Search Results (3,062)

Agricultural watersheds must simultaneously support multiple Ecosystem Services (ESs), yet the coordination between Ecosystem Service Value (ESV) growth and synergies of ESs remains poorly understood. Taking the Liaohe River mainstream Basin (LRMB), a typical agricultural watershed, as a case, this study investigates the spatiotemporal dynamics of ESV and trade-offs among ESs, along with their driving factors. Five key ESs—Food Production (FP), Water Conservation (WC), Water Purification (WP), Soil Conservation (SC), and Landscape Aesthetics (LA)—were selected. The InVEST model, Function-based Valuation Method, Root Mean Square Deviation (RMSD), and Coupling Coordination Degree (CCD) were comprehensively applied to assess the spatiotemporal variations in ESV, trade-off intensity, and their coupling coordination degree in the watershed from 2000 to 2023. Furthermore, the Optimal Parameters-based Geographical Detector (OPGD) and Multiscale Geographically Weighted Regression with Spatial Auto-correlation (MGWR-SAR) were employed to explore the driving mechanisms underlying changes in ESV and trade-off intensity, and to identify the major driving factors and their spatial heterogeneity. The results reveal the following: (1) From 2000 to 2023, total ESV in the LRMB increased by 69.5% from 77.66 to 131.59 billion yuan, with WC and FP accounting for 42.8% and 41.9% of this growth. Spatially, ESV shifted from a west-to-east increasing gradient to a U-shaped pattern, with high values concentrated in mountainous areas and low values along the mainstream. (2) Mean trade-off intensity remained stable at approximately 0.29, yet exhibited pronounced spatial polarisation. High trade-off zones shifted from the southwestern estuary toward the mainstream corridor, driven primarily by intensifying conflicts between FP and other ESs. (3) Despite a stable watershed-average CCD of 0.71–0.73, the CCD along the Liaohe River mainstream declined by over 15%, forming a corridor of coordination decay and revealing that ESV growth occurs at the expense of internal synergy. (4) Nonlinear interactions dominated ES dynamics, with the interaction of precipitation and human disturbance intensity exhibiting the highest explanatory power (q-values of 0.61 for ESV and 0.58 for RMSD). (5) Natural climatic factors (precipitation, temperature) predominantly enhanced synergy in mountainous areas, whereas human and landscape factors (human disturbance intensity, Shannon’s Diversity Index, PLAND of water) intensified trade-offs along the mainstream and central plains. This study establishes an integrated “ESV–trade-off–CCD” diagnostic framework and proposes a differentiated management strategy, offering a potentially transferable paradigm for sustainable governance in agricultural watersheds. Full article

Land-use and land cover (LULC) change is a key driver of environmental dynamics in semi-arid Mediterranean watersheds, strongly influencing hydrological processes, soil degradation, and ecosystem stability. In this context, understanding and predicting spatial–temporal land transformations is essential for sustainable watershed management. This study proposes a nonlinear spatial–temporal modeling framework integrating a hybrid Artificial Neural Network (ANN), Cellular Automata (CA), and Markov chain approach to simulate LULC dynamics in the Sebdou watershed, northwestern Algeria. Multi-temporal Landsat imagery (1985, 2005, and 2025), combined with topographic, socio-economic, and accessibility variables (slope, population density, distance to roads, and hydrographic network), was used to reconstruct historical land-use patterns and identify key driving forces of change. A supervised Maximum Likelihood classification achieved high accuracies, with overall accuracy ranging from 92.87% to 96.26% and Kappa coefficients between 0.85 and 0.91. The ANN model was trained to estimate nonlinear transition potentials, while the CA component incorporated spatial neighborhood effects to simulate land allocation processes. Markov chain analysis provided temporal transition probabilities, enabling the construction of a coupled ANN–CA–Markov framework for scenario-based prediction. Model validation against observed 2025 LULC maps indicated strong agreement in quantity distribution (Kappa histogram = 0.767), while spatial agreement (Kappa = 0.3566) reflected inherent spatial displacement typical of CA-based stochastic allocation. Simulation results for 2045 indicate continued urban expansion along major transport corridors, progressive decline of dense forest cover, and increasing bare soil areas, while agricultural land remains dominant but increasingly fragmented. These trends highlight the growing influence of anthropogenic pressure and accessibility factors on landscape restructuring in semi-arid environments. The proposed hybrid framework provides a robust decision-support tool for anticipating land-use dynamics and assessing future environmental pressures in Mediterranean drylands. Its integration with hydrological and erosion models can further support sustainable watershed planning under combined socio-economic and climatic changes. Full article

