Search Results (651)

Organic fertilization is a key strategy for improving soil structure and fertility in China’s Vertisols, yet the trade-offs among soil quality enhancement, grain yield performance, and nutrient use efficiency under different organic amendment regimes remain insufficiently elucidated. Based on a unique 43-year field fertilization experiment, this study systematically evaluated the effects of long-term chemical fertilization (NPK) alone, low-dose (NPKLS) and high-dose straw incorporation (NPKHS), combined chemical fertilizer with cattle manure (NPKCM), and pig manure (NPKPM) fertilization on soil physical, chemical properties, crop yields and plant nutrient utilization efficiency. The results showed that NPKCM and NPKPM significantly improved soil physical properties by reducing soil bulk density, improving soil pore structure, and enhancing soil water retention capacity and saturated hydraulic conductivity. Although long-term manure application led to slight soil salt accumulation, the rate of accumulation remained substantially lower than that associated with commercial organic fertilizers and did not approach the crop salinity damage threshold, suggesting low ecological risk. Compared with NPK treatment, manure amendment effectively counteracted soil acidification induced by prolonged chemical fertilization, while also significantly increasing soil total phosphorus and available phosphorus content, and elevated the proportion of active phosphorus (PAC). The improved soil phosphorus activation capacity and comprehensive soil quality further contributed to substantial increases in wheat grain yield under NPKCM and NPKPM treatments. Despite these agronomic benefits, the additional nitrogen and phosphorus inputs from manure resulted in soil nutrient surpluses, which considerably reduced nitrogen and phosphorus partial factor productivity as well as agronomic efficiency. In contrast, straw incorporation treatments (NPKLS, NPKHS) sustained stable crop yield without notable declines in nutrient efficiency, positioning them as a greener and more sustainable approach to balancing grain production with resource use efficiency. These findings highlight the need to integrate nutrient credits from manure into fertilization program. Given the 43-year evidence, fertilization strategy should consider not only the nutrients supplied by manure but also the quantities exported through harvested products, with adjustments based on annual soil fertility analyses. Such nutrient budgeting is essential to maximize fertilizer use efficiency, prevent excessive phosphorus accumulation, and maintain balanced soil fertility over time. Full article

This study investigates the macromolecular interaction mechanisms between linear-anionic xanthan gum (XG) and branched-nonionic guar gum (GG) biopolymers in three mineralogically distinct soils: Bentonite Clay (BC), Zeolite Silty Soil (ZS), and Red Clay (RC). Mıxtures were prepared by dry mixing of soil powders with biopolymer powders at designated ratios (0%, 1%, 2%, 3%, and 4% by dry weight). The prepared mixtures were characterized using X-Ray Diffraction (XRD), X-Ray Fluorescence (XRF), Scanning Electron Microscopy (SEM), and standard compaction and shear strength tests. The results show that geotechnical macro-behavior is primarily influenced by polymer chain conformation and mineral interfacial reactions. In ZS-XG mixture, hydraulic conductivity increased approximately 26-fold (from 0.107 × 10⁻⁹ to 2.83 × 10⁻⁹ m/s), a phenomenon attributed to the Donnan electrostatic exclusion effect, where linear anionic XG chains repel zeolite surfaces and generate low-friction macro-flow paths. Conversely, the addition of GG to RC formed a strongly interconnected hydrogel network through hydrogen bonding with trivalent iron and magnesium oxides, resulting in a 20.8% increase in cohesion (up to 70.05 kPa). In contrast, GG addition decreased cohesion in BC and ZS. These findings confirm that sustainable biopolymer-based soil remediation depends on customizing the polymer morphology according to the properties of the soil. In engineering applications, ZS-XG mixtures should be evaluated for drainage projects requiring high permeability, whereas the RC-GG4 mixture should be considered a primary option for infiltration barriers (e.g., landfill liners) requiring low permeability and high cohesion. Full article

