In recent years, microbial induced calcium carbonate precipitation (MICP) and vegetation slope protection technology have been proven to be feasible. However, since the process of MICP-improved vegetation slope protection is affected by factors such as vegetation type, the research on the adaptability and stability of roots in the root-soil complex is still not in-depth. Therefore, this paper carried out plant adaptability and erosion resistance tests and triaxial tests on MICP-solidified root-soil complexes to explore the changing laws of vegetation germination adaptability, erosion resistance and soil mechanical properties under the action of MICP solidification. The results showed that: (1) Tall fescue germination potential declined from 72.3 to 40.7% with increasing reaction solution concentration, and more sharply from 66 to 26.5% as the number of spray applications increased. Paspalum notatum showed a similar trend, decreasing from 11.4 to 2.2% (concentration) and from 8.5 to 1.2% (spray applications). Overall, the number of spray applications exerted a greater inhibitory effect than concentration. (2) Microorganisms can enhance the ability to resist erosion. After 6 sprayings, the erosion rate is only 1.5%. Microorganisms combined with plants can significantly inhibit continuous rainfall; (3) The stress-strain curve of MICP-reinforced root-soil composite shows an upward trend and is a strain hardening type. Roots can promote the formation of calcium carbonate, cement the soil and fill the pores, so that the c and φ values of the MICP-reinforced root-soil complex are positively correlated with the calcium carbonate and root content, and the C value increases more significantly; (4) MICP technology has a great influence on the root The strength increase ratio of soil composite strength is very important, and its MICP strength increase ratio is as high as 80% under the optimal root content. MICP can effectively improve the adaptability of vegetation slope protection technology and improve the stability of slopes. Therefore, it can be considered that MICP has important significance for improving the stability of slopes by improving vegetation slope protection technology.

Functional performance of constructed technosols as a soil management solution for urban green infrastructure.

Limited climate benefits of rewetting a shallow-drained peatland when interannual variabilities in CO2 and CH4 fluxes are considered.

Unpowered Automatic Fertigator (FONi) is an automatic irrigation technology without the use of electrical power that has been proven to be able to provide water as well as dissolved nutrients directly to the root area according to plant needs (evapotranspiration). In this activity, FONi was introduced to the Saung Kiray Farmer Group (Mitra) of Tasikmalaya City for the cultivation of organic fruit and leaf vegetables in a neglected yard. The problem faced by Partners is the difficulty in watering plants which has been done manually and limited access to appropriate technology. This service aims to turn the abandoned yard into a productive one. By applying FONi, the yard can now produce various types of organic fruit and leaf vegetables that are beneficial for health and become a pilot and learning location, especially for the surrounding community. The students involved now have skills and experience in designing and making FONi and together with Partners have mastered its installation and use. Also produced, means of disseminating information on this service activity in the form of posters, videos and news and articles that can be a reference for the wider community. To ensure its sustainability, it is necessary to synergise with the Ministry of Agriculture's Sustainable Food Yard (P2L) programme and the local Agriculture Office, especially to support the National Free Nutritious Meal.

Abstract Introduction Restoration in drylands is challenging because of harsh climates, requiring creative methods and organisms like biocrusts for restoration of degraded lands. Biocrusts are thin, coherent soil surface layers prevalent in drylands, engineered, and inhabited by communities of organisms including mosses, lichens, and cyanobacteria. Objectives Biocrusts are critical to dryland ecosystem recovery, enhancing soil stability and fertility, capturing dust, and sometimes enhancing native plant establishment. We tested the efficacy of biocrust capsules—a novel, cost‐effective biocrust delivery method using commercially available pill capsules filled with biocrust inoculum—as a method for biocrust restoration in four experiments. Methods We evaluated the compatibility of multiple capsule types with: field‐collected and cultivated biocrusts from different sources (Sonoran, Mojave and Sonoran cultivated), native seeds, anti‐herbivory agents (diatomaceous Earth alone and with: habanero powder, coyote urine, pectin, or bergamot oil), and water‐holding additives (activated charcoal and hydrogel, alone or together) in four experiments. We watered by hand in the greenhouse, monitoring breakdown rate, biocrust activity, and seed germination. Results Biocrust capsule types broke down at different rates. Tapioca and vegetable cellulose types were compatible with all biocrust sources, including cultivated inoculum. Capsule breakdown and biocrust activity varied by biocrust source and capsule material. Capsules did not inhibit native seed germination. Anti‐herbivory additives had little effect on herbivory, suggesting that encapsulated biocrusts may not be appealing to some herbivores. Conclusions Biocrust capsules show significant promise as a scalable, practical method for biocrust distribution.

