Search Results (518)

Soil erosion remains a critical global concern, yet long-term catchment-scale assessments that explicitly link historical land-use transitions with erosion responses remain limited. This study evaluates how ±240 years record of historical and projected land-use changes influence soil erosion in the Myjava Basin by integrating parcel-level land-use reconstructions from 1787 to 2030 into a distributed USLE-2D framework. R, K, and parcel-based C and P factors were temporally standardized, and LS was derived using an ensemble of four widely applied algorithms. A PCA was applied to quantify the relative contribution of RUSLE factors across time, and all analyses were performed within a reproducible geospatial modelling environment. The results indicated a long-term decline in total erosion of ±78% at the landscape scale and ±60% within arable land from the 19th century to the present, driven mainly by a major reduction in arable land (from ±62% to ±37%) and expansion of forest and shrub vegetation. Despite this decline, persistent hotspots remain concentrated on steep upland slopes with high LS (>10%), while agricultural parcels experienced erosion rates 10–20 times higher than the basin-wide mean across all periods. PCA shows that LS and rainfall erosivity dominate erosion variability (PC loadings ±0.78–0.84), while C and P factors increase in influence in recent and projected periods, contributing up to ±40% of total explained variance. These findings demonstrate that long-term land-use transitions have substantially reduced basin-scale erosion risk. Full article

The dung beetle family Geotrupidae (Scarabaeoidea) plays a vital ecological role in nutrient cycling and soil health, yet the scarcity of complete mitochondrial genome (mitogenome) data has hindered phylogenetic and comparative studies within this family. Here, we sequenced, assembled, and annotated the first complete mitogenomes of Geotrupes stercorarius and Phelotrupes auratus, collected from the Qinghai–Tibetan Plateau. Comparative analysis of these two novel mitogenomes with eight existing mitogenomes revealed conserved architectural features across Geotrupidae, such as gene arrangement, tRNA secondary structures, and small intergenic spacers. Nucleotide composition was largely conserved, though marked divergence occurred at the third codon positions. Substantial structural variation was observed in non-coding regions, particularly in the control region and the nad2-trnW spacer. Evolutionary analyses indicated strong purifying selection across all protein-coding genes, with no evidence of widespread positive selection linked to high-altitude adaptation. Phylogenetic reconstruction consistently recovered the relationships (Bolboceratinae, (Lethrinae, Geotrupinae)), with Anoplotrupes and Geotrupes forming sister genera within Geotrupinae. This study provides additional mitogenomic resources and a well-supported phylogenetic framework for Geotrupidae, resolving key taxonomic uncertainties and establishing a basis for future evolutionary and ecological research. Full article

Forest surveying and inspection face significant challenges due to unstructured environments, variable terrain conditions, and the high costs of manual data collection. Although mobile robotics and artificial intelligence offer promising solutions, reliable autonomous navigation in forest, terrain-aware path planning, and tree parameter estimation remain open challenges. In this paper, we present the results of the AI4FOREST project, which addresses these issues through three main contributions. First, we develop an autonomous mobile robot, integrating SLAM-based navigation, 3D point cloud reconstruction, and a vision-based deep learning architecture to enable tree detection and diameter estimation. This system demonstrates the feasibility of generating a digital twin of forest while operating autonomously. Second, to overcome the limitations of classical navigation approaches in heterogeneous natural terrains, we introduce a machine learning-based surrogate model of wheel–soil interaction, trained on a large synthetic dataset derived from classical terramechanics. Compared to purely geometric planners, the proposed model enables realistic dynamics simulation and improves navigation robustness by accounting for terrain–vehicle interactions. Finally, we investigate the impact of point cloud density on the accuracy of forest parameter estimation, identifying the minimum sampling requirements needed to extract tree diameters and heights. This analysis provides support to balance sensor performance, robot speed, and operational costs. Overall, the AI4FOREST project advances the state of the art in autonomous forest monitoring by jointly addressing SLAM-based mapping, terrain-aware navigation, and tree parameter estimation. Full article