When comparing soil water sensors installed in close proximity, observed differences in readings may reflect spatial heterogeneity in soil hydraulic properties, intrinsic sensor variability, or both. Partitioning sources of variability is essential for informed sensor selection and placement. This study used variogram analysis to quantify small-scale (9 m) soil water variability at three irrigated corn sites in southeastern North Dakota during the 2022 and 2023 growing seasons. Volumetric water content (θ_v) was measured along transects parallel and perpendicular to the crop rows using an Acclima TDR-310H sensor and contrasted with previously deployed sets of different types of soil water sensors. Descriptive statistics of the collected θ_v data revealed variability within transects, with mean θ_v ranging from 0.19 to 0.24 cm³ cm⁻³. Directional variograms showed anisotropic spatial structures, where perpendicular transects exhibited a higher nugget effect than parallel transects (square roots of nugget 0.019–0.030 cm³ cm⁻³ vs. 0.012–0.022 cm³ cm⁻³, respectively), likely due to stem flow patterns along corn rows. Similarly, sill and range parameters were also higher for perpendicular than for parallel transects. Comparison of variogram-derived variances with sensor-to-sensor differences indicated that the observed variability arises from both spatial heterogeneity and sensor type. For sensor comparison studies, these results demonstrate that sensors installed parallel to crop rows are less affected by spatial variability compared with sensors installed perpendicular to crop rows and that such installation minimizes confounding factors of spatial heterogeneities, while demonstrating the broader use of variogram analysis for partitioning measurement variability. Full article

We present a global metadata comprising results from studies investigating the effects of cover crops (CCs) on six key soil hydraulic properties, namely total porosity, infiltration rate, saturated hydraulic conductivity, water retention at field capacity and permanent wilting points, and available water holding capacity. This data repository is the result of a global meta-analysis entitled “Cover Crop Performance and Functional Groups Regulate Improvements in Soil Hydrology: A Global Meta-analysis”. Globally, numerous studies have investigated the role of CCs on soil hydraulic properties, but the results have varied across sites and years. Hence, the objective of the meta-analysis was to synthesize the existing knowledge base to assess the overall effects of CCs on these soil hydraulic properties and evaluate how environmental and management factors moderate these overall CC responses. We searched for peer-reviewed research articles published through 5 October 2024 in the ISI Web of Science database, with reference checking following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 146 relevant articles were identified from which data on CC responses were extracted. The metadata consists of 1007 pairwise observations comparing CC vs. no-CC controls across diverse geographic regions worldwide. Moreover, we collected associated metadata for each pairwise comparison that includes a broad set of bibliographic, geographic, soil, climate, and management variables. Categorical variables were grouped into pre-defined factor levels or classes. Missing soil and climate data were filled using publicly available data products. Our data repository can be a valuable resource for the field and modeling community to identify knowledge gaps and guide future research. Full article

Understanding the environmental fate of microplastics (MPs) in agricultural soils remains a major challenge, particularly under field conditions where soil structure and hydraulic processes jointly regulate particle transport and retention. This study investigated whether hydro-physical soil functioning can explain the distribution and accumulation of MPs in pistachio orchard soils from a semi-arid region of southeastern Türkiye. A total of 42 soil samples were analyzed for MP abundance, size distribution, and morphology, together with key hydro-physical properties including texture, porosity, bulk density, aggregate stability, organic matter content, and soil water retention characteristics. To identify the dominant controls on MP occurrence, explainable machine learning approaches combining Random Forest (RF), Gradient Boosting Decision Trees (GBDT), and SHAP (SHapley Additive exPlanations) analysis were employed. Microplastic abundance differed among management systems. Former landfill or construction sites represented the largest proportion of the total recorded microplastic abundance (40.9%), followed by conventionally managed (25.2%), manure-amended (24.5%), and sewage-sludge-amended orchards (9.4%). Median microplastic abundances were 1433, 667, 4633, and 633 particles kg⁻¹ soil, respectively. Fine-sized MPs constituted the dominant particle fraction and exhibited strong associations with pore-system characteristics, indicating that pore-size compatibility governs their retention and mobility within the soil matrix. Morphology-specific analyses further revealed contrasting relationships between soil hydro-physical properties and individual MP forms, suggesting distinct retention pathways for granules, films, fragments, and fibers. Explainable AI analysis identified organic matter, silt content, bulk density, and water retention characteristics as the most influential predictors of MP occurrence. Among the tested models, RF demonstrated superior predictive robustness and generalization capacity. The findings demonstrate that hydro-physical soil functioning plays a central role in determining microplastic fate in agricultural soils and highlight the value of interpretable machine learning frameworks for uncovering the mechanisms underlying contaminant retention and redistribution. Integrating soil structural indicators with explainable artificial intelligence offers a promising pathway for improving microplastic risk assessment in agroecosystems. Full article

