Search Results (733)

This study aims to explore whether the dual-scale management model, formed by integrating service-scale management with land-scale management, can further break through the benefit limits of single land-scale management and unlock additional profit potential in agricultural scale operations. This study used data from a 2024 questionnaire survey of 2166 farming households in Anhui Province and employed a coupling coordination degree model to measure the level of dual-scale management. Subsequently, we utilized OLS regression and mediation effect models to empirically examine the impact of dual-scale management on agricultural operational benefit and their underlying mechanisms. We find that dual-scale management significantly improves agricultural operational benefit. Our measurements show that dual-scale management not only breaks through the upper limit of the optimal operating area inherent in single land-scale management but also yields a greater improvement in agricultural operational benefit than single land-scale management. Heterogeneity analysis reveals that dual-scale management significantly enhances the agricultural operational benefit of farmers in plain areas and farmers with fully developed high-standard farmland. Mechanism analysis indicates that dual-scale management enhances agricultural operational benefit through an endogenous efficiency improvement mechanism and an exogenous risk-burden-sharing mechanism. These findings suggest that fostering a synergistic development system for land-scale management and service-scale management is conducive to improving the economic returns for land scale operators and unlocking new dividend spaces for agricultural scale operation in China’s post-land transfer era. Full article

Agrivoltaics (APV) mitigates land-use competition between photovoltaic installations and agricultural activities, thereby supporting multifaceted policy objectives in energy transition and sustainability. The availability of organic residuals from agrifood practices may also open the way to their energy valorization. This paper examines a small-scale farm in the Basilicata Region, southern Italy, to investigate the potential installation of an APV plant or a combined APV and bioenergy system to meet the electrical needs of the existing processing machinery. A dynamic numerical analysis is performed over an annual cycle to properly size the storage system under three distinct APV configurations. The panel shadowing effects on the underlying crops are quantified by evaluating the reduction in incident solar irradiance during daylight and the consequent agricultural yield differentials over the life period of each crop. The integration of APV and a biomass-powered cogenerator is then considered to explore the possible off-grid farm operation. In the sole APV case, the single-axis tracking configuration achieves the highest performance, with 45.83% self-consumption, a land equivalent ratio (LER) of 1.7, and a payback period of 2.77 years. For APV and bioenergy, integration with a 20 kW cogeneration unit achieves over 99% grid independence by utilizing a 97.57 kWh storage system. The CO₂ emission reduction is 49.6% for APV alone and 100% with biomass integration. Full article

Landslides are a persistent and destructive hazard in Angra dos Reis, located in the highlands of Rio de Janeiro State, southeastern Brazil, where steep slopes, intense orographic rainfall, and unregulated urban expansion converge to trigger recurrent mass movements. In this study, we applied Multiscale Geographically Weighted Regression (MGWR) to examine the spatially varying relationships between landslide occurrence and topographic, hydrological, geological, and anthropogenic factors. A detailed inventory of 319 landslides was compiled using high-resolution PlanetScope imagery after the December 2023 rainfall event. Following multicollinearity testing and variable selection, thirteen predictors were retained, including slope, rainfall, lithology, NDVI, forest loss, and distance to roads. The MGWR achieved strong performance (R² = 0.94; AICc = 134.99; AUC = 0.99) and demonstrated that each factor operates at a distinct spatial scale. Slope, rainfall, and lithology exerted broad-scale controls, while road proximity had a consistent global effect. In contrast, forest loss and land use showed localized significance. These findings indicate that landslide susceptibility in Angra dos Reis is primarily driven by the interaction of orographic rainfall, steep terrain, and geological substrate, intensified by human disturbances such as road infrastructure and vegetation removal. The study underscores the need for targeted adaptation strategies, including slope stabilization, restrictions on road expansion, and vegetation conservation in steep, rainfall-prone sectors. Full article

The Building Houses on Rented Land Model (BHRLM) was a pivotal land development model that drove Shanghai’s urbanization in the early modern era. This research examines the spatial generative mechanism of the Prepaid Building Houses on Rented Land Model (PBHRLM), prevalent during 1938–1941. It reveals how the wartime economic environment enabled interest alliances constituted with developers, landowners, and tenants to stimulate urban spatial growth. Firstly, we aim to analyze the features of architectural types linked to the PBHRLM using data-driven methods. Secondly, we aim to apply financial capital theory to investigate the innovations of financing methods. Finally, we draw on speculation theory to establish connections between the features of architectural types and the innovations of financing methods. The results include the following: (1) The PBHRLM’s dominant architectural types—new-styled lane houses, semi-shikumen lane houses, and garden houses—shared low-rise, high-density spatial features. (2) The PBHRLM’s innovations of financing methods lie in its convergence of financing and profitability, reflecting developers’ speculative intent. The research concludes that the PBHRLM operated as a spatial actuarial practice. Through risk games, the developers utilized the model to liberate land development from the control of financial capital and achieved multi-stakeholder synergy, generating small-scale, dispersed land development patterns. At the same time, surging housing demand thus perpetuated architectural types catering to the middle class with low-rise, low-tech tectonics and independent dwelling styles that continued to densely populate Shanghai concessions. Full article

