Soil Texture Research Articles

A binary prediction model to assess the oxidation of Gley Lowland soils in Japan

ABSTRACT This study aims to investigate the environmental conditions that promote oxidation in over-wet paddy fields in Japan and to discuss their predictability. The vertical lowering of Gley horizons in soil profiles (Gley horizon lowering), mainly due to groundwater drainage and paddy-upland rotation, is frequently observed in Japanese paddy fields. This results in changes in soil type (e.g. from Gley Lowland soils to Gray Lowland soils) and decreases the reliability of conventional soil maps. Updating these maps to reflect the current distribution of Gley Lowland soils requires numerous new field surveys, which are costly and time-consuming. Therefore, to facilitate efficient and data-driven soil map updates, it is essential to clarify and predict the environments where Gley Lowland soils are likely to change. To achieve this, a dataset indicating Gley horizon lowering was constructed by comparing 1,744 points from recent soil inventory surveys with past soil distributions according to the 1:50,000 comprehensive soil map (legacy soil map). In addition, local land use records and the environmental information from the open-sourced maps, such as surface and subsurface soil texture, landform, geology, and climate, were added to each soil survey result as soil formation factors related to dry/wet conditions. Using this dataset, we discussed the environmental factors that promote the Gley horizon lowering and constructed the binary classification model designed to estimate the Gley horizon lowering with the XGBoost algorithm. Our results suggested that the Gley horizon lowering was particularly influenced by the rate of paddy use and annual precipitation and was regulated under a topographical environment where wet conditions are retained. The obtained model successfully learned these pedogenic trends and was characterized by high precision for the test data. This model would contribute to the practical use for assessing the change of dry/wet conditions of Gley Lowland soils, providing conservative and reliable information to reduce the effort involved in new field surveys in soil map updating. In conclusion, our results demonstrated the feasibility of constructing a machine-learning model to support soil map updating by combining open GIS data and newly collected field survey results.

Assessing heavy metal contamination in agricultural soils: a new GIS-based Probabilistic Pollution Index (PPI) – case study: Guarda Region, Portugal

ABSTRACT Soil contamination by heavy metals is a global agricultural problem, as these elements can be absorbed by plants and transferred to humans through the food pathway. Current contamination assessment methods rely on in situ sample collection, which restricts the evaluation process due to the number of samples that can be analysed and the associated costs. This study addresses the need for a more efficient and cost-effective approach to identifying areas at risk of heavy metal contamination without the logistical constraints of physical sampling. To meet this challenge, we developed a new Probabilistic Pollution Index (PPI), calculated by integrating GIS tools with an 8-parameter probability-risk matrix to identify agricultural areas potentially contaminated by heavy metals. The factors considered included roads, industrial sites, pH levels, soil organic matter content, terrain slope, soil texture, mining areas, and drainage. Each parameter was classified and reclassified to produce a contamination risk map, categorizing each pixel into five levels of risk. To test the PPI, data analysis, parameter classification, and reclassification were applied in the district of Guarda, Portugal. The PPI map revealed that the central portion of the Guarda municipality, along with specific zones in Celorico da Beira, Sabugal, Mêda, and Pinhel, exhibited a high-probability risk of contamination. Given the agricultural expanse within each municipality, enhanced monitoring of heavy metal levels was recommended for Mêda, Pinhel, and Vila Nova de Foz Côa. This index provides a scalable, cost-effective, and easily replicable tool for identifying potential contamination hotspots and the sources and processes of contamination. Designed as a first-line method for large-scale assessments, it supports governmental decision-making and facilitates targeted risk mitigation strategies through a low-cost approach.

