Search Results (1,528)

Canopy fractional vegetation cover (FVCc) is a critical indicator for evaluating the effectiveness of ecological restoration, and its accurate estimation provides valuable data for regional ecological management. In this study, Sentinel-2 and MODIS data were integrated to develop an angular relationship model for MODIS reflectance, which was then used to estimate Sentinel-2 reflectance at a 45° viewing angle. Background reflectance at a 10 m spatial resolution was derived using a four-scale model, and total and shrub-herb fractional vegetation cover were estimated using a pixel dichotomy model. Finally, an empirical model tailored to the characteristics of the study area was developed to retrieve FVCc. Cross-validation results demonstrated that the multi-angle retrieval method proposed in this study achieved higher accuracy than the single-angle approach. The spatial distribution of FVCc in Changting County is characterized by higher values in peripheral areas and lower values in the central region. Temporal transitions among fractional vegetation cover classes were predominantly upward, indicating an overall trend of continuous improvement. These findings provide important technical support and a scientific basis for estimating and monitoring dynamic changes in forest canopy fractional vegetation cover. Full article

Operational agricultural monitoring in the Central European lowlands requires timely parcel boundaries; however, unmarked field edges produce minimal spectral contrast in single-date imagery. Previous works demonstrated that harmonic NDVI encoding enables zero-shot field delineation using foundational models, but the influence of the spectral index choice on temporal boundaries remained unquantified. This study systematically evaluates eleven vegetation indices—NDVI, GNDVI, NDRE, EVI, EVI2, SAVI, MSAVI, NDWI, CIg, CIre, and NDYVI—within a fixed harmonic phenology encoding pipeline. A one-year PlanetScope time series (15 × 15 km, Slavonija, Croatia) was decomposed via annual sinusoidal regression to extract per-pixel phase, amplitude, and mean parameters. These harmonic descriptors were mapped to HSV colour channels and segmented using the Segment Anything Model without fine-tuning. Official agricultural parcels (PAAFRD, 2025) provided ground truth for pixel-wise, object-wise, and size-stratified evaluation. Performance stratified into three tiers based on object-wise metrics. Soil-adjusted and enhanced-greenness indices (MSAVI, EVI, EVI2, and SAVI) achieved F1 = 0.51–0.52, and mIoU = 0.70–0.71, statistically outperforming standard ratio formulations (NDVI: F1 = 0.49) and chlorophyll indices (CIg, CIre: F1 = 0.45–0.47). Pixel-wise scores remained compressed (F1 > 0.88 across all indices), indicating consistent interior coverage but index-dependent boundary precision. Error analysis revealed scale-dependent patterns: merging dominated small parcels (<10,000 m²), while fragmentation increased with parcel size. Results demonstrate that spectral formulation is a systematic design factor in phenology-based delineation, with soil background correction and dynamic range compression improving seasonal trajectory separability. The harmonic parameters generated by this framework provide feature-ready input for crop classification, suggesting that integrated boundary extraction and crop mapping workflows merit further investigation. Full article

Background/Objectives: Antimicrobial agents have revolutionized disease management in humans and animals; however, their misuse and overuse have accelerated the emergence and spread of antimicrobial resistance (AMR) and antimicrobial resistance genes (ARGs). Dairy farms are recognized as potential hotspots for ARG dissemination, particularly through Escherichia coli, which acts as a reservoir and vector of ARGs, enabling their horizontal transfer via plasmids and other mobile genetic elements. This study aimed to characterize the genomic diversity, ARG profiles, plasmid content, and phylogenetic relationships of E. coli isolated from dairy farm environments and milk using whole-genome sequencing. Methods: A total of 31 E. coli isolates recovered from soil, effluent, cow dung, and milk samples underwent deoxyribonucleic acid extraction, library preparation, and sequencing on the Illumina MiSeq platform, followed by comprehensive bioinformatic analysis. Results: The E. coli isolates exhibited 20 distinct sequence types, including one novel sequence type. Plasmids were detected in 71% of the isolates, with the IncF plasmid family being the most predominant. Furthermore, 12 ARG groups were identified, with β-lactam resistance genes detected in 67.7% of isolates. Notably, blaCTX-M genes were identified in all phenotypically confirmed extended-spectrum β-lactamase-producing isolates. Additional ARGs, including those conferring resistance to tetracyclines (tet(A), tetX4), quinolones (qnrS1), aminoglycosides (aph, aad, ant), and folate pathway inhibitors (dfr and sul), were widely distributed throughout the samples. Phylogenetic analysis revealed clustering of isolates from different sample types, particularly among ST58 isolates, suggesting cross-environmental transmission. Conclusions: This study demonstrates that E. coli from dairy farm environments harbor diverse ARGs and plasmids, confirming their role as reservoirs of AMR. These findings underscore the importance of prudent antimicrobial use, routine genomic surveillance, and enhanced biosecurity measures to limit cross-environmental transmission. Full article

