Carbon Stocks Research Articles

Green Economic and Local Wisdom toward Sustainable Development in West Papua, Indonesia

Sustainable development is currently being implemented globally in response to the need for global societies to ensure a balance of development between economic and environmental issues (such as forests) and local communities. Local governments have committed to managing their forests for development in accordance with green economic principles. In addition, the adat community also has a significant role in promoting local wisdom in managing natural resources in Papua. This article attempts to investigate the role of green economic programs on development in the Papuan's local communities. The study found that the political, traditional structural system adopted local wisdom on the patterns of agricultural farming in the Papuan community. The study found that the green economy program is the alternative solution for developing the local community due to the economic benefit to Indigenous people and the lack of damage to Papua's natural resources.

Exploring the role of canopy cover and environmental factors in shaping carbon storage in Desa’a forest, Ethiopia

BackgroundDry Afromontane forests play a vital role in mitigating climate change by sequestering and storing carbon, as well as reducing greenhouse gas emissions. Despite previous research highlighting the importance of carbon stocks in these ecosystems, the influence of canopy cover and environmental factors on carbon storage in dry Afromontane forests has been barely assessed. This study addresses this knowledge gap by investigating the effects of environmental factors and vegetation cover on carbon stocks in Desa’a forest, a unique and threatened Afromontane dry forest ecosystem in northern Ethiopia. Data on woody vegetation, dead litter, grass biomass, and soil samples were collected from 57 plots. A one-way analysis of variance (ANOVA) was performed at a 95% confidence level (α = 0.05) to examine the influence of canopy cover and environmental factors on the carbon stocks of various pools.ResultsAmong the 35 woody species identified, Juniperus procera was the most dominant, while Carissa edulis Vahl and Eucalyptus globulus were the least dominant. The average total carbon stock was 92.89 Mg ha−1, with contributions from aboveground carbon, below-ground carbon, litter carbon, grass carbon, and soil organic carbon. Among the carbon pools, soil organic carbon had the highest carbon stock, accounting for 76.8% of the total, followed by above-ground biomass carbon at 17.7%. Significant variations in carbon stocks were found across altitude class and canopy level but not slope and aspect factors.ConclusionsIn summary, altitude and canopy level were found to significantly influence carbon stocks in Desa’a forest, providing valuable insights for conservation and climate change mitigation efforts in dry Afromontane forests. Forest intervention planning and management strategies should consider the influence of different environmental variables and tree canopy levels.

Advancing forest carbon stocks’ mapping using a hierarchical approach with machine learning and satellite imagery

Remote sensing of forests is a powerful tool for monitoring the biodiversity of ecosystems, maintaining general planning, and accounting for resources. Various sensors bring together heterogeneous data, and advanced machine learning methods enable their automatic handling in wide territories. Key forest properties usually under consideration in environmental studies include dominant species, tree age, height, basal area and timber stock. Being proxies of stand productivity, they can be utilized for forest carbon stock estimation to analyze forests’ status and proper climate change mitigation measures on a global scale. In this study, we aim to develop an effective machine learning-based pipeline for automatic carbon stock estimation using solely freely available and regularly updated satellite observations. We employed multispectral Sentinel-2 remote sensing data to predict forest structure characteristics and produce their detailed spatial maps. Using the Extreme Gradient Boosting (XGBoost) algorithm in classification and regression settings and management-level inventory data as reference measurements, we achieved quality of predictions of species equal to 0.75 according to the F1-score, and for stand age, height, and basal area, we achieved an accuracy of 0.75, 0.58 and 0.56, respectively, according to the R2. We focused on the growing stock volume as the main proxy to estimate forest carbon stocks on the example of the stem pool. We explored two approaches: a direct approach and a hierarchical approach. The direct approach leverages the remote sensing data to create the target maps, and the hierarchical approach calculates the target forest properties using predicted inventory characteristics and conversion equations. We estimated stem carbon stock based on the same approach: from Earth observation imagery directly and using biomass and conversion factors developed for the northern regions. Thus, our study proposes an end-to-end solution for carbon stock estimations based on the complexation of inventory data at the forest stand level, Earth observation imagery, machine learning predictions and conversion equations for the region. The presented approach enables more robust and accurate large-scale assessments using limited annotated datasets.