Against the backdrop of global change, frequent and severe droughts pose major threats to ecosystems, and quantifying ecosystem anomalies driven by hydrothermal stress remains challenging. Based on this, we propose a drought-monitoring framework centered on solar-induced chlorophyll fluorescence (SIF) and develop an SIF-based Vegetation Health Index (SHI) to improve monitoring performance. Compared with existing SIF-based drought indices (e.g., TFDI and TSWDI), SHI provides a more direct representation of photosynthetic stress, making it more suitable for elucidating drought-response mechanisms. In addition, we use net ecosystem productivity (NEP) to represent carbon sequestration and apply multiple correlation analyses to investigate NEP responses to drought and their spatiotemporal differentiation across vegetation types. Results indicate an overall wetting trend in the study region during 2001–2024, and SIF-based indices perform better in characterizing drought and vegetation responses. The dominant coupling scale between NEP and drought is annual, with an overall lag of 0–3 months: croplands show the strongest coherence and the shortest lag (0–1 month), grasslands are intermediate, and forests show longer lags (2–5 months) as well as a more persistent response window. This study highlights SHI’s advantages for drought monitoring and carbon sink diagnostics, supporting differentiated drought mitigation and management in NWC.

The food industry increasingly relies on automated vision systems to ensure product quality, consistency, and safety. However, the visual classification of vegetables remains challenging due to high intra-class variability, illumination differences, and subtle morphological similarities between categories. This study evaluates the effectiveness of combining CNNs with four advanced Vision Transformer (ViT) architectures: DeiT (Data-efficient Image Transformer), CoaT (Co-Scale Conv-Attentional Transformer), CvT (Convolutional Vision Transformer), CrossViT (Cross-Attention Vision Transformer) for the automatic classification of 15 vegetable types. All models were implemented within a unified CNN–ViT hybrid framework to enhance both local feature extraction and global contextual reasoning. We processed all images under identical conditions to ensure a fair comparison and reproducibility. Results demonstrate that the hybrid architectures significantly outperform the standalone CNN baseline, with CvT achieving an approximate global accuracy in the range of 96.6–98.88% and consistently strong performance across visually complex classes such as cabbage, brinjal, and pumpkin. These findings confirm that hybrid CNN–ViT models are highly effective for visual food analysis, offering a robust and scalable solution for quality control, automated inspection, and classification of agricultural products. The methodology presented here may also be extended to other food items, including gels and processed products, highlighting its versatility and industrial relevance.