The heavy metal pollution linked to extractive activities has attracted broad public attention. To examine the current state of heavy metal pollution in river sediments around iron tailing zones, this study was carried out to evaluate the distribution features, potential sources, and environmental hazards of heavy metals (HMs, Cr, Cd, Ni, Cu, Zn, Pb, As, and Hg) in the surface sediments of rivers in the Xiangshan area of Ma’anshan City. Results indicated that, except for Cr, the mean heavy metal concentrations exceeded the soil background levels in Anhui’s Huaihe River Basin. Variability in metal concentrations among the sediments was moderate, exhibiting an uneven spatial distribution. Significant positive correlations were detected between various HMs in the sediments, suggesting a common pollution source. Source analysis findings revealed that the HMs primarily originate from agricultural fertilization, mining, and smelting activities. Evaluation results from both the single-factor pollution index and the Nemerow comprehensive index indicated that the upstream section of the Caishi River is severely polluted by HMs. The potential ecological risk index evaluation results demonstrated that 85% of sediment samples from sampling points achieved a high comprehensive potential ecological risk level for HMs, with Cd, Cu, and Hg identified as the key contributors. The human health risk assessment demonstrated that both adults and children are subjected to carcinogenic risks from heavy metal exposure, with children exhibiting a higher risk level. This study offers valuable insights into managing heavy metal contamination in river sediments adjacent to iron tailings regions. Full article

Water-based drill cuttings generated during onshore natural gas development are complex solid wastes that may pose environmental risks if improperly managed. This study evaluates the feasibility of reutilizing water-based drill cuttings as pavement base materials after stabilization using a novel composite stabilizer composed of cement, stabilizer liquid agent, and water-reducing powder (CLP stabilizer). Mix proportion optimization was conducted through compaction and 7-day unconfined compressive strength tests, followed by evaluation of road performance, including strength, compressive rebound modulus, water stability, and temperature shrinkage, with stabilized powder stabilized soil as a control. Microstructural characteristics were analyzed using X-ray diffraction and scanning electron microscopy, and environmental safety was assessed through heavy metal leaching tests and background soil investigation. The results show that the optimal mixture ratio of curing agent (5% cement + 2% liquid stabilizer + 8% superplasticizer powder) satisfies the strength requirement for pre-drilling road bases, exhibiting superior performance compared to the control group. When the stabilizer dosage reaches 9%, the 7-day unconfined compressive strength achieves a maximum of 3.38 MPa, representing a 51% increase over the control group. At a stabilizer dosage of 12%, the splitting tensile strength reaches a peak value of 0.901 MPa, showing a 60.3% improvement. These results indicate enhanced deformation resistance, water stability, and reduced temperature shrinkage rates. Microstructural analysis indicates that the formation of calcium silicate hydrate (C-S-H) gel and ettringite (AFt phase) leads to a denser structure and enhanced durability. Heavy metal concentrations comply with relevant standards, demonstrating controllable environmental risks and supporting sustainable pavement base application. Full article

