Search Results (596)

Community-managed forests within agroforestry landscapes are vital for both carbon sequestration and agricultural sustainability. This study assesses the Hariyali Community Forest (HCF) in western Nepal, emphasizing its role in carbon storage within a Sal (Shorea robusta)-dominated lowland forest containing diverse native and medicinal species. Stratified field inventories combined with satellite-derived biomass and land-use/land-cover data were used to quantify carbon stocks and spatial trends. In 2022, the mean aboveground carbon density was 165 tC ha⁻¹, totaling approximately 101,640 tC (~373,017 tCO₂e), which closely matches satellite-based trends and indicates consistent carbon accumulation. Remote sensing from 2015–2022 showed a net tree cover gain of 427 ha compared to a 2000 baseline of 188 ha, evidencing effective community-led regeneration. The 615 ha Sal-dominated landscape also sustains agroforestry, small-scale horticulture, and subsistence crops, integrating livelihoods with conservation. Temporary carbon declines between 2020 and 2022, linked to localized harvesting and management shifts, highlight the need for stronger governance and local capacity. This study, among the first integrated carbon assessments in Nepal’s lowland Sal forests, demonstrates how community forestry advances REDD+ (Reducing Emissions from Deforestation and Forest Degradation, and the role of conservation, sustainable management of forests, and enhancement of forest carbon stocks in developing countries) objectives while enhancing rural resilience. Linking field inventories with satellite-derived biomass and land-cover data situates community forestry within regional environmental change and SDG (Sustainable Development Goals) targets (13, 15, and 1) through measurable ecosystem restoration and livelihood gains. Full article

Soil organic carbon (SOC) plays a crucial role in climate change mitigation by regulating atmospheric CO₂ and maintaining ecosystem balance; however, its stability is influenced by land use in anthropized areas such as the tropical Andes. This study developed a dynamic compartmental model based on ordinary differential equations to simulate carbon fluxes among litter, humus, and microbial biomass under four land uses in the Las-Piedras River basin (Popayán, Colombia): riparian forest (RF), ecological restoration (ER), natural-regeneration (NR), and livestock (LS). The model includes two decomposition rate constants: k₁, for the transformation of fresh organic matter, and k₂, for the turnover of humified organic matter. It was calibrated using field data on soil physicochemical and biological properties, as well as carbon inputs and outputs. The results showed clear differences in SOC dynamics among land uses: RF had the highest SOC stocks (148.7 Mg ha⁻¹) and microbial biomass, while LS showed the lowest values and the greatest deviation due to compaction and low residue input. The humus fraction remained the most stable pool (k₂ ≈ 10⁻⁴ month⁻¹), confirming its recalcitrant nature. Overall, the model reproduced SOC behavior accurately (MAE = 0.01–0.30 Mg ha⁻¹) and provides a framework for improving soil carbon management in mountain ecosystems. Full article

Mining activities have caused widespread land degradation and contamination, affecting millions of hectares worldwide and posing persistent ecological risks. However, reclamation substrates are constrained by limited availability and compromised quality, which restricts their ability to fully support mine ecological restoration. Among various amendment materials, biomass-based amendments have been widely applied due to their broad availability, renewability, biodegradability, and low cost. In recent years, their role has expanded beyond simple nutrient supplementation to encompass multiple functions, including structural optimization, pollutant stabilization, and microbial regulation. This review highlights the valorisation of biomass-derived solid wastes as multifunctional amendments for mine ecological restoration. By converting agricultural and industrial wastes into green materials, these amendments improve substrate structure, stabilize heavy metals and organic pollutants, enhance nutrient cycling, and stimulate microbial activity. Potential risks, including nutrient leaching, secondary pollution, and greenhouse gas emissions, are critically assessed, with emphasis on their variability under different environmental conditions. By integrating functional benefits with ecological risks, this work underscores the critical role of biomass-based amendments as waste-to-resource strategies in advancing sustainable mine reclamation, contributing to circular economy goals, and supporting environmental engineering practices. Full article