Magnetic sensors offer a physically grounded and non-invasive approach to probing biological processes that remain inaccessible to optical, electrochemical, and radio-frequency techniques in complex agricultural environments. In recent years, advances in both classical and quantum magnetic sensors have enabled the detection of bioelectromagnetic signals across plants, soils, animals, and aquatic systems, spanning spatial scales from ionic currents to organ-level electrophysiology and population-level dynamics, positioning magnetometry as an emerging modality within the broader biosensor landscape. This review surveys the evolution of magnetic sensing technologies for agricultural and animal systems, from robust classical sensors used in navigation and soil mapping to quantum-enabled platforms, including Optically Pumped Magnetometers (OPMs) and Nitrogen-Vacancy (NV) centers, capable of resolving pT to fT biomagnetic signals. We synthesize the characteristic amplitudes, frequency ranges, and physiological origins of agriculturally relevant magnetic signals, and critically assess how techniques originally developed for medical magnetoencephalography, magnetocardiography, and low-field magnetic resonance imaging (LF-MRI) are being translated into field-deployable agricultural applications. Beyond sensing hardware, we highlight the essential role of artificial intelligence in extracting weak biological signals from dominant environmental noise, enabling synthetic gradiometry, low-field image reconstruction, and scalable interpretation in unshielded settings. Finally, we discuss how the integration of magnetic biosensing with digital twins supports predictive, multiscale monitoring of plant, animal, and ecosystem health. Together, these developments position magnetometry as an enabling technology for next-generation biosensors in precision and sustainable agriculture. Full article

Urbanisation and impervious-cover expansion are reshaping riparian landscapes, particularly in mountain cities where steep terrain concentrates development along valley floors. This study examined spatiotemporal land-cover change within the regulated riparian corridors of Thimphu City, Bhutan, over a 25-year period from 1997 to 2022 using Landsat imagery, Random Forest classification and Google Earth Engine. Results show substantial transformation of riparian land cover, with impervious cover increasing from 26.14% to 32.63%, equivalent to an overall increase of 24.83%, while agriculture/barren/low-vegetation declined from 30.59% to 26.01%, equivalent to an overall decrease of 14.98%. A modest increase in detectable vegetation cover was also observed, although this should be interpreted cautiously because the study measured land-cover extent rather than vegetation condition, floristic composition or ecological quality. Classification performance was robust, with overall accuracies ranging from 89.9% to 94.5%, exceeding the commonly accepted 85% benchmark, although uncertainty remains in narrow riparian corridors due to Landsat’s 30 m spatial resolution. Mann–Kendall analysis provided supplementary evidence of monotonic land-cover trends, but the limited number of temporal observations means these results should be interpreted as indicative, rather than definitive. Spatial analysis revealed uneven transformation, with the southern valley recording the greatest increase in impervious cover. These findings demonstrate sustained development pressure within legally regulated riparian buffers and highlight the need for routine spatial monitoring, place-specific buffer management and stronger integration of riparian protection into urban planning. The study provides a quantitative baseline for assessing future riparian land-cover change and supporting more resilient land governance in rapidly urbanising Himalayan mountain cities. Full article

