soil water when the rainfall infiltrates and recharges the deep soil layer. Since R. pseudoacacia is sensitive to precipitation, it can quickly adjust its water absorption depth range during the short-term rainfall period to absorb as much precipitation as possible.