Search Results (233)

Ecosystem services—the benefits humans derive from nature—are foundational to environmental sustainability and economic well-being, with carbon sequestration and storage standing out as critical regulating services in the fight against climate change. This study presents a comprehensive financial valuation of the carbon sequestration, storage and product substitution ecosystem services provided by the Hungarian forest-based sector. Using a multi-scenario framework, four complementary valuation concepts are assessed: total carbon storage (biomass, soil, and harvested wood products), annual net sequestration, emissions avoided through material and energy substitution, and marketable carbon value under voluntary carbon market (VCM) and EU Carbon Removal Certification Framework (CRCF) mechanisms. Data sources include the National Forestry Database, the Hungarian Greenhouse Gas Inventory, and national estimates on substitution effects and soil carbon stocks. The total carbon stock of Hungarian forests is estimated at 1289 million tons of CO₂ eq, corresponding to a theoretical climate liability value of over EUR 64 billion. Annual sequestration is valued at approximately 380 million EUR/year, while avoided emissions contribute an additional 453 million EUR/year in mitigation benefits. A comparative analysis of two mutually exclusive crediting strategies—improved forest management projects (IFMs) avoiding final harvesting versus long-term carbon storage through the use of harvested wood products—reveals that intensified harvesting for durable wood use offers higher revenue potential (up to 90 million EUR/year) than non-harvesting IFM scenarios. These findings highlight the dual role of forests as both carbon sinks and sources of climate-smart materials and call for policy frameworks that integrate substitution benefits and long-term storage opportunities in support of effective climate and bioeconomy strategies. Full article

Invasive plant species pose an increasing threat to mangroves globally. This study assessed the impact of Paspalum vaginatum invasion on carbon loss and early recovery following four years of restoration in a mangrove forest with Rhizophora racemosa in Benin. Organic carbon was quantified in the total biomass, including both aboveground and belowground components, as well as in the soil to a depth of −50 cm. In addition, soil gas fluxes of CO₂, CH₄, and N₂O were measured. Three sites were evaluated: a conserved mangrove, a site degraded by P. vaginatum, and the same site post-restoration via hydrological rehabilitation and reforestation. Invasion significantly reduced carbon storage, especially in soil, due to lower biomass, incorporation of low C/N ratio organic residues, and compaction. Restoration recovered 7.8% of the total biomass carbon compared to the conserved mangrove site, although soil organic carbon did not rise significantly in the short term. However, improvements in deep soil C/N ratios (15–30 and 30–50 cm) suggest enhanced soil organic matter recalcitrance linked to R. racemosa reforestation. Soil CO₂ emissions dropped by 60% at the restored site, underscoring restoration’s potential to mitigate early carbon loss. These results highlight the need to control invasive species and suggest that restoration can generate additional social benefits. Full article

The priming effect (PE), a microbially mediated process, critically regulates the balance between carbon sequestration and mineralization. This study used soils from different soil depths (0–20 cm, 20–40 cm, and 40–60 cm) under Picea schrenkiana forest in the Tianshan Mountains as the research object. An indoor incubation experiment was conducted by adding three concentrations (1% SOC, 2% SOC, and 3% SOC) of ¹³C-labelled glucose. We applied ¹³C isotope probe-phospholipid fatty acid (PLFA-SIP) technology to investigate the influence of readily labile organic carbon inputs on soil priming effect (PE), microbial community shifts at various depths, and the mechanisms underlying soil PE. The results indicated that the addition of ¹³C-labeled glucose accelerated the mineralization of soil organic carbon (SOC); CO₂ emissions were highest in the 0–20 cm soil layer and decreased trend with increasing soil depth, with significant differences observed across different soil layers (p < 0.05). Soil depth had a positive direct effect on the cumulative priming effect (CPE); however, it showed negative indirect effects through physico-chemical properties and microbial biomass. The CPE of the 0–20 cm soil layer was significantly positively correlated with ¹³C-Gram-positive bacteria, ¹³C-Gram-negative bacteria, and ¹³C-actinomycetes. The CPE of the 20–40 cm and 40–60 cm soil layers exhibited a significant positive correlation with cumulative mineralization (CM) and microbial biomass carbon (MBC). Glucose addition had the largest and most significant positive effect on the CPE. Glucose addition positively affected PLFAs and particularly microbial biomass. This study provides valuable insights into the dynamics of soil carbon pools at varying depths following glucose application, advancing the understanding of forest soil carbon sequestration. Full article