Abstract Climate change has increased the likelihood of extreme events, increasing the number of days with dangerous fire weather conditions, resulting in fires with increased severity, frequency and extent. This can greatly impact vegetation communities by reducing diversity and slowing recovery. The role of in situ soil seed banks in mediating impacts of shifting fire regimes is often unclear and may vary between different vegetation types. In particular, the impact of high fire severity, an increasingly common fire regime shift, may increase the likelihood of temperatures lethal to seeds in the soil, while higher fire frequencies may reduce diversity via increased immaturity risk. Here we aim to assess how fire severity impacts the species' and functional group diversity of soil seed banks in a threatened mesic forest community. We collected 396 soil samples from 12 sites within wet sclerophyll forest in the Blue Mountains of New South Wales, Australia that had been burnt during the 2019/2020 Australian megafires at differing fire severities (moderate, high and extreme), as well as from unburnt (control) sites, 3 years post‐fire. Soil samples were split into the leaf litter and soil, both treated with smoke and heat to break fire‐related dormancy, and regularly watered in a greenhouse to observe germination for a year. This was compared to floristic surveys conducted at each site. Our data showed a hump‐shaped relationship between species richness and fire severity in the extant vegetation. The lowest richness occurred at unburnt sites peaking at moderate severity burn sites and declining slightly at high and then extreme severity sites. This relationship was not significant in the soil seed bank, indicating it may buffer against losses in diversity long term. Obligate resprouters generally declined as severity increased. A distinct difference in composition between extant vegetation and in the soil seed bank emphasises that a significant portion of the species richness within these communities solely exists in the soil seed bank, fluctuating as environmental conditions change. Composition in both extant vegetation and the soil seed bank shifted with increasing fire severity, suggesting potential impacts on the future functioning of these ecosystems. Read the free Plain Language Summary for this article on the Journal blog.

The growing frequency and extent of wildfires constitute a significant environmental challenge, posing serious threats to ecosystems, biodiversity, and human livelihoods. This study presents a comprehensive wildfire susceptibility assessment for El Tarf Province, one of the most fire-prone yet understudied regions in Algeria. Long-term Landsat imagery (1995-2024) combined with four machine learning algorithms was used to produce high-resolution susceptibility maps and identify the key environmental and bioclimatic drivers of wildfire occurrence. Ten conditioning factors representing topographic, vegetative, edaphic, and climatic conditions were integrated, with elevation, Enhanced Vegetation Index (EVI), wind speed, and precipitation emerging as dominant predictors. Among the tested models, Random Forest achieved the highest predictive performance (ROC-AUC = 0.897), closely followed by XGBoost (0.896), while LightGBM provided an optimal balance between accuracy (0.875) and computational efficiency. Logistic Regression, though simpler, performed reasonably well (0.794). The Landsat-derived wildfire inventory comprised approximately 622,221 burned pixels and was subsequently split into a pre-2017 training set (72.8%) and a post-2017 testing set (27.2%) to evaluate model generalization over time. Spatial block cross-validation was applied to reduce spatial autocorrelation and enhance model generalization. This methodological framework, combining spatial and temporal validation, temporal hold-out, and spatial blocking, strengthens the robustness and reliability of wildfire susceptibility modeling. Interpretability analyses based on SHAP values, Gini importance, and permutation importance identified the contributions of underexplored variables, including vegetation type, soil type, and soil organic carbon (SOC). The resulting susceptibility maps provide valuable insights for spatial planning and ecosystem management, supporting evidence-based strategies to enhance environmental resilience and biodiversity conservation in Mediterranean landscapes.

ABSTRACT Background High Nature Value farmland (HNVf) supports biodiverse, semi-natural habitats through extensive farming but is increasingly threatened in Ireland by land intensification and abandonment. Results-based payment schemes (RBPS) aim to maintain and enhance these habitats, yet their broader biodoversity benefits remain unclear. Aims This study examined the relationship between an RBPS scoring system developed for the protected hen harrier (Circus cyaneus) and semi-natural grassland plant communities. Methods Thirty grassland fields spanning a gradient of RBPS ecological condition scores from (0-10) were surveyed. Vegetation surveys were carried out in three 2 m × 2 m quadrats per fieldalong a ‘W’ transect walk, supplemented with additional environmental data as proxies for environmental conditions and management pressure. Non-metric Multidimensional Scaling was used to analyse species composition, and fields were assigned to a vegetation type using the Irish Vegetation Classification ERICA tool. Results Higher RBPS scores were positively associated with greater number and cover of positive indicator species, increased bryophyte cover, and higher floristic diversity indices. Lower scoring fields were associated with greater cover of negative indicator species, increased rush cover and higher Ellenberg’s Nitrogen and Reaction values. Eight wet grassland community types were identified. Conclusions Our findings indicate that an RBPS designed to incentivise ecological conditions for a target raptor species can also reflect wider plant diversity, supporting multiple ecosystem services in HNVf systems.