Ecosystem transpiration (T) is the core process in terrestrial water and carbon cycles. Accurately estimating T is critical to improving evapotranspiration (ET) models and understanding global ecosystem responses to climate change. In this study, we evaluated four ET partitioning methods (TEA, Z16, L19, and Y21) using 368 global eddy covariance (EC) sites and 15 sap flow sites. Intercomparison results showed that TEA, Z16, and Y21 maintained good consistency, whereas L19 exhibited lower agreement, primarily due to its high sensitivity to energy closure errors and poor non-linear fitting accuracy under extreme conditions. Validation against sap flow data indicated that Z16 performed best (R² = 0.45, KGE = 0.52), followed by Y21, while TEA had the lowest accuracy due to systematic overestimation driven by unremoved persistent background soil evaporation in its training dataset. Global analysis revealed that mean annual T ranged from 213 mm yr⁻¹ (Z16) to 294 mm yr⁻¹ (TEA), with annual T/ET varying between 0.45 (Z16) and 0.63 (TEA). Trend analysis further showed consistent increasing trends across all four methods for both annual T (0.33–0.83 mm·yr⁻²) and annual T/ET (0.0015–0.0019 yr⁻¹). Additionally, a notably stronger relationship was found between gross primary productivity (GPP) and T than between GPP and ET. Despite substantial differences in model structures, these methods effectively capture the temporal dynamics of T and the coupled relationships between ecosystem carbon and water fluxes. Our findings provide critical benchmarks for terrestrial water cycle modeling and sustainable water resource management under a changing climate. Full article

Identifying the specific sources of heavy metal accumulation in intensive agricultural landscapes is essential for ensuring soil sustainability and food security. In this study, we independently carried out a high-density regional geochemical survey and high-resolution field sampling in the Yangtze–Huaihe Watershed, Eastern China, and used the original sample dataset to distinguish between geogenic backgrounds and anthropogenic enrichments. By employing the APCS-MLR model, four distinct pollution sources were quantitatively identified: natural pedogenesis, agricultural activities, traffic emissions, and industrial inputs. Results demonstrated that while most heavy metal concentrations remained below national safety thresholds, Cd and Hg exhibited significant topsoil enrichment, signaling potential ecological risks. Source apportionment revealed that natural sources primarily controlled As, Cr, Ni, and Pb, with the contribution ranging from 41% to 70%. In contrast, traffic emissions (e.g., tire wear and fuel combustion) emerged as the dominant source for Cd (68%), Zn (55%), and Cu (34%), while industrial activities accounted for a substantial 89% of Hg accumulation via atmospheric deposition. Notably, despite the region’s intensive cultivation, agricultural practices played a surprisingly minor role in heavy metal accumulation. These findings highlight that the accumulations from traffic and industry now account for approximately 50% of the total heavy metal load in the region. Our results underscore the critical importance of high-resolution spatial data for precise source identification and suggest that implementing vegetative buffer zones and stricter industrial emission controls are imperative to mitigate further soil degradation in similar agricultural watersheds. Full article

Background/Objectives: Primary amoebic meningoencephalitis (PAM) is a rare, fulminant, and often fatal central nervous system infection caused by the opportunistic free-living amoeba Naegleria fowleri. Although Naegleria species are widely present in freshwater and soil worldwide, human disease is associated specifically with pathogenic N. fowleri rather than the many nonpathogenic environmental species, and virulence may vary across N. fowleri isolates. This systematic review aimed to synthesize contemporary global data from 2000 to 2024 to identify recent trends in epidemiology, clinical presentation, diagnosis, treatment, and outcomes. Methods: A systematic literature search was conducted across PubMed, Scopus, and the Cochrane Library, identifying 58 eligible publications encompassing 66 individual cases. Results: Most reports originated from the United States, India, and China. The median patient age was 14 years, with 78% of cases occurring in males. Annual case reports increased from one per year (2000–2005) to over four per year (2020–2024), reflecting either a true rise in incidence or improved detection. Common presenting symptoms included fever, headache, and altered mental status. Diagnosis was confirmed via polymerase chain reaction (PCR) testing or post-mortem biopsy in nearly one-third of cases. Treatment regimens varied, with amphotericin B and miltefosine being the most frequently used agents. Overall mortality was 83%, with survival strongly associated with early initiation of combination therapy. Pediatric patients had a higher survival rate (22%) compared to adults (7.1%). Conclusions: The findings highlight the need for heightened clinical awareness, especially in the context of climate-driven ecological changes that may expand N. fowleri’s geographic range. This review underscores critical gaps in surveillance and diagnostics and emphasizes the importance of a One Health approach to addressing emerging threats like PAM. Further research into novel therapeutics, rapid diagnostics, and global case reporting systems is urgently needed. Full article

