Search Results (683)

Mediterranean agroecosystems are highly vulnerable to climate change, soil degradation, and declining soil organic carbon (SOC), threatening long-term agricultural sustainability. Carbon farming and regenerative agriculture have emerged as complementary approaches to restore soil functionality while contributing to climate change mitigation. This review synthesizes peer-reviewed literature published between 2015 and 2025 to assess the agronomic effectiveness of key regenerative and carbon farming practices in Mediterranean systems. A structured bibliographic analysis using Scopus and Web of Science evaluated practices influencing SOC dynamics, erosion control, water regulation, and associated ecosystem services. Evidence indicates that the introduction of cover crops in the crop rotation and reduced or no-tillage are the most consistently effective practices for enhancing SOC stocks, particularly when combined with organic amendments and diversified rotations. Crop diversification, intercropping, and agroforestry further support SOC accumulation and erosion control, especially in perennial systems such as vineyards and olive orchards. Organic inputs stimulate microbial-mediated carbon stabilization, while regenerative grazing contributes to nutrient cycling under context-specific conditions. Across practices, integrated management consistently delivers greater and more stable benefits than single interventions. Regenerative agriculture thus provides a systems-based foundation for carbon farming in Mediterranean agroecosystems. Long-term field experiments and improved monitoring frameworks remain essential to quantify carbon persistence and support policy implementation. Full article

Carbon storage dynamics in dryland and semi-arid ecosystems remain a major uncertainty in global carbon cycle assessments, particularly in regions like the Yellow River Basin (YRB). Using the Arid Ecosystem Model (AEM), we simulated the spatiotemporal evolution of four major carbon pools—total carbon (TOTC), vegetation carbon (VEGC), soil organic carbon (SOC), and litter carbon (LTRC)—from 1981 to 2060 under factorial climate scenarios. During 1981–2020, TOTC increased by 0.09 Pg C (+3.54%), driven by gains in VEGC (+0.03 Pg C, +21.43%) and SOC (+0.06 Pg C, +2.78%). LTRC showed minimal net change but was highly sensitive to interannual variability. From 2021 to 2060, under the high-emission SSP5 scenario, TOTC is projected to increase by 0.114 Pg C (+4.81%), with VEGC contributing most of the gain (+23.87%). CO₂_only simulations showed similar increases, underscoring the dominant role of CO₂ fertilization. In contrast, warming and precipitation alone produced weaker and more variable effects. Spatially, upper YRB regions are expected to maintain strong sink capacity, while the Loess Plateau and central-western subregions remain vulnerable to warming and moisture decline. LTRC exhibited the highest variability across scenarios (−18% to +22%), highlighting its role as a sensitive indicator of sink stability. These findings emphasize the need to account for nonlinear climate–carbon interactions and regional heterogeneity. Region-specific, adaptive strategies that integrate ecological restoration and climate adaptation will be critical to enhancing carbon sinks and supporting China’s carbon neutrality targets in the Yellow River Basin. Full article

For advancing carbon peaking and neutrality objectives and regional socio-ecological sustainability, it is critical to examine how land use change and ecosystem carbon storage may evolve under different development scenarios, and to reveal the spatiotemporal patterns and key drivers of carbon sink capacity across the Lower Yangtze River Basin. Such analysis bears both substantial scientific insight and practical relevance. By coupling the PLUS, InVEST, and Geographical Detector models, the present study conducted a comprehensive assessment of land use and carbon storage dynamics in the Lower Yangtze River region from 2000 to 2025. We further explored how different factors drive the spatiotemporal variation in carbon storage, and predicted the potential land use patterns and associated carbon storage values in the research area by 2030 under three hypothetical scenarios. Collectively, our analysis yielded four core conclusions. (1) Between 2000 and 2025, the land use transformation in the research area was dominated by the continuous shrinkage of arable land and the expansion of construction land. (2) The total carbon storage in the study area declined steadily throughout the study period, showing distinct phased characteristics with a steep drop in the early stage and a slower decline thereafter. (3) Implementing the S2 scenario could effectively curb regional carbon storage loss, whereas the S3 Scenario would result in the most severe carbon stock depletion. (4) The spatial configuration of carbon storage is primarily structured by natural environmental factors. In light of these research outcomes, several recommendations are proposed to guide regional sustainable development. Specifically, efforts should be made to improve the intensive use of urban construction land, thereby minimizing carbon storage loss caused by urbanization. Additionally, develop scientific and targeted ecological conservation policies based on the spatial distribution patterns of high carbon storage zones. Finally, implementing regionally tailored management measures will help achieve coordinated and sustainable development across the study area. Full article