Land degradation and declining groundwater availability threaten the sustainability of rural livelihoods across semi-arid regions. This study evaluates the hydrological performance of contour-based trenches as a low-cost and replicable nature-based solution (Nbs) for soil restoration, runoff regulation, and potential distributed managed aquifer recharge (MAR) in Guerrero, Mexico. The structures were installed on 12% slopes and designed using a simplified water balance criterion based on trench storage capacity, runoff coefficient, and representative rainfall events. Each trench was constructed along contour lines with overflow notches and connecting micro-trenches to improve hydraulic continuity, reduce erosion, and enhance infiltration opportunities under degraded field conditions. After one year of field monitoring, the trenches reached an average filling efficiency of approximately 90% per effective rainfall event, with estimated infiltration rates ranging from 0.0069 to 0.011 L·s⁻¹. Soil moisture in the upper soil layer showed a relative increase of approximately 10–18% compared to adjacent untreated areas, while visible reductions in runoff velocity, sediment transport, and surface erosion were observed across the treated plot. Based on trench storage capacity, observed infiltration behavior, and assumed deep percolation fractions, the potential induced recharge was estimated between 216 and 360 m³·yr⁻¹ (43–72 mm·yr⁻¹). These values represent indicative plot-scale estimates rather than direct measurements of aquifer recharge, since no tracer studies or piezometric validation were performed. The results demonstrate that contour-based trenches contribute not only to infiltration enhancement and runoff control, but also to short-term soil restoration and improved water availability in rainfed agricultural systems. Their low-cost implementation, combined with community-based maintenance and adaptation to local environmental conditions, makes them a viable complementary strategy for strengthening decentralized water management, soil resilience, and climate adaptation in semi-arid rural landscapes. However, long-term effectiveness remains dependent on maintenance continuity, institutional support, and local governance conditions. Further multi-year monitoring and direct hydrogeological validation are recommended to improve the design and replicability of decentralized MAR systems. Full article

Across the Saharan region of North Africa, oasis territories constitute the dominant form of human settlement. In Algeria, the Sahara is undergoing rapid urban and agricultural expansion, resulting in significant spatial and demographic transformations and increased environmental pressures on oasis systems. Despite these critical dynamics, existing studies have addressed oasis sustainability only superficially, lacking quantitative, territory-scale indicators that integrate both spatial and demographic dimensions. As a result, preserving oasis territories has become a critical challenge for national economic and industrial development. Spatial planning and demographic balance are key drivers for oasis landscape sustainability. This study focuses on the Tolga oasis territory, one of the largest in North Africa, to investigate the spatial and demographic relationships among the built environment, urban perimeters, population dynamics, and palm grove areas. The methodology combines: (1) historical cartographic analysis using georeferenced maps from 1900 to 2020 processed in QGIS (RMSE < 5 m); (2) GIS-based digitization of built-up areas (BuA) and palm grove areas (PGA) across four reference periods (1900, 1940, 1980, 2020); (3) polynomial regression modeling for urban perimeter vs. inter-oasis distance; and (4) least squares method for the population–palm tree correlation. Using spatial and statistical analyses, the results indicate that the built-up area should remain below a threshold ratio of 0.05 relative to the cultivated area to maintain the oasis landscape. Strong polynomial correlations (0.5876 ≤ R² ≤ 0.974) confirm the structural link between urban perimeter growth and inter-oasis distance, outperforming linear regression (mean ΔR² = +0.226). In addition, a strong correlation is identified between population size and palm tree abundance, as expressed by the relationship PT = 1.6376 Po + 755,050, where P denotes population size (F-statistic = 178.4; p < 0.01; N = 24; 95% CI of slope = ±0.24). Adopting a territorial-scale approach, this study proposes novel quantitative indicators, including ratio and formula-based models that can be integrated into Saharan territorial planning strategies to support sustainable oasis development. Full article