Cost-efficiency analysis of multiple ecosystem services across forest management regimes

Forest management is at the crossroads of economic, environmental, and social goals, often requiring strategic trade-offs. As global demands surge, it's vital to employ management strategies fostering multifunctional landscapes, enabling ecosystem integrity while procuring resources. Historically, the boreal forest in Fennoscandia has been intensively managed for timber, causing environmental shifts and conflicts with biodiversity conservation and climate mitigation policies. Application of current management practices while increasing harvests are a threat to both biodiversity and carbon stocks. To explore this issue, we quantify the cost-efficiency of two forest management regimes: rotation forestry (RF) and continuous cover forestry (CCF), considering specific forest attributes like soil type (mineral and peat soils), site type (fertility classes) and tree stand age, which have been underexplored in previous research. We simulated 45,559 forest stands for 100 years in Northern boreal forests of Finland. We proposed two straightforward cost-efficiency indices (CEI) to evaluate the performance of these management regimes, specifically focusing on their impact on economic output, biodiversity conservation (measured as a biodiversity index for six forest vertebrates, including five bird species and one mammal) and carbon stock. Our findings suggest that continuous cover forestry holds the potential to deliver more cost-efficient ecosystem services and maintain greater biodiversity compared to rotation forestry approaches. Continuous cover forestry, however, is not optimal for all at management units, which calls for alternative management options depending on the stand characteristics. The cost-efficiency indices performance of rotation forestry and continuous cover forestry depend on the characteristics of the initial stand which is largely determined by the previous management of the stand. Our results contribute to guiding forest management towards enhanced sustainability and ecological balance. The great variation in stand characteristics suggest a need for diverse management strategies to create multifunctional landscapes. Our proposed cost-efficiency indices could serve as practical tools for decision-making.

Modelling the nexus between green energy, agricultural production, forest cover, and population growth towards climate change for the transition towards a green economy

Modelling the nexus between green energy, agricultural production, forest cover, and population growth towards climate change for the transition towards a green economy

Analysis of Temporal and Spatial Carbon Stock Changes and Driving Mechanism in Xinjiang Region by Coupled PLUS-InVEST-Geodector Model

Based on the goal of "dual-carbon" strategy, it is important to explore the impacts of land use change on carbon stock and the drivers of spatial differentiation of carbon stock in Xinjiang. Here, we predicted the land use types in Xinjiang in 2035 under different scenarios and analyzed the impacts of land use on carbon stock, which is of great theoretical and practical importance for policy formulation, land use structure adjustment, and carbon neutrality target achievement in Xinjiang. The coupled PLUS-InVEST-Geodector model was used to explore the spatial and temporal patterns of carbon stock change under the scenarios of rapid development, natural change, arable land protection, and ecological protection in Xinjiang in 2035 and to quantitatively reveal the attribution of influences on the changes in carbon stock from the perspectives of land use change and the combination of nature-socioeconomic-accessibility. The results showed that： ① From 1990 to 2020, the area of arable land and construction land in Xinjiang increased, and in terms of the transfer direction, it was mainly shifted from unutilized land to grassland. ② On the time scale, the carbon stock in Xinjiang showed the fluctuation of "decrease-increase-decrease," with an overall increasing trend. The transfer of unutilized land to grassland was the main reason for the increase in carbon stock； on the spatial scale, the carbon stock in the Altai Mountains in the north, the Tianshan Mountains in the middle, and the Kunlun Mountains in the south was higher, whereas the carbon stock in the Tarim Basin and the Junggar Basin was lower. ③ In 2035, the carbon stock of the natural development and rapid development scenarios decreased by 27.24 Tg and 71.17 Tg compared with 2020, respectively, and the ecological protection and arable land protection scenarios increased by 492.55 Tg and 46.67 Tg. The ecological protection scenario could significantly increase the carbon stock of the Xinjiang Region compared with that in the other scenarios, and the distribution pattern of the carbon stock in the four scenarios was more or less the same as that in 2020. In addition to land transformation, soil erosion intensity was the main driver of spatial differentiation of carbon stocks in Xinjiang （q value of 0.3501）, followed by net primary productivity of vegetation. The results of multifactor interactions showed that the spatial differentiation of carbon stocks in Xinjiang was the result of the joint action of multiple factors. All the factors had a synergistic enhancement under the interactions. The interaction between soil erosion intensity and the net primary productivity of vegetation was the main driver of the spatial differentiation of carbon stocks in Xinjiang.

