Soil Moisture Content Research Articles

Influence of Rice Residue Management on Sunflower Growth, Bulk Density and Moisture Content of Soil in a Rice-sunflower Cropping System

A field experiment was conducted during 2022-23 and 2023-24 to assess the impact of kharif rice residue management on sunflower growth and soil bulk density and moisture content in a rice-sunflower cropping system on Alfisols using a randomized block design. Results indicated that the highest dry matter accumulation per plant (20.59 g plant⁻¹) was observed in the treatment involving straw incorporation with a C:N:P ratio adjusted to 30:1:0.3 prior to incorporation (T7), outperforming residue removal (T2) and burning (T1) treatments. Bulk density (BD) was highest (1.38 Mg m⁻³) in residue removal and burning treatments with RDF application and lowest (1.34 Mg m⁻³) in residue retention with zero tillage sunflower and RDF (T3). Additionally, the higher gravimetric moisture (GM) content was recorded in treatments with a C:N:P ratio adjustment of 30:1:0.3 before incorporation, while the lowest GM content was observed in residue removal with RDF application.

Changes in Soil Properties, Organic Carbon, and Nutrient Stocks After Land‐Use Change From Forests to Grasslands in Kumaun Himalaya, India

ABSTRACTLand‐use changes are anticipated to be a substantial contributor to global change climate, substantially causing significant modifications in soil characteristics. This study addressed the impact of land‐use change from native forests to grasslands on the soil physico‐chemical properties in entirely replicated grasslands of three different forest zones (Oak, Pine and Cypress) in temperate region of Kumaun Himalaya. A total of 162 soil samples (6 sites × 3 plots × 3 seasons × 3 depths = 162 samples) were randomly collected from each site in triplicates from depths. The soil texture, bulk density (bD), porosity, water holding capacity, soil moisture content, pH, organic carbon (SOC), total nitrogen (TN), available phosphorus (P) and available potassium (K) were determined at different depths in forest and grassland sites. Results showed that soil bD, pH, SOC, TN, P and K significantly (p < 0.05) decreased with increasing depth. Moreover, conversion of forests into grassland reduced nutrient concentrations, physical qualities (bD and porosity), and pH levels. The decreasing trend of nutrient along the soil depth explains that the zone of nutrient accumulation is not well established in these grasslands because of the substantial leaching effect. Our findings indicate that conversion of natural forests into grasslands resulted in significant losses of SOC and TN stocks which can be attributed to the disturbance of natural forests. Therefore, while making land‐use change plans, the impact of these alterations on soil nutrients must be considered. These findings emphasize the value of establishing natural vegetation (forests) in these areas to retain nutrients and safeguard soil against runoff and erosion. However, anticipating the physico‐chemical impacts of land‐use alteration necessitates a better comprehension of its relations with other drivers of global change, such as changing climate and nitrogen deposition.

Inversion of Soil Moisture Content in Silage Corn Root Zones Based on UAV Remote Sensing

Accurately monitoring soil moisture content (SMC) in the field is crucial for achieving precision irrigation management. Currently, the development of UAV platforms provides a cost-effective method for large-scale SMC monitoring. This study investigates silage corn by employing UAV remote sensing technology to obtain multispectral imagery during the seedling, jointing, and tasseling stages. Field experimental data were integrated, and supervised classification was used to remove soil background and image shadows. Canopy reflectance was extracted using masking techniques, while Pearson correlation analysis was conducted to assess the linear relationship strength between spectral indices and SMC. Subsequently, convolutional neural networks (CNNs), back-propagation neural networks (BPNNs), and partial least squares regression (PLSR) models were constructed to evaluate the applicability of these models in monitoring SMC before and after removing the soil background and image shadows. The results indicated that: (1) After removing the soil background and image shadows, the inversion accuracy of SMC for CNN, BPNN, and PLSR models improved at all growth stages. (2) Among the different inversion models, the accuracy from high to low was CNN, PLSR, BPNN. (3) From the perspective of different growth stages, the inversion accuracy from high to low was seedling stage, tasseling stage, jointing stage. The findings provide theoretical and technical support for UAV multispectral remote sensing inversion of SMC in silage corn root zones and offer validation for large-scale soil moisture monitoring using remote sensing.

