Search Results (149)

China regards shale gas as a key energy source for ensuring energy security, promoting the transformation of the energy structure, and addressing climate change. However, at this stage, the scarcity of land resources, coupled with various institutional restrictions, has brought numerous practical obstacles to the large-scale commercial development of shale gas. By analyzing the restrictive provisions concerning shale gas development in China’s current laws, this paper points out three major institutional constraints faced by the use of agricultural land for shale gas development: first, stringent land use control policies; second, the legal acquisition system for surface land remains unstable; third, institutional gaps in the supervision of subsurface space on collectively owned land. To overcome these institutional barriers, this study proposes fundamental reform measures for the current land legal framework. If comprehensive reform cannot be achieved immediately, partial breakthroughs may be sought within the existing institutional framework. The sequence has three phases. Near-term one to three years: negative-list quotas, refined land classification, land linkage, benefit balance, and community guidance. No law changes needed; provinces can act. Medium-term three to seven years: regulations and the mining land chapter in the revised Mineral Resources Law. Long-term beyond seven years: constitutional amendment for collective land transfer and dual-track supply reform. This study provides a theoretical reference for solving the land use issues in China’s shale gas development, and its conclusions also provide a reference for resolving the conflicts between shale gas development and agricultural land use in other jurisdictions. Full article

Accurate crop yield prediction increasingly relies on diverse data streams, including satellite observations, meteorological reanalysis, soil composition, and topographic information. However, despite advances in machine learning, many existing approaches remain crop- or region-specific and require substantial bespoke data engineering, limiting scalability and reproducibility. This study introduces UniCrop, a generalisable, configuration-driven data engineering pipeline that standardises the acquisition, harmonisation, and feature construction of multi-source agro-environmental data. Rather than proposing a new predictive model, UniCrop addresses a key bottleneck in agricultural machine learning: the lack of reproducible and scalable data preparation workflows. For any given location, crop type, and temporal window, the pipeline automatically retrieves, harmonises, and engineers over 160 environmental variables from heterogeneous sources (Sentinel-1/2, MODIS, ERA5-Land, NASA POWER, SoilGrids, and SRTM), reducing them to a compact, analysis-ready feature set using a structured feature selection process based on minimum redundancy maximum relevance (mRMR). The effectiveness of the pipeline is demonstrated through a case study, where the generated datasets enable robust baseline modelling across multiple machine-learning algorithms. Using a selected subset of 15 features, four baseline models (LightGBM, Random Forest, Support Vector Regression, and ElasticNet) were evaluated under rigorous cross-validation. LightGBM achieved the best single-model performance (RMSE = 465.1 kg/ha, $R^2=0.6576$), while a constrained ensemble provided a marginal improvement (RMSE = 463.2 kg/ha, $R^2=0.6604$). SHAP-based analysis further confirms that the selected features capture agronomically meaningful relationships across data modalities. UniCrop contributes a scalable and transparent data engineering pipeline that enables consistent, reproducible, and transferable dataset construction for crop yield prediction. By decoupling data specification from implementation and supporting flexible configuration across crops, regions, and temporal contexts, the framework provides a practical foundation for large-scale agricultural analytics. Full article

Plastic pollution is ubiquitous in the environment, with microplastics (particles < 5 mm) a worldwide emergent contaminant of concern; however, no studies have explored historical patterns in freshwater macroinvertebrates in connection to species traits and changes in land use. We measured microplastics in macroinvertebrates collected from 1998–2019 at six forest, urban, and agriculture dominated streams in the Schuylkill River watershed (PA, USA). We selected representative macroinvertebrates that underwent peroxide oxidation digestion and were examined for microplastics under a dissecting microscope with Rose Bengal dye to confirm microplastic identification. Microplastics were present in 134 individuals across 24 taxa and all functional feeding groups (FFGs). Abundance of microplastics in macroinvertebrates decreased through time with an increase of agriculture land use, whereas microplastics increased with an increase of urban land use. Collector-gatherer FFG had the greatest microplastic abundance, which may be linked to Elmidae and Psephenidae (Coleoptera), which tended to have the highest abundances of microplastics compared to all other taxa in some years. This relationship may be explained by the feeding mode of these families of macroinvertebrates and that gathering particles in the water column and scraping particles from biofilms may be similarly effective in the acquisition of microplastics. Overall, understanding microplastic temporal patterns and their connections to species traits and variations in land and water use is critical to developing a predictive framework for explaining microplastic abundance in the environment and interactions with aquatic biota. Full article