Allotment—the division of Native American reservations into individually-owned plots of land—has been extensively studied; yet there exists a paucity of reservation-level studies at granular geospatial scales, i.e., at the level of examining the impacts of allotment on individuals, families, and clan or tribal groups. In previous research, we described a new semi-automated method for creating detailed GIS allotment databases and discussed the policies and processes that that lay behind allotment at the Standing Rock Reservation. In this study, we employed our Standing Rock database to map and explore allotment patterns in detail. We primarily focused on patterns of clustering versus dispersion of allotment parcels for individuals, families, and tribal groups by calculating median distance (and other descriptive statistics) and standard distance in GIS. Throughout, we used mapped representations of allotment patterns as visualization tools, both for confirming hypotheses and raising new questions. As anticipated, we discovered patterns of both gain and loss. On the one hand, as we had found earlier, the people at Standing Rock gained land through their insistence on allotments for married women and for children born after the beginning date of allotment (“later-born children”), land they otherwise would not have received. We also confirmed that married women only received half the land that their husbands received and that the early sale of “surplus” reservation lands deprived a future generation of children of the opportunity to receive their own land. Perhaps most importantly, however, we discovered that the belated timing of allotments to married women and later-born children caused their allotments to be located at some distance from those of their husbands or fathers, creating disjunct and dispersed patterns of family land holdings that would have significantly hampered the creation of viable farming and ranching operations. Full article

This study presents a techno-economic environmental risk analysis (TERA) of large-scale green hydrogen production using Alkaline Water Electrolysis (AWE) and Proton Exchange Membrane (PEM) systems. The analysis integrates commercial data, market insights, and academic forecasts to capture variability in capital expenditure (CAPEX), efficiency, electricity cost, and capacity factor. Using Libya as a case study, 81 scenarios were modelled for each technology to assess financial and operational trade-offs. For AWE, CAPEX is projected between $311 billion and $905.6 billion for 519 GW (gigawatts) of installed capacity, equivalent to 600–1745 $/kW. PEM systems show a wider range of $612 billion to $1020 billion for 510 GW, translating to 1200–2000 $/kW. Results indicate that AWE, while requiring greater land use, provides significant cost advantages due to lower capital intensity and scalability. In contrast, PEM systems offer compact design and operational flexibility but at substantially higher costs. The five most economical scenarios for both technologies consistently feature low CAPEX and high efficiency, while sensitivity analyses confirm these two parameters as the dominant cost drivers. The findings emphasise that technology choice should reflect context-specific priorities such as land availability, budget, and performance needs. This study provides actionable guidance for policymakers and investors developing cost-effective hydrogen infrastructure in emerging green energy markets. Full article

To enhance the efficiency and reduce the operational costs of large-scale Unmanned Aerial Vehicle (UAV) inspection missions limited by endurance, this paper addresses the coupled problem of dynamically positioning landing/takeoff sites and routing the UAVs. A novel Hierarchical Reinforcement Learning (H-DRL) framework is proposed, which decouples the problem into a high-level strategic deployment policy and a low-level tactical routing policy. The primary contribution of this work lies in two architectural innovations that enable globally coordinated, end-to-end optimization. First, a coordinated credit assignment mechanism is introduced, where the high-level policy communicates its strategic guidance to the low-level policy via a learned “intent vector,” facilitating intelligent collaboration. Second, an Energy-Aware Graph Attention Network (Ea-GAT) is designed for the low-level policy. By endogenously embedding an energy feasibility model into its attention mechanism, the Ea-GAT guarantees the generation of dynamically feasible flight paths. Comprehensive simulations and a physical experiment validate the proposed framework. The results demonstrate a significant improvement in mission efficiency, with the makespan reduced by up to 16.3%. This work highlights the substantial benefits of joint optimization for dynamic robotic applications. Full article