Organic Fertilization Leads to N Limitation Rather than P Limitation in Both Vegetable Soils

Organic amendments are widely used to enhance soil fertility and nutrient cycling in greenhouse cultivation, but their effectiveness can vary depending on their origin and composition. This study investigated the impact of four organic materials (rice husk, coconut coir, biochar, and sheep manure) on nutrient cycling and enzyme activities in two of greenhouse tomato soils. The distribution of soil nutrients and enzyme activities was analyzed under controlled conditions during a pot experiment. The addition of organic amendments, regardless of their source, significantly altered the microbial resource allocation, reducing the carbon limitation while increasing the nitrogen demand. The effect on soil nutrient pools was largely determined by the chemical composition of the amendments. In clayey soils, biochar and rice husk additions most effectively promoted enzyme activities related to carbon, nitrogen, and phosphorus acquisition. Under sandy soil conditions, sheep manure increased carbon and phosphorus acquisition enzymes, while biochar most effectively enhanced nitrogen acquisition enzymes. Biochar emerged as a particularly effective amendment, enhancing organic carbon sequestration across different soil types. The chemical composition of the amendments, specifically, the content of carboxyl C, aromatic C, and O-alkyl C, played a crucial role in influencing soil nutrient limitations. In clayey soils, the mean C:N:P ratios for CK, T1, T2, T3, and T4 treatments were 1:1.375:0.625, 1:1.244:0.662, 1:0.839:0.610, 1:1.161:0.689, and 1:1.038:0.549, respectively. In sandy soils, the ratios were 1:1.117:0.698, 1:1.18:0.75, 1:1.096:0.731, 1:1.217:0.689, and 1:1.06:0.669, respectively. These findings suggest that the addition of organic amendments can improve nutrient retention and enzyme activities, but their effects on soil nutrient pools are influenced by both the composition of the amendments and the soil texture. This research enhances our understanding of organic amendments and soil nutrient transformations, and aids in optimizing the application of organic materials for improved soil management in greenhouse cultivation.

Dehydrogenase: A key soil health indicator for Thar Desert, India

A field study was conducted to identify a potential fertility indicator for the soils of the Thar Desert. The study area included eight districts, covering a total of 156,580 km². This region experiences a wide range of climatic conditions, with annual rainfall varying from 177 mm to 409 mm and temperatures fluctuating between 8°C and 46°C. Surface soil samples (0-10 cm depth) were collected from agricultural fields across the region, representing various soil properties and cropping patterns. The soil texture varied from sandy loam to loamy sand, and the wet colour ranged from dark reddish-brown to dark yellowish-brown. The physicochemical and biological properties of the soil samples from different areas of the Thar Desert were as follows: moisture content ranged from 2.19% to 8.73%, bulk density from 1.18 to 1.33 Mg/m³, particle density from 1.82 to 4.11 Mg/m³, pore space percentage from 26.74% to 68.53%, solid space percentage from 31.47% to 73.26%, pH values from 7.69 to 8.43, and electrical conductivity from 0.12 to 0.17 dS/m. Furthermore, the soil organic carbon content ranged from 0.82% to 1.21%, while organic matter content varied between 1.41% and 2.09%. The available nitrogen was found to be between 285.69 and 365.87 kg/ha, phosphorus ranged from 19.84 to 24.77 kg/ha, potassium levels ranged from 214.29 to 314.72 kg/ha, and sulfur levels varied between 16.08 and 23.62 ppm. Additionally, nitrogenase retention time was recorded at 1.391 to 1.547 minutes, phosphatase activity ranged from 269.44 to 343.15 µg p-nitrophenol g⁻¹ h⁻¹, and dehydrogenase enzyme activity ranged from 250.33 to 309.34 µg TPF/g/24 h. The results demonstrated that soil properties varied across the Thar Desert. This study provided valuable insights into the physicochemical and biological characteristics of the soil in the Thar Desert of Rajasthan, India. Notably, a significant positive correlation (r² value of 0.95) was found between dehydrogenase enzyme activity and various soil fertility parameters, suggesting that dehydrogenase could serve as a potential biological indicator of soil fertility.