Wildfires are a dominant ecological disturbance in Mediterranean ecosystems, yet how post-fire aboveground carbon dynamics and vegetation characteristics govern soil organic carbon stock (SOC) remains poorly understood. This study characterized the dynamics of the carbon pool and changes in the plant community in cork oak shrublands in southern Portugal 11 and 19 years after a fire. Unburned stands were used as a reference. Five plots of each scenario were selected, for a total of 45 independent transects (15/scenario). Aboveground carbon, litter carbon, SOC, and community structural and diversity traits were quantified. Random Forest regression was used to identify the structural and compositional predictors at each stage. The results showed that 11 and 19 years after the fire, the stands held approximately 3:1 and 2:1 more aboveground carbon than the unburned sites due to higher post-fire shrub growth. Total litter carbon amount reached the highest value at 19 years, accompanied by a transient increase in species richness. However, SOC did not differ significantly across any scenario, revealing a clear decoupling between above- and belowground pools, likely constrained by Mediterranean drought conditions. Random Forest analysis showed that the nature of the vegetation–SOC relationships shifted qualitatively across the chronosequence, from structurally dominated in unburned stands to increasingly species-identity-driven. These findings highlight the limitations of assuming monotonic post-fire carbon recovery and underscore the role of species functional identity in mediating long-term SOC dynamics in Mediterranean shrublands. Full article

Bacterial density, cell morphology, and carbon stock (C stock) were quantified in seven Brazilian reservoirs (Serra da Mesa, Manso, Itumbiara, Corumbá, Furnas, Mascarenhas de Moraes, and Luis Carlos Barreto) to evaluate spatial and seasonal patterns in these tropical freshwater systems. Subsurface water samples were collected before, during, and after the rainy season. Bacterial density, cell volume, elongation, and biomass were determined using epifluorescence microscopy, and bacterial C stock was estimated from biomass integrated over the 0.5 m sampling depth. C stock varied among reservoirs and sampling periods, with the highest values consistently observed in the largest reservoir (Serra da Mesa, 1.9·10⁻⁵ g C). Although bacterial densities showed limited temporal variation, biomass peaked before the rainy season. Density and biomass were negatively correlated with water transparency and positively correlated with turbidity, suggesting that particle-associated organic and inorganic matter influences bacterial biomass accumulation. These findings highlight how environmental conditions shape bacterial biomass and carbon storage in tropical reservoirs, contributing to a broader understanding of microbial carbon pools in these ecosystems. Full article