In this research we analyzed land cover/use processes and their impact on biodiversity in the Megalopolis of Mexico City. We used land cover/use databases from 1976 and 2018, both validated, improved and adapted for conducting landscape dynamic analysis. We also included records of 159 threatened species of fungi, vascular plants and vertebrates to construct spatially explicit biodiversity richness models based upon niche ecological algorithms. The results showed that human settlement encroachment (35%, 1892 km²) was the main factor driving land cover/use changes, significantly affecting rural and natural landscapes. The extent and location of the dramatic shrinking of agricultural land was clearly demonstrated (47.22%). Afforestation was the second most important land cover/use process occurring mainly on conversion of native grasslands and shrubland into forest cover mainly with non-local tree species. Biodiversity richness was depleted substantially, affecting 36.7% of the largest hotspots by human settlement encroachment. On the mountain peaks, as vestiges of temperate Nearctic ecosystems, with a large number of endemic and threatened species, remnants of the high potential richness of biodiversity are still conserved. The results are discussed in the light of interdisciplinary methodological approaches, potential water recharge, governance of territorial disputes, loss of cultural heritage and poorly implemented environmental policies. Furthermore, the study highlights the urgent need to generate an innovative model for development which gives equal importance to the conservation of natural and rural landscapes as a fundamental form of subsistence for human settlements. Protecting biocultural heritage is of paramount importance. The region’s genetic resources and cultural diversity are unique and have played a fundamental role in providing various benefits from nature to urban and rural inhabitants. These findings can serve as a guide for other similar megacities around the world. Full article

Peri-urban zones are critical interfaces where urban expansion, agricultural production, and ecological processes overlap. However, ecological-network planning in these areas is often constrained by uncertain boundary definition and insufficient integration between habitat quality and landscape connectivity. Taking Zhengzhou, China, as a case study, this paper proposes a resilience-oriented Ecological Security Membrane planning framework that links peri-urban boundary delineation with the prioritization of ecological sources, corridors, and critical points. A deep neural network was used to distinguish the urban core, urban fringe, and peri-urban zone from multi-source land-use and socioeconomic indicators, achieving an overall classification accuracy of 93.1%. Priority ecological sources were then identified by coupling biodiversity quality, patch morphology, area thresholds, and connectivity contribution, while corridors and critical points were prioritized to support network reinforcement. The results reveal a peri-urban ecological structure characterized by source concentration in the western mountainous and eastern agroforestry areas, insufficient ecological continuity along the Yellow River corridor, and key bottlenecks at transport and urban-expansion interfaces. The proposed framework advances peri-urban ecological planning by translating source–corridor–node analysis into a spatially explicit planning structure. Future research should test the robustness of this framework under multi-year, multi-seasonal, and scenario-based urban-growth conditions. Full article

Multi-source agricultural land-cover mapping is important for food-system monitoring, sustainable land management, and climate-adaptation planning, yet obtaining reliable labels remains difficult in heterogeneous landscapes. This paper presents a climate-aware, label-efficient active learning framework for reducing annotation demands in agricultural mapping. The framework uses multi-source predictors derived from Sentinel-1, Sentinel-2, ERA5 climatological variables, and topographic context to prioritize informative samples under constrained labeling budgets. In the main benchmark, uncertainty-guided and diversity-aware sampling achieved a macro F1-score of 0.827 while using fewer than 40% of the training labels used by the same-pool supervised baseline. Additional feature-group and cross-regional experiments showed that agro-environmental context improves robustness, particularly under regional domain shift. Overall, the results indicate that uncertainty-guided, diversity-aware active learning is a practical Geo-AI strategy for reducing annotation requirements in climate-relevant agricultural monitoring within the scope of binary crop/non-crop mapping over ML-ready feature time series. Full article

The San Juan Islands are one of the few places where native temperate grasslands are found in western Washington State. These ecosystems are important reservoirs of biodiversity and sources of ecosystem services, support many rare and endemic species, and have profound cultural significance to the Coast Salish peoples. These ecologically and culturally valuable ecosystems have become scarce due to the combined pressures of changes in land use, the introduction of non-native invasive species, and the exclusion of fire from the landscape. A lack of historical context and ecological baseline knowledge has made it impossible to fully understand the long-term trends in the extent and distribution of this ecosystem. To address this knowledge gap, we used historical land cover data and multispectral imagery to create a high-resolution, spatially explicit database of grassland extent on the San Juan Islands at multiple time periods since the early years of Euro-American colonization. Our spatial analysis of these data revealed significant decreases in grassland extent between time periods, with an overall 77% net decrease in the extent of non-agricultural grasslands and a loss of 93% of the area of persistent, old-growth grasslands since 1890 across the region. These changes are primarily a result of conversion to agriculture and conifer encroachment or succession to forest. The spatial data and analyses created in this study help to develop the historical baseline of native temperate grasslands on the San Juan Islands, adding to our understanding of the lingering legacy that changes in land use have had on this ecosystem, with the potential to aid in the development of effective conservation and restoration practices. Full article