Delineation of potential management zones for cowpea by factorial kriging and spatial Fuzzy C-Means of yield and soil chemical and texture properties

ABSTRACT In precision agriculture, accurate delineation of management zones and understanding spatial variability of soil properties and crop yields are critical for optimizing resource allocation and improving productivity. Spatial variability of different environmental factors (soil and plants) is evident in several studies. Associations between the texture and chemical properties of the soil and cowpea yield have been tested, but a large, unexplained variance of ranges between kriged maps is usually reported. This suggests that a deeper exploration into the soil properties of these spatial interactions may help develop our understanding on how to reduce the number of soil property maps to delineate management zones and simplify interpretation. The main objective of this study was to investigate whether factorial kriging analysis can be used as an auxiliar variable to cokriging of soil properties and cowpea yield, and what is the potential of Spatial Fuzzy c-Means associated with factorial kriging analysis to delineate management zones. This study employed factor maps and spatial clustering to classify the cowpea field in management zones based on a multivariate and geostatistical analysis using soil texture and chemical properties. From Farmer, 66 soil samples were collected at a layer of 0.00-0.20 m, at points with a regular spacing of 12 m, at Agropecuária Milênio in the municipality of Tracuateua, Pará State, to make the technology applicable to the most common data available to farmers. It also used Spatial Fuzzy c-Means to generate estimated maps. Only the kriged maps of soil properties were inefficient in delineating management zones. Factor maps and Spatial Fuzzy c-Means were efficient in delineating the two management zones. Factorial kriging analysis can be used in cokriging to estimate soil properties and the cowpea field. The proposed method is a practical tool to delineate management zones, performing better and more efficiently compared with soil multiple property maps. The optimal number of management zones for cowpea cultivation was determined to be two. This encompasses soil management, yield considerations, and site-specific choices, all aimed at mitigating the impacts of precision agriculture on high productivity.

Forest land-use change affects soil organic carbon in tropical dry forests of the Peruvian Amazon

Aim of study: The loss of forest cover is a global problem that alters ecosystems, contributing to carbon emissions. This study measured the soil organic carbon (SOC) at different soil depths in tropical dry forests of the Huallaga Central in the Peruvian Amazon. Area of study: San Martín Region, Peruvian Amazon. Material and methods: A total of 24 plots of 100 m2 were selected in primary (~200 years), intervened (~50 years since intervention), and deforested forests (10 years ago), with 120 soil samples collected across five depths. Soil texture (hydrometer), bulk density (cylinder method), SOC content, SOC density, and erodibility (K parameter) were calculated. Main results: SOC content in the 0-20 cm soil horizon was 79.5±21.3 t ha-1 for the primary forest, 58.5±11.8 t ha-1 for the intervened forest, and 41.8±10 t ha-1 for the deforested forest. A soil erodibility K of 0.065 was observed for primary forests and 0.076 and 0.093 for intervened and deforested forests. In average, the SOC density obtained in this study was 7.6±5.1 t ha-1 in the primary forest, 6.2±3.6 t ha-1 in the intervened forest, and 4.7±2.7 in the deforested forest. Research highlights: Primary forests had the highest SOC content and SOC density, followed by intervened and deforested forests, while the opposite pattern was found for soil erodibility. These patterns were especially marked in the first 40 cm of soil depth.

Investigating the Effect of Soil Texture on Dielectric Properties of Soil by Using Square Patch Sensor

Investigating the Effect of Soil Texture on Dielectric Properties of Soil by Using Square Patch Sensor

Soil Residual Activity of Tetflupyrolimet and the Influence of Soil Moisture and Flood Timeliness on Barnyardgrass Efficacy