Slope stability assessment remains a fundamental challenge in geotechnical engineering because of the complex nonlinear interactions among soil properties, slope geometry, and hydraulic conditions, particularly variations in pore-water pressure. This study investigates the reliability of Artificial Neural Network–Multi-Layer Perceptron (ANN–MLP) models for predicting the Factor of Safety (FoS) of homogeneous soil slopes through a systematic comparison of three gradient-based optimization algorithms: Adam, Mini-Batch Gradient Descent (MBGD), and Nesterov Accelerated Gradient (NAG). A database comprising 2014 slope cases, compiled from published studies and numerically generated using Limit Equilibrium Method (LEM) and Finite Element Method (FEM) analyses, was used for model development and k-fold cross-validation. Beyond statistical evaluation, the developed models were validated using two classical dry-slope benchmark frameworks based on the Taylor stability charts and Bishop–Morgenstern stability coefficients, followed by two documented engineering case studies from Hulu Kelang and Pahang, Malaysia, to assess predictive performance under both dry and variable hydraulic conditions. Adam achieved the highest cross-validated predictive accuracy (R² = 0.988; RMSE = 0.212), whereas MBGD demonstrated the closest overall agreement with the reference LEM solutions across the validation cases and under increasing pore-water pressure ratios. NAG generally produced more conservative predictions while exhibiting greater sensitivity to hyperparameter selection. All models successfully reproduced the expected nonlinear reduction in FoS with increasing pore-water pressure, consistent with established geotechnical behaviour. The results demonstrate that optimizer selection significantly influences ANN–MLP prediction behaviour and that properly validated gradient-based ANN models can serve as efficient decision-support tools for rapid slope stability assessment under hydraulic variability. Full article

Agrivoltaic (AV) systems represent a promising strategy for integrating renewable energy production and agricultural activity on the same land unit, while contributing to soil water conservation under increasingly frequent drought conditions. This preliminary, single-site case study investigates the effects of a horizontal biaxial tracking AV system on soil water dynamics in a durum wheat field in the Po Valley (Borgo Virgilio, Mantua, Italy) over a full monitoring period, covering the final crop growth stages and the post-harvest bare soil phase (May–December 2024). Monitoring of soil temperature, volumetric water content (VWC), and soil water potential (SWP) was conducted at four depths (15, 30, 45, and 60 cm) at one representative monitoring station per treatment, comparing soil under AV cover (AVC) and in unshaded conditions (UC), located 10 m apart. Paired VWC and SWP measurements were used to derive site-specific soil water characteristic curves (SWCCs) and to calibrate the agro-hydrological model CRITERIA-1D, which was used to estimate available water (AW) in the first 80 cm of depth for both treatments. Measured VWC values were higher in the AVC profile than in the UC profile at all monitored depths throughout the May–September period, with differences persisting, although at lower values through October–December. Estimated AW was consistently higher under AVC than in UC during both the dry and wet periods. Despite higher VWC, the AVC profile showed more negative average SWP values at all depths during summer. This pattern is consistent with the shape of the derived SWCCs and may point to differences in water-retaining capacity between the two profiles, possibly related to structural modifications induced by 13 years of AV system operation. These preliminary findings suggest that AV systems could potentially improve soil water availability in the root zone of rainfed cereal crops and propose the hypothesis that long-term AV cover may act as a driver of changes in soil hydraulic properties, with implications for the sustainability and climate resilience of dryland farming systems. However, given the design of this case study, with only one monitoring point per treatment, the observed differences reflect the specific monitored locations and cannot fully disentangle the AV treatment effect from pre-existing spatial heterogeneity in soil properties. The preliminary results obtained in this study should therefore not be generalised beyond the specific conditions of this case study, and the interpretations proposed here should be treated as unproven hypotheses rather than established conclusions. Further studies with spatial replication and multi-year monitoring are needed to confirm these patterns. Full article