Impact of Land Use and Land Cover (LULC) Changes on Carbon Stocks and Economic Implications in Calabria Using Google Earth Engine (GEE).

Terrestrial ecosystems play a crucial role in global carbon cycling by sequestering carbon from the atmosphere and storing it primarily in living biomass and soil. Monitoring terrestrial carbon stocks is essential for understanding the impacts of changes in land use on carbon sequestration. This study investigates the potential of remote sensing techniques and the Google Earth Engine to map and monitor changes in the forests of Calabria (Italy) over the past two decades. Using satellite-sourced Corine land cover datasets and the InVEST model, changes in Land Use Land Cover (LULC), and carbon concentrations are analyzed, providing insights into the carbon dynamics of the region. Furthermore, cellular automata and Markov chain techniques are used to simulate the future spatial and temporal dynamics of LULC. The results reveal notable fluctuations in LULC; specifically, settlement and bare land have expanded at the expense of forested and grassland areas. These land use and land cover changes significantly declined the overall carbon stocks in Calabria between 2000 and 2024, resulting in notable economic impacts. The region experienced periods of both decline and growth in carbon concentration, with overall losses resulting in economic impacts up to EUR 357.57 million and carbon losses equivalent to 6,558,069.68 Mg of CO 2 emissions during periods of decline. Conversely, during periods of carbon gain, the economic benefit reached EUR 41.26 million, with sequestered carbon equivalent to 756,919.47 Mg of CO 2 emissions. This research aims to highlight the critical role of satellite data in enhancing our understanding and development of comprehensive strategies for managing carbon stocks in terrestrial ecosystems.

Effects of Grazing on Plant Diversity and Their Carbon Stocks in Different Types of Grasslands

With the drying and warming of the climate and irrational grazing, various types of grasslands in Inner Mongolia have been degraded to different degrees, and different management modes will inevitably affect the plant diversity and vegetation carbon stock of soil grasslands. To clarify the changes and influencing factors of plant diversity and carbon stock in different types of grasslands under different management modes, plant species composition, aboveground biomass, and vegetation carbon were analyzed based on 18 sentinel monitoring stations across three different types of grasslands in Inner Mongolia. The results showed that grazing increased the dominance of typical grassland and desert grassland, whereas meadow grassland decreased, and the evenness index and Shannon Wiener diversity index increased less in meadow grassland and desert grassland. Grazing decreased graminaceous biomass in meadow grassland and typical grassland, whereas it increased in desert grassland. Above-ground vegetation and below-ground root carbon stocks were much higher than those in grazing areas, 1.5 and 1.2 higher, respectively, but vegetation carbon stocks in long-term grazing sites were significantly lower than those in short-term grazing. Further, the structural equations showed that the effects of geographic location, climatic factors, and soil factors on the biomass and vegetation carbon stocks of the three grassland types differed significantly. The results can provide a reference for the ecologically sustainable development of grassland and the optimization of management mode.

Carbon and Nitrogen Stocks in Vineyard Soils Amended with Grape Pomace Residues

Fruit crops under soil conservational management might sequester carbon (C) in soils and mitigate greenhouse gases emissions. Using grape pomace residues as soil amendment holds promise for sustainable viticulture. However, its actual capability to increase soil organic carbon (SOC) and nitrogen (N) is unknown, especially in subtropical climates. This research aims to investigate whether grape pomace compost and vermicompost can increase SOC, total N (TN), and C and N stocks in subtropical vineyards. Two vineyards located in Veranópolis, in South Brazil, one cultivated with ‘Isabella’ and the other with ‘Chardonnay’ varieties, were annually amended with these residues for three years. We quantified SOC and TN in each condition in different soil layers, as well as C and N content in two different granulometric fractions: mineral-associated organic matter (MAOM) and particulate organic matter (POM). C and N stocks were also calculated. Despite potential benefits, neither treatment enhanced SOC, its fractions, or C stocks. In fact, vermicompost was rapidly mineralized and depleted SOC and its fractions in the 0.0 to 0.05 m layers of the ‘Isabella’ vineyard. Our findings indicate that the tested grape pomace residues were unable to promote C sequestration in subtropical vineyards after a three-year period.