ABSTRACT Tetflupyrolimet is the first herbicide with a novel site of action to be commercialized for use in agronomic crops in three decades. Direct-seed rice field experiments were conducted at research facilities near Stuttgart (silt loam), AR, and Keiser (clay), AR, to evaluate tetflupyrolimet as a preemergence herbicide versus commercial standards. Greenhouse experiments determined the influence of soil moisture on pre- and postemergence (POST) barnyardgrass control with tetflupyrolimet and clomazone, and the impact of a delayed flood on efficacy when POST-applied. For the field experiments, clomazone, tetflupyrolimet, and quinclorac were applied individually PRE at 336 and 560, 134 and 224, and 336 and 560 g ai ha-1, respectively, on a silt loam and clay soil, along with clomazone plus tetflupyrolimet and clomazone plus quinclorac at the same rates. The soil moisture experiment included a single PRE and POST application of clomazone at 336 g ai ha-1, tetflupyrolimet at 134 g ai ha-1, and a mixture at the respective rates, on a silt loam soil at 50, 75, and 100% of field capacity. For the flood timing experiment, tetflupyrolimet was applied to 2- to 3-leaf barnyardgrass at 134 g ai ha-1, and a flood was established at 4 hr after treatment (HAT) and 5 and 10 d after treatment (DAT). Barnyardgrass control with a tetflupyrolimet and clomazone mixture was comparable to clomazone plus quinclorac when averaged over all evaluations on silt loam and clay texture soils (≥91%). Soil moisture interacted with herbicide treatments for PRE and POST barnyardgrass efficacy when averaged over DAT, with tetflupyrolimet plus clomazone generally providing the greatest and most consistent control across regimes. Flooding barnyardgrass at 4 HAT provided superior control to later flood timings. Tetflupyrolimet is an effective residual barnyardgrass herbicide, and the addition of clomazone will aid in providing consistent control across varying soil moisture conditions.

Effects of a chemical dispersant on the dispersibility of Moso bamboo phytoliths in a particle-size analysis

ABSTRACT Phytoliths produced by higher plants are biogenic minerals with a wide range of particle sizes that correspond to clay, silt, and sand in mineral soil. Phytoliths supplied to soils through plants’ death function as soil particles and affect soil physicochemical properties. Investigations of the functions of phytolith particles as a subset of soil particles are facilitated by fractionation and quantification of the phytoliths based on soil particle-size distribution. Fractionation is typically conducted through particle-size analysis (PSA) methods developed for soil texture analyses. However, the effects of chemicals used to achieve particle dispersion in these methods on phytolith dispersibility remain poorly understood. The objective of this study was to examine the effects of chemical dispersants on phytolith dispersibility. Based on the results, several recommendations are provided with respect to the dispersal conditions. The phytoliths used in this study were extracted from Moso bamboo leaf litter through wet digestion and dispersed by ultrasound followed by two chemical procedures: pH control using hydrochloric acid or sodium hydroxide and the addition of sodium hexametaphosphate as a dispersant. The extent of dispersion was determined by quantifying the clay-sized fraction (<2 µm) through gravity sedimentation and the pipette method. The results showed that the content of clay-sized phytoliths in dispersions remained constant from pH 3 to 9, then increased significantly at pH 10–11, likely due to phytolith dissolution, which also led to overestimation of the clay-sized fraction. This finding demonstrated that dispersion at higher pH (>10), as is often the case in PSA methods for soils, is unsuitable for phytoliths; instead, soil particles should be dispersed at pH < 9 to avoid phytolith dissolution.

Assessing the Impacts of Coal Mining on Soil Quality in Kogi East, Nigeria

This study investigates the effects of coal mining on soil physical and chemical properties in Kogi East, Nigeria, focusing on two mined sites (Ika-Ogboyaga and Okaba) and one unmined control site (Abache). Soil samples were collected from 32 plots across mined areas and 16 plots in the unmined site using a 200m x 200m belt transect method. Samples were analyzed for particle size, bulk density, soil pH, organic matter, nitrogen, phosphorus, exchangeable cations, and heavy metal concentrations using standard laboratory methods. Results show minimal differences in soil texture (sandy clay loam) across all sites, with slightly higher bulk density in mined areas (1.89±0.2g/cm3, 1.87±0.6 g/cm3), indicating soil compaction from mining activities. Chemical properties, including pH, organic carbon, nitrogen, and cation exchange capacity, showed minor variations, while heavy metal concentrations (Zn, Cu, Fe, Pb) were within safe limits across all sites. Analysis of Variance (ANOVA) (F-value=0.00449; F-crit=3.1907; P-value=0.995511) showed no significant differences in most soil properties between mined and unmined areas, suggesting limited impact of coal mining on soil quality in the region. To improve soil health and sustainability the study recommended mitigating soil compaction, implementing vegetation rehabilitation with native species, conducting continuous soil monitoring, raising community awareness, and promoting sustainable mining practices.