Abstract Background Microbial necromass carbon (MNC) is a critical component of persistent soil organic carbon (SOC). However, the elevational patterns of MNC accumulation and its contribution to SOC across different soil layers remain poorly understood in climate-sensitive alpine ecosystems. We investigated these dynamics across topsoil and subsoil layers along a subtropical elevational gradient (1700–3500 m) in southwest China, which encompasses five vegetation types from evergreen broadleaved forests to alpine meadows. Results The results showed that MNC content increased linearly with elevation, with a more pronounced accumulation in topsoil. This pattern resulted from a clear depth-dependent shift in primary regulators, where fine root biomass and mean annual temperature dominated topsoil MNC accumulation while total phosphorus dominated subsoil accumulation. Notably, the contribution of MNC to SOC remained consistent across soil depths (averaging 29.9 ± 3.3%) and exhibited a quadratic elevational trend, peaking in high-elevation meadows. Structural equation modeling revealed that iron oxides and total phosphorus exerted significant direct effects on the MNC contribution to SOC, while fine root biomass had an indirect effect by enhancing phosphorus availability. Conclusions These findings highlight alpine meadows as key reservoirs of MNC and reveal distinct depth-dependent mechanisms governing its accumulation. This study provides a mechanistic framework for predicting SOC dynamics under climate change in vulnerable alpine ecosystems.

Abstract Major advances have been made in understanding carbon emissions from boreal forest fires, yet substantial knowledge gaps remain for tundra fires. Tundra ecosystems are increasingly susceptible to fire activity due to climate change, posing a threat to their large organic soil carbon stocks and the permafrost they insulate. Additionally, studies assessing remote sensing fire severity metrics in tundra landscapes are limited. Here, we report carbon (C) combustion estimates from two large tussock tundra fires (1048 km²) that occurred in Southwest Alaska, USA, in 2022. We quantified above-and belowground carbon stocks and combustion (kg C m⁻²) at 45 field plots (36 burned, 9 unburned) one year post-fire. Soil burn depth was determined using paired moss patches, and aboveground carbon stocks were calculated using allometric equations for shrubs and tussocks. Fire severity was estimated using the field-based geometrically structured Composite Burn Index (GeoCBI) at these 45 plots and at 41 additional plots where only GeoCBI was measured. We upscaled our field-based findings using the differenced normalized burn ratio (dNBR) derived from Sentinel-2 imagery. Tussock tundra landscapes emitted an average of (± standard deviation) 1.59 ± 0.55 kg C m⁻², consuming 11% of the total pre-fire carbon stock. The majority of the loss (75%) came from belowground stocks, with an average burn depth of 6.9 ± 2.1 cm. By scaling carbon combustion with the dNBR ( R² = 0.42, p < 0.001), we estimated total carbon emissions from tussock tundra across both fires at 0.81 ± 0.22 Tg. GeoCBI estimates were moderately correlated with the dNBR ( R² = 0.63, p < 0.001) across vegetation types. This work provides important information on the impacts of fires on the carbon balance of tundra ecosystems and demonstrates that dNBR, a well-established remote sensing proxy for forest fires, is also effective for mapping fire severity in tundra landscapes.