Historical cropland reconstruction is crucial for modeling long-term agricultural dynamics and assessing their climatic and ecosystem impacts, while also providing critical regional benchmarks for improving global land-use datasets. This study presents a millennium-long reconstruction of cropland area at the provincial level for the Korean Peninsula by integrating multi-source historical cropland records, land surveys, and modern statistical and remote-sensing-based data. Then, a land suitability model for cultivation and a spatial allocation model were developed by incorporating topographic, climatic, and soil variables to generate 10 km resolution gridded cropland data over the past millennium. Our analysis revealed a long-term increasing trend in cropland area at the provincial level over the past millennium, with significant spatial and temporal variations. Spatially, cropland was primarily distributed in western coastal areas, with historical southward expansion. After the peninsula’s division, trends diverged, with continued growth in the north Korea but a decrease in the south Korea by 2000. The spatial allocation model validation results show strong spatial and quantitative agreement between the reconstructed historical cropland and the remote-sensing-based data, with 72.12% of grids differing by less than ±20%. This high consistency confirms the feasibility of the applied reconstruction method. Full article

The presented dataset contains spatial models of cones formed from lunar soil simulants. The cones were formed in a laboratory by allowing the soil to fall freely through a funnel. Then, the cones were measured using three methods: a high-precision handheld laser scanner (HLS), photogrammetry, and a low-cost LiDAR system integrated into an iPad Pro. The dataset consists of two groups. The first group contains raw measurement data, and the second group contains the geometry of the cones themselves, excluding their surroundings. This second group was prepared to support the calculation of the cones’ volume. All data are provided in standard 3D file format (.STL). The dataset enables direct comparison of resolution and geometric reconstruction performance across the three techniques and can be reused for benchmarking 3D processing workflows, segmentation algorithms, and shape reconstruction methods. It provides complete geometric information suitable for validating automated extraction procedures for parameters such as cone height, base diameter, and angle of repose, as well as for further research into planetary soil and granular material morphology. Full article

After the completion of open-pit coal mining, land reclamation is implemented to restore the disturbed eco–hydrological system, for which accurate soil moisture characterization is essential. We evaluated the feasibility and performance of an Auto-Regressive Moving Average (ARMA)-based ground-penetrating radar (GPR) inversion scheme for estimating soil moisture in a reclaimed mine area. GPR data were acquired over a reconstructed three-layer soil profile in a reclaimed open-pit coal mine, and soil moisture content was independently determined using the oven-drying method on core samples. An ARMA model was used to describe the relationship between the GPR reflections and soil electromagnetic properties and to invert the vertical distribution of soil moisture. The ARMA-derived GPR estimates reproduced the measured moisture profile well within the depth interval of 1.4–3.0 m and revealed the clear vertical zonation of soil moisture associated with the engineered layering. Correlation coefficients between the ARMA-inverted GPR estimates and oven-drying measurements ranged from 0.64–0.78 for 0–1.4 m, 0.84–0.93 for 1.4–2.2 m, and 0.98–0.99 for 2.2–3.0 m, indicating that inversion accuracy improves systematically with depth. These results demonstrate that ARMA-based GPR inversion provides a reliable and non-destructive approach for quantifying soil moisture in reclaimed mine soils and offers practical support for monitoring and assessing the effectiveness of reclamation in open-pit coal mining areas. Full article

Breeding wheat varieties that are resilient to arid climates, which impart a complex combination of stresses, including excessive Ca, high pH, nutrient deficiency, and aridity, is important. Afghan landrace wheat is assumed to have evolved with a specific prototypical pattern of traits to adapt to its challenging, composite stress environment. Here, a useful semi-hydroponic double cup screen aiding proteomic analysis was exploited to reconstruct the combined excessive Ca²⁺ (100 ppm) and extreme pH (11.0) of the soils and to dissect specific morpho-physiological characteristics and adaptation strategies in Kihara Afghan wheat landrace (KAWLR). When compared to other cultivars and growth habits, several winter-type KAWLR showed lower unused N-K-P and greater rhizosphere pH stability in the bottom cup and higher tolerance in terms of greater root allocation shift, and most of their above ground traits (shoot biomass, chlorophyll content, and stomatal conductance) were strongly correlated with root length and biomass under stress conditions. Quantitative proteomics on the roots of a tolerant winter-type KAWLR, Herat-740 (KU-7449), showed a strong decreasing trend in changed proteins (12 increased/816 decreased). The proteins (such as mitochondrial phosphate carrier protein, cytoskeleton-related α-, and β-tubulin) that increased in abundance were associated with energy transport and cell growth. A metabolism overview revealed that most proteins that were mapped to glycolysis, fermentation, and the TCA cycle decreased in abundance. However, proteins related to cell wall and lipid metabolism pathways remained unchanged. Our results suggest that winter-type KAWLR adopts a homeostatic stress adaptation strategy that globally downshifts metabolic activity, while selectively maintaining root growth machinery. Root allocation shift, rhizosphere pH stabilization (nutrient solubilization), and a selective proteome response maintaining the root growth machinery in winter-type KAWLR could be breeding selection markers for early-stage screening in calcareous-alkaline arid land. Full article