Urbanisation and land-use change are among the main pressures on soil health in periurban areas, but the multifunctionality of grassland soils is still not sufficiently recognised. In this study, the physical and chemical properties of soils under grassland, forest and croplands in the periurban area of Zagreb were investigated in a two-year period. Grasslands consistently exhibited multifunctional benefits, including high organic matter content (4.68% vs. 2.24% in cropland), improved bulk density (1.14 vs. 1.24 g cm⁻³) and an active carbon cycle indicated by increased CO₂ emissions (up to 1403 kg ha⁻¹ day⁻¹ in 2021). Forest soils showed the highest aggregate stability (91.4%) and infiltration (0.0006 cm s⁻¹), while croplands showed signs of structural degradation with the highest bulk density and lowest water retention (39.9%). Temporal variation showed that grassland was particularly responsive to favourable climatic conditions, with soil porosity and water content improving yearly. Principal component analysis showed that soil structure, biological activity and moisture regulation were linked, with grassland plots favourably positioned along the axes of resilience. The absence of tillage and the presence of permanent vegetation cover contributed to their high capacity for climate and water regulation and carbon sequestration. These results emphasise the importance of protecting and managing grasslands as an important component of urban green areas. Practices such as mulching, minimal disturbance and continuous cover can maximise the ecosystem services of grassland soils. In addition, the results highlight the potential risk of trace metal accumulation in cropland and grassland soils located near urban and farming infrastructure, underlining the need for regular monitoring in periurban environments. Integrating grassland functions into urban planning and policy is essential for improving the sustainability and resilience of periurban landscapes. Full article

Nitrous oxide (N₂O) emissions from paddy soils significantly contribute to global warming; however, the regulatory mechanisms of microbial denitrification remain poorly understood. This study investigated the biotic and abiotic drivers of N₂O production and reduction across seven paddy soils spanning China’s major rice-growing regions, using integrated qPCR, incubation experiments, and multivariate analyses. Results demonstrated niche partitioning among denitrifying microorganisms. Pearson correlation analysis demonstrated significant positive correlations between potential N₂O production rates and the abundances of denitrification genes (nirS, nirK, and fungal nirK), as well as between N₂O reduction rates and nosZ gene abundances (both clade I and II). Key soil chemical properties, including pH, total carbon (TC), and NH₄⁺-N content, showed significant relationships with both potential N₂O production rates and reduction rates. Furthermore, random forest analysis identified nirS, fungal nirK, TC, and pH as key predictors of N₂O production, while nosZ (clade I and II), TC, and pH governed N₂O reduction. Structural equation modeling revealed that nirS-type bacteria predominantly drove N₂O production, whereas nosZ II-encoded microorganisms primarily mediated N₂O reduction. Moreover, TC exhibited direct positive effects on both processes, while pH indirectly influenced N₂O production by regulating nirS abundance and affected reduction via nosZ Ⅱ modulation. These findings provide a mechanistic framework for mitigating agricultural denitrification-derived N₂O emissions through a targeted management of soil carbon and pH conditions to optimize complete denitrification. Full article

Upland fields are a significant source of N₂O emissions. Thus, an accurate estimation of these emissions is essential. This study employed four classical modeling approaches—the Stepwise Regression Model, Decision Tree Regression, Support Vector Machine, and Random Forest (RF)—to simulate soil N₂O emissions from Chinese upland fields. The upland crops considered in this study covered food crops, oil crops, cash crops, sugar crops, fruits, and vegetables, excluding flooded rice. Comparative analysis revealed that the RF algorithm performed the best, with the highest R² at 0.66 and the lowest root mean square error at 0.008 kg N₂O ha⁻¹ day⁻¹. The application rate of mineral nitrogen fertilizers, mean temperature during the growing season, and soil organic carbon content were the key driving factors in the N₂O emission model. Utilizing the RF model, total N₂O emissions from Chinese upland fields in 2020 were estimated at 183 Gg. Future projections under Representative Concentration Pathway (RCP) scenarios indicated a 2.80–5.92% increase in national N₂O emissions by 2050 compared to 2020. The scenario analysis demonstrated that the proposed nitrogen reduction strategies fail to counteract climate-driven emission amplification. Under the combined scenarios of RCP8.5 and nitrogen reduction strategies, a net 4% increase in national N₂O emissions was projected, highlighting the complex interplay between anthropogenic interventions and climate feedback mechanisms. This study proposes that future attention should be paid to the development of nitrogen optimization strategies under the impact of climate change. Full article

