Search Results (4,148)

Hurricanes cause severe impacts on lives, livelihoods, and essential systems. Hurricane Melissa impacted Jamaica as a Category 5 cyclone, resulting in estimated losses of approximately 41% of national GDP (US$8.8 billion) and eliciting widespread damage to housing, healthcare, agriculture, and urban infrastructure. Agriculture sustained heavy losses, with 41,000 hectares of damaged farmland and the loss of more than 1 million livestock animals. These impacts resulted in exposed animal closures with biological hazards. Using systems thinking, the PESTHEEL framework, and a One Health lens, we argue for viewing Hurricane Melissa as series of cascading inter-related One Health threats of waterborne and vector-borne diseases, zoonoses, antimicrobial resistance, degraded indoor and outdoor air quality, chemical pollution, and shifting migration and border dynamics. These each unfold at different timings. A structured synthesis for Jamaica and other Caribbean Small Island Developing States is provided by integrating systems thinking, One Health, and the PESTHEEL framework. Immediate and lagged risk pathways are identified, and practical risk reduction actions are proposed to support anticipatory, multisectoral recovery: enhanced syndromic, laboratory, wastewater, vector, and rodent surveillance; resilient WASH and shelter systems; non-insecticidal and integrated vector management; biosecure aid and border protocols; environmental toxicology monitoring; and climate–health intelligence. Full article

Global climate change poses escalating ecological challenges, with agriculture contributing approximately 30% of anthropogenic greenhouse gas emissions, primarily from nitrous oxide (N₂O) and methane (CH₄). The farmland carbon-nitrogen cycle represents a key nexus for coordinating pollution control and carbon mitigation. This study applies bibliometric methods, including co-occurrence analysis, clustering, and burst detection, to 1286 publications retrieved from the Web of Science Core Collection (1990–2025) and CiteSpace 6.2.R4. Results indicate that China (444 papers, centrality 0.42), the United States (211 papers), and Germany (151 papers) are leading contributors, with major institutions forming a multi-centered international collaboration network. Keyword analysis identified 11 core clusters (modularity Q = 0.82, silhouette S = 0.91), with nitrous oxide emerging as the central theme (frequency 670). The field has evolved through three stages: fundamental emission mechanism studies (1990–2005), agricultural management practices (2006–2015), and integrated mitigation strategies with microbial mechanism exploration (2016–2025). Current frontiers emphasize microbial-mediated carbon-nitrogen cycling and yield-scaled emission assessments bridging theory and practice. Future research should prioritize cross-scale coupling analysis, multi-objective management frameworks, smart agricultural technologies, and policy integration. This study provides a systematic bibliometric mapping of the evolution of synergistic carbon-nitrogen research in agricultural systems, offering a quantitative overview of development trends and research gaps. Full article

Non-grain use of cultivated land (NGUCL) in ecologically fragile regions has become a major challenge to food security and land sustainability, yet its spatiotemporal dynamics, spatial spillover effects, and associated factors remain insufficiently understood. Taking Ningxia, China, as a typical semi-arid to arid transition zone, this study developed a phenology-informed framework that combined multi-temporal Landsat imagery, random forest classification, spatial autocorrelation analysis, centroid and standard deviation ellipse models, and a spatial lag model to identify and analyze NGUCL in 2005, 2010, 2015, and 2020. Within the cultivated land boundary, NGUCL was further decomposed into cash crop-cultivated farmland (CCCF) and farmland abandonment (FA). The results show that the classification framework achieved robust performance, with overall accuracies above 85% across the benchmark years. Food-crop mapping reached an OA of 86.38–90.12% and a Kappa of 0.80–0.85, while FA mapping reached an OA of 85.60–86.74% and a Kappa of 0.70–0.72. NGUCL in Ningxia exhibited strong subregional differentiation under the gradients of northern irrigation, central arid, and southern mountainous conditions. CCCF was more closely associated with irrigated and agriculturally productive areas, whereas FA was concentrated in ecologically constrained counties and showed stronger dispersion and migration complexity. Spatial econometric results further indicate significant spatial spillover effects, suggesting that NGUCL-related processes in one county are associated with those in neighboring counties. The effects of natural, socioeconomic, and agricultural production factors also varied by type and period, indicating that NGUCL in ecologically fragile regions is not a homogeneous land-use transition process. By distinguishing CCCF from FA, this study provides a more nuanced interpretation of NGUCL and offers empirical evidence for understanding cultivated land transition and governance in ecologically fragile areas. Full article