Monitoring vegetation edges in dynamic coastal zones is essential for understanding long-term shoreline change and supporting effective coastal management, particularly as climate change accelerates erosion, sea-level rise, and ecosystem shifts. This study provides the first validation of VedgeSat, an automated Vegetation Edge (VE) detection toolkit, in contrasting tropical coastal environments, with relevance for coastal monitoring worldwide. In Sumatra, Indonesia, fifteen sites were assessed, encompassing diverse vegetation and sediment types, a range of water clarity, and varying wave exposures in both open and sheltered coastal settings. Vegetation edge detection was conducted with high-resolution PlanetScope imagery and differential Global Positioning System (dGPS) field surveys. VedgeSat performed reliably in areas with dense vegetation, regardless of the type of vegetation or sediment, and water clarity achieving sub-pixel root mean square errors (RMSE) of less than 7 m, R 2 values up to 0.89 and minimal positional bias. Performance declined in areas with sparse and patchy vegetation, such as pioneer mangroves and grasses in sandy environments, resulting in higher RMSE and reduced R 2 values. A sensitivity analysis demonstrated that tuning the threshold of Normalized Difference Vegetation Index (NDVI) values can optimize edge detection across diverse vegetation types and environments. Overall, the results confirm the robustness of VedgeSat for scalable monitoring of vegetated coasts without retraining, while also identifying limitations in sparsely vegetated settings. This study provides the first benchmark for automated vegetation edge detection in tropical systems and demonstrates the potential of satellite-based approaches to enable large-scale, repeatable, and cost-effective coastal monitoring in data-scarce regions.

Background Pesticides are essential in agriculture for protecting crops from pests, diseases, and weeds, but improper use can lead to health issues like neurological disorders and carcinogenic effects. Strengthening regulatory frameworks, promoting integrated pest management strategies, and raising farmer awareness can mitigate pesticide misuse. In Ethiopia, widespread pesticide use in vegetables raises concerns about consumer exposure to pesticide residues. This study determined the pesticide residue in vegetables in Southwest Ethiopia. Methods The study was conducted in randomly selected districts of the Jimma zone. Samples of tomatoes, potatoes, cabbage, and onions were collected from vegetable farmers. The modified QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) methods were used for sample preparation and extraction. The analysis of pesticide residues was performed using gas chromatography coupled with a mass spectrometer (GC-MS) and an ion trap analyzer with an automatic injection. The pesticide detection levels among types of vegetables and locations were analyzed using one-way Analysis of variance (ANOVA) with statistical significance set at p < 0.05. Results Pesticide residues were detected in vegetables from Seka Chokorsa, Goma, and Dedo districts. Detected pesticides include Lindane, Aldrin, Chlorpyrifos, 4,4-DDE, Hexachlorobenzene, and Endosulfan II. The highest concentrations were found in the Dabo Gibe onion sample (Seka Chokorsa), the Waro Kolobo potato sample (Dedo), and the Ganji Dalacho cabbage sample (Goma). Lindane residues were found in onion and potato (Seka Chokorsa), exceeding the Maximum Residue Limit (MRL) of 10–50 µg/kg. Chlorpyrifos residues were detected below MRLs across all districts, while 4,4-DDE residues were also detected in some vegetables, indicating historical use of banned pesticides. ANOVA results showed small variation between groups, and there was no statistically significant difference across the four groups (p-value = 0.305). The study highlights the need for stricter regulation, farmer education, and residue monitoring to ensure food safety.

Fractional vegetation cover of crops (CropFVC) is a critical indicator for remote sensing-based crop monitoring. However, existing inversion models are largely developed for general vegetation types, limiting their effectiveness for crop-specific applications. Here, we developed a gap-fraction-refined hybrid CropFVC model that integrates crop-specific PROSAIL calibration, an ALA (averages of leaf angle) -based dynamic projection function, and a Random Forest model. The model was validated with 43343 CropFVC samples of four major crops (winter wheat, rice, maize, and soybean) across China during March to August 2024, spanning key phenological stages, and further compared against SNAP (10 m) and GEOV3 (300 m) products. Results showed that (1) the proposed model achieved stable performance across diverse canopy structures, with average RMSE < 9.3% for wheat, rice, maize, and soybean; (2) compared with SNAP (10 m), RMSE decreased by 4.83%, 3.10%, 7.51%, and 8.63% for wheat, rice, maize, and soybean, respectively; compared with GEOV3 (300 m), reductions reached 7.88%, 9.49%, 13.63%, and 19.75%, respectively. Further observations showed that the model-derived CropFVC captured intra-field variability and abnormal crop conditions well, enabling more accurate monitoring of crop-specific FVC dynamics across phenological stages. The proposed operational framework enhances CropFVC estimation by improving canopy structural representation and reducing retrieval bias. By enabling more accurate 10 m CropFVC mapping at the field scale, the crop-specific approach provides practical support for precision agriculture and crop-related food security monitoring.