Although tropical ecosystems have become increasingly vulnerable to fire over the past century, the mechanisms by which fire disturbance influences N₂O emissions in these regions remain poorly understood. This study investigated the effects of fire on nitrous oxide (N₂O) emissions, the gross nitrification rate (GN), denitrification genes, and carbon (C) and nitrogen (N) fractions in a tropical forest. The results showed that fire increased the GN by 41.5%. The abundance of the nirK and nirS genes encoding nitrite reductase increased by 16.3% and 27.5%, respectively, while the abundance of the nosZI gene encoding N₂O reductase increased by 28%, suggesting a potentially enhanced denitrification capacity. This enhancement in nitrification and denitrification was mainly due to increased easily oxidizable organic C (EOC, +35%), light fraction organic C (LFOC, +32%), hydrolyzable ammonium N (HAN, +13%), and amino sugar N (ASN, +11%), which provided additional substrates for nitrification and denitrification. As a result, soil N₂O emissions increased by 18% in response to fire. Soil N₂O emissions showed a significant and positive linear correlation with GN, EOC, LFOC, HAN, nirK, nirS, and nosZI. Thus, the post-fire increase in N₂O emissions is likely driven by enhanced nitrification and denitrification processes, facilitated by the elevated availability of labile C and N fractions. Our findings provide new evidence for the role of soil C and N fractions in controlling N₂O emission and nitrification–denitrification under fire disturbances in tropical soils. Full article

China has approximately 3.43 million hectares of tea plantations, which offer significant potential for carbon sequestration and the reduction of CO₂ emissions. However, the mechanisms underlying the stability and mineralization of soil organic carbon (SOC) in different tea plantations remain unclear. This study aimed to comprehensively evaluate the effects of chemical, physical, and microbial factors on SOC mineralization in tea plantations with different methods of forest conversion to tea plantations and different ages of tea plants. Our findings indicate that forest conversion to tea plantation methods and tea planting age significantly influence SOC mineralization. Specifically, the SOC mineralization in tea plantations reclaimed by clear-cutting and burning (FMT4) was lower than in those reclaimed by partial cutting (MT3, MT30, and MT150). This variation is attributed to differences in the chemical structure of SOC, which showed higher proportions of aromatic C (33.4%) and carbonyl/carboxyl C (7.8%), alongside lower proportions of O-alkyl C, in the FMT4 tea plantation compared to the others. Additionally, SOC mineralization was significantly higher in the MT150 tea plantation (15.23 g C kg⁻¹ SOC) than in the MT3 (10.11 g C kg⁻¹ SOC), MT30 (10.38 g C kg⁻¹ SOC), and MT200 plantations (9.13 g C kg⁻¹ SOC). Notably, although the MT200 tea plantation had a higher proportion of O-alkyl C (42.4%) than the MT3 and MT30 plantations (36.4%), and was similar to the MT150 plantation (43.1%), its SOC mineralization remained lower due to the higher clay content (278 g kg⁻¹). Correlation analysis and random forest analysis further revealed that physical properties, particularly clay content, are the most significant factors regulating SOC mineralization, followed by the chemical structure, such as O-alkyl C and aromatic C, as well as other physicochemical properties like the carbon-to-nitrogen (C/N) ratio, and microbial properties like Gram-positive bacteria. In conclusion, our study highlights the complex interplay of soil physical properties and SOM chemical structure and microbial properties in regulating SOC mineralization, providing valuable insights for improving carbon management in tea plantations. Full article

Replacing peat with biochar in nursery growing media could help offset carbon emissions and reduce environmental degradation caused by mining wetlands for peat. However, the effects of replacing peat with biochar on CO₂ emissions are little known. In this study, we measured CO₂ flux rates in growing media with varying proportions of biochar (0%, 25%, 50%, 75%, and 100% levels) as a replacement for peat. Overall, we found that higher biochar levels (≥75%) in growing media resulted in a reduction in CO₂ fluxes compared to pure peat (0% biochar), approaching near-zero emissions. In contrast, lower biochar levels (≤25%) had little to no effect on CO₂ fluxes. When the growing media was fertigated or irrigated, we observed a decrease in CO₂ fluxes in mixes containing 25%, 50%, and 75% biochar, though this effect was absent in mixes that were pure peat or pure biochar, suggesting that irrigation and fertilization regimes could be strategized to enhance biochar’s carbon emission impacts. Our study offers insights into the development of sustainable growing media to reduce the carbon footprint of horticulture and forestry nursery production systems and may help balance productivity with environmental conservation. Full article