The Coupling and Coordination Between Digital Economy and Green Economy: Evidence from China

ABSTRACT This paper tries to empirically calculate the coupling and coordinate degree between digital economy and green economy by utilizing the data for 28 Chinese provinces from 2015 to 2020 via coupling coordination model. Specifically, we measure the development level of digital economy and green economy by utilizing the entropy weight method, which supports that while the digital economy and green economy increases quickly among all sample provinces, there exists problem of digital divide among different regions and the speed of digital economy is higher than that of green economy. Additionally, we calculated the degree of coupling and coordination between digital economy and green economy, which suggests that coordination and coupling between digital economy and green economy experiences sharp increase. Finally, we also investigated the regional heterogeneity for the coupling and coordination between such two factors and offered that while the absolute level of coupling and coordination between digital economy and green economy in western and eastern regions is lower than that in eastern regions, its growth rate is higher in western and eastern regions than that in eastern regions.

Spatio-Temporal Evolution and Multi-Scenario Modeling Based on Terrestrial Carbon Stocks in Xinjiang

The increase in atmospheric CO2 leads to global warming and ecological environment deterioration. Carbon storage modeling and assessment can promote the sustainable development of the ecological environment. This paper took Xinjiang as the study area, analyzed the spatial and temporal evolution of land use in four periods from 1990 to 2020, explored the spatial relationship of carbon stocks using the InVEST model, and coupled the GMOP model with the PLUS model to carry out multiple scenarios for the future simulation of land use in the study area. We found (1) Over time, the types with an increasing area were mainly impervious and cropland, and the types with a decreasing area were grassland, snow/ice, and barren; spatially, the types were predominantly barren and grassland, with the conversion of grassland to cropland being more evident in the south of Northern Xinjiang and north of Southern Xinjiang. (2) The evolutionary pattern of terrestrial carbon stocks is increasing and then decreasing in time, and the carbon sink areas are concentrated in the Tarim River Basin and the vicinity of the Ili River; spatially, there are differences in the aggregation between the northern, southern, and eastern borders. By analyzing the transfer in and out of various categories in Xinjiang over the past 30 years, it was obtained that the transfer out of grassland reduced the carbon stock by 5757.84 × 104 t, and the transfer out of Barren increased the carbon stock by 8586.12 × 104 t. (3) The land use layout of the sustainable development scenario is optimal under the conditions of satisfying economic and ecological development. The reduction in terrestrial carbon stocks under the 2020–2030 sustainable development scenario is 209.79 × 104 t, which is smaller than the reduction of 830.79 × 104 t in 2010–2020. Land optimization resulted in a lower loss of carbon stocks and a more rational land-use layout. Future planning in Xinjiang should be based on sustainable development scenarios, integrating land resources, and achieving sustainable economic and ecological development.

L-Band Synthetic Aperture Radar and Its Application for Forest Parameter Estimation, 1972 to 2024: A Review.

Optical remote sensing can effectively capture 2-dimensional (2D) forest information, such as woodland area and percentage forest cover. However, accurately estimating forest vertical-structure relevant parameters such as height using optical images remains challenging, which leads to low accuracy of estimating forest stocks like biomass and carbon stocks. Thus, accurately obtaining vertical structure information of forests has become a significant bottleneck in the application of optical remote sensing to forestry. Microwave remote sensing such as synthetic aperture radar (SAR) and polarimetric SAR provides the capability to penetrate forest canopies with the L-band signal, and is particularly adept at capturing the vertical structure information of forests, which is an alternative ideal remote-sensing data source to overcome the aforementioned limitation. This paper utilizes the Citexs data analysis platform, along with the CNKI and PubMed databases, to investigate the advancements of applying L-band SAR technology to forest canopy penetration and structure-parameter estimation, and provides a comprehensive review based on 58 relevant articles from 1978 to 2024 in the PubMed database. The metrics, including annual publication numbers, countries/regions from which the publications come, institutions, and first authors, with the visualization of results, were utilized to identify development trends. The paper summarizes the state of the art and effectiveness of L-band SAR in addressing the estimation of forest height, moisture, and forest stocks, and also examines the penetration depth of the L-band in forests and highlights key influencing factors. This review identifies existing limitations and suggests research directions in the future and the potential of using L-band SAR technology for forest parameter estimation.