Unmanned aerial vehicle (UAV) path planning is one of the core technologies for realizing precision agricultural operations. In complex farmland environments involving terrain obstacles, tall tree canopies, high-voltage power lines, and restricted no-fly zones, this problem is transformed into a typical multi-objective and multi-constraint optimization problem. Dense constraints drastically narrow the feasible solution space and impose stringent requirements on the convergence, real-time performance, and robustness of planning algorithms. To address this issue, this paper proposes a novel meta-heuristic algorithm: the Agricultural Planting Whole-Cycle Management Optimization (APWMO) algorithm. By integrating the cultivation strategy aligned with crop growth cycle dynamics, the demonstration farmland-based elite guidance mechanism, and the elite archive pruning operation, it achieves a dynamic balance between global exploration and local exploitation. Comparative experiments with 15 advanced meta-heuristic algorithms on the 30-dimensional CEC2017 benchmark test suite show that APWMO achieves the best performance in terms of convergence accuracy, convergence speed, and search stability. Furthermore, the effectiveness of the proposed algorithm is verified in four 3D farmland path planning tasks with different objective weights and complexity levels. Experimental results confirm that APWMO has excellent path planning performance in complex farmland environments and can provide efficient technical support for practical agricultural UAV tasks such as plant protection spraying, crop growth monitoring, and farmland surveying. Full article

Accurate farmland boundary mapping from high-resolution aerial imagery is vital for precision agriculture, yet existing methods struggle with complex geospatial boundaries and texture degradation in fragmented plots. To address irreversible detail loss under downsampling, difficulty in capturing both sharp boundaries and large-scale textures, and weak boundary supervision without extra annotations, we propose PaintingFormer, an enhanced UNet-based segmentation framework. It introduces three targeted innovations: an original feature retention module (OFRM) that injects raw RGB images into the deepest decoder layer to recover lost details; a dual attention–MLP design combining FeaAttention (full-resolution global attention with linear complexity) and TWLK-MLP (cascaded 3 × 3, 5 × 5, and 7 × 7 depthwise separable kernels within an MLP) to capture multi-scale spatial patterns; and a deep edge loss from the encoder’s bottleneck that enforces boundary constraints without manual edge labels. PaintingFormer surpasses mainstream methods, achieving 84.5% mIoU and 91.5% F1 on Vaihingen, 87.3% mIoU on Potsdam, 53.7% on LoveDA, and 84.2% on our private dataset. This work offers an effective solution for fine-grained farmland segmentation, improving boundary accuracy and texture preservation. Full article

The temporal variations of evapotranspiration (ET) and its controlling factors occur across time scales ranging from seconds to decades, with significant differences in the lag effects of ET drivers under varying water conditions. Therefore, identifying the dominant time scales of the relationships between ET and its controlling factors under varying water conditions is crucial for optimizing irrigation strategies of crops grown in a greenhouse. In our study, we utilized two years of continuous lysimeter observations of greenhouse tomato ET, and applied two water treatments: well-irrigated (0.9E_pan, E_pan is the cumulative pan evaporation) and deficit-irrigated (0.5E_pan). Wavelet transform technology served as the core method to systematically examine the temporal variations of ET and its controlling factors. Observations indicated that the power spectra of ET featured pronounced peaks at daily and seasonal scales. The cospectra between ET and soil water content for greenhouse tomato revealed strong temporal correlation at 2~5 day scales, confirming the regulatory effect of irrigation cycles on ET. Moreover, ET variations were largely synchronous with net radiation, with ET lagging net radiation but leading vapor pressure deficit and air temperature at daily scales. In addition, significant disparities in phase angles between ET and individual meteorological variables were identified under 0.9E_pan and 0.5E_pan water conditions. Partial wavelet coherence revealed that net radiation was the primary meteorological driver of greenhouse tomato ET across multiple time scales, particularly at the daily scale, followed by vapor pressure deficit. These findings provide scientific evidence for selecting appropriate ET models at different time scales and offer valuable insights for optimizing water-saving irrigation for crops grown in greenhouses. Full article