Sequential extreme hydrological events driven by climate change pose severe risks to both crop survival and groundwater quality in flood-prone agricultural regions. The current study investigated the long-term soil hydrodynamics and potential perfluoralkyl and polyfluoralkyl substance (PFAS) leaching in a mountain tea (Sideritis raeseri) plantation in Thessaly plain, Greece, before, during and after the consecutive extreme storms Daniel and Elias (in September 2023). An 8-year simulation was performed, covering the years 2018–2025, including an initial calibration/validation phase (2018) followed by the 2019–2025 simulation of hydrodynamics and potential short-chain PFAS leaching, under two hypothetical solute transport scenarios. Model HYDRUS-1D was calibrated and validated using field data (precipitation, irrigation, evaporation, transpiration and biomass) from the 2018 growing season under four irrigation treatments (100%, 75%, 50% and 0% of ET_c, where ET_c is crop evapotranspiration) to secure reliable soil hydraulic properties and was subsequently extended to simulate soil water dynamics until 2025, when crop failure was observed. To quantify environmental risks, two hypothetical solute transport scenarios with different initial depths of contamination (0–20cm and 0–100 cm) and an initial short-chain PFAS soil pore water concentration of 0.1 ppb (equal to upper EU limit) were executed exclusively for the 2019–2025 period to simulate the potential leaching of the contaminants through the soil. The simulation successfully captured the agronomic reality, showing that prolonged waterlogging conditions persisted, suggesting a severe risk of root anoxia, potentially leading to the subsequent crop failure in 2025. Furthermore, solute transport modeling captured the leaching process and predicted that under typical Mediterranean hydrological conditions, short-chain PFAS had already potentially leached beyond the root zone prior to the extreme events. However, the sequential floods acted as an acute hydraulic flush, evacuating the residual chemicals towards deeper soil layers. Moreover, the 0–100 cm contaminated profile represents the most critical hazard to the underlying aquifer, delivering a higher load and elevated risks for the groundwater table than the shallow (0–20 cm) contamination profile. Full article

The CSM-CROPGRO-Lentil model (DSSAT v4.8.5) was calibrated and validated for 43 ICARDA lentil genotypes across three growing seasons (2019–2022) under semi-arid rainfed conditions in southeastern Türkiye. Calibration against the 2019–2020 season yielded excellent performance for phenological traits (anthesis date nRMSE = 0.8%; maturity date nRMSE = 1.2%), thousand-seed weight (nRMSE = 1.6%), and good performance for grain yield (nRMSE = 19.3%; d = 0.935) and biological yield (nRMSE = 11.1%; d = 0.941). Phenological simulations remained robust (anthesis and maturity dates nRMSE = 6.8% and 1.6%), but grain and biological yield simulations failed (nRMSE = 43.0–74.7%) with opposing bias directions in both validation seasons. Spearman rank correlation collapsed to non-significant levels in both validation years (rho = −0.020 and +0.263), indicating a loss of genotype-ranking capacity, in which only three accessions (G37, G3695, and G21151) maintained simulation error below 40% across both drought seasons. Model bias was not significantly associated with PEG-derived drought tolerance classifications, based on composite stress tolerance index-based reclassification with balanced group sizes (n = 5 vs. n = 5; Mann–Whitney p ≥ 0.548), suggesting that limitations in species-level water stress response functions may have contributed to model failure, although the potential influence of interannual differences in soil hydraulic properties cannot be excluded. Late-maturing genotypes were simulated significantly more accurately than early and mid-maturing genotypes in 2021–2022 (Kruskal–Wallis p = 0.013), while large-seeded genotypes showed disproportionately greater underestimation in 2020–2021 (rho = −0.410, p = 0.006). These findings demonstrate that CROPGRO-Lentil reliably simulates phenological diversity under favorable conditions but requires genotype-specific drought stress parameterization and improved seed-filling dynamics to support genotype evaluation under increasingly variable semi-arid moisture regimes. Full article