The detection of subsurface stromatolites remains challenging due to their complex morphology and heterogeneous composition. This study assesses the combined application of Electrical Resistivity Tomography (ERT) and Ground Penetrating Radar (GPR) for identifying microbialites in two contrasting geological and climatic settings: the Neoproterozoic Salitre Formation in Brazil and the Mesozoic microbialite-bearing limestones in northern Spain. High-resolution ERT profiles processed with raster-based blob detection algorithms revealed subcircular high-resistivity anomalies consistent with the studied microbialite morphologies, with strong resistivity contrasts observed between microbialites and host matrices despite variations in absolute values linked to lithology and soil moisture. In parallel, GPR surveys analyzed with a peak detection algorithm delineated domal reflectors and clusters of high-amplitude reflections that directly captured the internal architecture of stromatolitic buildups. With decimetric vertical resolution, GPR offered unrivaled insights into internal morphology, complementing the broader-scale imaging capacity of ERT. The complementary strengths of both methods are clear: ERT excels at mapping distribution and stratigraphic context, while GPR provides unparalleled resolution of internal structures. Crucially, this work advances previous efforts by explicitly demonstrating that integrated ERT-GPR approaches, when combined with algorithm-based interpretation, can resolve microbialite morphology, distribution and internal architecture with a level of objectivity not previously achieved. Beyond methodological refinement, these findings open new avenues for reconstructing microbialite development and preservation in ancient carbonate systems and hold strong potential for application in other geological contexts where complex carbonate structures challenge traditional geophysical imaging. Full article

Soil profile moisture (0–200 cm) in agricultural fields is a critical variable determining root-zone water storage and irrigation scheduling accuracy, yet continuous deep-layer monitoring is constrained by equipment costs and installation difficulties. This study developed a near-real-time reconstruction model for soil moisture profiles across the 0–200 cm depth based on shallow-layer (0–20 cm, 20–40 cm) real-time monitoring data and multi-day accumulated meteorological features. Using field measurements from 2023 to 2025, Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Support Vector Regression (SVR) models were compared across different input scenarios and cumulative time windows. The results showed that using only surface moisture as input (Scenario A), prediction R² ranged from 0.87 to 0.93 for shallow layers (≤80 cm) but decreased to 0.58 for deep layers (140–200 cm). Incorporating multi-day meteorological accumulation (Scenario B) improved R² by 0.05–0.08. When dual-layer moisture and meteorological drivers were combined (Scenario D), shallow-layer R² reached 0.96–0.98 with RMSE < 7 mm, mid-layer performance maintained at 0.85–0.90, and deep layers still achieved 0.76–0.84. Optimal time windows exhibited depth-dependent patterns: 5–10 days for shallow layers, 10–15 days for mid-layers, and ≥20 days for deep layers. Rolling validation demonstrated high consistency between model predictions and observations in the 0–80 cm range (R² > 0.90, RMSE < 10 mm), enabling stable estimation of 0–200 cm profile dynamics. This approach eliminates the need for deep probes while achieving low-cost, interpretable, and deployable near-real-time deep moisture estimation, providing an effective technical pathway for precision irrigation and water management in semi-arid regions. Full article