Effects of Straw Decomposition on Soil Surface Evaporation Resistance and Evaporation Simulation

As a prominent agricultural country, China has widely implemented returning straw to the field in agricultural production. However, as the decomposition of straw progresses, the physical properties of the soil change, inevitably leading to alterations in the soil surface evaporation model. This study investigated the variations in soil evaporation rate, soil moisture content over 60 days after returning straw to the field, and bare soil through two leaching pond experiments. Through soil moisture retention curves at different degrees of decomposition, this study analyzed the impact of straw decomposition on soil’s water retention capacity. Based on measured data, this study formulated models for the soil surface evaporation resistance of bare soil and varying degrees of straw decomposition. With the comparison and contrast between the models, this study clarified the impact of straw decomposition on soil surface evaporation resistance. The main conclusions are the following: The moisture content of the surface soil decreases exponentially over time and, after 40 days of straw decomposition, the water content of the soil under decomposition is higher than that of bare soil. As the moisture content decreases, the cumulative evaporation from the soil increases linearly. The cumulative evaporation of the decomposed straw soil is lower than that of bare soil, with a relative reduction ranging from 3.08% to 32.2%. The straw decomposition significantly enhances the water retention capacity of the soil in the medium-to-high suction range. The straw decomposition enhances the evaporation resistance of the soil surface, and the greater the degree of decomposition, the more significant the enhancement effect. The research findings not only provide a scientific basis for agricultural water management, but also possess practical implications for guiding farmers to adopt more effective moisture retention measures.

Natural recovery of different soil types after passes by wheeled military truck: Implications for soil as a ground for follow-on mobility operations

The trafficking of off-road military vehicles generates disturbed soil conditions. Thus, former active combat areas can have a serious effect on subsequent operations. More precisely, an uneven soil surface will remarkably increase the vibration of travelling vehicles, and over-compacted as well as displaced soil layers affect soil water dynamics that, in turn, have a long-lasting effect on soil strength. In addition, soil as a habitat for plants must provide air, water, and nutrients for the plants to grow. Luckily, soil can naturally recover from being disturbed thanks to freezing-thawing cycles, wetting–drying cycles, and biological activity. However, the rate of recovery depends on the physical parameters of soil. The objective of this study was to monitor and summarize the natural recovery rate of soils. As an experiment, single pass and repeated passes with military trucks (total weight 70 kN) were carried out. The ruts that formed were monitored and soil samples were collected up to 2 years after wheeling. The rut depth, cone index values, bulk density, soil moisture content as well as field-saturated hydraulic conductivity were measured and soil pore size distribution was determined in a laboratory during the entire studied recovery period. The results about the natural recovery on different soils are presented herein.

Rapid Detection of Soil Available Phosphorus using Capacitively Coupled Contactless Conductivity Detection

Background: In China, the traditional method for analyzing soil available phospho-rus is inadequate for large-scale soil assessment and nationwide soil formulation demands. To address this, we propose a rapid and reliable method for soil-available phosphorus detection. The setup includes an on-site rapid pre-treatment device, a non-contact conductivity detection device, and a capillary electrophoresis buffer solution system composed of glacial acetic acid and hydroxypropyl-β-cyclodextrin. Methods: The on-site rapid pre-treatment process includes fresh soil moisture content detec-tion (moisture rapid detector), weighing (handheld weighing meter), stirring (handheld rapid stirrer), and filtration (soil rapid filter) to obtain the liquid sample, and direct injection (capil-lary electrophoresis detector). The phosphate ion detection parameters include capillary size, separation voltage, injection parameters, and electric injection. We used Liaoning brown soil, Henan yellow tidal soil, Heilongjiang black soil, and Anhui tidal soil as standard samples. Additionally, we used mathematical modeling methods and machine learning algorithms to analyze and process research data. Results and Conclusion: Following calibration with standard samples, the experimental blind test samples demonstrated conformity with the national standard method, exhibiting a relative standard deviation of less than 3%. The proposed pre-treatment device and non-contact con-ductivity detector are powered by lithium-ion batteries, rendering them ideal for extended field operations. The non-contact conductivity detector obviates the need for direct contact with test samples, mitigating environmental pollution. Furthermore, the neural network model exhibited the highest level of goodness of fit in chemical data analysis.

