Search Results (217)

Gravity erosion is one of the main physical processes of soil erosion and sediment sources in catchments, and its spatiotemporal patterns and driving mechanisms are seriously understudied, mainly due to the the great difficulties in monitoring and quantifying. This study obtained gravity erosion amounts by runoff scouring experiments on the field slope of the hilly–gully region of the Chinese Loess Plateau. The terrain point cloud before and after gravity erosion was obtained based on the TLS, SfM and the fusion of single-scan TLS and SfM, and then the gravity erosion was estimated by four terrain change detection algorithms (DoD, C2C, C2M and M3C2). Results showed that the M3C2 algorithm plus fused data had the highest quantization accuracy among all the algorithms and data sources, with a relative error of 14.71%. The fused data combined with M3C2 algorithm performed much better than other algorithms and data sources for the different gravity erosion magnitudes (mean relative error < 17.00%). The DoD algorithm plus TLS data were preferable for collapse areas, while the M3C2 algorithm plus TLS was suitable for the alcove area. This study provides a useful reference for the monitor and quantitative research of gravity erosion in complex topographic areas. Full article

Situated in the southern hilly-mountain belt of China’s “Three Zones and Four Belts Strategy”, Gannan region is a critical ecological shelter belt for the Ganjiang River. Decades of intensive mineral extraction and irrational agricultural development have rendered it into an ecologically fragile area. Consequently, multiple restoration initiatives have been implemented in the region over recent decades. However, it remains unclear how relationships among ecosystem services have evolved under these interventions and how future ecosystem management should be optimized based on these changes. Thus, in this study, we simulated and assessed the spatiotemporal dynamics of five key ESs in Gannan region from 1990 to 2020. Through integrated correlation, clustering, and redundancy analyses, we quantified ES interactions, tracked the evolution of ecosystem service bundles (ESBs), and identified their socio-ecological drivers. Despite a 31% decline in water yield, ecological restoration initiatives drove substantial improvements in key regulating services: carbon storage increased by 6.9 × 10¹² gC while soil conservation rose by 4.8 × 10⁸ t. Concurrently, regional habitat quality surged by 45% in mean scores, and food production increased by 2.1 × 10⁵ t. Critically, synergistic relationships between habitat quality, soil retention, and carbon storage were progressively strengthened, whereas trade-offs between food production and habitat quality intensified. Further analysis revealed that four distinct ESBs—the Agricultural Production Bundle (APB), Urban Development Bundle (UDB), Eco-Agriculture Transition Bundle (ETB), and Ecological Protection Bundle (EPB)—were shaped by slope, forest cover ratio, population density, and GDP. Notably, 38% of the ETB transformed into the EPB, with frequent spatial interactions observed between the APB and UDB. These findings underscore that future ecological restoration and conservation efforts should implement coordinated, multi-service management mechanisms. Full article

The scenario adaptability of agricultural machinery chassis in hilly and mountainous regions has become a key area of innovation in modern agricultural equipment development in China. Due to the fragmented nature of farmland, steep terrain (often exceeding 15°), complex topography, and limited suitability for mechanization, traditional agricultural machinery experiences significantly reduced operational efficiency—typically by 30% to 50%—along with poor mobility. These limitations impose serious constraints on grain yield stability and the advancement of agricultural modernization. Therefore, enhancing the scenario-adaptive performance of chassis systems (e.g., slope adaptability ≥ 25°, lateral tilt stability > 30°) is a major research priority for China’s agricultural equipment industry. This paper presents a systematic review of the global development status of agricultural machinery chassis tailored for hilly and mountainous environments. It focuses on three core subsystems—power systems, traveling systems, and leveling systems—and analyzes their technical characteristics, working principles, and scenario-specific adaptability. In alignment with China’s “Dual Carbon” strategy and the unique operational requirements of hilly–mountainous areas (such as high gradients, uneven terrain, and small field sizes), this study proposes three key technological directions for the development of intelligent agricultural machinery chassis: (1) Multi-mode traveling mechanism design: Aimed at improving terrain traversability (ground clearance ≥400 mm, obstacle-crossing height ≥ 250 mm) and traction stability (slip ratio < 15%) across diverse landscapes. (2) Coordinated control algorithm optimization: Designed to ensure stable torque output (fluctuation rate < ±10%) and maintain gradient operation efficiency (e.g., less than 15% efficiency loss on 25° slopes) through power–drive synergy while also optimizing energy management strategies. (3) Intelligent perception system integration: Facilitating high-precision adaptive leveling (accuracy ± 0.5°, response time < 3 s) and enabling terrain-adaptive mechanism optimization to enhance platform stability and operational safety. By establishing these performance benchmarks and focusing on critical technical priorities—including terrain-adaptive mechanism upgrades, energy-drive coordination, and precision leveling—this study provides a clear roadmap for the development of modular and intelligent chassis systems specifically designed for China’s hilly and mountainous regions, thereby addressing current bottlenecks in agricultural mechanization. Full article