Soil organic carbon (SOC) and total nitrogen (TN) are fundamental indicators of soil fertility and long-term agricultural sustainability. However, intensive cultivation, residue removal, and imbalanced fertilization have resulted in substantial declines in SOC and TN across many agroecosystems, particularly in Northeast China. This study investigated SOC and TN dynamics within the 0–35 cm profile of four representative soils in Northeast China under a continuous maize cropping system. Five treatments were assessed: conventional tillage (CT), deep tillage (DT), deep tillage with straw (SDT), deep tillage with organic fertilizer (MDT), and deep tillage combined with straw and organic fertilizer (SMDT). Compared with DT, organic amendment treatments increased SOC and TN contents in the 0–20 cm layer by 9.41–57.57% and 5.29–60.76%, respectively. The SMDT treatment achieved the highest SOC and TN stocks (65.03 Mg ha⁻¹ and 7.91 Mg ha⁻¹) and enhanced nutrient accumulation in the 20–35 cm layer. In the subsoil, the ratio of soil C and N (C/N) under SMDT increased by 3.11%, 11.08%, 2.10%, and −7.01% across the four soils, indicating improved C–N balance and reduced nutrient stratification. SOC and TN stocks were linearly correlated with cumulative C input, confirming that organic amendments were among the main drivers of C and N sequestration. Mantel and path analyses further revealed that clay content and mean annual precipitation enhanced SOC and TN storage by improving soil structure and C–N balance through increased C input and reduced bulk density. Overall, deep tillage combined with amendments strengthened C–N coupling, improved soil fertility, and provided a mechanistic basis for reconstructing fertile tillage layers and sustaining productivity in Northeast China. Full article

Groundwater depletion is among the most critical hydrological threats to sustainable agriculture and water security in semi-arid regions. This study presents a high-resolution, multi-sensor assessment of groundwater storage (GWS) dynamics across the Lower Chenab Canal (LCC) command area in Punjab, Pakistan—an intensively irrigated agro-hydrological system within the Indus Basin. We integrated downscaled GRACE/GRACE-FO-derived total water storage anomalies with CHIRPS precipitation, MODIS evapotranspiration (ET) and vegetation indices, TerraClimate soil moisture, land surface temperature (LST), land use/land cover (LULC), and population density using the Google Earth Engine (GEE) platform to reconstruct spatiotemporal GWS changes from 2002 to 2020. The results reveal a persistent and accelerating decline in groundwater levels, averaging 0.52 m yr⁻¹, which intensified to 0.73 m yr⁻¹ after 2014. Cumulative GWS losses exceeded 320 mm yr⁻¹, with severe depletion (up to −3800 mm) in northern districts such as Sheikhupura, Gujranwala, and Narowal. Validation with borewell data (R² = 0.87; NSE = 0.85) confirms the reliability of the remote sensing estimates. Statistical analysis indicates that anthropogenic drivers (population growth, urban expansion, and intensive irrigation) explain over two-thirds of the observed variability (R² = 0.67), whereas precipitation contributes only marginally (R² = 0.28), underscoring the dominance of human-induced stress over climatic variability. The synergistic rise in evapotranspiration, land surface temperature, and cultivation of high-water-demand crops such as rice and sugarcane has further amplified hydrological imbalance. This study establishes an operational framework for integrating satellite and ground-based observations to monitor aquifer stress at basin scale and highlights the urgent need for adaptive, data-driven groundwater governance in the Indus Basin. The approach is transferable to other data-scarce semi-arid regions facing rapid aquifer depletion, aligning with the global targets of Sustainable Development Goal 6 on water sustainability. Full article

Historical cropland cover change reconstruction is essential for understanding long-term agricultural reclamation dynamics, particularly for modeling carbon and nitrogen cycles and assessing their climatic impacts. Such reconstructions also provide critical regional benchmarks for improving global land-use datasets. In this study, we integrated historical documents and land survey records spanning the Heian period (794–1185 CE) to the present with modern remote sensing data to develop a spatially explicit methodology for reconstructing Japan’s cropland extent over the past millennium. Our analysis revealed four distinct phases of cropland area change, (1) slow expansion (800–1338 CE), (2) gradual decline (1338–1598 CE), (3) rapid growth (1598–1940 CE), and (4) sharp contraction (1940–2000 CE), with significant regional variations. Spatially, cropland progressively expanded from the core Kansai and Kantō regions toward the southwestern and northeastern frontiers. Cropland cover changes in Japan over the past millennium were driven by a combination of socio-political factors—such as technological innovations in agriculture, feudal conflicts, demographic shifts, agricultural industrialization, and urbanization—as well as natural conditions, including topography, climate, and soil texture. Validation against year-2000 remote sensing data demonstrated high accuracy, with 69.12% of grid cells showing ≤20% absolute difference and only 0.15% exceeding ±80% deviation. Full article