While terrestrial carbon storage is vital for achieving global carbon neutrality, its spatiotemporal evolution in ecologically fragile regions—such as the Pisha sandstone area—is complicated by intense erosion and complex environmental drivers. Widely known as the Pisha sandstone area, often referred to as the “Earth’s ecological cancer” due to its unique geological instability (“hard as rock when dry, soft as mud when wet”), this area is a critical but vulnerable carbon sink in the Yellow River Basin. This study aims to clarify these dynamics and identify their non-linear driving mechanisms by integrating a coupled PLUS–InVEST model with an XGBoost–SHAP framework to simulate land-use cover change and quantify carbon sequestration potential from 1990 to 2040. Our results reveal: (1) a robust path dependence in land use, where grassland remained the dominant landscape matrix (>75%), which partly explains the stable regional carbon-stock structure and the moderate FoM value of the PLUS validation; (2) carbon storage followed a fluctuating but overall increasing trajectory, projected to reach a peak of 3.19 × 10⁵ tC by 2040 under the Ecological Conservation Scenario (ECS), which significantly outperforms the economic-driven and natural growth modes; (3) hot spot analysis showed that statistically notable low-carbon cold spots were concentrated mainly along valley corridors, marginal transition zones, and locally disturbed patches, whereas high-carbon hot spots were spatially limited; and, (4) crucially, XGBoost–SHAP results should be interpreted as model-based associations rather than direct causal proof; the whole-region model and the regional models jointly suggest that topography, water availability, socioeconomic pressure, and erosion-related factors contribute differently across bare, loess-covered, and sand-covered Pisha sandstone units. These findings support differentiated land-use and restoration strategies rather than uniform regional management. The findings suggest that future management in the Pisha sandstone area should transition from general restoration toward targeted and differentiated regulation to improve regional ecosystem services. Full article

This study quantifies coastal mangrove carbon stocks and their interannual variability along the Pakistan coastline by developing a multi-sensor fusion framework integrated with a process-based light use efficiency (LUE) modeling approach. To ensure high-cadence monitoring and overcome persistent cloud cover over the Indus Delta, data from multiple satellite sensors including Landsat 8/9 and Sentinel-2 within Google Earth Engine were utilized. Sentinel-2-derived Normalized Difference Vegetation Index (NDVI) data composited for the January–March period was processed to estimate vegetation productivity. Field-based validation of biomass estimates was conducted using 57 georeferenced sampling points, cross-compared with Sentinel-2 data. Mangrove extent was delineated through land use and land cover (LULC) classification into water bodies, mangroves, mudflats, land parcels, and sand surfaces. The LUE model incorporated environmental stress scalars, including temperature, vapor pressure deficit (VPD), salinity, and photosynthetically active radiation (PAR) to estimate gross primary productivity and derive total biomass, which was subsequently converted into carbon stocks. Results indicate a mean carbon stock of 31.95 Mg C ha⁻¹ (equivalent to 117.3 Mg CO₂ ha⁻¹), with significant interannual variation (coefficient of variation = 19.8%). A significant decline in carbon stocks was observed in 2021 (−11.11%; 3.56 Mg C ha⁻¹), corresponding to a reduction in NDVI value (0.55 compared to 0.58 in other years). Spatial analysis revealed substantial heterogeneity in carbon distribution (20.51 to 55.93 Mg C ha⁻¹), primarily influenced by localized salinity gradients and water stress conditions. This study mapped mangrove extent, quantified environmental stress, and estimated carbon stocks across Pakistan’s coast from 2020 to 2024, delivering a spatially resolved, multi-year baseline for coastal carbon assessment and ecosystem monitoring in arid tidal environments. Full article

Mangrove ecosystems are critical for climate change mitigation; however, monitoring these environments in high-precipitation regions, such as the Colombian Pacific coast, is often hindered by persistent cloud cover and complex terrain. This study addresses these challenges by implementing the novel Google AlphaEarth Foundations (AEF) technology, leveraging 64-dimensional embeddings integrated with Digital Elevation Models (DEM) and Slope data. For classification, a Random Forest (RF) algorithm was deployed using a subset of only 7 embedding dimensions alongside topographical variables. The model estimated a mangrove extent of 209,262 ha, compared to a reference baseline of 137,732 ha. This discrepancy is hypothesized to stem from the model’s ability to map changes in the mangrove forest due to anthropogenic and natural factors and species migrating upstream, areas frequently overlooked in traditional inventories. The classification performance, evaluated on a spatially independent hold-out validation set, yielded an Overall Accuracy of 0.9844 and a Kappa Index of 0.9747. Regarding biomass estimation, the RF algorithm utilized 4 embedding dimensions plus DEM and Slope to achieve a Coefficient of Determination ($R^2$) of 0.5844, a Root Mean Square Error (RMSE) of 18.99 Mg/ha, and a Mean Absolute Error (MAE) of 14.88 Mg/ha within a range of 100–300 Mg/ha. These metrics represent a notable advancement, successfully mitigating the physical signal saturation that typically constrains traditional single-sensor remote sensing models at high biomass thresholds. Significant advantages of this methodology include the complete elimination of cloud interference and a drastic reduction in processing time. These findings demonstrate that the synergy between foundational models and machine learning provides a robust, scalable, and efficient framework for managing blue carbon stocks in critical tropical regions. Full article