Geological hazards pose significant threats to ecological stability, human lives, and infrastructure, necessitating precise and robust risk assessment methodologies. This study evaluates geological hazard risks in Dawukou District, Shizuishan City, Ningxia Hui Autonomous Region, using the information value (IV) model. The study systematically identifies susceptibility, hazard, and vulnerability factors influencing geological disaster risks by integrating diverse datasets encompassing geological conditions, meteorological parameters, and anthropogenic activities. The key findings reveal that hilly landforms, slope gradients, and vegetation indices are the dominant contributors to hazard development. Additional factors, including lithology, fault proximity, and precipitation, were also found to play critical roles. The results categorize the district into four risk zones: high-risk areas (1.55% of the total area), moderate-risk areas (10.16%), Low-risk areas (23.32%), and very-low-risk areas (64.97%). These zones exhibit a strong spatial correlation with geomorphic features, tectonic activity, and human engineering interventions, such as mining and infrastructure development. High-risk zones are concentrated near mining regions and fault lines with steep slopes, while low-risk zones are predominantly in flat plains and urban centers. The reliability of the risk assessment was validated through cross-referenced geological hazard occurrence data and Receiver Operating Characteristic (ROC) curve analysis, achieving a high predictive accuracy (AUC = 0.88). The study provides actionable insights for disaster prevention, mitigation strategies, and urban planning, offering a scientific basis for resource allocation and sustainable development. The methodology and findings serve as a replicable framework for geological hazard risk assessments in similar regions facing diverse environmental and anthropogenic challenges. Full article

Understanding a catchment’s geomorphological and erosion processes is essential for sustainable land management and soil conservation. This study investigates the Xynias drained lake catchment in Central Greece using a twofold geospatial modelling approach that combines morphometric analysis with the Revised Universal Soil Loss Equation (RUSLE) to evaluate the area’s landscape evolution, surface drainage features, and soil erosion processes. The catchment exhibits a sixth-order drainage network with a dendritic and imperfect pattern, shaped by historical lacustrine conditions and the carbonate formations. The basin has an elongated shape with steep slopes, high total relief, and a mean hypsometric integral value of 26.3%, indicating the area is at an advanced stage of geomorphic maturity. The drainage density and frequency are medium to high, reflecting the influence of the catchment’s relatively flat terrain and carbonate formations. RUSLE simulations also revealed mean annual soil loss to be 1.16 t ha⁻¹ y⁻¹ from 2002 to 2022, along with increased erosion susceptibility in hilly and mountainous areas dominated by natural vegetation. In comparison to these areas, agricultural regions displayed less erosion risk. These findings demonstrate the effectiveness of combining GIS with remote sensing for detecting erosion-prone areas, informing conservation initiatives. Along with the previously stated results, more substantial conservation efforts and active land management are required to meet the Sustainable Development Goals (SDGs) while considering the monitored land use changes and climate parameters for future catchment management. Full article