A Soil Refractive Index (SRI) Model Characterizing the Functional Relationship Between Soil Moisture Content and Permittivity

A Soil Refractive Index (SRI) Model Characterizing the Functional Relationship Between Soil Moisture Content and Permittivity

Multi-decadal stability of water ages and tracer transport in a temperate-humid river basin

Abstract The temporal dynamics of water ages provide crucial insights into hydrological processes and transport mechanisms, yet there remains a significant gap in quantifying water age variability across different temporal scales. This study utilizes a comprehensive dataset spanning 70 years of hydrological observations and tritium records (1953–2022) with a semi-distributed hydrological model with integrated tracer routing routine based on StorageAge Selection functions SAS, to explore the temporal evolution of water ages in the 4000 km2 Upper Neckar River basin, Germany. Our findings indicate a systematic convergence of the variability of young water fractions and other metrics of water age in riverflow and evaporation towards stable values when averaging over increasing time scales. While at daily scales exhibiting considerable variability with young water fractions in riverflow F wy,Q ∼ 0.01–0.91 and in evaporation F wy,E ∼ 0.02–0.75, the variability of F wy,Q and F wy,E gradually reduces with increasing averaging time scales and converge to 0.45–0.47 and 0.96–0.97, respectively, between individual decades. Liquid water input (P L), comprising rainfall and snow melt, emerges as the dominant driver of F wy,Q across all time scales. In contrast, F wy,E shows varying controls with time scale: soil moisture content governs daily fluctuations, whereas P L dominates at the decadal scale. Overall, water ages demonstrate remarkable stability with only minor deviations in response to climatic variability: a 20% fluctuation in average decadal P L results in only ∼4% variation in F wy,Q and ∼1% in F wy,E over the study period. These findings suggest a lack of major long-term dynamics in water ages. Consequently, the results suggest that the physical transport dynamics in the Upper Neckar River basin, and potentially in comparable river basins with similar water age characteristics, can be considered near-stationary over multiple decades.

Unexpected species diversity in the understanding of selenium-containing soil invertebrates

Yutangba, situated in Enshi City, Hubei Province, is globally noted for its high selenium (Se) content. Soil invertebrates are essential to the functionality and services of terrestrial ecosystems, yet their community composition in this region remains under-explored. This study utilized environmental DNA metabarcoding to investigate the interrelations among environmental factors, soil invertebrate diversity, and community characteristics concerning soil Se content, pH, and moisture content in the region. Environmental factors such as Se concentration, water content, and pH were strongly associated with species beta diversity of soil invertebrates in Se-rich areas, reshaping their distribution and abundance, while alpha diversity remained relatively stable. Among these, Se notably emerges as the primary regulatory factor increasing soil invertebrate diversity. The acidic soil pH, along with moisture, plays a fine-tuning role in regulating the species richness and community composition. Unexpectedly, certain species, such as the Formicidae (ants, e.g., Odontomachus troglodytes), the Noctuidae (e.g., Diarsia rosaria), and the annelid Haplotaxida Perionyx excavates, exhibit a strong positive association with Se, indicating a high level of Se tolerance among the native species. This novel perspective reveals the complex role of Se in soil ecosystems, emphasizing the necessity of understanding its ecological functions and potential implications for ecosystem health and stability.

Impact of Management Practices on Coffee-Pine Agroforestry: Coffee Yield and Soil Respiration

The coffee-pine agroforestry system offers a promising solution to enhance coffee yields and maintain soil health on degraded lands. This study aims to evaluate the impact of various agroforestry management practices on coffee yield and soil respiration. The experiment was conducted using a complete randomized block design across five management treatments: without management, without fertilization, organic fertilization, mixed fertilization, and recommended management by Perhutani. The observed parameters included coffee yield, soil respiration, soil moisture, soil temperature, litter biomass, canopy cover, and soil organic carbon (SOC) content. Results indicated that the recommended management (RM) plot achieved the highest coffee yield (834 kg ha⁻¹), attributed to wider planting spacing, which reduced resource competition between coffee and pine trees. The RM plot also displayed stable soil moisture and temperature, supporting coffee growth. Meanwhile, soil respiration showed no significant differences across treatments, though the mixed fertilization (MF) plot exhibited the highest respiration rate, indicating higher microbial activity due to combined fertilizer use. In conclusion, optimal management in agroforestry systems can enhance coffee productivity while preserving soil health. Keywords: Agroforestry, Coffee Yield, Soil Management, Soil Moisture, Soil Respiration.