Soil salinization severely threatens agricultural sustainability in saline–alkali regions, and high-throughput, efficient screening of salt-tolerant rice varieties is critical to mitigating this threat. Traditional evaluation methods are constrained by low throughput, limited spatiotemporal resolution, and the lack of standardized indicators. To address these gaps, this study established a multi-scale spectral phenotyping framework integrating ground-based hyperspectral, UAV-borne multispectral, and Sentinel-2 satellite remote sensing data for high-throughput screening of salt-tolerant rice. Field experiments were conducted with 12 rice lines at five key growth stages in Ningxia, China, with synchronous ground spectral measurements and UAV image acquisition on the same day for each stage. Five feature selection methods were employed to screen salt stress-sensitive hyperspectral bands, with classification accuracy validated via a Support Vector Machine (SVM) model. The results showed that: (1) rice spectral characteristics varied dynamically across growth stages, and first-order differential transformation effectively amplified subtle spectral variations in stress-sensitive regions; (2) the Minimum Redundancy–Maximum Relevance (mRMR) method outperformed other methods, achieving 100% classification accuracy at key growth stages, with sensitive bands dominated by red edge bands (58.33%); (3) the constructed Salt Stress Index (SIR) showed strong correlations with classical vegetation indices and rice yield, and could clearly distinguish salt-tolerant and salt-sensitive rice varieties, with stable performance against field environmental noise; and (4) band matching between UAV and Sentinel-2 data enabled multi-scale data fusion and regional-scale salt stress monitoring. This framework realizes the transformation from qualitative spectral description to quantitative salt tolerance evaluation, providing standardized technical support for salt-tolerant rice breeding and precision management of saline–alkali lands. Full article

Urban sprawl promotes significant changes in land use and occupation by interfering with the dynamics of functional ecosystems. Among other things, it encourages forest fragmentation, the degradation of woodland edges, and altered habitat integrity. This study aims to propose a simplified and low-cost methodological framework that integrates open data and open-source tools to monitor the potential of ecosystem services (ESs) at the municipal scale. Guided by the hypothesis that rapid suburbanization leads to measurable declines in ecological integrity, the InVEST Habitat Quality model was used as a proxy to analyze the landscape’s capacity to support ES. The procedure included data acquisition and organization, land use reclassification, and scores for the threats and sensitivities, implemented through the InVEST software 3.14.2. Results indicated that urban areas more than doubled between 1985 and 2005, while habitat quality scores declined across Campinas, reflecting a decrease in the potential for ES provision. Urban expansion, mainly concentrated in the central region, occurred at the expense of agricultural and pasture areas. Forest remnants, which currently occupy only 8.5% of the municipal territory, are small and fragmented, intensifying edge effects and reducing the potential capacity to provide regulatory ES. Fragmentation and adjacent land use changes limit these habitats’ capacity to provide ES. The proposed methodology demonstrates the potential for simple and reproducible monitoring of ecosystem services at the municipal scale, providing support to local governments with limited financial and technical capacity in geospatial data processing. This framework enables municipalities to incorporate environmental indicators into planning tools, offering a scalable approach for monitoring ecosystem dynamics in urbanized regions. Full article

Soil erosion, particularly ephemeral gully (EG) erosion, poses a significant threat to agricultural sustainability and ecosystem health. Despite their substantial impact on soil degradation, EGs have been relatively understudied, primarily due to their temporary nature and the limitations of existing measurement techniques. This study introduces an integrated approach for quantifying and analyzing EGs, addressing the critical need for accurate and scalable measurement methods. Our methodology combines three key components: (1) an updated portable field tool (Gulliometer), which improves upon existing designs to enhance data collection in diverse field conditions; (2) a standardized image acquisition protocol that ensures consistent, high-quality data capture; and (3) an image processing technique leveraging easy repetitive analysis of gully cross-sections. Laboratory validation using known geometric shapes demonstrated the high precision of our methodology, with error rates below 1%. Field applications in two distinct locations in Ontario, Canada, further confirmed the practicality and effectiveness of our approach under varied environmental conditions. This approach not only advances our understanding of ephemeral gully erosion but also aids in the development of effective soil conservation strategies and informed decision-making in land management. Full article