Arctic ecosystems are highly vulnerable to ongoing and projected climate change. Rapid warming and growing anthropogenic pressure are driving a profound transformation of these regions, increasingly positioning the Arctic as a persistent, globally significant source of greenhouse gases. In the Russian Arctic—a critical zone for national economic growth and transport infrastructure—intensive development is replacing natural ecosystems with anthropogenically modified ones. In this context, Nature-based Solutions (NbS) represent a vital tool for climate change adaptation and mitigation. However, many NbS successfully applied globally have limited applicability in the Arctic due to its inaccessibility, short growing season, low temperatures, and permafrost. This review demonstrates the potential for adapting existing NbS and developing new ones tailored to the Arctic’s environmental and socioeconomic conditions. We analyze five key NbS pathways: forest management, sustainable grazing, rewilding, wetland conservation, and ecosystem restoration. Our findings indicate that protective and restorative measures are the most promising; these can deliver measurable benefits for both climate, biodiversity and traditional land-use. Combining NbS with biodiversity offset mechanisms appears optimal for preserving ecosystems while enhancing carbon sequestration in biomass and soil organic matter and reducing soil emissions. The study identifies critical knowledge gaps and proposes priority research areas to advance Arctic-specific NbS, emphasizing the need for multidisciplinary carbon cycle studies, integrated field and remote sensing data, and predictive modeling under various land-use scenarios. Full article

Global population growth, climate change, and the environmental impact of livestock production have accelerated the search for sustainable and efficient protein sources. Fruiting bodies (mushrooms) and mycelial biomass have emerged as promising alternatives due to their high nutritional quality, low ecological footprint, and compatibility with circular bioeconomy principles. This review highlights the nutritional, biotechnological, and environmental aspects of fungal proteins obtained from both fruiting bodies and mycelial biomass of Basidiomycetes. Emphasis is placed on amino acid composition, protein digestibility, and advances in cultivation and fermentation systems for large-scale production. Submerged and solid-state fermentation processes are analyzed in terms of scalability, resource efficiency, and integration with agro-industrial residues for sustainable bioprocessing. Comparative analyses reveal that mycelial biomass production achieves high protein yields with significantly reduced land, water, and energy requirements compared to conventional protein sources. Emerging fungal species such as Schizophyllum commune and Auricularia polytricha demonstrate strong potential for producing protein-rich mycelia applicable to functional and plant-based foods. Finally, the review discusses current technological innovations, regulatory frameworks, and market perspectives that position fungal biomass as a strategic component in the ongoing global protein transition. Full article

This study aimed to assess the potential of machine learning models applied to high spatial resolution images from UAVs for estimating the aboveground biomass (AGB) of forage grass cultivated in the Brazilian semiarid region. The fresh and dry AGB were determined in Cenchrus ciliare plots with an area of 0.04 m². Spectral data were obtained using a multispectral sensor (Red, Green, and NIR) mounted on a UAV, from which 45 vegetation indices were derived, in addition to a structural variable representing plant height (H95). Among these, H95, GDVI, GSAVI2, GSAVI, GOSAVI, GRDVI, and CTVI exhibited the strongest correlations with biomass. Following multicollinearity analysis, eight variables (R, G, NIR, H95, CVI, MCARI, RGR, and Norm G) were selected to train Random Forest (RF), Support Vector Machine (SVM), and XGBoost models. RF and XGBoost yielded the highest predictive performance, both achieving an R² of 0.80 for AGB—Fresh. Their superiority was maintained for AGB—Dry estimation, with R² values of 0.69 for XGBoost and 0.67 for RF. Although SVM produced higher estimation errors, it showed a satisfactory ability to capture variability, including extreme values. In modeling, the incorporation of plant height, combined with spectral data obtained from high spatial resolution imagery, makes AGB estimation models more reliable. The findings highlight the feasibility of integrating UAV-based remote sensing and machine learning algorithms for non-destructive biomass estimation in forage systems, with promising applications in pasture monitoring and agricultural land management in semi-arid environments. Full article

This study presents a comprehensive methodology for assessing the technical potential of hay biomass from permanent grasslands (TUZ) in Poland, aimed at evaluating its energy use possibilities. This research was based on detailed data from the Agency for Restructuring and Modernization of Agriculture (ARiMR) and included both environmentally subsidized and non-subsidized parcels. Using statistical hay yield values adjusted for drought impacts through the Climatic Water Balance (KBW), a realistic estimation of technical hay potential was obtained. Results show a total theoretical hay potential of 15 million tonnes in 2024. The results indicate that the total theoretical hay potential in the country in 2024 amounted to 15 million tons, but its technical potential is reduced to almost zero. The methane productivity of this biomass could generate 3.5 Mt CH₄ (at STP) if most of it could not be used for animal feeding purposes. The findings highlight the underutilized energetic potential of grasslands and the critical role of land use policy in unlocking sustainable bioenergy resources. Research into the potential of biomass is important in view of supporting energy independence, sustainable use of agricultural resources and agroecological synergy by combining production, energy and environmental objectives. It should be remembered that biomass potential studies are subject to limitations resulting from the uncertainty of statistical data, variability of climatic and soil conditions and model assumptions, which may affect the accuracy and comparability of the obtained results. Full article