Characteristics and Influencing Factors of Soil Particulate and Mineral-associated Organic Carbon in Cropland Soil across Sichuan Basin

Exploring the composition of regional soil organic carbon （SOC） components and identifying their influencing factors are of utmost importance to deeply understand the potential mechanisms of SOC change in cropland soil. Based on data from 871 soil sampling points, this study explored the characteristics of soil particulate and mineral-associated organic carbon （POC and MAOC） in the surface soil of cropland and the relationships with climate, terrain, soil texture, agricultural land-use type, and fertilization across the Sichuan basin using analysis of variance, correlation analysis, and a random forest model. The results showed that the average content of POC and MAOC in the surface soil of cropland was 5.01 g·kg-1 and 9.79 g·kg-1, respectively, accounting for 32.5% and 67.5% of the total SOC； the proportion of POC increased with SOC. The two SOC fractions showed a similar spatial distribution pattern, with the content being higher in peripheral areas and lower in the middle of the basin, which were determined together by structural and random factors. The results of the random forest model showed that the relative importance of agricultural land-use type was much more important than other factors. NPP, mean annual temperature, and soil texture showed the largest correlations with the two SOC fractions in dryland； mean annual temperature and slope had larger correlations with POC and mean annual temperature and soil texture in the dryland-paddy rotation land； and soil texture was the most relevant factor to POC and MAOC in the paddy field. The above results suggested that MAOC was the main component of SOC, and agricultural land-use type was the dominant control and regulated the effects of other factors on the two SOC fractions.

Azotobacter biodiversity in Egypt using microbiological, biochemical, and molecular-biology multidisciplinary approach.

The presence of Azotobacter bacteria in the soil plays an important role in increasing its fertility and enhancing plant health. Azotobacter diversity depends on several environmental factors, particularly soil texture, pH, and nutrient content. The current study investigated the diversity of Azotobacter in various soil samples collected from 10 different governorates along the river Nile valley and its delta, Northern Mediterranean shore, Sinai, and Upper Egypt regions. The sampling sites spanned different environmental and ecological conditions of the Egyptian land either cultivated (agricultural land) or uncultivated (desert land). Fifty Azotobacter isolates were isolated and characterized based on cell morphology, culture properties, physiological, biochemical, and molecular characteristics. In addition, the alginate production capacity of the isolates was investigated. The results indicated that Egyptian soils are rich in Azotobacter diversity. The isolates were Gram-negative short rods, appearing either as single cells or in diploid structures. The isolates showed high variability in alginate production where two isolates (BH3 and AST4) were the highest alginate producers (3.12 and 4.22g alginate L- 1), respectively. 16S-rDNA sequencing and 16S-rDNA RFLP analyses indicated that despite the presence of Azotobacter salinestris and Azotobacter vinelandii in the Egyptian soil, Azotobacter chroococcum was the predominant species. In addition, sequence analysis of the gene coding for the transcription factor AlgU confirmed the results of 16S-rRNA gene sequence analysis. RAPD-REP and BOX-PCR were used to study the polymorphism among the isolates. High levels of microbial diversity were found using these DNA primers as 6-9 fingerprinting profiles were retrieved.

Soil erodibility properties experimental quantification: a meta-analysis.