Arable land quality is of the essence for the sustenance of grain production and food security. The continuous monitoring of the physical and chemical properties of arable land is instrumental in facilitating a comprehensive understanding of the evolution patterns of soil quality. This, in turn, provides fundamental evidence that is crucial for the optimization of cultivation practices, the establishment of appropriate plough layers, and the enhancement of soil quality. The near-surface sensing methodologies facilitate the acquisition of soil data at reduced scales, thus signifying a pivotal research trajectory for the procurement of soil-related information. The present study undertakes an examination of the current state of research on acquiring key parameters of farmland soil and provides an overview of the fundamental ground-level techniques employed for the assessment of farmland soil parameters. These techniques encompass single-parameter fixed-point detection, encompassing Soil Moisture Content (SMC), Soil Electrical Conductivity (EC), and nutrient analysis, multi-parameter fusion detection, and dynamic parameter monitoring. The study systematically reviews field sensing methods for major soil physicochemical parameters (such as SMC, Soil Penetration Resistance (SPR), EC, and nutrients) while analyzing the current application of Artificial Intelligence (AI) in soil parameter detection. The present paper proposes a developmental trajectory that shifts from “single-parameter static” to “multi-parameter dynamic” monitoring. This trajectory is proposed as a building upon the analysis of existing research. This evolution emphasizes intelligent algorithm-driven data enhancement to improve detection accuracy, forming a closed-loop progression of “dynamic detection—precise modeling—decision support”. This framework provides a reference for the advancement of soil sensing monitoring technologies and the scaling of precision agriculture applications. Full article

High-spatial-resolution remote sensing imagery provides a data foundation for fine-grained land use classification. However, due to long revisit cycles and susceptibility to cloud cover, large-area imagery often suffers from temporal inconsistency, which severely limits the classification accuracy of traditional unified models. To address this issue, this study proposes a geographic entity-oriented, spatiotemporally coupled land use classification method for high-resolution remote sensing imagery, with agricultural land (including paddy fields, dry farmland and gardens) as an example for validation. In this method, the study area is first divided into multiple sub-regions based on image acquisition time, ensuring temporal consistency within each sub-region. A dedicated deep texture feature extraction model is then constructed for each sub-region. This model is adapted from the advanced CAPTN texture recognition network: its classification head is removed, and a multi-scale feature fusion module is introduced, transforming it into an encoder focused on extracting spatial texture feature maps. Additionally, a self-supervised loss function combining masked feature reconstruction and cross-view consistency is designed to improve the quality of the learned texture features. During the prediction stage, the corresponding feature extractor is invoked based on the temporal phase of the imagery to generate a full-region texture feature map. This feature map is then cropped using land parcel vectors, and statistical feature vectors describing the texture attributes of each parcel are formed by calculating the mean and standard deviation of the features within each parcel. Finally, a Random Forest classifier is employed to determine the land parcel categories. This study uses the Jiangjin District of Chongqing City as the experimental area. The results show that, compared to training a unified deep learning model directly on full-region multi-temporal imagery or using traditional texture features, the proposed spatiotemporally coupled classification framework achieves significant improvements in overall accuracy and Kappa coefficient, reaching 92.3% and 0.89, respectively. Full article

Accurate estimation of tree biomass and volume is essential for sustainable forest management, climate change mitigation, and ecosystem service assessment. Recent advances in unmanned aerial vehicle (UAV) technology enable the acquisition of ultra-high-resolution optical and three-dimensional data, providing a resource-efficient alternative to traditional field-based inventories. This review synthesizes 181 peer-reviewed studies on UAV-based estimation of tree biomass and volume across forestry, agricultural, and urban ecosystems, integrating bibliometric analysis with qualitative literature review. The results reveal a clear methodological shift from early structure-from-motion photogrammetry toward integrated frameworks combining three-dimensional canopy metrics, multispectral or LiDAR data, and machine learning or deep learning models. Across applications, tree height, crown geometry, and canopy volume consistently emerge as the most robust predictors of biomass and volume, enabling accurate individual-tree and plot-level estimates while substantially reducing field effort and ecological disturbance. UAV-based approaches demonstrate particularly strong performance in orchards, plantation forests, and urban environments, and increasing applicability in complex systems such as mangroves and mixed forests. Despite significant progress, key challenges remain, including limited methodological standardization, insufficient uncertainty quantification, scaling constraints beyond local extents, and the underrepresentation of biodiversity-rich and structurally complex ecosystems. Addressing these gaps is critical for the operational integration of UAV-derived biomass and volume estimates into sustainable land management, carbon accounting, and climate-resilient monitoring frameworks. Full article