Perennial species diversity, ecosystem carbon stocks and carbon income in coffee-based agroforestry systems along an elevation gradient in South-eastern Ethiopia

In the current context of deforestation, coffee-based agroforestry system (CAFS) is credited for climate change (CC) mitigation and biodiversity conservation while supporting local livelihoods. Despite integrating shade tree species in CAFS, empirical studies to support this assertion are inadequate in Eastern Africa, and hence, its ecosystem services provisions are less understood. We evaluated perennial species diversity, carbon (C) stocks in the biomass and soil organic C (SOC) along an elevation gradient of 72 plots of shade coffee, while 36 plots were selected for without-shade coffee systems within three elevations, namely, low (1600–1750 masl), mid (1750–1850 masl) and high (1850–2000 masl) elevations in Southeastern Ethiopia. The perennial species diversity and biomass, SOC, fine root and litter C stocks were evaluated. Perennial species Shannon diversity significantly differed among the studied elevations (p < 0.001). Shaded coffee had significantly higher ecosystem C stocks than without shaded coffee systems (p < 0.05). The highest C stocks were found in the soil in both coffee systems. However, we found a weak relationship between the Shannon diversity and biomass C. The C income in shaded coffee was 70 % higher than without shaded coffee systems. The present study showed that shaded coffee accumulates more C and provides additional benefits from C credits. Hence, CAFS deliver ecosystem services that enhance biodiversity conservation and CC mitigation while generating an additional C income for farmers. However, we learned that the impact of perennial plant diversity on C stock and C income is context and site-specific.

Forecasting Climate Change Patterns to Improving Rice Harvest Using SVR for Achieving Green Economy

The consistently declining rice harvest will cause several economic and environmental problems. The unstable and unpredictable climate change was believed as the main problem of the declining rice harvest. We proposed a method for forecasting climate change to help the farmer in their rice cultivation. We used Support Vector Regression (SVR) to improve algorithm steps such as normalizing the data and applying an Adaptive Linear Combiner (ALC) to optimize the dataset before we processed it with the algorithm. Our model gets 95% accuracy as measured with the confusion matrix. We believe our model will help the farmers in their rice cultivation with good climate forecasting. A further benefit of this research we belief that with the well-forecasted climate, the usage of pesticides will decrease and will help the vision of the Indonesian government with a green economy

Evaluation of GEDI footprint level biomass models in Southern African Savannas using airborne LiDAR and field measurements

Savannas cover more than 20% of the Earth and account for the third largest stock of global aboveground biomass yet estimates of their above ground biomass density (AGBD) are very inaccurate. The Global Ecosystem Dynamic Investigation (GEDI) sensor provides near-global full-waveform LiDAR data with 25 m footprints, from which various structural metrics are derived that are used to predict footprint level AGBD. The current GEDI L4A AGBD product uses a comprehensive Forest Structure and Biomass Database (FSBD) to develop models for specific plant functional types and geographic regions, but southern African savannas have been underrepresented in the reference data. The objectives of this study were to (i) validate GEDI L4A AGBD in South African savannas using field measurements and ALS datasets and (ii) develop and evaluate local GEDI footprint-level AGBD estimates from multiple L2A and L2B metrics. The local GEDI AGBD models outperformed GEDI L4A AGBD (R2 = 0.42, RMSE = 12 Mg/ha, %RMSE = 79.5%) with higher R2 and smaller error measures. The local GEDI AGBD using a random forest model (RF) had the highest R2 of 0.71 and lowest %RMSE of 53.3%, while the generalized linear model (GLM) results provided the lowest Relative Mean Systematic Deviation (RMSD) of 9.2%, which was half that of RF model. L4A significantly underestimated AGBD with an RMSD up to −37%. This highlights the importance and benefits of local calibration of biomass models to unlock the full potential of GEDI metrics for estimating AGBD. The field and ALS data have subsequently been contributed to the GEDI FSBD and should be used in calibration of future versions of GEDI L4A AGBD product. This research paves the way for the integration of the local GEDI AGBD estimates with other sensors, notable the eminent NISAR mission, to derive regional to global gridded AGBD products that will enable the monitoring of savanna carbon stocks.