Groundwater vulnerability refers to an aquifer’s susceptibility to contamination based on natural hydrogeological properties, including geology, soil, topography, and unsaturated zone characteristics. In low-recharge arid systems, limited recharge reduces dilution and flushing, allowing contaminants introduced through anthropogenic activities to persist over time. This study assesses groundwater vulnerability in El-Farafra, El-Kharga, and Tushka using a GIS-based DRASTIC approach, enhanced with a Land-Use DRASTIC model to incorporate human activities. Parameters, including the depth-to-water table, net recharge, aquifer media, soil media, topography, the vadose zone, and hydraulic conductivity, were spatially analyzed to generate vulnerability indices. Sentinel-2 imagery was used for land-use classification. In addition, expert elicitation from twenty hydrogeology specialists provided alternative parameter weightings, which were compared with the standard DRASTIC weights. Results show that incorporating land use and expert-based weights refines vulnerability patterns, particularly in agricultural, urban, and industrial zones. El-Farafra exhibits the highest vulnerability due to intensive land use and hydrogeological conditions, El-Kharga shows moderate vulnerability, and Tushka shows lower vulnerability, where recharge from Lake Nasser enhances dilution and reduces contaminant persistence. The study highlights the importance of integrating land-use information and expert knowledge to improve vulnerability assessment in data-scarce arid environments and supports improved groundwater management strategies. Full article

Groundwater levels in gated agricultural drainage networks respond to canal-stage changes, rainfall, antecedent storage, and changing operating conditions. We examined groundwater and surface-water records from the Chezegou Watershed, Huaibei Plain, China (2019–2024), using analytical solutions of the linearized Boussinesq equation. Groundwater was measured mostly at intervals of about five days, and analyses used the original observation dates. Using the half-power criterion |Z|² = 1/2 and hydraulic diffusivities of 5.27 × 10³–1.05 × 10⁴ m² d⁻¹, cutoff periods were 56.1–111.7 d at 150 m and 399.1–794.1 d at 400 m; the half-power distance for a 30 d cycle was 77.7–109.7 m. The record also includes a 106 mm storm on 12 July 2020. Groundwater depth at J5 (490 m from the canal) decreased from 2.23 to 0.54 m in 48 h, a 1.69 m water-level rise, while J9 (1020 m) rose by 1.79 m over five days. These observations show a rapid shallow-groundwater head response, although water-level records alone do not separate vertical recharge from hydraulic-pressure transmission. Canal influence depends on forcing duration and aquifer properties, while rainfall responses also reflect lateral boundaries and the shrink-swell behavior of Shajiang black soil. The calculated time-distance relations provide site-specific reference values for canal operation. Full article

Urban rocky desertification areas are characterized by shallow soils, rock–soil mosaics, and strong human disturbance, so infiltration processes may differ from those in homogeneous soils. However, interactions among multiple controlling factors remain insufficiently quantified. This study compared soil infiltration under artificially restored vegetation (planted grassland (PG) and planted woodland (PW)), and natural secondary vegetation (secondary grassland (SG) and secondary woodland (SW)). Saturated hydraulic conductivity (Ks) and falling-head duration (T) were measured using falling-head tests on undisturbed soil columns. Soil physical properties were then integrated with partial least squares structural equation modeling (PLS-SEM) to assess the effects of rocky desertification, soil aggregates, and porosity. Soil bulk density was significantly lower under artificially restored vegetation, whereas capillary porosity, non-capillary porosity, and water-holding capacity were significantly higher (p < 0.05). Infiltration performance followed PW > PG > SW > SG. PLS-SEM indicated that rocky desertification (−0.78), porosity (0.51), and aggregates (−0.03) jointly regulated infiltration, with non-capillary porosity as the dominant positive factor. Higher infiltration in artificially restored plots was mainly associated with improved pore structure. These findings support vegetation configuration and soil–water management in urban rocky desertification areas. Full article