This study takes the Jinfoping Small Watershed in the Loess Hilly Region as the research area. Through field investigation and laboratory analysis, combined with methods such as spatial autocorrelation analysis, the ordinary least squares method (OLS), and the geographically weighted regression model (GWR), it deeply explores the spatial distribution characteristics and driving factors of Formicidae in the study area. The research results are as follows: (1) Spatial autocorrelation analysis indicates that the distribution of Formicidae is significantly regulated by spatial dependence and has significant spatial autocorrelation (global Moran’s I = 0.332; p < 0.01). (2) The spatial visualization analysis of the GWR model reveals that soil physical and chemical properties and topographic factors have local influences on the spatial distribution of Formicidae. Available phosphorus (AP) and slope (SLP) were significantly positively correlated with the number of ants. Hydrogen peroxidase (HP) and topographic relief (TR) were significantly negatively correlated with the number of ants. This study reveals the spatial distribution pattern of Formicidae in the Loess Hilly Region and its complex relationship with environmental factors, and clarifies the importance of considering spatial heterogeneity when analyzing ecosystem processes. The research results provide a scientific basis for the protection and management of soil ecosystems, and also offer new methods and ideas for future related research. Full article

Precipitation typically leads to the accumulation of soil moisture, which causes slope instability and triggers landslides. However, due to the lag nature of this process, landslides usually do not occur on the day of heavy rainfall. Therefore, it is essential to incorporate antecedent effective precipitation as a factor in landslide prediction models that allow for the creation of more comprehensive landslide susceptibility maps. In this study, six machine learning models are compared, with antecedent effective precipitation included as a conditioning factor for model training. The optimal model is selected to simulate landslide susceptibility maps under four return periods (5, 10, 20, and 50 years). Additionally, the mean decreases in the Gini and SHAP values are employed to identify the most significant factors contributing to landslides. The results indicate the following: (1) Effective antecedent precipitation is the most influential factor in landslide occurrence, ranging from one to two times higher than other factors. (2) Most meteorological stations in the study area show antecedent effective precipitation that follows a lognormal distribution, mainly in coastal areas, with a secondary fit to the general extreme value distribution. The spatial distribution of antecedent effective precipitation is more prominent in the coastal and western mountainous regions, with lower values that then increase with longer return periods in central areas. (3) The XGBoost model achieves the best performance, with an area under the curve of 0.96 and an accuracy of 89.02%. (4) The landslide susceptibility maps for the four return periods reveal three high-risk zones: the southern coastal mountains, the western Zhejiang mountains, and the areas surrounding the hilly region of Shaoxing to Taizhou in central Zhejiang. This study provides dynamic decision-making support for the prevention and control of rainstorm-induced landslide risks. Full article

Cropland slope structure is a key factor influencing agricultural sustainability and ecological risk, especially in topographically complex regions. This study proposes a novel framework that integrates slope spectrum analysis with H3 hexagonal grid partitioning to examine the spatiotemporal dynamics of cropland slope across China from 1990 to 2023. Using 30 m CLCD land cover data, we derived key indicators, including the T-value, upper slope limit (ULS), peak area proportion (PaP), slope at maximum area (SMA), and cropland slope change index (CSCI). This grid-based, multi-indicator approach enables the fine-scale detection of slope structure transitions. Results show that the average slope of cropland fluctuated at around 4.12°, peaking at 4.18° in 2003, while the ULS remained stable at 17°, with 95% of cropland below this threshold. Regionally, cropland in southwest and northwest China was concentrated on steeper slopes (ULS > 26°, PaP < 10%), whereas flatter areas in north and south China had cropland mainly below 15°. From 1990 to 2023, upslope expansion was evident in south China (CSCI > 10), while downslope shifts aligned with high-slope cropland in the western regions. Geographically weighted regression revealed significant positive correlations between increasing ULS and CSCI and elevated cropland fragmentation and soil erosion in hilly areas. These findings highlight the ecological risks of cropland expansion into steep terrain. The proposed framework offers a spatially explicit perspective of cropland slope evolution and supports targeted strategies for land management and ecological restoration. Full article