Climate challenges, along with a projected global population increase of 2 billion by 2080, are intensifying pressures on agricultural systems, leading to biodiversity loss, land use constrains, soil fertility declining, and changes in water cycles, while crop yields struggle to meet the rising food demand. These challenges, coupled with evolving legislation and rapid technology advancements, require innovative sustainable agricultural solutions. By reshaping farmers’ daily operations, real-time data acquisition and predictive models can support informed decision-making. In this context, smart farming (SM) applied to plant breeding can improve efficiency by reducing inputs and increasing outputs through the adoption of digital and data-driven technologies. Examples include the investment on common ontologies and metadata standards for phenotypes and environments, standardization of HTP protocols, integration of prediction outputs into breeding databases, and selection workflows, as well in building multi-partner field networks that collect diverse envirotypes. This review outlines how AI and machine learning (ML) can be integrated in modern plant breeding methodologies, including genomic selection (GS) and genetic algorithms (GAs), to accelerate the development of climate-resilient and sustainably performing crop varieties. While many reviews address smart farming or smart breeding independently, herein, these domains are bridged to provide an understandable strategic landscape by enhancing breeding efficiency. Full article

Efficient nitrogen (N) management is critical for improving productivity and sustainability in intercropping systems, especially in semi-arid regions. Maize and peanut, the two dominant local crops, were selected to represent a typical cereal/legume intercropping system with contrasting nitrogen acquisition strategies. To investigate how spatial configuration regulates nitrogen uptake and nitrogen use efficiency in maize/peanut intercropping systems, a 3-year field (2022–2024) experiment was conducted on sandy soils in semi-arid northwest Liaoning, China. Six cropping systems were evaluated, including sole maize, sole peanut, and four intercropping configurations differing in strip width and crop proportion, including M2P2 (two rows of maize intercrop with two rows of peanut, M indicates maize and P indicates peanut), M2P4, M4P4, and M8P8. The total land equivalent ratio (LER) varied from 0.65 to 1.09, indicating that yield advantages were highly dependent on spatial configuration. Maize consistently exhibited stronger competitiveness than peanut, resulting in suppressed peanut growth in narrow-strip systems. Increasing strip width and peanut proportion alleviated interspecific competition and improved fertilizer nitrogen equivalent ratio (FNER) and nitrogen equivalent ratio (NER) in intercrops. Although intercropping did not consistently enhance total nitrogen uptake, nitrogen use efficiency was significantly improved. Narrow-strip systems (M2P2 and M2P4) increased nitrogen use efficiency, whereas wide-strip systems (M4P4 and M8P8) achieved yield benefits mainly through enhanced nitrogen uptake. Overall, the results highlight that spatial configuration plays a key role in regulating nitrogen uptake and interspecific competition in maize/peanut intercropping under semi-arid sandy conditions. Optimizing strip width and crop proportion is therefore critical for stabilizing yield and improving resource use efficiency in maize/peanut intercropping systems in dryland agriculture. Full article

South Africa’s land reform program is a cornerstone of efforts to redress historical injustices, guided by the 1997 White Paper on Land Reform Policy and structured around restitution, redistribution, and tenure reform. Three decades into implementation, this study systematically reviews the policy performance from 1994 to 2024, focusing on these pillars and related governance measures. Despite repeated policy revisions and extensive public debate, significant gaps persist between objectives, such as equitable access, tenure security, and poverty reduction—and actual outcomes. Using PRISMA guidelines, 94 peer-reviewed articles were selected from Scopus (1994–2024) alongside key policy documents and official reports. Evidence shows that land reform has consistently fallen short of its targets. Restitution claims remain largely cash-based, undermining tenure security, while redistribution has transferred less than 14% of agricultural land, far below the 30% target. Beneficiaries under the Proactive Land Acquisition Strategy (PLAS) often hold insecure lease agreements, and most households in former homelands lack title deeds, perpetuating vulnerability. The weak institutional capacity, poor coordination, and inadequate post-settlement support further constrain progress. The review concludes that the most fundamental policy priority is establishing a unified national framework that guarantees secure land tenure through the issuance of title deeds, complemented by integrated post-settlement support and transparent beneficiary selection. Strengthening tenure security is essential for enabling investment, improving livelihoods, and achieving equitable and sustainable land reform. Full article