Benggang (Benggang), a typical landform characterized by severe erosion and a geohazard in the red-soil hilly regions of southern China, is characterized by a fragmented texture, irregular boundaries, and high similarity to background objects such as bare soil and roads, which poses a dual challenge of “multiscale variability + strong noise” for automated identification at regional scales. To address insufficient information from a single modality and the limited representation of cross-scale features, this study proposes a dual-stream feature-fusion network (DF-Net) for multisource data consisting of a digital orthophoto map (DOM) and a digital elevation model (DEM). The method adopts ResNeSt50d as the backbone of the two branches: on the DOM side, a Canny-edge channel is stacked to enhance high-frequency boundary information; on the DEM side, derived terrain factors, including slope, aspect, curvature, and hillshade, are introduced to provide morphological constraints. In the cross-modal fusion stage, a multiscale sparse attention fusion module is designed, which acquires contextual information via multiwindow average pooling and suppresses noise interference through top-K sparsification. In the decision stage, a multibranch ensemble is employed to improve classification stability. Taking Anxi County, Fujian Province, as the study area, a coregistered dataset of GF-2 (1 m) DOM and ALOS (12.5 m) DEMs is constructed, and a zonal partitioning strategy is adopted to evaluate the model’s generalization ability. The experimental results show that DF-Net achieves 97.44% accuracy, 85.71% recall, and an 82.98% F1 score in the independent test zone, outperforming multiple mainstream CNN/transformer classification models. This study indicates that the strategy of “multimodal feature enhancement + sparse attention fusion” tailored to Benggang erosional landforms can significantly improve recognition performance under complex backgrounds, providing technical support for rapid Benggang surveys and governance-effectiveness assessments. Full article

Establishing the soil geochemical baseline and background values is critical for agricultural soil environmental management. This study collected 5207 topsoil (0–20 cm) and 1311 subsoil (150–180 cm) samples from an intensive agricultural area in Eastern China to quantify the element enrichment and depletion patterns, evaluate the integrated soil fertility, and assess the potential ecological risks, with a focus on disentangling the links between human activities and soil environmental changes. The results showed that most elements had higher baseline/background values than national averages, except for CaO, Mo, MgO, Sr, Na₂O, and Br, reflecting the control of homogeneous parent material. Topsoil elements largely inherited subsoil characteristics, while anthropogenic disturbances such as fertilization and industrial activities caused the enrichment of Cd, Se, TN, TP, S, and SOC, and the depletion of I, V, and Mn. Soil fertility presented an obvious vertical heterogeneity, in which the topsoil had moderate-to-rich nutrients with a mean SOC of 10.05 g kg⁻¹ and mean TN of 1.10 g kg⁻¹, whereas the subsoil was severely deficient with a mean SOC of 1.96 g kg⁻¹ and TN of 0.66 g kg⁻¹. The integrated fertility index (IFI) indicated that the topsoil and subsoil in Changfeng and western Feixi exhibited higher fertility levels, while Feidong and Hefei had lower fertility levels. An ecological risk assessment identified western Feidong as a high-risk hotpot, with Cd as the primary contributor to potential ecological risk. The source analysis confirmed Ni, As, and Cr as geogenic, Cd as anthropogenic, and Pb and Cu as mixed natural–industrial–agricultural sources. Our findings highlight the necessity of adopting zoned precision fertilization to improve the nutrient efficiency and applying organic amendments to immobilize Cd and reduce the ecological risk. This study provides targeted strategies for soil fertility improvement, precision fertilization, and Cd risk control, supporting sustainable agricultural development. Full article