The focus in the present study is on the quantification soil erodibility properties (representing an erosion threshold (such as the critical shear stress) and a resistance property (e.g., the soil erosion coefficient)). These are necessary for an adequate assessment of soil erosion mechanisms affecting earth-made hydraulic structures (e.g., dams, dykes and levees). The paper gives a quantitative statistical analysis of the aforementioned soil erodibility parameters. To do so, a wide range of experimental tests, used for the study of internal erosion (hole erosion test (HET) and slot erosion test (SET)) and surface erosion (jet erosion test (JET), flume test (FT), erosion function apparatus (EFA), and rotating cylinder test (RCT)), were examined. A dataset of previously published experimental data was collected, harmonized, structured, treated, and used in a multicriteria analysis. The outcomes of the study provide a better understanding of the limitations and ambiguity in the assessment soil erodibility properties, highlight the differences among tests and between processes, and assess their inter-useability. Correlations between the erodibility properties themselves and between each of them and an in-study-defined midrange soil texture diameters were evaluated at specific level of erosion process, erosion test, and/or soil texture. Furthermore, a set of new empirical formulas has been proposed linking soil erodibility properties to themselves or each erodibility properties to the midrange soil texture diameter. A set of reference values and ranges and trends for the studied erodibility properties, useful for design or risk assessment purposes or for evaluating the quality of experimental data, are derived.

Predicting Soil Salinity Based on Soil/Water Extracts in a Semi-Arid Region of Morocco

Soil salinity is a major constraint to soil health and crop productivity, especially in arid and semi-arid regions. The most accurate measurement of soil salinity is considered to be the electrical conductivity of saturated soil extracts (ECe). Because this method is labor-intensive, it is unsuitable for routine analysis in large soil sampling campaigns. This study aimed to identify the best models to estimate soil salinity based on ECe in relation to a rapid electrical conductivity (EC) measurement in soil/water (referred to as S:W henceforward) extracts. We evaluated the relationship between ECe and the ECS:W extract ratios (1:1, 1:2, and 1:5) in salt-affected soils from the semi-arid Sehb El Masjoune region of Morocco. The soil salinity in this region is 0.5 to 235 dS/m, as determined by the ECe method. A total of 125 soil samples, from topsoil (0–15 cm) and subsoil (15–30 cm) with mainly fine to medium textures, were analyzed using linear, logarithmic, and second-order polynomial regression models. The models included all samples or grouped samples according to soil texture (fine, medium) or specific textural classes. The mean ECe values were 2.6, 3.1, and 7.9 times greater than the EC of 1:1, 1:2, and 1:5 S:W extracts, respectively. Polynomial regression models had the best predictive accuracy, R2 = 0.98, and the lowest root mean square error of 10.6 to 10.7 dS/m for the ECS:W extract ratios of 1:5 and 1:2. The polynomial models could represent the non-linear relationships between ECe and salinity indicators, especially in the 80–170 dS/m salinity range, where other models typically underestimate the salinity. These results confirm that advanced regression techniques are suitable for predicting soil salinity in a salt-affected semi-arid region. The site-specific models outperformed previously published models, because they consider the spatial variability and heterogeneity of the salinity in the study area explicitly. This confirms the importance of calibrating soil salinity models according to the local soil and environmental conditions. Consequently, we can undertake soil salinity assessments in hundreds of samples by using the simple, rapid ECS:W extraction method as a direct indicator of EC and extrapolate to ECe with a polynomial regression model. Our approach enables the widespread soil salinity assessments that are needed for land-use planning, irrigation management, and crop selection in salt-affected landscapes.

Research on the relationship between the density of Anatolian ground squirrel (Spermophilus xanthoprymnus Bennet, 1835) nest burrows and soil texture

This study examined the correlation between the nest density of the Anatolian ground squirrel (Spermophilus xanthoprymnus) and the characteristics of soil texture and stoniness. This study was conducted in Gençali plain, Isparta province, Türkiye. A mechanical analysis of the soil revealed that clay content varied from 24% to 51%, sand content ranged from 33% to 55%, and dust levels were between 13% and 28%. It was observed that S. xanthoprymnus exhibited a preference for loamy clay soils, with an occurrence rate of 82% in the sampled areas. The analysis indicated that the presence of stones in the soil had a statistically significant positive effect (p&lt;0.01) on the species' nest site selection, while the amount of sand had a negative impact (p&lt;0.05). Nest density increased in habitats with stoniness levels between 9% and 9.5%, whereas a decrease in nest numbers was recorded in areas with stoniness levels either below or above this range. Key words: Anatolian ground squirrel, Spermophilus xanthoprymnus, ecology, nest density, soil texture