Benggangs, a type of soil erosion widely distributed in the hilly and mountainous regions of South China, pose significant challenges to land management and ecological conservation. Accurate identification and assessment of their location and scale are essential for effective Benggang control. With advancements in technology, deep learning has emerged as a critical tool for Benggang classification. However, selecting suitable feature extraction and fusion methods for multi-source image data remains a significant challenge. This study proposes a Benggang classification method based on multiscale features and a two-stream fusion network (MS-TSFN). Key features of targeted Benggang areas, such as slope, aspect, curvature, hill shade, and edge, were extracted from Digital Orthophotography Map (DOM) and Digital Surface Model (DSM) data collected by drones. The two-stream fusion network, with ResNeSt as the backbone, extracted multiscale features from multi-source images and an attention-based feature fusion block was developed to explore complementary associations among features and achieve deep fusion of information across data types. A decision fusion block was employed for global prediction to classify areas as Benggang or non-Benggang. Experimental comparisons of different data inputs and network models revealed that the proposed method outperformed current state-of-the-art approaches in extracting spatial features and textures of Benggangs. The best results were obtained using a combination of DOM data, Canny edge detection, and DSM features in multi-source images. Specifically, the proposed model achieved an accuracy of 92.76%, a precision of 85.00%, a recall of 77.27%, and an F1-score of 0.8059, demonstrating its adaptability and high identification accuracy under complex terrain conditions. Full article

Occupational Health and Safety (OHS) in agriculture is a critical concern worldwide, with self-propelled machinery accidents, particularly tip/roll-overs, being a leading cause of injuries and fatalities. In such a context, while great attention has been paid to machinery safety improvement, a major challenge is the lack of studies addressing the analysis of the work environment to provide farmers with precise information on field slope steepness. This information, merged with an awareness of machinery performance, such as tilt angles, can facilitate farmers in making decisions about machinery operations in hilly and mountainous areas. To address this gap, the Italian Compensation Authority (INAIL) launched a research programme to integrate georeferenced slope data with the tilt angle specifications of common self-propelled machinery, following EN ISO 16231-2:2015 standards. This study presents the first results of this research project, which was focused on vineyards in the alpine region of the Autonomous Province of Trento, where terrestrial LiDAR technology was used to analyze slope steepness. The findings aim to provide practical guidelines for safer machinery operation, benefiting farmers, risk assessors, and manufacturers. By enhancing awareness of tip/roll-over risks and promoting informed decision-making, this research aims to contribute to improving OHS in agriculture, particularly in challenging terrains. Full article

Nitrogen (N) and phosphorus (P) losses from sloping agricultural lands through runoff are a significant environmental concern, yet their transport mechanisms across different slope gradients are not well understood. Therefore, we built an experimental site in a subtropical hilly region of China to explore the patterns of nitrogen and phosphorus loss in tea plantations under typical slopes. We set two slope gradients of 20° and 30°, with three plots for each gradient. We quantified the loss of nitrogen (N) and phosphorus (P) through surface flow and interflow on these two slope gradients. We also collected meteorological data through the meteorological station we built. A total of 17 rainfall events were recorded. Results showed that total nitrogen (TN) and phosphorus (TP) concentrations in surface flow on the 30° slope were 8.9% and 31.6% higher, respectively, than on the 20° slope. In interflow, the differences were even more pronounced, with TN and TP concentrations 68.5% and 218.1% higher on the 30° slope. Overall nutrient loss loads (combining surface and interflow pathways) were significantly greater on the steeper slope, with TN and TP loss loads being 2.58 and 3.43 times higher on the 30° slope than on the 20° slope. The composition analysis revealed that dissolved nitrogen (DN) dominated nitrogen transport, accounting for 68.6% of TN in surface flow and 97.8% in interflow, while dissolved phosphorus (DP) represented 35.0% of TP in surface flow and 57.0% in interflow. Initially, TN and TP concentrations in surface flow were high and decreased as runoff generation time increased. Correlation analysis showed that higher temperatures increased TN and TP concentrations in surface flow. On the 30° slope, increased soil moisture promoted higher concentrations of soluble P. Instantaneous rainfall intensity was significantly correlated with TN and TP concentrations in surface flow under both slope gradients. This study revealed N and P loss patterns in tea gardens on steeper slopes, offering guidance for controlling nutrient loss in sloping farmland. Full article