Elemental migration and enrichment are important processes influencing tea plant growth and the assembly of rhizosphere bacterial communities within the rock–soil–plant continuum. This study explores how soil parent materials (granite, quartz schist, and sericite schist) are potentially associated with these processes and their observed associations with the elemental composition of tea leaves. Exploratory statistical analyses revealed distinct, lithology-specific biogeochemical patterns that serve as a foundation for hypothesis generation. In granite soils, chlorite correlated with the mobility of Cr, Pb, Cu, Ni, Mg, and Na, coinciding with shifts in the relative abundances of Verrucomicrobia, Armatimonadetes, and Chloroflexi. In quartz schist, kaolinite exhibited notable correlations with the dynamics of Pb, Cr, Ni, Zn, and As, which were statistically linked to Planctomycetes, Proteobacteria, and Acidobacteria. Complex mineral–microbe interactions were observed in sericite schist soils, where clay minerals (e.g., chlorite, illite) were closely associated with the migration of multiple elements (Pb, K, Ca, Cd, As, Al, Fe, Zn), paralleling structural variations in communities of Actinobacteria, Planctomycetes, Chloroflexi, and Proteobacteria. Potassium (K), calcium (Ca), and manganese (Mn) showed bioaccumulation tendencies in tea leaves across all lithologies, with an enrichment capacity order of Ca > K > Mn > Mg > Na > Al. Exploratory Classification and Regression Tree (CART) analysis suggested that the migration of K, Ca, Cu, Zn, and Hg corresponded most closely with their soil concentrations. Manganese (Mn) exhibited a mineral-associated trend, with kaolinite content as a potential correlate, while cadmium (Cd) migration was statistically linked to the relative abundance of Armatimonadetes. These findings highlight potential candidate relationships between mineralogy, microbes, and elemental mobility rather than confirming causal mechanisms, emphasizing the need for further validation in larger or experimental datasets. Full article

Cadmium (Cd) contamination in agricultural soils, especially in regions with a naturally high geochemical background such as Southwest China, poses a serious threat to food safety and the health of terrestrial ecosystems. Although arbuscular mycorrhizal fungi (AMFs) are known to enhance plant tolerance to heavy metals, the specific mechanisms by which dominant AMF species in karst soils—such as Funneliformis mosseae (Fm) and Rhizophagus intraradices (Ri)—immobilize Cd are not yet fully understood. In this study, a pot experiment with pepper plants was conducted to investigate the effects of Fm and Ri inoculation on Cd geochemistry in both the rhizosphere and bulk soil. Key results showed that AMF inoculation, especially with Fm, significantly reduced total Cd (by up to 33.8%) and bioavailable Cd (by up to 36.3%) concentrations in the soil, with a more pronounced effect within the rhizosphere. Accordingly, Cd content in pepper shoots was reduced by up to 15.0%. Inoculation also increased soil pH, organic matter, available phosphorus, and glomalin-related soil protein (GRSP) content. Redundancy analysis identified soil pH and total extractable GRSP as primary factors negatively correlated with Cd bioavailability. The study concludes that AMFs, particularly Fm, represent a potent bioremediation strategy by effectively immobilizing Cd in contaminated soils through mechanisms linked to GRSP production and pH elevation, thereby reducing its phytoavailability and translocation to edible plant parts. Full article