The lack of an established seaweed aquaculture industry in the Atlantic Southeast reflects the persistent challenges in identifying macroalgal species that can consistently produce year-round under regional environmental conditions. As a result, in this study, locally abundant Charlestonian Ulva spp. were selected as sustainable algal candidates for a pilot investigation, due to their resilience to abiotic (e.g., seasonal changes in temperature and nutrients) and biotic (e.g., predation and epiphytes) factors, thus allowing for practical land-based aquaculture. Ulva spp. were analyzed for their seasonal biomass and potential bioremediation applications using the existing land-based aquaculture infrastructure of the SCDNR in Charleston, South Carolina. The biomass of tank-cultivated Ulva spp. was monitored on a biweekly basis for 16 months and was found to be highest (31.8 kg) in the spring, increasing by 22% in just two weeks as water temperatures rose. A synthetic nutrient fertilizer was incorporated into aquaculture at the latter stages of this study to observe the effects on algal biomass while simulating an anthropogenic event. Interestingly, inorganic supplementation did not induce growth but was absorbed by the algal tissue, significantly lowering the δ¹⁵N to <7‰. Additionally, Vibrio spp. bacteria proliferated following the inorganic nutrient spike, while coliform populations decreased. Biochemical composition analyses comparing tank-cultivated and wild in situ Ulva spp. revealed variations in essential trace element (e.g., potassium: tank—19,530; wild—5520 mg/kg) concentrations, yet shared similar trace metal (e.g., arsenic: tank—4.47; wild—4.52 mg/kg) and pesticide (e.g., DEET: tank—0.048; wild—0.040 mg/kg) concentrations. This is the first reported macroalgal aquaculture research in South Carolina and serves as a pilot study for future research or commercialization in the Lowcountry and the greater southeastern coastal communities of the United States. Full article

This research, carried out within the framework of the European Space Agency’s second Scout mission (HydroGNSS), seeks to utilize CYGNSS Level 1B products over land for soil moisture estimation. The approach involves a novel physically based algorithm, which inverts a semiempirical forward model of surface reflectivity proposed in the literature. An Artificial Neural Network (ANN) algorithm has also been developed. Both methods are implemented in the frame of the HydroGNSS mission to make the most of the reliability of an approach rooted in a physical background and the power of a data-driven approach that may suffer from limited training data, especially right after launch. The study aims to compare the results and performance of these two methods. Additionally, it intends to evaluate the impact of auxiliary data. The static auxiliary data include topography, Above Ground Biomass (AGB), land cover, and surface roughness. Dynamic auxiliary data include Vegetation Water Content (VWC) and Vegetation Optical Depth (VOD) from Soil Moisture Active Passive (SMAP), as well as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) from Moderate Resolution Imaging Spectroradiometer (MODIS), on enhancing the accuracy of retrievals. The algorithms were trained and validated using target soil moisture values derived from SMAP L3 global daily products and in situ measurements from the International Soil Moisture Network (ISMN). In general, the ANN approach outperformed the semiempirical model with RMSE = 0.047 m³ m⁻³ and R = 0.91. We also introduced a global stratification framework by intersecting land cover classes with climate regimes. Results show that the ANN consistently outperforms the semiempirical model in most strata, achieving around RMSE = 0.04 m³ m⁻³ and correlations above 0.8. The semiempirical model, however, remained more stable in data-scarce conditions, highlighting complementary strengths for HydroGNSS. Full article