Microtopography plays a crucial role in regulating soil moisture in arid and semi-arid regions, thereby significantly influencing vegetation growth and distribution. The Loess Plateau, characterized by a deeply incised and fragmented landscape, necessitates an in-depth understanding of the microtopograph–soil moisture–vegetation relationship to guide effective vegetation restoration. This study, based on field investigations and laboratory analyses in the hilly-gully region of the Loess Plateau, employed one-way ANOVA, Duncan’s multiple range test, and structural equation modeling to examine the effects of microtopography on vegetation community characteristics. The results revealed that microtopography significantly affects vegetation diversity and stability. Vegetation diversity and stability were higher on shady slopes than on sunny slopes, with diversity indices increasing by approximately 38% in certain regions. Additionally, downslope positions exhibited greater vegetation diversity than upslopes, with richness indices increasing by approximately 33% and the M. Godron index decreasing by 8.49, indicating enhanced stability. However, the effects of gullies varied significantly across different regions. Soil moisture content was higher on shaded slopes than on sunny slopes and greater at downslope positions than at upslopes, reaching up to 12.89% in gullies. Slope position exerted a direct and significant positive effect on soil moisture, which, in turn, indirectly influenced vegetation diversity and stability. This study reveals the dominant regulatory role of slope position in soil moisture, vegetation diversity, and stability, providing new perspectives and evidence for developing vegetation restoration strategies on the Loess Plateau and promoting the sustainable growth of regional vegetation. Full article

Beijing’s royal gardens represent the highest artistry in the artificial modification and utilization of natural hill and lake landforms. They also encompass the most concentrated ancient Chinese royal architectural heritage complexes. Their sustainable development has drawn significant attention, particularly in detecting and identifying areas of land subsidence and analyzing its influencing factors, which are crucial for preserving Beijing’s royal architectural heritage. This study employed time-series interferometric synthetic aperture radar (InSAR) technology to collect 148 SAR datasets from 2019 to 2023. It compares the persistent scatterer (PS)–InSAR and small baseline subset (SBAS)–InSAR techniques for cross-validation analyses to systematically assess the spatial characteristics of land subsidence of the most valuable architectural heritage complexes in the four most representative Beijing’s royal gardens. The study identified several areas with concentrated subsidence. Further analysis of the types of ancient building locations reveals that buildings situated in hilly areas (Type C), waterside buildings (Type A1), and near-water buildings (Type A2) are more significantly affected by land subsidence. Through an analysis of the causes of subsidence, it was found that, affected by the “excavating lakes and piling hills” landscape modification method and the utilization of natural hilled terrain approach, the subsidence observed in most Type C architectural heritage complexes within the study area may be associated with the Holocene sediments in the underlying soils beneath the shallow foundations of architectural heritage, localized bedrock instability caused by exposure and weathering, and slope instability. Type A building complexes’ subsidence and localized uplift may be associated with Holocene sediments beneath their foundations. The cross-comparison between SBAS-InSAR and PS-InSAR provides a reference framework for exploring land deformation research in architectural heritage sites where detection methods are constrained. Full article