Hemp (Cannabis sativa L.) is an important crop, yet little is known about how herbivory and soil microbial communities interact to influence plant performance. In this study, two hemp cultivars, BaOx and Cherry Citrus, were grown under identical greenhouse conditions and exposed to naturally occurring background populations of the two-spotted spider mite (Tetranychus urticae). Plant traits were measured, and rhizosphere soil was sampled for 16S rRNA gene sequencing to compare bacterial community composition and diversity between cultivars. Spider mite injury was assessed using a standardized 0–5 visual damage scale commonly applied in integrated pest management studies. Although the cultivars did not differ significantly in growth traits, Cherry Citrus experienced significantly less spider mite damage than BaOx, suggesting greater tolerance or resistance to herbivory under shared conditions. Rhizosphere bacterial communities differed markedly between cultivars despite identical soil and environmental conditions. BaOx rhizospheres were enriched in Actinobacteria, including taxa associated with decomposition and antimicrobial compound production, whereas Cherry Citrus rhizospheres were enriched in Alphaproteobacteria, particularly nitrogen-cycling and root-associated taxa such as Rhizobium and Reyranella. Alpha diversity metrics did not differ between cultivars; however, beta diversity analyses revealed significant cultivar-level separation, particularly in phylogenetic community structure. Because herbivore pressure and microbial communities were not experimentally manipulated, this observational study identifies ecological associations rather than direct causal relationships. Nevertheless, the results demonstrate that hemp cultivar identity is associated with distinct rhizosphere microbiomes and differential susceptibility to spider mite damage. These findings highlight the potential for integrating cultivar selection and microbiome-informed strategies into sustainable pest management programs for hemp. Full article

Accurate and stable acquisition of the spatial distribution of soybean planting areas is essential for supporting precision agricultural monitoring and ensuring food security. However, crop remote-sensing mapping for specific regions still faces critical data bottlenecks: high-precision, large-scale pixel-level annotation is costly, resulting in scarce available labeled samples that make it difficult to construct large-scale training datasets. Although parameter-intensive models such as FCN and SegNet can achieve sufficient end-to-end training on large-scale public remote sensing datasets like LoveDA, when directly applied to the data-limited dataset in this study area, the models are prone to overfitting, leading to a significant decline in generalization ability. To address these issues, this study proposes a lightweight U-shaped semantic segmentation model, SimSDRU-Net. The model utilizes a pre-trained VGG-16 backbone to extract shallow texture and deep semantic features. The pre-trained weights mitigate the impact of overfitting in data-limited settings. In the decoding stage, a parameter-free lightweight SimAM attention module enhances effective soybean features and suppresses soil background redundancy, while an embedded S-DRU unit fuses multi-scale features for deep complementary reconstruction to improve edge detail capture. A label dataset was constructed using Sentinel-2 images as the data source and Menard County (USA) as the study area. The USDA CDL was used as a foundation for the dataset, with Google high-resolution images serving as visual interpretation aids. In the context of the experiment, Deeplabv3+ and U-Net++ were compared with U-Net under identical conditions. The results demonstrated that SimSDRU-Net exhibited optimal performance, with MIoU of 89.03%, MPA of 93.81%, and OA of 95.96%. Specifically, SimSDRU-Net uses the SimAM attention module to generate spatial attention weights by analyzing feature statistical differences through an energy function, so as to adaptively enhance soybean texture features. Meanwhile, the S-DRU unit groups, dynamically weights, and cross-branch reconstructs multi-scale convolutional features to preserve fine boundary details and achieve accurate segmentation of soybean plots. The present study demonstrates that SimSDRU-Net integrates lightweight design and high precision in data-limited scenarios, thereby providing effective technical support for the rapid extraction of soybean planting areas in North America. Full article

Nanoplastics (NPs) are increasingly detected in agricultural soils, yet their influence on arsenic (As) transfer and plant toxicity remains unclear. Lettuce (Lactuca sativa L.) was cultivated in farmland soil with a naturally high As background (98.8 mg·kg⁻¹) to assess how polyethylene nanoplastics (PE NPs) affect rhizosphere conditions, As accumulation, and plant performance. PE NPs partially buffered soil acidification but reduced rhizosphere water content, while total soil As remained largely unchanged. Leaf As increased by 35–39%, with reduced biomass (up to 30%) and lower chlorophyll status (SPAD ~7% lower). Metabolomic analyses indicated dose-dependent alterations in central carbon metabolism and phenylalanine-related antioxidant metabolites, including suppressed tricarboxylic acid cycle intermediates at higher PE levels. Overall, PE NPs enhanced transfer of background As to edible leaves and intensified phytotoxicity, underscoring the need to consider nanoplastics in risk assessment of As-affected soils. Full article