Managing ecosystems in an era of rapid change is inherently challenging not only because of uncertainty in future climate but also due to diverse responses of ecosystems to climate. Projections of ecological transformation alongside information about plausible vegetation trajectories can help land managers explore divergent scenarios and consider how modeled outcomes match their observations. Climate-analog impact models (AIMs) compare environmental information (e.g., vegetation types) between sets of climatically similar locations to infer change and can be used to identify multiple outcomes. We used AIMs to project changes in vegetation across the western United States under a mid-21st century climate scenario, characterize ecological transformation vulnerability based on projection divergence, and demonstrate how AIMs can inform decision-making. We projected high or very high vulnerability to ecological transformation across 29% of the western US, nearly 1 M km2. Vulnerability varied among vegetation groups; 75% of alpine vegetation had high or very high vulnerability vs. 6% of desert scrub. We estimate that 9% of the study area faces a high likelihood of transformation based on combined measures of vulnerability and projection agreement. Transformation at the vegetation type (n = 50) level is projected for 40% (1.4 M km2) of the study area, based on primary projections. As vegetation shifts towards types supported by a more arid climate, forested area is expected to contract by 9% and subalpine forests specifically by 54%. Elsewhere, vulnerability is low or trajectories are uncertain, implying opportunities for managers to intervene. Dry forests, for example, could be stabilized through vegetation management and intentional fire use. Our findings suggest likely ecological transformations with significant downstream consequences for ecosystem services and natural resources. They are best used within decision-making frameworks that draw on multiple lines of evidence including local expertise and complementary knowledge systems.

Characteristics of sediment microbial community structure under different vegetation types with various restoration durations in Hongze Lake.

Abstract Plant wax hydrogen isotopes (δ 2 H) preserved in lake sediments provide valuable insights into past climatic changes. However, lake catchments often experience local shifts in vegetation type that can yield distinct isotopic signatures in the sediments, potentially obscuring hydroclimatic signals. Here, we compile regional plant wax data and test different approaches to correct for the impact of changes in vegetation type over time to isolate precipitation δ 2 H values (δ 2 H prc ) since the Younger Dryas (12.9–11.7 ka) from a sediment record from Rotsee (central Switzerland). Our method intercomparison shows that n ‐alkane relative abundances produced the most accurate δ 2 H prc estimates, agreeing with an independent speleothem fluid‐inclusion δ 2 H record from Milandre Cave. This indicates that sedimentary plant wax δ 2 H values represent a vegetation‐weighted community signal. Precipitation was 2 H‐depleted during the Younger Dryas (∼−75‰), and then δ 2 H prc values increased sharply into the Holocene. During the early Holocene (∼10 ka), δ 2 H prc values of ∼−55‰ were reached, consistent with maximum summer insolation, followed by a gradual long‐term decline toward the present, reflecting Neoglacial cooling and consistent with modern δ 2 H prc values (∼−67‰). Importantly, while plant wax δ 2 H values declined sharply due to deforestation beginning in the Roman period, this impact is effectively corrected for by using the relative abundance of n ‐alkanes. These findings underscore the need for site‐specific vegetation corrections to produce robust hydroclimate reconstructions from plant‐wax isotopes, especially in lakes with small catchments, thereby enhancing comparability of sedimentary records with climate models and deepening our understanding of past climate–vegetation–human interactions.

Climate-driven vegetation greening in Southwest China's Karst region: A multi-scale kNDVI analysis.

Biodegradation reshapes chlorine reactivity of litter-derived dissolved organic matter from herbaceous and woody plants.