Precision agriculture techniques, such as variable rate seeding (VRS) and hybrid switching, play an important role in optimizing crop yield and reducing input costs. This study evaluates the effectiveness of hybrid switching and the application of VRS technology in maize production, focusing on the accuracy of seeding rate and hybrid placement under varying field conditions. Conducted over two years, the research compares the performance of a precision planting system in flat (2023) and hilly (2024) terrain in north-eastern Hungary. The study examines seed placement uniformity, furrow quality and seed drop rates, with a focus on how terrain affects the success of these operations. A data analysis shows that hybrid switching and VRS result in better seed placement and more uniform furrows in downhill operations, with lower seed drop rates compared to uphill operations. In addition, the paper discusses the importance of accurate seeding equipment calibration and data cleaning. The findings highlight the critical need for accuracy and reliability in precision agriculture and provide insights to improve future crop management strategies and ensure sustainable farming practices. The study evaluates the accuracy of hybrid switching in maize across different terrain types and its impact on operational efficiency. The results show variation in hybrid switching distances, with an average transition length of 5.1 m on flat terrain, 5.80 m on uphill, and 4.22 m on downhill. The longest transitions occurred on uphill terrain due to increased mechanical adjustment delays, while the shortest transitions were observed on downhill slopes where seed flow remained more stable. The results highlight the importance of terrain-adaptive control mechanisms in precision planting systems to minimize transition delays, improve seed placement accuracy, and increase overall yield potential. Full article

Agropyron michnoi is a perennial grass with rhizomes in the genus Agropyron. It has a strong tolerance to drought and low temperature, and it is an established species in sandy flat and hilly slope lands, which constitute sandy grassland. So, it is an important forage species in dry grassland and desert steppes. Rhizomes not only enable asexual reproducibility but also confer strong resilience to stresses in A. michnoi. However, during production and utilization, it has been found that there are significant differences in the development of rhizomes among individuals of A. michnoi, yet the regulatory mechanism remains unclear. Therefore, in this study, the A. michnoi ‘Baiyinxile’ was used as the material, and the anatomical structures of the rhizomes, roots, and stems were analyzed using the paraffin sectioning technique. The results showed that the anatomical structure composition of the cross-section of the rhizome was similar to that of the root, while the arrangement of the vascular bundles in the stele was different from that of the root but similar to that of the stem. Subsequently, the Agropyron michnoi plants were classified into two types: plants with rhizomes and plants without rhizomes. Root, stem, and rhizome samples were collected from each type, and RNA sequencing was conducted. De novo transcriptomic analysis was performed to identify the candidate genes involved in rhizome development. From the RNA sequencing, a total of 103.73 Gb clean bases were obtained, from which 215,282 unigenes with an average length of 905.67 bp were assembled. Among these unigenes, 161,175 (74.87%) were functionally annotated based on seven common public databases. From pairwise comparisons of differentially expressed genes between the five samples, 129 candidate genes that are potentially specifically expressed in rhizomes were selected. Pathway enrichment analysis revealed that the rhizome-expressed genes are highly enriched in pathways of phenylpropanoid biosynthesis and starch and sucrose metabolism. The rhizome-specific expression pattern of 10 of the 129 candidate genes was further validated using qRT-PCR. Through the analysis of metabolites, 11 metabolites closely related to rhizome development, such as choline and betaine, were successfully identified. CYP family genes were selected for functional verification, and phylogenetic analysis revealed that CYP86B1 was grouped with CYP 86B1 of species such as Triticum aestivum and Lolium rigidum and was named AmrCYP86B1. The cloning results showed that its size was 1599 bp, and its subcellular localization was in the endoplasmic reticulum. Through stable genetic transformation, the study found that AmrCYP86B1 can promote the development of plant roots and stems and increase the dry matter content of the roots. Hormone detection showed that overexpression of AmrCYP 86B1 decreased the content of ABA hormone and increased the content of GA3 hormone in the plants. Combined with previous studies, it was determined that AmrCYP 86B1 promoted rhizome elongation by regulating ABA and GA3 hormones. The selected candidate genes involved in rhizome development, along with the preliminary functional verification, provide a preliminary mechanistic interpretation of rhizome development. This will contribute to in-depth research on the molecular mechanism of rhizome development in A. Michnoi. Full article