Accurate extraction of building features from remotely sensed data is essential for supporting research and applications in urban planning, land management, transportation infrastructure development, and disaster monitoring. Despite the prominence of deep learning as the state-of-the-art (SOTA) methodology for building extraction, substantial challenges remain, largely stemming from the diversity of building structures and the complexity of background features. To mitigate these issues, this study introduces DeepSwinLite, a lightweight architecture based on the Swin Transformer, designed to extract building footprints from very high-resolution (VHR) imagery. The model integrates a novel local-global attention module to enhance the interpretation of objects across varying spatial resolutions and facilitate effective information exchange between different feature abstraction levels. It comprises three modules: multi-scale feature aggregation (MSFA), improving recognition across varying object sizes; multi-level feature pyramid (MLFP), fusing detailed and semantic features; and AuxHead, providing auxiliary supervision to stabilize and enhance learning. Experimental evaluations on the Massachusetts and WHU Building Datasets reveal the superior performance of DeepSwinLite architecture when compared to existing SOTA models. On the Massachusetts dataset, the model attained an OA of 92.54% and an IoU of 77.94%, while on the WHU dataset, it achieved an OA of 98.32% and an IoU of 92.02%. Following the correction of errors identified in the Massachusetts ground truth and iterative enhancement, the model’s performance further improved, reaching 94.63% OA and 79.86% IoU. A key advantage of the DeepSwinLite model is its computational efficiency, requiring fewer floating-point operations (FLOPs) and parameters compared to other SOTA models. This efficiency makes the model particularly suitable for deployment in mobile and resource-constrained systems. Full article

Fiber Bragg grating (FBG) sensing systems face significant challenges in resolving overlapping spectral signatures when multiple sensors operate within limited wavelength ranges, severely limiting sensor density and network scalability. This study introduces a novel Transformer-based neural network architecture that effectively resolves spectral overlap in both uniform and mixed-linewidth FBG sensor arrays, operating under bidirectional drift. The system uniquely combines dual-linewidth configurations with reflection and transmission mode fusion to enhance demodulation accuracy and sensing capacity. By integrating cloud computing, the model enables scalable deployment and near-real-time inference even in large-scale monitoring environments. The proposed approach supports self-healing functionality through dynamic switching between spectral modes during fiber breaks and enhances resilience against spectral congestion. Comprehensive evaluation across twelve drift scenarios demonstrates exceptional demodulation performance under severe spectral overlap conditions that challenge conventional peak-finding algorithms. This breakthrough establishes a new paradigm for high-density, distributed FBG sensing networks applicable to land monitoring, soil stability assessment, groundwater detection, maritime surveillance, and smart agriculture. Full article

The green transformation of agricultural systems is crucial for environmental protection and food security, yet smallholder-dominated systems face immense structural barriers. This study investigates whether agricultural socialized services (ASSs)—an emerging institutional innovation—can serve as a catalyst for this transition. Using household survey data from the China Land Economy Survey (CLES), this study examines the direct impact and mediating pathways of ASSs on farmers’ adoption of green production behaviors. We also reveal the heterogeneity effects of household operating scale. The results show the following: (1) Agricultural socialized services positively impact farmers’ adoption of green production behaviors, which can contribute to advancing sustainable agricultural development. (2) ASSs do not simply increase the quantity of machines. Instead, they facilitate a shift from costly asset ownership to efficient mechanization-as-a-service. (3) Furthermore, a heterogeneity analysis reveals that the positive impacts of ASSs are heterogenous at different levels. ASSs more significantly influence farmers’ adoption of green practices for small-scale farms (operating at a size less than 4.8 mu). It provides robust empirical evidence that ASSs can effectively “decouple” green modernization from large-scale farmers to overcome structural barriers. These findings help to provide policy implications for promoting ASSs and sustainable agriculture production. Full article

This article presents the results of a rockfall analysis conducted for the limestone walls of a former quarry that is now used as an urban park. The performed simulations (2D statistical analysis using Rigid Body Impact Mechanics—RBIM and Discrete Element Modelling—DEM) enabled the determination of the maximum displacement range during the ballistic phase and the maximum rebound height at the slope base, which facilitated the delineation of a safe land-use zone. A hazard zone was also identified, within which public access must be strictly prohibited due to the risk posed by flying debris. Based on slope stability assessments (safety factor values and rockfall trajectories), recommendations were formulated for slope reinforcement measures and appropriate management actions for designated sections to ensure safe operation of the site. Three mitigation strategies were proposed: (1) no protective measures, (2) no structural reinforcements but with installation of a rockfall barrier, and (3) full-scale stabilisation to allow unrestricted access to the quarry walls. The first option—leaving slopes unsecured with only designated safety buffers—is not recommended. Full article