Drivers of microbial carbon biomass variability in two oceanic regions of the Gulf of Mexico

The microbial plankton community is an integral part of the pelagic ecosystem. It hosts essential functional groups that play a vital role in organic carbon production, release, uptake, and degradation within open-ocean ecosystems. Given its significance, carbon biomass estimates are urgently needed, especially in oligotrophic regions, to provide and enhance our knowledge of biogenic carbon pools. They also aid in validating biogeochemical models that characterize the functioning of these extensive marine ecosystems within the global carbon cycle. This study addresses the temporal variability of microbial community biomass in two oceanic zones: the west-central (Perdido) and southern (Coatzacoalcos) areas of the Gulf of Mexico. During three seasonally contrasting periods (nortes, rainy, and dry seasons), seawater samples were collected from the euphotic zone in both regions to estimate the carbon biomass of different pico- (<2–3 µm), nano-, and microplankton groups (>3–200 µm). Carbon biomass assessments for the microbial groups were based on their abundance and carbon conversion factors. Overall, we found a significant contribution of pico-prokaryotic components (heterotrophic bacteria, Prochloroccocus, and Synechoccocus) to the total microbial carbon stock of the euphotic zone (84–89 % global estimates). The finding suggests these microorganisms are key functional groups that drive carbon production and fate in the Gulf of Mexico ecosystem. Pico-cyanobacteria, especially Prochloroccocus, were the dominant primary producers (68–82 % total autotrophic carbon), mainly in the upper layer of the oligotrophic euphotic zone. This vertical pattern implies that the deep chlorophyll-a maximum (DCM) depth level was unrelated to a net increase in phytoplankton biomass in the three study periods. The distribution of microbial carbon biomass exhibited striking differences associated with winter mixing (the nortes season), high river discharge accompanied by cross-shelf transport (the rainy season), and the dynamics of mesoscale structures. Ecological aspects, such as the habitat preference of the organisms and the seasonal complementary development of mixotrophic and heterotrophic grazers and their prey, were also essential drivers in regulating the microbial carbon pool of both oceanic regions. The microbial carbon assessments conducted in this study contribute to identifying and quantifying key planktonic functional groups involved in the biogeochemical carbon cycle in the Gulf of Mexico open-ocean ecosystem.

Carbon balance in Maize (Zea mays L.) with various combinations of organic and inorganic fertilizers

Fertilization in agricultural cultivation has an important role in the carbon cycle. This study aims to evaluate using organic fertilizers with inorganic fertilizers that can produce high carbon sequestration with low CO2 emission levels in maize (Zea mays L.) cultivation. There were seven combinations of NPK and organic fertilizers, namely ¼ NPK + 1 OF (C), ½ NPK + 1 OF (D), ¾ NPK + 1 OF (E), 1 NPK + 1 OF (F), ¾ NPK + ¼ OF (G), ¾ NPK + ½ OF (H), ¾ NPK + ¾ OF (I), and two controls, namely no fertilizer (A) and standard NPK (350 kg/ha, SP36 150 kg/ha, KCl 75 kg/ha) (B). Organic fertilizer was applied one week before planting, SP-36 and KCl fertilizer at planting, and urea fertilizer at 0, 14, and 28 HST. Maize was planted with a spacing of 20 x 70 cm. Parameters observed included CO2 emissions, soil pH, C-Organic, C-microbial, and plant growth parameters. The combined use of NPK and organic fertilizer significantly increased soil carbon stocks (33.25-64.04 Mg/ha) and carbon sequestration by plants (3.76-5.98 Mg/ha). Therefore, using organic and inorganic fertilizer can be considered more environmentally friendly and effective in managing carbon balance on farmland and has great potential to contribute to climate change mitigation through increased soil and plant carbon sequestration.