The growing demand for renewable energy, together with the need to mitigate climate change and promote more sustainable agriculture systems, has stimulated interest in energy crops. In this context, invasive alien plant species (IAPS), which have progressively colonized abandoned farmland, degraded ecosystems, and marginal areas, represent a key bioresource. IAPS have a dual nature combining high ecological invasiveness and fast growing rate with notable energetic potential. These aspects have generated a still ongoing debate among farm managers, ecologists, and policymakers regarding their role within the future bioeconomy. The present study provides a review of the IAPS black locust (Robinia pseudoacacia L.) on its real benefits as a source of bioenergy, ecological impact, and the management strategies adopted. We examine the trade-offs between containment efforts and use for renewable bioenergy production, particularly in marginal areas where few alternatives exist. This review highlights the need for stratified site-specific approaches that balance biodiversity conservation with bioresource exploitation. Finally, this study also contributes to the ongoing discussion on whether IAPS should be regarded primarily as a management challenge or a multifunctional bioresource, as in the production of bioenergy. Full article

Soil degradation driven by inappropriate soil management is a serious global challenge, while climate change-induced yield declines are increasing the conversion of natural ecosystems to agricultural land. This review examines how soil structure influences soil health, focusing on organo-mineral complexes derived from microbial biomass and soil organic carbon-to-clay (SOC/Clay) ratio as an indicator of structural quality. Regenerative agriculture based on conservation farming practices helps mitigate SOC depletion and aligns with the nature-based solutions framework. In Hokkaido, Japan, 10 years of clean agricultural applications (cover crops and organic matter application) increased SOC storage in farmland affected by volcanic eruption. This was associated with improved bulk density, porosity, cation exchange capacity, and phosphate absorption capacity, indicating improved soil health. The increased SOC rose SOC/Clay ratio to levels comparable with unaffected farmland (≥1/13). When the SOC/Clay ratio exceeded 1/13 (soil carbon storage level of 30 t C/ha/15 cm), carbon sequestration rate became negative. This suggests that improved soil health and structural quality may promote carbon saturation and stimulate microbial decomposition of existing SOC. While the threshold for SOC/Clay ratio varies depending on soil type, vegetation type, climatic conditions, and land use, changes in the SOC/Clay ratio can provide insights into changes in soil health and structural quality. Full article

Soil microbes play pivotal roles in nutrient cycling and ecosystem functioning across diverse farmland systems. Orchard grass coverage has been demonstrated to effectively alter microbial community structure and promote nutrient cycling. However, the effects of soybean intercropping on soil bacterial community characteristics and nutrient contents in citrus orchards remain poorly understood. In this study, a field experiment was conducted in a citrus orchard involving three planting patterns: clean tillage (CT), natural grass (NG), and soybean intercropping (SI). The physicochemical properties and bacterial community structure of the topsoil (0–40 cm depth) were determined. Results showed that compared with CT, NG and SI significantly increased cation exchange capacity (CEC), soil organic matter (SOM), alkali-hydrolyzable nitrogen (AN), and available potassium (AK). SI further elevated soil pH and available phosphorus (AP) relative to CT and NG. Bacterial diversity ranked SI > NG > CT, with PCoA showing lower community variation under SI. A total of 31 bacterial phyla were detected in the citrus orchard soil, with Cyanobacteria (17.20~40.81%), Proteobacteria (15.04~24.19%), Acidobacteriota (8.95~14.66%), and Chloroflexi (3.93~21.13%) identified as the dominant phyla. SI enriched Cyanobacteria and Proteobacteria but reduced Acidobacteriota, Chloroflexi, and Actinobacteriota. Mantel tests confirmed CEC and SOM as key drivers of bacterial community structure. Overall, soybean intercropping improves soil microecology and exhibits great potential for soil quality improvement in citrus orchards under local conditions. Full article