Fishery production in the Gulf Cooperation Council (GCC) region faces growing threats from overfishing, climate change, marine pollution, and habitat degradation, which reduce stock regeneration and ecosystem stability. Inadequate management systems and limited technological adoption further constrain productivity, posing risks to food security and economic stability. This study examines the dynamic impact of climate change shocks on fishery sustainability in GCC countries, using the Panel Vector Autoregression (PVAR) framework to examine both short- and long-run interactions between climate variables and fishery production. The study observed a long-run cointegration between total fisheries production and climate variables. Results reveal strong dynamic linkages, with temperature and carbon emissions exhibiting stable long-term trends and relatively low forecast errors. In contrast, precipitation and fishery output show higher volatility and greater sensitivity to short-term shocks. Temperature shocks have a significant negative effect on fishery production, highlighting the need for climate adaptation policies that protect marine ecosystems, enhance monitoring, and promote sustainable fishing practices. The findings highlight the importance of considering climate variability and adaptive strategies to ensure sustainable fisheries in the region. The novelty of this study is applying a dynamic PVAR approach to GCC fisheries, accounting for short-run and long-run climate impacts and providing region-specific policy-relevant insights that address sustainability under climate variability. Full article

Vegetated coastal wetlands, especially mangroves, seagrass beds, and salt marshes, are biodiversity-rich ecosystems whose blue carbon outcomes depend on living communities, sediment dynamics, hydrological connectivity, and landscape context. Biodiversity conservation and blue carbon management are often assessed through separate scientific, monitoring, and policy frameworks. This review uses a staged literature search and thematic synthesis to examine biodiversity–blue carbon linkages across the three major vegetated coastal wetland types. It considers how taxonomic, genetic, functional, and habitat diversity influence productivity, sediment stabilization, trophic exchange, carbon stocks, carbon burial, and carbon retention. It also evaluates how climate change, habitat fragmentation, hydrological alteration, pollution, and anthropogenic disturbance weaken these linkages. The synthesis compares representative carbon-stock and burial-rate baselines, examines conservation and restoration synergies and trade-offs, and expands the discussion of seagrass regime shifts. Field surveys, remote sensing, unmanned aerial vehicles, environmental DNA, and AI-enabled data integration are placed within a tiered monitoring framework. The review further develops an operational decision pathway for biodiversity-centered blue carbon management. Persistent blue carbon benefits arise where conservation and restoration maintain native communities, hydrological exchange, sediment stability, habitat complexity, migration space, and long-term stewardship capacity. Full article

Estimating carbon stocks in semi-urban ecosystems remains challenging due to spatial heterogeneity and the scale limitations of conventional datasets. This study aims to estimate and analyse the spatial and temporal distribution of carbon stocks at Universitas Padjadjaran using high-resolution remote sensing imagery and to support sustainable campus and green campus initiatives. Multi-temporal data from WorldView-2 (2015, 2017), WorldView-3 (2021), and Legion-03 (2025) were used to derive vegetation indices, followed by aboveground biomass (AGB) modelling through regression analysis. Carbon stock was calculated using a standard conversion factor of 0.5. The results show a consistent increase in vegetation density and carbon stock, with average values rising from 20.381 tonnes/ha in 2015 to 29.160 tonnes/ha in 2025. The use of the MSAVI produced an accurate model for predicting AGB (R² = 0.987–0.993). This study introduces a novel integration of high-resolution imagery using MSAVI to improve AGB estimation at the campus scale, providing a more detailed and reliable approach for carbon assessment in heterogeneous semi-urban environments and contributing to the implementation of sustainable, environmentally friendly campus management strategies. Full article