For the development of a green future, managerial decision making at the local level plays an important role. The study is dedicated to the analysis of the current state of green areas, and development and rehabilitation of green areas in the territorial communities of Ukraine. The goal of the study is the development of a set of measures to create a sustainable green infrastructure at the local level in Ukraine. The main trends of green land policies by territorial communities were substantiated: keeping the natural afforestation of agricultural land; the development and rehabilitation of water conservation zones, windbreak belts, anti-erosion forests, green belts of inhabited areas, and nature conservation or recreation areas; and promoting gardening. A land reallotment methodology, which allows for the expansion of a spatial environment for the development and rehabilitation of green areas was suggested. The methods and approaches presented were tested in the Petrivska Territorial Community of Kyiv Region. The presented measures allow for an increase the green area of a territorial community by 1,084,352 m². The approach allows for the minimization of the condemnation of land from landowners, creates a more comfortable environment for the population, facilitates the effectiveness of agriculture due to containing the erosion, and conservation of natural landscapes. The research findings approved that the main challenges for the implementation of green policies are the acquisition of land for green areas in the environment of the historically established land use, and controlling the sustainable use of green areas and their surroundings responsibly to prevent their violation. Full article

Agrivoltaic (APV) systems co-locate agricultural production and photovoltaic (PV) electricity generation on the same land to maximize land use efficiency. This study proposes an integrated assessment framework that jointly evaluates crop yield and electricity generation in APV systems. Unlike many previous APV studies that estimated crop responses from empirical PAR–photosynthesis relationships, this framework explicitly couples a process-based rice growth model (DSSAT-CERES-Rice) with irradiance and PV performance simulations (Honeybee-Radiance and PVlib) in a single workflow. The five-stage framework comprises (i) meteorological data acquisition and processing; (ii) 3D modeling in Rhinoceros; (iii) calculation of module front and rear irradiance and crop height irradiance using Honeybee; (iv) crop yield calculation with DSSAT; and (v) electricity generation calculation with PVlib. Using bifacial PV modules under rice cultivation in Gochang, Jeollabuk-do (Republic of Korea), simulations were performed with ground coverage ratio (GCR) and PV array azimuth as key design variables. As GCR increased from 20% to 50%, crop yield reduction (CYR) rose from 12% to 33%, while land equivalent ratio (LER) increased from 128% to 158%. To keep CYR within the domestic guideline of 20% while maximizing land use, designs with GCR ≤ 30% were found to be appropriate. At GCR 30%, CYR of 17–18% and LER of 139–140% were achieved, securing a balance between agricultural productivity and electricity generation. Although PV array azimuth had a limited impact on crop yield and electricity generation, southeast or southwest orientations showed more uniform irradiance distributions over the field than due south. A simple economic assessment was also conducted for the study site to compare total annual net income from rice and PV across GCR scenarios. The proposed framework can be applied to other crops and sites and supports design-stage decisions that jointly consider crop yield, electricity generation, and economic viability. Full article

The comprehensive, accurate, and rapid acquisition of large-scale surface deformation using Interferometric Synthetic Aperture Radar (InSAR) technology provides crucial information support for regional eco-geological safety assessments and the rational development and utilization of groundwater resources. The Linfen-Yuncheng Basin in Shanxi Province is one of China’s historically most frequented regions for geological hazards in plain areas, such as land subsidence and ground fissures. This study employed the coherent point targets based Small Baseline Subset (SBAS) time-series InSAR technique to interpret a dataset of 224 scenes of 5 m resolution RADARSAT-2 satellite SAR images acquired from January 2017 to May 2024. This enabled the acquisition of high-resolution spatiotemporal characteristics of surface deformation in the Linfen-Yuncheng Basin during the monitoring period. The results show that the area with a deformation rate exceeding 5 mm/a in the study area accounts for 12.3% of the total area, among which the subsidence area accounts for 11.1% and the uplift area accounts for 1.2%, indicating that the overall surface is relatively stable. There are four relatively significant local subsidence areas in the study area. The total area with a rate exceeding 30 mm/a is 41.12 km², and the maximum cumulative subsidence is close to 810 mm. By combining high-resolution satellite images and field survey data, it is found that the causes of the four subsidence areas are all the extraction of groundwater for production, living, and agricultural irrigation. This conclusion is further confirmed by comparing the InSAR monitoring results with the groundwater level data of monitoring wells. In addition, on-site investigations reveal that there is a mutually promoting and spatially symbiotic relationship between land subsidence and ground fissures in the study area. The non-uniform subsidence areas monitored by InSAR show significant ground fissure activity characteristics. The InSAR monitoring results can be used to guide the identification and analysis of ground fissure disasters. This study also finds that due to the implementation of surface water supply projects, the demand for groundwater in the study area has been continuously decreasing. The problem of ground water over-extraction has been gradually alleviated, which in turn promotes the continuous recovery of the groundwater level and reduces the development intensity of land subsidence and ground fissures. Full article