PERFORMANCE OF A LOCALLY FABRICATED ONE-ROW POTATO HARVESTER FOR WALK-TRACTOR

This study evaluates the performance of a locally fabricated one-row mechanized potato harvester designed for small and medium-scale farms in Kenya, focusing on its efficiency across different soil conditions. Mechanized harvesting addresses inefficiencies and post-harvest losses in potato farming, particularly in Kenya's diverse agricultural environments. The harvester was tested under dry soil (moisture content below 15%) and moderately wet soil (moisture content between 20% and 25%) conditions. Key performance metrics assessed included harvesting efficiency, tuber damage rate, fuel consumption, and field capacity. The harvester achieved an average efficiency of 93.4%, with higher performance observed in moderately wet soil (94.7%) compared to dry soil (92.1%). The average tuber damage rate was 4.2%, with more significant damage occurring in dry soil (4.8%) than in wet soil (3.6%). Fuel consumption remained consistent at 2.5 liters/hectare, and field capacity was measured at 0.4 hectares/hour. These results demonstrate the harvester's reliability and adaptability to local farming conditions, providing a mechanical potato harvesting solution for small-scale farmers in Kenya.

Subsurface Irrigation With Ceramic Emitters Improved the Water Productivity and Nitrogen Use Efficiency of Greenhouse Melons.

ABSTRACTIncreasing nitrogen use efficiency in agricultural fields is crucial for sustainable agricultural development. In a 2‐year greenhouse study, we compared two methods, subsurface irrigation with ceramic emitters (SICE) and subsurface drip irrigation (SDI), to evaluate their effects on soil moisture and nitrogen distribution, photosynthetic efficiency and plant stoichiometry. The results showed that SICE can reduce the soil nitrate nitrogen content by approximately 20% while maintaining the soil moisture content at 70%–80%, improving the photosynthetic efficiency and melon yield. Under the optimal yield treatment (applying 60.67 kg of nitrogen fertilizer per hectare), compared with SDI, SICE increased yield by 8%, water productivity by 13%, nitrogen absorption efficiency by 4% and nitrogen use efficiency by 3%. In addition, by analysing the stoichiometric characteristics of melon at different nitrogen fertilizer application rates and growth stages under the two irrigation methods, we found that the main reason for the increase in SICE yield was the coupled effect of the increase in plant photosynthetic efficiency and the need for plants to maintain their internal stoichiometric balance.

Relationship between Topographic Wetness Index and Soil Physical Properties in Cikapundung Sub-watershed Using Geographic Information Systems

Purpose: This study aimed to analyze the relationship between Topographic Wetness Index (TWI) and soil physical properties (permeability, porosity, bulk density, and soil moisture content) and their spatial distribution in the Cikapundung Sub-watershed. Methods: A survey method was conducted through descriptive, correlation, and interpolation approaches. Thirty soil samples were collected based on a two-way stratification method using TWI and slope gradient. TWI was derived from Digital Elevation Model (DEM) data using GIS. Soil physical properties were analyzed in laboratory and their spatial distribution was mapped using interpolation techniques. Results: TWI showed varying degrees of correlation with soil physical properties: significant positive correlation with soil permeability (r = 0.37), weak negative correlation with soil moisture content (r = -0.30), and very weak correlations with bulk density and soil porosity (r = -0.03 and r = 0.03). Spatial analysis revealed heterogeneous distribution patterns of soil physical properties across the sub-watershed. Conclusion: TWI can only be effectively used as a single predictor for soil permeability, but not for bulk density, porosity, and soil moisture content in the Cikapundung Sub-watershed. This suggests the need for additional environmental parameters when predicting these soil physical properties.