The Qinghai Lake Basin, a typical ecologically vulnerable, high-altitude, cold region, requires coordinated ecosystem conservation and socio-economic development to achieve territorial sustainability. Based on the Production–Living–Ecological Space (PLES) framework, this study used land use data from five periods between 2000 and 2020 and integrated the PLUS and InVEST models to examine and simulate the evolution of PLES patterns and carbon stock under four scenarios—natural development, ecological protection, economic development, and sustainable development—in 2035. The results show that the PLES pattern in the Qinghai Lake Basin remained generally stable from 2000 to 2020, with ecological space dominating the landscape, while production and living spaces expanded slowly. Carbon stock increased from 214.73 × 10⁶ Mg to 264.70 × 10⁶ Mg, representing a growth rate of 23.27%. Its spatial distribution is highly consistent with the PLES pattern, with ecological space being the main contributor. By 2035, carbon stock is projected to slightly increase under the natural development scenario; under the ecological protection scenario, the expansion of ecological space leads to an increase in carbon stock; it decreases under the economic development scenario due to the encroachment of ecological space by construction land expansion; and under the sustainable development scenario, which balances economic development and ecological protection, carbon stock increases by 4.87 × 10⁶ Mg, achieving the best overall performance. Therefore, it is essential to properly coordinate the relationships among PLES components to achieve synergistic enhancement of ecosystem services and regional sustainable development. The findings provide methodological references and decision support for sustainable development in the Qinghai–Tibet Plateau and other ecologically vulnerable regions. Full article

National forest parks play an important role in maintaining the integrity of ecosystems, the sustainability of biodiversity, and the improvement of ecological service functions. Aboveground carbon storage (AGC) is an important indicator for evaluating forest ecosystem functions. Qianjiangyuan–Baishanzu National Park, located in the southern part of Lishui City, serves as a typical representative of the mid-subtropical evergreen broad-leaved forest ecosystem. It is characterized by high biodiversity and serves as a concentration area for rare and endangered species. Therefore, assessing the spatiotemporal dynamics of forest AGC in the typical subtropical forests of Qianjiangyuan–Baishanzu National Park is of great scientific significance. Taking Qianjiangyuan–Baishanzu National Park as a case study, this research utilized three periods of Landsat satellite remote sensing data (2009, 2014, and 2019) alongside contemporaneous ground-based AGC survey samples. Feature variables were extracted and subsequently screened using the Boruta algorithm. There were three algorithms, including least squares (LS), support vector regression (SVR), and random forest (RF), constructed to estimate forest AGC. The optimal AGC estimation model was selected, and the spatiotemporal variation characteristics of forest AGC within the national park were systematically analyzed. Research has shown that (1) texture features are important parameters for constructing forest AGC estimation models, accounting for up to 71%, with the 7 × 7 window having the greatest impact. (2) All three models can achieve high accuracy in estimating the forest AGC and its spatial distribution in Qianjiangyuan Baishanzu National Park. Among them, the RF model has the highest overall accuracy in estimating forest AGC, with a training set R² of 0.938, RMSE of 5.550 Mg/ha, rRMSE of 12.517%, a test set R² of 0.954, RMSE of 4.653 Mg/ha, and rRMSE of 10.087%. (3) The distribution of forest AGC in Qianjiangyuan Baishanzu National Park shows significant spatial heterogeneity, with higher carbon storage in the central, southern, and southeastern regions, while the northern region has relatively lower carbon storage. From 2009 to 2019, the forest AGC in the Qianjiangyuan–Baishanzu National Park exhibited a steady annual increase, with AGC density rising from 37.64 Mg/ha to 66 Mg/ha and total AGC stock increasing from 2.16 Tg C to 4.36 Tg C. These findings provide precise data support and a scientific basis for decision-making regarding differentiated ecological carbon enhancement and functional zone management within the national park. Full article