Intercropping is an effective approach for enhancing soil organic carbon (SOC) sequestration. However, the effects of intercropping on SOC dynamics and the underlying factors in rhizosphere and bulk soils are still unclear. In this study, we examined the impacts of sugarcane monoculture and sugarcane–watermelon intercropping on soil properties, soil respiration, SOC fractions, and microbial C limitation with continuous two years in 2023–2024 years in the Nala area of Guangxi Province. Our results revealed that intercropping significantly decreased CO₂/SOC by 25% and microbial C limitation by 21% in the rhizosphere, with more pronounced reductions observed in bulk soil by 33% and 25%, respectively. This means that the intercropping reduced soil respiration and this effect can be offset by the rhizosphere effects. Additionally, the sugarcane–watermelon intercropping increased the contents of mineral-associated organic carbon (MAOC) by 15~18% and particulate organic carbon (POC) by 34~46%. The random forest analysis indicated that enzyme activities (explaining 20~38% of variation) and soil properties (explaining 22% of variation) were the primary drivers of reduced CO₂ emissions. The PLS-PM showed that intercropping decreased microbial C limitation by influencing soil pH and soil water content (SWC), and then increased MAOC, which finally led to a decline in CO₂ emissions. Overall, these findings highlight the decreasing CO₂ emissions during the use of the intercropping system and the importance of microbial C limitation in the soil C cycle via soil respiration and SOC fractions. Full article

The effects of climate change are particularly pronounced in cities, where urban green infrastructure—such as trees, parks, and green spaces—plays a vital role in both climate adaptation and mitigation. This study assesses the carbon sequestration potential of urban forests in Budapest, the capital city of Hungary, which lies at the intersection of the Great Hungarian Plain and the Buda Hills, and is traversed by the Danube River. The city is characterized by a temperate climate with hot summers and cold winters, and a diverse range of soil types, including shallow Leptosols and Cambisols in the limestone and dolomite hills of Buda, well-developed Luvisols and Regosols in the valleys, Fluvisols and Arenosols in the flood-affected areas of Pest, and Technosols found on both sides of the city. The assessment utilizes data from the National Forestry Database and the Copernicus Land Monitoring Service High Resolution Layer Tree Cover Density. The results show that Budapest’s urban forests and trees contribute an estimated annual carbon offset of −41,338 tCO₂, approximately 1% of the city’s total emissions. The urban forests on the Buda and Pest sides of the city exhibit notable differences in carbon sequestration and storage, age class structure, tree species composition, and naturalness. On the Buda side, older semi-natural forests dominated by native species primarily act as in situ carbon reservoirs, with limited additional sequestration capacity due to their older age, slower growth, and longer rotation periods. In contrast, the Pest-side forests, which are primarily extensively managed introduced forests and tree plantations, contain a higher proportion of non-native species such as black locust (Robinia pseudoacacia) and hybrid poplars (Populus × euramericana). Despite harsher climatic conditions, Pest-side forests perform better in carbon sink capacity compared to those on the Buda side, as they are younger, with lower carbon stocks but higher sequestration rates. Our findings provide valuable insights for the development of climate-resilient urban forestry and planning strategies, emphasizing the importance of enhancing the long-term carbon sequestration potential of urban forests. Full article

In Vietnam, models for estimating Above-Ground Biomass (AGB) to predict carbon stock are primarily based on diameter at breast height (DBH), tree height (H), and wood density (WD). However, remote sensing has increasingly been recognized as a cost-effective and accurate alternative. Within this context, the present study aimed to develop correlation equations between Total Above-Ground Carbon (TAGC) and vegetation indices derived from Sentinel-2 imagery to enable direct estimation of carbon stock for assessing emissions and removals. In this study, the remote sensing indices most strongly associated with TAGC were identified using principal component analysis (PCA). TAGC values were calculated based on forest inventory data from 115 sample plots. Regression models were developed using Ordinary Least Squares and Maximum Likelihood methods and were validated through Monte Carlo cross-validation. The results revealed that Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Near Infrared Reflectance (NIR), as well as three variable combinations—(NDVI, ARVI), (SAVI, SIPI), and (NIR, EVI — Enhanced Vegetation Index)—had strong influences on TAGC. A total of 36 weighted linear and non-linear models were constructed using these selected variables. Among them, the quadratic models incorporating NIR and the (NIR, EVI) combination were identified as optimal, with AIC values of 756.924 and 752.493, R² values of 0.86 and 0.87, and Mean Percentage Standard Errors (MPSEs) of 22.04% and 21.63%, respectively. Consequently, these two models are recommended for predicting carbon stocks in Evergreen Broadleaf (EBL) forests within Vietnam’s South Central Coastal Ecoregion. Full article