Soil quality is a critical determinant of crop productivity. This study assessed the soil quality of 61 barley farmlands in central and northern Hubei Province based on ten soil chemical properties: pH, soil organic matter (SOM), ammonium nitrogen (NH₄⁺-N), nitrate nitrogen (NO₃⁻-N), hydrolyzable nitrogen (HN), available phosphorus (AP), available potassium (AK), exchangeable calcium (Exc-Ca), exchangeable magnesium (Exc-Mg), and available sulfur (AS). A total of 68.85% of the farmlands were acidic (pH < 6.5). The average levels of SOM, NH₄⁺-N, NO₃⁻-N, and HN were deficient, while AP was moderate, according to the Second State Soil Survey of China (SSSSC). AK, Exc-Ca, Exc-Mg, and AS were, on average, at moderate-to-abundant levels. Differences in preceding crops led to significant differences in pH and SOM between paddy and dryland fields. A minimum data set was established using six soil properties (HN, AS, AK, Exc-Ca, Exc-Mg, and NH₄⁺-N) to calculate the soil quality index (SQI). SQI ranged from 0.27 to 0.69, with an average of 0.45, indicating overall low soil quality in the region. Both accuracy importance and R²-weighted importance revealed that HN was the most influential factor driving SQI variation among the soil properties examined. This study elucidates the status of soil nutrients, offering a diagnostic basis for developing targeted fertilization strategies for barley in this region. Full article

According to an FAO report, the total area of saline-alkali land worldwide is approximately 954 million hectares, accounting for about 20% of global cultivated land. Saline-alkali stress significantly reduces soybean (Glycine max L.) yield and quality, and saline-alkali-tolerant plant growth-promoting bacteria (PGPB) have shown important application value for soybean planting in such farmlands. In this study, 15 strains of saline-alkali-tolerant bacteria were isolated from saline-alkali soil in Anda City, Heilongjiang Province, China, and identified morphologically, belonging to the genera Enterobacter, Bacillus, Chryseobacterium, Acinetobacter, Enterococcus, and Pseudomonas. Through tests for nitrogen fixation, phosphorus solubilization, potassium solubilization, hydrolase production (including pectinase, amylase, and protease), and germination promotion assays, Bacillus pumilus AD14 was identified as having the best growth-promoting effect on soybean seedlings. Pot experiments in saline-alkali soil showed that AD14 significantly promoted soybean seedling growth, increasing plant height by 5.63–6.37 cm and root length by 3.58–3.99 cm compared to the control. AD14 also enhanced saline-alkali tolerance by improving the activity of antioxidant enzymes including superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) and increasing soluble sugar and protein contents. Meanwhile, soil pH decreased by 10.94–12.15% and soluble salt content decreased by 9.59–13.39% after planting, and soil enzyme activities (including urease, sucrase, and catalase) increased markedly. These results demonstrate the great potential of AD14 for soybean planting in saline-alkali soil. This study provides a relevant reference for enriching the resources of saline-alkali-tolerant PGPB and developing new biological agents suitable for soybean planting in saline-alkali soils. Full article