Inland water management is increasingly important under climate change due to the need for landscape-scale water retention, but in situ studies are limited by fluctuating, shallow, and intermittent water cover. This study simulated prolonged waterlogging under controlled laboratory conditions. Four agricultural soils (Calcisol, Arenosol, Chernozem, and Solonetz) were flooded for 40 days using identical 1:5 soil-to-water ratios at two temperature regimes, at 4 and 22 °C. Given that periodic water cover may conflict with agricultural production, particular attention was paid to crop-relevant indicators, including pH, water-soluble salts, and N, P, K. The laboratory simulation revealed significant differences among soil types and between temperature treatments. Elevated Mg concentrations limited the irrigation suitability of leachate derived from Calcisol, with Mg% values ranging from 57 to 64%, exceeding the 50% guideline threshold. Soil buffering capacity controlled phosphorus and potassium dynamics, resulting in stable or slightly increasing AL-soluble nutrient levels, except in low-buffering sandy soils where up to 3–4-fold variability was observed. Reductive conditions developed early in the Calcisol samples, supported by dissolved oxygen saturation values below 20% during the first days of the experiment. Oxygen saturation increased later, only exceeding 60% twice in the cooled Calcisol treatment, while nitrate–ammonium dynamics reflected changing redox conditions. Temperature significantly affected solubility and nutrient mobility, partly through its influence on microbial activity. These findings improve our understanding of inland water–soil interactions and support the development of sustainable, water-retentive land management strategies. Full article

Extracting rural road networks from remote sensing images is crucial for data-driven precision agriculture planning. However, traditional semantic segmentation methods often struggle to achieve both high-precision boundary delineation and topological integrity, especially in heterogeneous rural landscapes. To address these issues, this study proposes MACER-UNet, a novel connectivity-aware road extraction model that integrates multi-scale perception and edge enhancement capabilities. Specifically, MACER-UNet employs ResNet-50 as the backbone network to extract robust deep semantic features. Within the encoder–decoder framework, an atrous spatial pyramid pooling module (ASPP) is embedded to capture rich multi-scale context cues, thereby enhancing robustness to varying road widths and inconsistent imaging conditions. During the decoding process, the convolutional block attention module (CBAM) recalibrates features to reduce noise from the agricultural background. The edge enhancement module (EEM) extracts high-frequency gradient cues for geometric correction and boundary sharpening. This architecture combines spatial attention and edge constraints to balance recognition accuracy and topological connectivity. On the public WHU-CR dataset, MACER-UNet achieved an intersection over union (IoU) of 50.37% and an F1 score of 67.02%, outperforming U-Net (44.27%), DeepLabv3+ (49.43%), and D-LinkNet (49.54%), and its connectivity was comparable to recent state-of-the-art road extraction methods such as C²Net (49.37%) and CGCNet (50.34%). On a self-built dataset with a 3 m resolution in Suihua, the model achieved an IoU of 42.56% and an F1 score of 59.71%. The evaluation results confirm that MACER-UNet provides a road network with geometric consistency and topological integrity for spatial analysis in rural environments. Full article