The global effort to combat climate change highlights the critical role of storing organic carbon in soil to reduce greenhouse gas emissions. Traditional methods of mapping soil organic carbon (SOC) have been labour-intensive and costly, relying on extensive laboratory analyses. Recent advancements in unmanned aerial vehicles (UAVs) offer a promising alternative for efficiently and affordably mapping SOC at the field level. This study focused on developing a method to accurately predict topsoil SOC at high resolution using spectral data from low-altitude UAV multispectral imagery, complemented by laboratory data from the Nogalte farm in Murcia, Spain, as part of the LIFE AMDRYC4 project. To attain this objective, Python version 3.10 was used to implement several machine learning techniques, including partial least squares (PLS) regression, random forest (RF), and support vector machine (SVM). Among these, the random forest algorithm demonstrated superior performance, achieving an R² value of 0.92, RMSE of 0.22, MAE of 0.19, MSE of 0.05, and EVE of 0.71 in estimating SOC. The results of the RF model were then visualised spatially using GIS and compared with simple spatial interpolations of soil analyses. The findings suggest that a multispectral sensor UAV-based modelling and mapping of SOC can provide valuable insights for farmers, offering a practical means to monitor SOC levels and enhance precision agriculture systems. This innovative approach reduces the time and cost associated with traditional SOC mapping methods and supports sustainable agricultural practices by enabling more precise management of soil resources. Full article

This study investigated the impact of grassland and cropland expansion on carbon (C) and nitrous oxide (N₂O) emissions using remote sensing data and machine learning models. The research focused on agricultural land-use changes in South Sumatra from 1992 to 2018, utilizing Landsat satellite imagery and Google Earth Engine (GEE) for spatial and temporal analysis. Machine learning algorithms, including gradient boosting trees (GBT), random forest (RF), support vector machines (SVM), and classification and regression trees (CART), were employed to estimate greenhouse gas emissions based on multiple environmental parameters. These parameters include enhanced vegetation index (EVI), land surface temperature (LST), normalized difference vegetation index (NDVI), albedo, elevation, humidity, population density, precipitation, soil moisture, and wind speed. The results revealed a strong correlation between agricultural expansion and increased C and N₂O emissions, with RF and GBT models demonstrating superior predictive accuracy. Specifically, GBT and RF achieved the highest R² value (0.71, 0.59) and the lowest error metrics in modeling emissions, whereas SVM performed poorly across all cases. The study highlights the effectiveness of machine learning in quantifying emission dynamics and underscores the necessity of sustainable land management strategies to mitigate greenhouse gas emissions. By integrating remote sensing and data-driven methodologies, this research contributes to climate change mitigation policies and precision agriculture strategies aimed at balancing food security and environmental sustainability. Full article

Soil microbial carbon use efficiency (CUE) is the ratio of carbon allocated to microbial growth to that taken up by microorganisms. Soil microbial CUE affects terrestrial ecosystem processes such as greenhouse gas emissions, carbon turnover, and sequestration, which is an important indicator of changes in the terrestrial carbon cycle. Firstly, we summarized the three methods of soil microbial CUE, stoichiometric modeling, ¹³C glucose tracing, and ¹⁸O water tracing, and compared the advantages and limitations of the three methods. Then, we analyzed the single or combined effects of different environmental factors on soil microbial CUE in grassland ecosystems, forest ecosystems, and wetland ecosystems. Finally, we suggested that future research should focus on the following aspects: the influence of management patterns on CUE (such as grazing and the prohibition of grazing in grassland ecosystems, forest gap, and thinning in forest ecosystems); effects of the strategies of microorganisms for adapting to environmental changes on CUE; effects of anaerobic metabolic pathways, especially in wetland ecosystems; and effects of microbial taxonomic level. This study contributes to the investigation of the microbial mechanisms of carbon cycling in terrestrial ecosystems to mitigate the impacts of climate change. Full article