From refuse to resource: exploring technological and economic dimensions of waste‐to‐energy

AbstractWaste valorization offers a sustainable approach to waste management and the generation of biofuels and bioenergy, mitigating the environmental impact of fossil fuel production and use. Carbon emissions and food‐source competition associated with first‐generation biofuels production can be mitigated. Reports on thermochemical and biochemical pathways for generating bio‐oil and syngas provide extensive coverage of production processes but lack recent insights into technological advances for upgrading these outputs into biodiesel, biomethanol, bioethanol, biogas, and biomethane. They also often omit information on commercial status, leading and prospective companies, market size, future market predictions, and associated challenges. The drawbacks of waste valorization do not appear to have been discussed widely in the literature – they include deforestation, land‐use changes, soil texture changes, and biodiversity and social impacts. To address these gaps in the literature, this review examines the factors mentioned above, focusing on the drawbacks of waste‐to‐energy conversion and proposing solutions targeted at governments, policymakers, investors, entrepreneurs, and researchers seeking to understand and mitigate the challenges in this sector.

Enhancing Soil Moisture Prediction in Drought-Prone Agricultural Regions Using Remote Sensing and Machine Learning Approaches

The North China Plain is a crucial agricultural region in China, but irregular precipitation patterns have led to significant water shortages. To address this, analyzing the high-resolution dynamics of root-zone soil moisture transport is essential for optimizing irrigation strategies and improving water resource efficiency. The Richards equation is a robust model for describing soil moisture transport dynamics across multiple soil layers, yet its application at large spatial scales is hindered by its sensitivity to boundary conditions and model parameters. This study introduces a novel approach that, for the first time, employs a continuous time series of near-surface soil moisture as the upper boundary condition in the Richards equation to estimate high-resolution root-zone soil moisture in the North China Plain, thus enabling its large-scale application. Singular spectrum analysis (SSA) was first applied to reconstruct site-specific time series, filling in missing and singular values. Leveraging observational data from 617 monitoring sites across the North China Plain and multiple spatial covariates, we developed a machine learning model to estimate near-surface soil moisture at a 1 km resolution. This high-resolution, continuous near-surface soil moisture series then served as the upper boundary condition for the Richards equation, facilitating the estimation of root-zone soil moisture across the region. The results indicated that the machine learning model achieved a correlation coefficient (R) of 0.92 for estimating spatial near-surface soil moisture. Analysis of spatial covariates showed that atmospheric forcing factors, particularly temperature and evaporation, had the most substantial impact on model performance, followed by static factors such as latitude, longitude, and soil texture. With a continuous time series of near-surface soil moisture, the Richards equation method accurately predicted multi-layer soil moisture and demonstrated its applicability for large-scale spatial use. The model yielded R values of 0.97, 0.78, 0.618, and 0.43, with RMSEs of 0.024, 0.06, 0.08, and 0.11, respectively, for soil layers at depths of 10 cm, 20 cm, 40 cm, and 100 cm across the North China Plain.

The Impact of Implementing Agarwood Agroforestry System (Aquilaria malacensis Lamk.) on Soil Texture, Topsoil Depth, and Water Retention Capacity in Oil Palm Lands (Elaeis guineensis Jacq.)

The impact of implementing an agroforestry system in oil palm plantations by integrating agarwood-producing plants (Aquilaria Malacensis Lamk.) with oil palm plants on the physical properties of the soil needs to be assessed. This study is crucial due to the shared land use by agarwood-producing and oil palm plants. The study aims to evaluate the effects of integrating agarwood-producing plants into oil palm plantations on soil physical properties, including soil texture, topsoil depth, and water retention capacity. The research was conducted from September 2024 to November 2024 in agroforestry land (1 ha) and monoculture land (1 ha) in Bukit Kemuning Village, Tapung Hulu Kampar - Riau. The study utilized survey methods and systematic sampling for soil sample collection. The findings indicated that the agroforestry system positively impacted soil physical properties, with improved soil texture (54.67% sandy clay), increased topsoil depth, and enhanced water retention capacity compared to oil palm monoculture land. This improvement can be attributed to the higher organic matter content resulting from the decomposition of agarwood plant litter. Therefore, the agroforestry system enhances soil fertility and promotes sustainable environmental management.