The Relationship between Renewable Energy Consumption and Economic Growth: Insights from Iceland and Azerbaijan

Iceland leads globally in per capita green energy production, while oil-dependent Azerbaijan endeavors to transition its energy policy towards sustainable alternatives. Investigating the impacts of renewable energy consumption on the economy represents a significant contemporary scientific inquiry. The objective of this research is to empirically examine the causal relationship between GDP per capita, a key metric of economic growth, and renewable energy consumption, a fundamental aspect of the green economy. In this research, the Toda-Yamamoto causality test framework of the vector autoregressive (VAR) model was utilized to examine the causal relationship between the variables. The outcomes of this investigation reveal a significant causality between renewable energy consumption and economic growth in Iceland and Azerbaijan.

Spatio-temporal Dynamics of Land Use/Cover Change and Associated Carbon Stocks in Kanyabaha Wetland in Rukiga District, Uganda

Wetlands play an important ecological function of sequestering atmospheric carbon dioxide and thereby moderating adverse impacts of climate change. It is therefore important to understand the dynamics of carbon stocks in wetland vegetation and soils. This study investigated the spatio-temporal dynamics of aboveground, belowground, and total carbon stocks in Kanyabaha Wetland, located in Rukiga District, Uganda, spanning from 1990 to 2021. Through field sampling and laboratory analysis, aboveground carbon stocks were assessed by harvesting vegetation biomass and converting it to carbon stock using established conversion factors. Soil samples collected at different depths (0-20cm, 20-50cm, 50-100cm) were analyzed for soil organic carbon content to determine belowground carbon stocks. The study reveals variable spatio-temporal patterns of carbon stocks across land use types, with papyrus-dominated areas exhibiting the highest aboveground carbon stocks (49.66 tC/ha), followed by small-scale farmlands (33.73 tC/ha) and tree plantations (23.01 tC/ha). Conversely, built-up areas exhibit the lowest carbon stocks (1.29 tC/ha). Temporal analysis reveals fluctuating patterns in carbon stocks, with increases observed in built-up areas and small-scale farmlands, and decreases in grasslands and tree plantations that could be due to changes in hydrological cycle. Belowground carbon stocks follow similar trends, with papyrus areas maintaining the highest stocks (39.96 tC/ha), particularly at deeper soil depths that exhibit the highest carbon accumulation due to its extensive network of papyrus rhizome. Changes in land use, especially reclamation of the wetlands for farming and settlements affected carbon capture and storage in the wetland ecosystem. These findings highlight the importance of targeted conservation of natural wetlands and sustainable land management strategies in the Kanyabaha Wetland catchment for enhanced carbon sequestration. Further, in depth studies in the variability of carbon stocks due to various eco-climatic factors and anthropogenic activities are necessary to support sustainable wetland land management practices in Uganda.

Soil carbon stocks and nutrient stratification in a volcanically active coffee-dominated landscape in south-central Guatemala

The volcanoclastic mountains of Central America offer elevations ideal for coffee production. Both vulcanism and land use can have significant impacts on soil formation and properties. We evaluated how the interactive impacts of soil formation and coffee-dominated land use modulate soil storage and vertical distribution of organic carbon and nutrient elements in a volcanically active landscape dominated by coffee agriculture in south-central Guatemala. Thirty-seven pedons under coffee production (n = 29), forest cover (n = 5) and pasture (n = 3) were characterized and classified, and concentrations and stocks of organic carbon and macronutrient and micronutrient elements were quantified across genetic horizons. Additionally, soil organic carbon (SOC) stock values calculated using the fixed depth (FD) and equivalent soil mass (ESM) methodologies were compared. The active stratovolcano in the region had a strong effect on the development of andic properties and soil horizon burial, with thirty-one pedons classified as Andisols, three as Inceptisols and three as Entisols. Land use management practices and soil horizon burial by deposition of volcanic material were partly reflected in the vertical distribution of SOC concentrations and stocks. The vertical distribution of macronutrients was generally more sensitive to land use than the vertical distribution micronutrients, which could potentially reflect differences in inputs via fertilizers and vegetation cover and in outputs with biomass harvest. Soil organic carbon stocks were similar when calculated by FD and ESM, reflecting relatively consistent depth-wise bulk density. These results demonstrate that in volcanically active landscapes, land use should be considered in concert with soil-forming factors to comprehensively understand organic carbon and nutrient element storage in soils.