The normalized difference vegetation index (NDVI) is widely used to track vegetation cover and ecological change. However, in arid watersheds where irrigated farmland and natural vegetation coexist, it remains unclear how irrigation changes the relative effects of climate, terrain, and soil on vegetation growth. Using the Hetao irrigation district watershed in Inner Mongolia, this study analyzed NDVI dynamics and their environmental controls from 2001 to 2024 through trend analysis, spatial autocorrelation, XGBoost-SHAP, GeoDetector, and geographically weighted regression. NDVI increased significantly across the watershed at 0.0035 yr⁻¹, but the increase was much stronger inside the irrigation district (mean NDVI = 0.58; slope = 0.0061 yr⁻¹) than outside it (mean NDVI = 0.26; slope = 0.0015 yr⁻¹). Global Moran’s I values remained above 0.86, showing persistent spatial clustering. The main drivers also differed by zone. DEM, SOC, and precipitation were most important for the whole watershed; SOC, TP, pH, and TN were more important inside the irrigation district; and precipitation and DEM were more important outside it. GeoDetector confirmed that paired drivers strengthened each other, including SOC ∩ DEM at the watershed scale and DEM ∩ TP outside the irrigation district. GWR further showed that rainfall effects were stronger outside the irrigation boundary, while soil-related effects were stronger in the irrigated agricultural belt. These results show that irrigation not only increases NDVI but also changes how vegetation responds to environmental conditions by weakening direct rainfall limitation and strengthening soil-related controls in managed landscapes. The findings provide evidence for zone-specific vegetation restoration and land-water management in dryland irrigation watersheds. Full article

Accurate identification of arable land, as the foundation of the high-standard farmland construction, impacts the crop layout, accurate management of water and fertilizers, and intelligent control. Due to the 10-m resolution limitation of Sentinel-2 imagery, there is feature overlap within individual pixels of the satellite imagery. This leads to fragmented semantic features during farmland identification, and adjacent plots often appear unclear and intertwined. To address these issues, a Hierarchical Agricultural Segmentation Network (HASNet) was proposed. Built upon the classic encoder-decoder structure, this HASNet model incorporates an expanded feature enhancer (DFE) module to recover weak features and reconstruct cropland features (e.g., edges and shapes) that are obscured by mixed pixels. It also introduces a lightweight strip spatial attention (LSSA) mechanism to capture long-range features unique to farmland. Furthermore, it used a pyramid decoding module (PDM) to refine cropland parcel boundaries. Taking a farm in Xinjiang Uygur Autonomous Region, a semantic segmentation dataset of cultivated land was constructed based on Sentinel-2 imagery. Through accuracy comparisons, visualizations, and inferences, HASNet achieved an MIoU of 88.52% and a Kappa coefficient of 87.82%, outperforming mainstream models such as Unetformer and MPFUnet. Ablation experiments confirmed the effectiveness of the DFE, LSSA, and PDM modules in feature capture and edge refinement. The large-scale image sliding inference experiment prevented the seam effect and demonstrated its practicality. In summary, HASNet provides low-cost technical and theoretical support for the intelligent monitoring of high-standard farmland. Full article

This study investigates how different rural landscape types shape visual attention and physiological responses, with the aim of informing more targeted rural landscape renewal. Four typical rural landscape types in the suburbs of Hefei, China, were examined: Flat Farmland (FF), Hilly Forest (HF), Developed Plain (DP), and Water-network Lowland (WNL). All four study villages are project villages in the suburban area of Hefei where rural revitalization is currently being advanced. This study therefore treats them as empirical cases within the context of rural revitalization in China, using them to examine perceptual differences among rural landscape types and their implications for rural landscape renewal. A two-stage research design was adopted to balance field realism and laboratory control. In the first stage, 40 representative scene images were selected by combining field video records with fluctuations in on-site skin conductance response (SCR). In the second stage, laboratory experiments were conducted while participants viewed the selected images, during which eye-tracking, skin conductance, and heart rate data were recorded simultaneously. These measures were used to characterize visual attention allocation and autonomic physiological responses across different rural landscape types, rather than to directly measure landscape preference. For Area of Interest (AOI) analysis, each image was coded into six landscape element categories: vegetation, buildings, roads, sky, vernacular buildings, and water bodies. The results revealed significant typological differences in overall visual search patterns and autonomic responses. Gaze hotspots were concentrated on identifiable targets and boundary regions in the foreground and midground, whereas the sky attracted relatively limited attention. FF primarily emphasized vernacular buildings and farmland boundaries, HF emphasized settlement interfaces and spatial transition nodes, DP emphasized road junctions and facilities along routes, and WNL emphasized water bodies and water–land interface zones. These findings suggest that a two-stage multimodal design can provide supporting evidence for understanding type-specific perceptual responses and can support more targeted strategies for rural landscape renewal. Full article