This study investigates the influence of various land use systems (LUSs) on soil physico-chemical properties, nutrient dynamics, and soil organic carbon (SOC) stocks in the Central Plain Zone of Uttar Pradesh, India. Soil samples were collected from six distinct LUSs, i.e., fallow, crop-based, horticulture-based, forest-based, vegetable-based, and barren land, and analyzed across three depth intervals (0–15 cm, 15–30 cm, and 30–60 cm). Soil pH increased steadily with depth, ranging from 7.43 to 8.58 at the surface layer to 7.55 to 10.32 in deeper layers. Horticulture-based LUSs recorded the lowest pH, while barren lands had the highest. Electrical conductivity (EC) also rose with depth, ranging from 0.12 to 3.63 dS m⁻¹, from the surface to subsoil layers, all below critical salinity thresholds. Soil organic carbon (SOC) content decreased with increasing soil depth across all land use systems. Among the studied systems, horticulture-based land use recorded the highest SOC content (0.77%), whereas barren land showed the lowest SOC content (0.21%). Due to greater organic matter inputs and reduced disturbances, horticultural systems also exhibited significantly higher levels of macronutrients (N: 17.98 kg ha⁻¹, P: 330.45 kg ha⁻¹, K: 374.81 kg ha⁻¹, S: 84.33 mg ha⁻¹) and micronutrients (Fe: 164.12 mg ha⁻¹, Mn: 60.89 mg ha⁻¹, Cu: 2.85 mg ha⁻¹, Zn: 1.80 mg ha⁻¹). Bulk density increased slightly with depth (1.46–1.63 Mg m⁻³), while soil moisture content remained relatively stable (43.43% to 42.31%), with moderate variability (CV: 24–27%). The mean total SOC stock was 10.77 t C ha⁻¹, ranging from 5.44 to 14.46 t C ha⁻¹. Microbial properties also varied among land uses: dehydrogenase activity (DEA), an indicator of microbial functionality, peaked in vegetable-based systems (30.54 µg TPF g⁻¹), whereas microbial biomass carbon (MBC) was highest in forest-based systems (184.83 µg g⁻¹). Correlation and regression analyses revealed a strong positive relationship between SOC and nutrient availability, with the highest correlation observed for Zn (R² = 0.99), followed by N (R² = 0.83) and K (R² = 0.75). Overall, barren lands showed the poorest soil quality indicators, while horticulture-based systems consistently demonstrated superior soil fertility and carbon sequestration potential. These findings emphasize the critical role of land use management in regulating soil fertility, SOC dynamics, and the long-term sustainability of agro-ecosystems in the region. Full article

Temperate seagrass carbon-stock data remain limited in northern China, especially for island meadow systems with mapped distribution and repeated field verification. This study quantified standing seagrass ecosystem carbon stocks in the Changshan Archipelago, Dalian, using a standardized field survey covering eight meadow zones, 39 sampling stations, and 323.37 ha of confirmed seagrass area. Plant biomass carbon and sediment organic carbon were assessed, and the 0–100 cm sediment profile was sampled at all stations. The mapped meadows stored 29,305.75 Mg C in total ecosystem carbon. Sediment organic carbon accounted for 28,965.71 Mg C, representing 98.84% of the total stock. Plant biomass carbon contributed 340.04 Mg C, or 1.16%. The area-weighted ecosystem carbon stock per unit area was 90.63 Mg C ha⁻¹. This per-area stock ranged from 52.11 Mg C ha⁻¹ in Xiaochangshan to 209.50 Mg C ha⁻¹ in Haiyang Island. Guanglu Island contained the largest total carbon stock, with 9247.73 Mg C, because of its large meadow area and relatively high per-area carbon stock. The results show how mapped meadow area, sediment carbon dominance, and local sediment setting jointly shape regional carbon-storage patterns. This standardized baseline provides field-based evidence for comparing northern Chinese seagrass meadows with other temperate Zostera systems. The estimates describe standing ecosystem carbon stocks. Annual carbon sequestration rates were outside the scope of the assessment. Full article