Accurate mapping of winter rapeseed cultivation areas is crucial for food security assessment and agricultural resource management, yet remains a persistent challenge in mountainous regions characterized by complex topography and highly fragmented field parcels. To address these challenges, this study develops a progressive deep learning framework using single growing-season data from the Hanzhong Basin. We conducted a structured comparison of remote sensing indices, machine learning, and deep learning approaches for rapeseed identification in heterogeneous landscapes. First, sensitivity analysis of the Flowering Index for Rapeseed was performed to identify the optimal parameterization, yielding high inter-class separability (ND = 0.959) during peak flowering and a threshold-based overall accuracy (OA) of 94.41%. Second, a multidimensional feature space was constructed by integrating Sentinel-2 spectral bands, image texture metrics, and topographic variables; Random Forest-based feature importance selection subsequently enhanced Support Vector Machine classification performance to an OA of 90.70%. Third, we proposed an innovative three-stage progressive UNet++ architecture: Stage1 focuses on binary rapeseed/non-rapeseed classification to establish spatial priors; Stage2 refines discrimination among spectrally similar vegetation classes (rapeseed and other vegetation); and Stage3 achieves comprehensive seven-class semantic segmentation. A weighted focal loss function combined with a weight inheritance mechanism was employed to mitigate class imbalance and facilitate inter-stage knowledge transfer. The final model attained an OA of 98.65% and a mean intersection over union of 95.29%, while effectively suppressing salt-and-pepper noise artifacts in geometrically fragmented parcels. Our findings demonstrate the substantial advantages of progressive deep learning strategies for crop monitoring in topographically constrained environments. Full article

The genus Burkholderia encompasses both plant pathogenic and beneficial species, yet the genomic determinants underlying this lifestyle divergence remain poorly understood. Using 16S rRNA sequencing of 100 rice cultivars, our companion study demonstrated that resistant varieties are enriched in beneficial Burkholderiaceae, leading to the isolation of three phenotypically contrasting strains. Here, we present comparative genomic analyses of non-pathogenic biocontrol strain Burkholderia vietnamiensis J14EpLeaf2 and pathogenic strains Burkholderia gladioli A1EpSeed5 and Burkholderia cepacia J14Eple. Pathogenic strains possess significantly larger genomes (8.36–8.46 Mb) enriched in mobile genetic elements compared to the streamlined 6.95 Mb genome of B. vietnamiensis. CAZyme analysis revealed broader repertoires of glycoside hydrolases and polysaccharide lyases in pathogens, consistent with enhanced plant cell wall degradation. B. gladioli possesses a complete T3SS and expanded T6SS with 301 predicted effectors, while B. cepacia lacks structural T3SS genes but harbors 271 candidate effectors predicted to be secreted via alternative secretion pathways, compared to 180 in B. vietnamiensis. Notably, B. cepacia harbors cystic fibrosis-associated markers (cable pili, ZmpA/ZmpB), raising significant biosafety concerns that preclude its agricultural application. LC-MS validated IAA, ornibactin, and AHL production in B. vietnamiensis, supporting its plant growth-promoting and biocontrol functions. Computational PPI networks predicted distinct interaction landscapes requiring experimental validation. This study provides a genomic framework for distinguishing pathogenic from beneficial Burkholderia and supports B. vietnamiensis as a safe biocontrol agent while cautioning against B. cepacia J14Eple. Full article

Host-finding behavior of insect herbivores is a key determinant of herbivory intensity in agricultural and forest ecosystems, which often drives excessive pesticide application for pest control. While host plant apparency theory explains herbivore host detection, and push–pull strategies manipulate this behavior, both produce inconsistent results and remain mechanistically disconnected. Existing frameworks like the Resource Concentration Hypothesis focus mainly on host density, ignoring the multidimensional, context-dependent nature of apparency. Here, we synthesize forest and agricultural research to develop the first unified framework linking these two concepts. We show that host plant apparency is not intrinsic but shaped by plant morphology, non-host identity, and spatial arrangement. Push–pull strategies exploit this relativity by redesigning the chemical and physical apparency landscape. We argue that: (1) push–pull system success requires reducing main crop apparency while enhancing trap crop apparency; (2) trap crops may fail when their dual functions, olfactory attraction or physical interception, are misinterpreted, with profound implications for spatial design; and (3) this integration resolves field contradictions by framing them within a common bottom-up mechanism. Our framework provides a generalizable principle for sustainable pest management: effective control depends on understanding what makes host plants apparent to target pests in their specific local environment. Full article