China aims to reduce carbon emissions but faces challenges from small-scale farmer operations. Previous studies have predominantly examined carbon density using macro-level data. This study employs a primary field survey involving 916 rice farmers, along with input–output data from their typical paddy plots, to calculate micro-level carbon emissions and assess the impact of land operation scale. The results indicate that operational scale enhances carbon emission productivity and has a nonlinear relationship with carbon emission intensity. From survey data, the carbon emission intensity of late rice is 4648.77 kg CO_2eq·ha⁻¹ in Guangdong province China, which differs by a mere 1.14% from the figure derived from yearbook macro data. The yield carbon emission productivity and yield value carbon emission productivity of rice production are 1.347 kg·kg CO_2eq⁻¹ and 2.166 CNY·kg CO_2eq⁻¹, respectively. The operational scale significantly positively enhances indirect carbon emission productivity, a key indicator of economic growth and environmental sustainability. However, it exhibits a U-shaped effect on carbon emission intensity. Our results underscore the critical role of expanding the operational scale among individual farmers to boost carbon emission productivity, facilitating the simultaneous development of grain crops and a reduction in carbon emissions. Full article

The bidirectional flow of population between urban and rural areas, not limited to rural-to-urban migration, influences the sustainable development of agricultural economic resilience in multiple ways. This study employs panel data from 31 provincial-level regions in China spanning 2017–2022 to comprehensively examine the impact of bidirectional urban–rural mobility on diverse dimensions of agricultural economic resilience, while further investigating its underlying mechanisms. Benchmark regression shows that the bidirectional urban–rural mobility exerts a suppressive effect on the agricultural economic resilience. Mechanism analyses indicate that such mobility contributes to strengthening agricultural economic resilience by catalyzing land-scale operational efficiency and amplifying labor productivity gains and that the advancement of smart agriculture technologies effectively mitigates the inhibitory impacts of bidirectional mobility on agricultural economic resilience. Furthermore, according to heterogeneity analysis, the mobility exerts a suppressive effect on the resistance (Res.) and reconstruction (Recons.) of agricultural economic resilience, while concurrently enhancing its restoration (Rest.). Meanwhile, the bidirectional mobility has significantly impeded the agricultural economic resilience of the eastern, central, and western regions, as well as the primary grain-producing areas, production and marketing balance areas, and the primary grain-selling areas. Further investigation reveals that the reverse mobility has a positive effect on the resistance but a negative effect on its restoration and reconstruction. Full article

Ensuring the safe landing of deep-sea mining vehicles (DSMVs) on soft seabed sediments is critical for the stability and operational reliability of subsea mineral extraction. However, deep-sea sediments, particularly in polymetallic nodule regions, are characterized by low shear strength, high compressibility, and rate-dependent behavior, posing significant challenges for full-scale experimental investigation and predictive modeling. To address these limitations, this study develops a high-fidelity finite element simulation framework based on the Coupled Eulerian–Lagrangian (CEL) method to model the landing and penetration process of full-scale DSMVs under various geotechnical conditions. To overcome the high computational cost of FEM simulations, a data-driven surrogate model using the random forest algorithm is constructed to predict the normalized penetration depth based on key soil and operational parameters. The proposed hybrid FEM–ML approach enables efficient multiparameter analysis and provides actionable insights into the complex soil–structure interactions involved in DSMV landings. This methodology offers a practical foundation for engineering design, safety assessment, and descent planning in deep-sea mining operations. Full article

This study examined long-term trends (1991–2019) in landings and fish community structure in the four Egyptian Nile Delta lakes. Using fisheries data, we explored trends in the catch per unit effort (CPUE) and temporal dynamics of landings and fishing effort. Non-metric Multidimensional Scaling (nMDS) and Similarity Percentage Analysis (SIMPER) were employed to assess long-term changes in fish community structure. The results revealed variable productivity across the lakes. Lake Manzala often exhibited higher yields between 1991 and 2004, and notably in 2013 (e.g., 62,372 tons), while Lake Burullus peaked at 81,399 tons in 2019. A reciprocal trend was often observed in their total yields. Lake Burullus catches were dominated by Tilapia and Mullets, while Edku and Mariout showed lower productivity. CPUE patterns varied, with Lake Manzala showing a notable increase, peaking at approximately 52 tons per boat per year in 2013, and Lake Burullus experienced a sharp increase to about 29 tons per boat per year in 2019. A shift towards amateur fishing was observed predominantly in Lake Manzala, alongside a decline in traditional licensing. An increase in fishers operating without boats was also noted across all the Northern Lakes, with contributions from Lake Edko and Lake Manzala. nMDS and SIMPER analyses revealed distinct temporal groupings of years within each lake, indicating significant shifts in fish community structure, likely in response to invasive species, pollution, and habitat degradation. These findings underscore the need for lake-specific management and long-term monitoring to address unsustainable fishing and ecological changes, ensuring biodiversity conservation and fisheries sustainability in the region. Full article