MECANIZAREA LUCRĂRILOR AGRICOLE PE PAJIȘTI PRIN VARIANTE TEHNOLOGICE CU INPUTURI MINIME. REVIEW

Grassland farming plays a vital role in sustainable agricultural systems, providing forage resources for livestock production and contributing to environmental conservation. However, the labor-intensive nature of grassland management requires significant challenges for farmers. The adoption of appropriate mechanization technologies can improve efficiency, reduce labor requirements, and enhance overall productivity. This paper investigates the mechanization of grassland farming through technological variants with minimal inputs. The incorporation of sensor technologies and data analytics facilitates real-time monitoring of grass growth, enabling farmers to make decisions regarding grazing rotations and forage quality. Additionally, the utilization of smart sensors for soil moisture and nutrient content allows for targeted application of inputs, reducing waste and optimizing resource utilization. Overall, this article highlights the potential of mechanization and technological variants with minimal inputs to make efficient the grassland farming, improving productivity, sustainability and the livelihoods of farmers.

Deep eutectic solvent-mediated extraction of lignin: A novel strategy for producing high-quality biopolymers in controlled-release mulching applications.

Microplastic contamination of low-density polyethylene mulch and nutrient loss from fertilizers present significant challenges in the crop-growing. In this study, the focus was on creating a biodegradable film that combines the advantages of plastic film, thermal insulation and water retention, as well as the controlled release of fertilizer. A key innovation was the efficient introduction of low molecular weight and low dispersibility of poplar lignin into chitosan and polyvinyl alcohol matrices. The lignin was extracted using deep eutectic solvents of binary carboxylic acids (choline chloride and maleic acid). The refined lignin was used as a superhydrophobic additive to improve the mechanical properties, hydrophobicity, and controlled nutrient release properties of the films through cross-linking. The mulch attained a tensile strength of 37.6 MPa, an elongation of 644.1 %, and a precise release of 53.1 % urea over 30 d at the ideal lignin content ratio (10 %). Furthermore, the film proficiently regulated soil temperature and moisture content. Successful enhancement of cabbage growth was achieved by actual measurements. This discovery provides innovative ideas for the development of nutrient slow-release high-strength integrated agricultural mulching films to promote sustainable, high-quality green agriculture.

Characteristics of Soil Microbial Community Structure in Different Land Use Types of the Huanghe Alluvial Plain

The Huanghe alluvial plain plays a crucial role in biodiversity conservation. However, its ecosystem has become sensitive and fragile due to long-term human disturbances. Enhancing the resilience of this ecosystem and promoting the sustainable use of land resources are key to addressing its ecological challenges. Soil microbial communities are vital to ecosystem functioning, and land use is a major human factor influencing their structure and diversity. Existing research on the Huanghe alluvial plain primarily focuses on soil physicochemical properties and moisture content, with relatively limited attention given to soil microorganisms. Therefore, this study, using the Wudi Tanyang Forest Farm in the Huanghe alluvial plain as a case study, employs high-throughput metagenomic sequencing to analyze the composition and diversity of soil bacteria, eukaryota, archaea, and virus communities in five different land use types (Tamarix chinensis forest, Fraxinus chinensis forest, farmland, wetland, and grassland). The results indicate that: (1) At the phylum level, the top three bacteria communities were Pseudomonadota, Acidobacteriota, and Actinomycetota; the top three in the eukaryota communities were Ascomycota, Mucoromycota, and Basidiomycotina; the top three in the archaea communities were Nitrososphaerota, Euryarchaeota, and Candidatus Thermoplasmatota; and the virus communities were dominated by Uroviricota; (2) The microbial community structure of the Tamarix chinensis forest and the Fraxinus chinensis forest was similar, and was significantly different from the other three land use types; (3) The land use type had a significant effect on the diversity of the soil microbial communities, with a higher diversity in the wetland and grassland soils; (4) The dominant species of the soil microbial communities under different land use types showed significant differences. This study provides theoretical support for land use optimization and sustainable soil management in the Huanghe alluvial plain region.