Benchmarking Measures for the Adaptation of New Irrigation Solutions for Small Farms in Egypt

The aim of this study is to construct and validate an expert system to predict the adaptation of irrigation technologies, water-saving strategies, and monitoring tools by small-scale farmers in Egypt. The research investigates the impact of economic, educational, environmental, and social factors on adaptation rates. To build the expert system, extensive knowledge was collected from experts, key concepts were identified, and production rules were created to generate tailored scenarios. These scenarios utilize the empirical cumulative distribution function (ECDF), selecting the scenario with the highest ECDF as the optimal irrigation technology. This approach ensures well-informed, data-driven decisions that are tailored to specific conditions. The expert system was evaluated under the conditions of ten small farms in Egypt. The results indicate that water cost and availability are significant drivers of technology adaptation. Specifically, subsurface drip irrigation (SDI) demonstrated an adaptation percentage of 75% at high water costs, with probabilities of 0.67 and 0.33, while soil mulching (SM) showed a 75% adaptation rate with a probability of 0.33 in high-cost scenarios. Conversely, when water availability was high, the adaptation percentage for all techniques was zero, but it reached 100% adaptation with a probability of 0.76 for SM and SDI and a probability of 1 for variable number of drippers (VND) and the use of sensors as monitoring tools during water shortages. Educational attainment and professional networks enhance the adaptation of advanced technologies and monitoring tools, emphasizing the role of knowledge and community engagement. Environmental conditions, including soil texture and salinity levels, directly affect the choice of irrigation methods and water-saving practices, highlighting the need for localized solutions. The source of irrigation water, whether groundwater or surface water, influences the preference for water-saving technologies. The study underscores the importance of tailored approaches to address the challenges and opportunities faced by small farmers in Egypt, promoting sustainable agriculture and efficient water management. The evaluation findings reveal that SDI is the most favored irrigation technology, with a probability of 0.55, followed by variable number of drippers (VND) at 0.38 and ultralow drip irrigation (ULDI) at 0.07 across various scenarios for small farmers. Regulated deficit irrigation (RDI) and SM are equally preferred water-saving strategies, each with a probability of 0.50. Sensors emerged as the preferred monitoring tool, boasting a high probability of 0.94. The analysis reveals the critical roles of economic pressures, educational levels, environmental conditions, and social networks in shaping the adaptation of sustainable agricultural practices.

The Spatial Land Suitability Evaluation for Corn (Zea mays L) Crops in Patilanggio District, Pohuwato Regency, Gorontalo Province

Agricultural land in Gorontalo had 60% suitable soil texture for maize crops. This shows the importance of agronomic interventions to support the optimization of maize production. The purpose of this study was 1) To assess the level of land suitability for corn cultivation in Patilanggio District; 2) To conduct a spatial analysis (map) of land suitability for corn cultivation in Patilanggio District; 3) To formulate strategies for sustainable corn land management in Patilanggio District. This research was conducted in one district, namely Patilanggio, located in Pohuwato Regency, which is known as the corn cultivation base area. The research method used site survey and soil sampling using a soil ring sampler, then tested and analyzed at the UGM Geography Soil Laboratory. The stages of GIS data analysis and synthesis include: (1) on-screen digitization; (2) overlaying tentative maps. Data analysis for land suitability was conducted using LCLP (Land Classification and Land Use Planning) software. The results showed that the level of land suitability varied, requiring agronomic interventions to improve land suitability. This level of land suitability indicates that Patilanggio District has considerable potential for corn cultivation. Spatial land suitability maps provide an overview of potential areas for corn development. Soil conservation-based land management strategies need to be implemented to maintain productivity and ecosystem sustainability, including crop rotation, balanced fertilization, and the use of adaptive high-yielding varieties.