Accurate numerical prediction of particle–tool interaction requires validation methods that closely reflect the complexity of real operating conditions. This study introduces a comprehensive methodology for validating the motion of particulate material modeled using the Discrete Element Method (DEM) under field-like conditions, with experimental measurements conducted directly during agricultural processing. The proposed framework integrates image analysis with manual extraction of experimental particle trajectories, providing an efficient, flexible, and cost-effective validation approach. A multilayer perceptron artificial neural network (ANN) trained on 94,939 calibration samples was employed to transform pixel coordinates from two synchronized cameras into 3D spatial positions. To the best of the authors’ knowledge, this represents the first application of an ANN-based trajectory reconstruction method under laboratory soil-channel conditions that replicate field-representative geometry and operating velocities. Experiments were conducted in a laboratory soil channel using a full-scale agricultural chisel operating at 1.0 and 1.5 m·s⁻¹, corresponding to realistic tillage velocities. The ANN achieved excellent accuracy (R² = 0.9994, 0.9993, and 0.9988 for the X-, Y-, and Z-axes; average deviation 2.7 mm), and the subsequent comparison with DEM simulations resulted in an average nRMSE error of 4.7% for 1 m·s⁻¹ and 9.41% for 1.5 m·s⁻¹. The results confirm that the proposed methodology enables precise reconstruction of particle trajectories and provides a robust framework for the validation and calibration of DEM models under conditions closely approximating real field environments. Full article

Root system analysis remains methodologically challenging in plant research: traditional soil cultivation obstructs comprehensive root observation, whereas hydroponic visualization lacks ecological relevance due to soil environment exclusion—a critical limitation for crops like soybean. This manuscript developed a cost-effective hybrid imaging system integrating transparent acrylic plates, semi-permeable membranes, and natural soil substrates with high-resolution imaging and controlled illumination, enabling non-destructive root monitoring in quasi-natural soil conditions. Complementing this hardware innovation, this manuscript proposed an unsupervised semantic segmentation algorithm that synergizes path planning with an enhanced DBSCAN framework, achieving the precise extraction of primary and lateral root architectures. Experimental validation demonstrated superior performance in soybean root analysis, with segmentation metrics reaching 0.8444 accuracy, 0.9203 recall, 0.8743 F1-score, and 0.7921 mIoU—significantly outperforming existing unsupervised methods ($p<0.01$). Strong correlations (R² > 0.94) with WinRHIZO in quantifying root length, projected area, dimensional parameters, and lateral root counts confirmed system reliability. This soil-compatible phenotyping platform establishes new opportunities for root research, with future developments targeting multi-crop adaptability and complex soil condition applications through modular hardware redesign and 3D reconstruction algorithm integration. Full article

Soil acid–base status fundamentally regulates biogeochemical cycling and agroecosystem resilience by controlling nutrient solubility, cation exchange, and redox equilibria. However, the long-term evolution of soil pH and its spatial divergence under intensive agricultural expansion remain poorly quantified. Herein, we integrate three nationwide soil surveys (1980, 2012, 2018) encompassing over 190,000 cropland observations into a harmonized 1 km dataset to reconstruct four decades of soil pH change across China. National mean soil pH declined from 7.1 in 1980 to 6.7 in 2012 and 6.6 in 2018, revealing a sustained acidification trend. Nearly one quarter of neutral soils (pH 6.5–7.5) have shifted into acidic classes (<6.5) since 1980, reflecting widespread depletion of soil buffering capacity under intensive fertilization, high rainfall, and carbonate exhaustion. By integrating current pH conditions with standardized pH change rate, we delineate nine bidirectional soil pH risk zones that capture contrasting acidification and alkalization processes along climatic and edaphic gradients. Acidification-prone zones dominate humid southern croplands, whereas alkalization risk prevails in arid northern regions. Our results provide nationally consistent, grid-level evidence of soil acid–base evolution across nearly four decades, offering a quantitative foundation for region-specific soil management to sustain productivity and mitigate environmental risks. Full article