Accurately quantifying grazing intensity (GI) is crucial for assessing grassland utilization and supporting sustainable management. Traditional livestock-based approaches cannot capture the spatial heterogeneity of grazing or its dynamic response to climate variability. The objective of this study was to develop a remote sensing-based quantitative framework for estimating GI across the Inner Mongolian grasslands. The framework integrates MODIS vegetation indices, ERA5-Land climate variables, topographic factors, and field-measured data and GI was quantified as the proportional difference between potential and satellite-derived aboveground biomass (AGB), providing a spatially explicit measure of forage utilization. In this framework, potential AGB (AGBp) represents the climate-driven growth capacity under ungrazed conditions reconstructed using machine learning models, whereas satellite-derived AGB (AGBs) denotes the standing AGB remaining under current grazing pressure. Validation using 324 paired grazed–ungrazed plots demonstrated strong agreement between modeled and observed GI (R² = 0.65, RMSE = 0.18). This AGB-difference-based approach provides an effective and scalable tool for large-scale rangeland monitoring, offering quantitative insights into grass–livestock balance, ecological restoration, and adaptive management in arid and semi-arid regions. Full article

This study presents an integrated framework for spatiotemporal mapping of carbon stocks in rubber plantations in Rayong Province, Eastern Thailand—an area undergoing rapid agricultural transformation and rubber expansion. Unlike most existing assessments that rely on Tier 1 IPCC defaults or coarse plantation age classes, our framework combines annual plantation age derived from Landsat time series, age-specific allometric growth models, and Tier 2 soil organic carbon (SOC) accounting. This enables fine-scale, age- and site-sensitive estimation of both tree and soil carbon. Results show that tree biomass dominates the carbon pool, with mean tree carbon stocks of 66.94 ± 13.1% t C ha⁻¹, broadly consistent with national field studies. SOC stocks averaged 45.20 ± 0.043% t C ha⁻¹, but were overwhelmingly inherited from pre-conversion land use (43.7 ± 0.042% t C ha⁻¹). Modeled SOC changes ($\mathsf{\Delta }\mathrm{SOC}$) were modest, with small gains (2.06 t C ha⁻¹) and localized losses (−9.96 t C ha⁻¹), producing a net mean increase of only 1.44 t C ha⁻¹. These values are substantially lower than field-based estimates (5–15 t C ha⁻¹), reflecting structural limitations of the global empirical ΔSOC model and reliance on generalized default parameters. Uncertainties also arise from allometric assumptions, generalized soil factors, and Landsat resolution constraints in smallholder landscapes. Beyond carbon, ecological trade-offs of rubber expansion—including biodiversity loss, soil fertility decline, and hydrological impacts—must be considered. By integrating methodological innovation with explicit acknowledgment of uncertainties, this framework provides a conservative but policy-relevant basis for carbon accounting, subnational GHG reporting, and sustainable land-use planning in tropical agroecosystems. Full article

Coastal wetlands are a critical buffer between land and water that are threatened by land use and climate change, necessitating improved monitoring for management and resilience planning. The recently launched NASA-ISRO L- and S-band SAR satellite (NISAR) will provide regular collections of fully polarimetric SAR imagery over the Great Lakes, allowing for unprecedented remote monitoring of the large expanses of coastal wetlands in the region. Prior research with polarimetric C-band SAR showed inconsistencies with common polarimetric analysis techniques, including the erroneous misattribution of double-bounce scattering in three-component scattering models. To prepare for NISAR and determine whether SAR-based coastal wetland analysis methods established with the C-band are applicable to the L- and S-bands, the NASA-ISRO airborne system (ASAR) collected imagery over western Lake Erie and Lake St. Clair coincident with a field data collection campaign. ASAR data were analyzed to identify common Great Lakes coastal wetland vegetation species, assess the extent of inundation, and derive biomass retrieval algorithms. Co-polarized phase difference histograms were also analyzed to assess the validity of three-component scattering decompositions. The L- and S-bands allowed for the production of wetland type maps with high accuracies (92%), comparable to those produced using a fusion of optical and SAR data. Both frequencies could assess the extent of flooded vegetation, with the S-band correctly identifying inundated vegetation at a slightly higher rate than the L-band (83% to 78%). Marsh vegetation biomass retrieval algorithms derived from L-band data had the best correlation with field data (R² = 0.71). Three component scattering models were found to misattribute double-bounce scattering at incidence angles shallower than 35°. The L- and S-band results were compared with satellite RADARSAT-2 imagery collected close to the ASAR acquisitions. This study provides an advanced understanding of polarimetric SAR for monitoring wetlands and provides a framework for utilizing forthcoming NISAR data for effective monitoring. Full article