Soil water models are increasingly required to support irrigation, drought assessment and sustainable water management, yet physical, artificial intelligence (AI)-based and hybrid approaches differ in process representation, data demand and transferability. This structured narrative review critically compared these approaches and used auxiliary publication-record mapping in Web of Science, Scopus and OpenAlex for 2015–2026; quantitative comparisons were based on the complete years 2015–2025. Aggregated annual database records increased from 21,795 to 38,899 for physical models (1.78-fold), from 203 to 4088 for AI-based models (20.14-fold), and from 61 to 669 for hybrid models (10.97-fold); because records overlapped across databases, these values indicate relative trends rather than unique publications. Physical models remained essential for mechanistic interpretation but were constrained by hydraulic parameterisation, boundary conditions, heterogeneity and scale mismatch. AI-based models enabled flexible multi-source prediction and remote-sensing integration but remained vulnerable to domain shift, weak extrapolation and limited process interpretability. Hybrid strategies provided specific benefits through parameter estimation, emulation, residual correction, data assimilation, physics-informed learning and differentiable coupling, while potentially inheriting uncertainty from both components. No model class was universally superior. Model selection should therefore be problem-oriented and supported by independent validation, uncertainty quantification, domain assessment and evaluation at root-zone and management-relevant decision thresholds. Full article

Over the past few decades, the oil sands mining industry has taken steps to find ways to speed up the filterability and consolidation of their tailings deposits, which would otherwise take decades to settle and reach the required strength. The initiative has led to deposits that are combinations of sands and fines (<44 µm) in proportions whose geotechnical behaviors have not yet been determined by the existing body of knowledge. The purpose of this study is to examine how the quantity of fines and their index characteristics affect the filterability and consolidation of particular deposits. Findings from this research show that these deposits exhibit characteristics of low-plasticity soils. The hydraulic conductivity of these materials is strongly influenced by the fines content. The deposits behave more like sand below a threshold point of about 35 percent fines content, and they exhibit low hydraulic conductivity above this point. Furthermore, the hydraulic conductivity of these deposits is influenced by other factors, including clay properties, sodium adsorption ratio, and effective stress. The results of finite-strain consolidation modeling show that mixtures of sand and fluid tailings with fines within the threshold range exhibit significantly improved consolidation performance. In particular, compared to the performance of traditional fluid tailings deposits, settlement time and depth are reduced by more than 50%, and the time needed for complete pore pressure dissipation is reduced by more than 80%. Findings from this study provide an insight to the industry on the optimal fines–sand blending proportions for best performing deposits. Since these findings are solely laboratory-based, it should be noted that the determined threshold fines content and consolidation behavior may alter in field-scale deposition. Full article

Soil amendments are widely applied for water conservation in urban turfgrass, yet whether establishment-phase benefits persist into a mature-stand remains unclear. This study evaluated biochar, biosolids, and greenwaste on tall fescue (Schedonorus arundinaceus) over a 108-day mature-stand trial under deficit (50% ET₀) and moderate (85% ET₀) irrigation, both below full replacement. Canopy performance was assessed by visual quality and NDVI, with van Genuchten soil-water retention modeling. Unlike the establishment-phase advantages reported for the organic amendments in Part I, the second-year results reversed sharply: moderate biochar (12.36 t ha⁻¹) was most hydraulically stable, holding the highest plant-available water (PAW ≈ 0.18 cm³ cm⁻³, above the control and organic amendments) and the most stable canopy. High-rate biochar (24.71 t ha⁻¹) underperformed the control under deficit irrigation, indicating constraints beyond water retention at the highest rate. Greenwaste and biosolids raised volumetric water content but provided lower PAW than moderate biochar. For greenwaste, a reduced field capacity offset this; for biosolids, an elevated permanent wilting point limited the extractable fraction. Biosolids failed to maintain acceptable quality even under the 85% ET₀. Because first-year success does not guarantee mature-stand resilience, amendment stability and rate optimization, rather than application volume, emerge as long-term management priorities under water-limited conditions. Full article