Design and simulation of an intelligent irrigation system using fuzzy logic

Farmers in developing countries employ various irrigation methods ranging from traditional techniques such as the use of irrigation cans to modern pressurized systems while irrigating short-seasoned crops that require continuous water supply especially during the dry season. Inspite of being cheap and easy to use, farmers find it challenging to optimize farming costs through the application of the neccessary amount of water onto the crops, leading to crop water logging and hence roting. Due to the growing demand for agricultural products, there was need to develop an irrigation system that considers the amount of moisture in the soil, air temperature and light intensity, which are important parameters in plants’ growth. This dissertation therefore presents the design, simulation and implementation of an intelligent irrigation system, utilizing multiple sensors and a fuzzy logic controller to optimize water usage while ensuring high farming yields. The system design integrates light, temperature, and soil moisture sensors to monitor environmental conditions and soil moisture content in real-time. The operation of the irrigation system is governed by a set of one hundred twenty five (125) fuzzy rules that process the inputs from these sensors, determining the appropriate irrigation duration based on the prevailing light intensity, ambient temperature, and soil moisture levels. The fuzzy logic controller employs a combination of membership functions and rule-based inference to handle the relationships between the environmental parameters and the irrigation needs of the soil. By continuously analyzing the data from the sensors, the system dynamically adjusts the water output, ensuring optimal soil moisture levels while minimizing water wastage. Additionally, the system is equipped to send notifications in form of calls to a smartphone connected to the GSM network in case of critical conditions or system malfunctions. Experimental results demonstrate the ability of the intelligent irrigation system to dynamically adjust the amount of water being pumped into the garden basing on the prevailing soil moisture, temperature and light intensity.

Sustainable and Low-Cost Greenhouse Soil Moisture Monitoring Using Battery-Free RFID Sensors

Intelligent irrigation based on measurements of soil moisture levels in every pot in a greenhouse can not only improve plant productivity and quality but also save water. However, existing soil moisture sensors are too expensive to deploy in every pot. We therefore introduce GreenTag, a low-cost RFID-based soil moisture sensing system whose accuracy is comparable to that of an expensive soil moisture sensor. Our key idea is to attach two RFID tags to a plant’s container so that changes in soil moisture content are reflected in their Differential Minimum Response Threshold (DMRT) metric at the reader. We show that a low-pass filtered DMRT metric is robust to changes both in the RF environment (e.g., from human movement) and in pot locations. In addition, we propose a fast DMRT acquisition algorithm and a time-efficient tag query protocol, which can reduce the sensing latency by 90%. In a realistic setting, GreenTag achieves a 90-percentile moisture estimation errors of 5%, which is comparable to the 4% errors using expensive soil moisture sensors. Moreover, this accuracy is maintained despite changes in the RF environment and container locations. We also show the effectiveness of GreenTag in a real greenhouse.

THE ACCEPTABLE COMPACTION ZONE CRITERIA BASED ON HYDRAULIC CONDUCTIVITY FOR A COMPACTED MIXTURE OF RESIDUAL SOIL WITH BENTONITE AS A SOIL LINER

The significant criteria for compacted clay liner in landfill construction are the compaction characteristics and hydraulic conductivity value. The primary parameter for designing an efficient soil liner is ensuring a hydraulic conductivity value below 1x10-9 m/s to prevent contaminated seepage into the aquifer. Therefore, the compaction criteria are crucial in controlling the construction of hydraulic barriers for liners. In this study, laboratory testing such as permeability tests at varying compaction energies, were carried out on a blend of residual soil and bentonite. The aim was to establish an acceptable compaction zone based on the allowable hydraulic conductivity value and to outline suitable design considerations for compacted residual soil mixed with bentonite as a soil liner. The design parameters investigated included hydraulic conductivity at different compaction efforts. A comparison was made between traditional, common, and modern approaches to ensure construction quality assurance for compacted soil liners mixed with bentonite. The results indicated that all methods, traditional, common, and modern, successfully met the design objectives for hydraulic conductivity, creating acceptable compaction zones on the compaction plane curve. These acceptable zones for the mixture of residual soil with bentonite align with the lower bound limit of the compaction optimum line, with the shapes of the shaded zones influenced by the soil's moisture content and compaction characteristics.

Machine Learning for Prediction and Risk Assessment of Landslides

Landslides pose a serious threat to human life and can cause substantial economic losses. It also consumes a lot of time and energy to deal with landslides. In order to address the occurrence of landslides, it is important to predict the probability of land- slides and assess the risk level. Since the occurrence of landslides is based on many factors, it is impossible for people to make an accurate judgment. Therefore, it is the best choice to train machines to help people to make predictions and risk assessment. In this paper, we discuss how to predict the occurrence of land- slides through random forests and assess the risk level according to the water content of the soil. The results show that the accuracy of prediction by Random Forest is very high. We will also predict future changes in soil moisture content to update the risk level assessment for real-time monitoring.