Agricultural land management is a major determinant of terrestrial carbon (C) fluxes and has substantial implications for greenhouse gas (GHG) mitigation strategies. This study evaluated the net ecosystem carbon balance (NECB) of an agricultural field in an organic integrated crop–livestock system (ICLS) with a ley-arable rotation in northern Germany over two years (2021–2023). Carbon dioxide (CO₂) fluxes were measured using the eddy covariance (EC) method to derive net ecosystem exchange (NEE), gross primary production (GPP), and ecosystem respiration (RECO). This approach facilitated an assessment of the temporal dynamics of CO₂ exchange, alongside detailed monitoring of field-based C imports, exports, and management activities, of a crop sequence including grass-clover (GC) ley, spring wheat (SW), and a cover crop (CC). The GC ley acted as a consistent C sink (NECB: −1386 kg C ha⁻¹), driven by prolonged photosynthetic activity and moderate biomass removal. In contrast, the SW, despite high GPP, became a net source of C (NECB: 120 kg C ha⁻¹) due to substantial export via harvest. The CC contributed to C uptake during the winter period. However, cumulatively, it acted as a net CO₂ source, likely due to drought conditions following soil cultivation and CC sowing. Soil cultivation events contributed to short-term CO₂ pulses, with their magnitude modulated by soil water content (SWC) and soil temperature (TS). Overall, the site functioned as a net C sink, with an average NECB of −702 kg C ha⁻¹ yr⁻¹. This underscores the climate mitigation potential of management practices such as GC ley systems under moderate grazing, spring soil cultivation, and the application of organic fertilizers. To optimize CC benefits, their use should be combined with reduced soil disturbance during sowing or establishment as an understory. Additionally, C exports via harvests could be offset by retaining greater amounts of harvest residues onsite. Full article

Terpenoids, as a class of natural products with extensive biological activities, hold broad application prospects in the fields of medicine, food, materials, and energy, with the global market scale projected to reach USD 10 billion by 2030. Traditional chemical synthesis and plant extraction methods rely on petroleum and plant resources, suffering from problems such as environmental pollution, cumbersome procedures, low yields from plant sources, enantioselectivity, geographical constraints, and competition for resources. Biocatalytic conversion of biomass feedstocks via microbial cell factories serves as an environmentally friendly alternative for the synthesis of terpenoids, but current production mostly depends on starch-based glucose, which triggers issues of food security and competition for arable land and water resources. This review focuses on the biocatalytic conversion of non-food alternative carbon sources (namely lignocellulose, acetate, glycerol, and waste oils) in the microbial synthesis of terpenoids, systematically summarizing the current research status and cutting-edge advances. These carbon sources exhibit potential for sustainable production due to their low cost, wide availability, and ability to reduce resource competition, but they also face significant technical bottlenecks. We systematically analyze the current problems in the biocatalytic conversion process and put forward some available solutions. It is hoped that this study will provide theoretical and technical suggestions for breaking through the bottlenecks in the biocatalytic conversion of non-food carbon sources and promoting the efficient and sustainable production of terpenoids. Full article

This research determines the techno-economic feasibility of valorizing as biofertilizer the nitrogen (N) and the phosphorus (P) from a municipal wastewater effluent using the microalgae Scenedesmus sp., contributing to phosphorus recycling, resource optimization, and diminishing eutrophication by capturing 74% of N, 97% of P, and 41% of chemical oxygen demand in effluents. The inoculum was conditioned in 20 L photobioreactors by weekly harvesting and refilling at room temperature (25 °C day, 12 °C night) with a 12:12 photoperiod and 4 L/min atmospheric air bubbling. The improved operational conditions were obtained using a Box–Behnken experimental design, establishing that 70% wastewater concentration (vol./vol.), 4.5% nutrient addition, and 3 days’ harvesting time were the best conditions. The estimated biomass production was 176 tons/year, and this represents a maximum net present value of 1.5 MUSD for a 6.8 Ha plant, capturing 10% of municipal wastewater effluent, which serves 64000 inhabitants. The representative operational costs (OPEX) were 32% for utilities, 30% labor costs, and 25% for raw materials, and the required capital expenditures (CAPEX) were 11 MUSD and are related to photobioreactors (64%) and land (21%). The findings demonstrate the potential of microalgae-based systems as a feasible and profitable approach to wastewater valorization, while also highlighting the need for scale-up validation and integration with existing treatment infrastructures, where land requirements and photobioreactor installation will be relevant for financial feasibility. Full article