Accurate estimating aboveground biomass (AGB) is essential for quantifying carbon stocks and assessing the climate-mitigation potential of agroforestry systems. However, many existing biomass models in Uganda and other eastern African countries were developed for natural forests, rely on limited sample sizes, or employ generalized equations that may not adequately represent trees grown under smallholder agroforestry setting This study developed species-specific allometric equations to estimate AGB for seven agroforestry tree species in northern Uganda: Acacia polyacantha, Albizia lebbeck, Gmelina arborea, Maesopsis eminii, Markhamia lutea, Melia volkensii, and Senna siamea. Destructive sampling was conducted on 36–44 trees per species across a range of diameter classes and management regimes in Gulu, Amuru, Nwoya, and Oyam districts. Diameter at breast height (D), tree height (H), and crown radius (CR) were fitted as predictors of AGB using nonlinear maximum-likelihood regression models. Model performance was assessed using coefficient of determination (R2), root mean square error (SE), prediction error (PE%), Akaike Information Criterion (AIC), and repeated k-fold cross-validation. The selected models explained most of the variation in AGB (R2 = 0.90–0.99) and PE% below 3.5%. Model structure varied among species: for M. eminii and S. siamea, D alone adequately explained AGB variation, whereas other species required combinations of D with H or CR. Comparison with previously developed models revealed that generalised or pantropical equations produced biased estimates. Therefore, the developed species-specific equations provide reliable tools for estimating AGB and carbon stocks in smallholder agroforestry systems and woodlots in northern Uganda, supporting carbon accounting and restoration monitoring.
Tag: Aboveground biomass
Refining land-use-specific carbon emission factors for commodity-driven deforestation monitoring in Colombia
Deforestation remains one of the most significant sources of greenhouse gas emissions globally. Tropical regions are particularly critical to climate mitigation due to their high carbon density and ongoing conversion to agricultural uses. While earth observation (EO) has become fundamental for assessing land-use change over time and space, key gaps persist in understanding associated carbon emissions (CE) and their distribution across production systems, information required by diverse international and European policy frameworks targeting greenhouse gas reduction and deforestation. To address this, the study integrates satellite-derived aboveground biomass (AGB) with machine learning-based land-use modeling to quantify commodity-specific CE from deforestation in Colombia. Our key findings reveal that pasture expansion for livestock dominates emissions (>67% in the Amazonas and Orinoquía regions, approximately 25% nationally), while cocoa and coffee, despite smaller cultivation areas, exhibit high emission factors (>25% of subregional totals in the Andes, Pacífico, and Caribe). Smallholder croplands contribute up to 16.7% regionally, and oil palm plays a modest role nationally. These results underscore the importance of targeting both extensive pasture systems and high-emission perennial crops in deforestation-free supply chain policies, while considering the diverse regional land-use dynamics. In conclusion, our framework offers spatially explicit, crop-specific emission estimates that address essential gaps in EO-based CE quantification. It provides clear, actionable baselines for monitoring policies aimed at promoting deforestation-free supply chains, such as the EU Deforestation Regulation (EUDR), across tropical landscapes. Ultimately, this data supports the creation of a comprehensive database of country-specific emission factors, a resource critical for enhancing national carbon inventories, guiding policy decisions related to high-risk deforestation commodities, and ensuring more transparent and traceable zero-deforestation supply chains.
Drivers of tree species diversity and aboveground carbon stocks in smallholder agroforestry systems in Laikipia East, Central Kenya
Agroforestry is highly valued for its role in carbon sequestration and biodiversity enhancement, yet empirical knowledge on how tree diversity and carbon stocks relate to environmental, farm-level, and household factors remain limited. This study combined whole-farm tree inventories and household surveys across 55 farms in Laikipia East Sub-County to assess patterns and drivers of woody species diversity and aboveground carbon stocks. A total of 108 woody species (6,756 individuals) across 38 families were recorded. Native species accounted for 56% of all species but only 40% of individuals, indicating dominance by a few exotic species. Species richness was highest in croplands (70 species) and boundary areas (59 species), which together accounted for 65% of all recorded trees. Young trees dominated the farms (>50%), mature trees made up about one-third, while large trees (<10%) were mostly confined to boundaries and croplands. Aboveground carbon stocks varied widely (mean: 16.5±7.1 t C/ha), with highest densities in woodlots (40.0±12.7 t C/ha) and homegardens (20.0±9.5 t C/ha). Multivariate analyses showed that plant density and average tree size were the strongest predictors of aboveground carbon (p< 0.01), while species richness was influenced primarily by the diversity of agroforestry practices and selected household characteristics, including respondent’s gender and household size (p < 0.05). Species diversity showed a weak relationship with aboveground carbon stocks (r < 0.20). These findings highlight both the ecological value and structural limitations of smallholder agroforestry. We recommend promoting tree conservation, native regeneration, and diversified agroforestry practices as strategies to enhance carbon storage and biodiversity outcomes in the region.
Species-specific allometric models for estimating aboveground biomass and carbon stocks of plantation forests in northcentral Ethiopia
Plantation forests are crucial for landscape rehabilitation and carbon sequestration. However, the efficacy of these plantations in carbon sequestration in Ethiopia remains uncertain due to a lack of robust, species- and site-specific biomass estimation models. This study addresses this gap by developing and validating allometric models for estimating aboveground biomass (AGB) and carbon (C) stocks of plantation forests. Allometric models were developed using data from 69 harvested trees of three species: Eucalyptus globulus, Cupressus lusitanica, and Pinus patula. AGB was regressed against diameter at breast height (DBH) as the sole predictor, with stepwise inclusion of height (H), crown area and wood density. Model performance was evaluated using fit statistics, including Pseudo-R2 and mean prediction error (MPE). The model that incorporated both DBH and H as predictors provided the best fit (Pseudo-R2 > 0.90, p < 0.001) and achieved the lowest MPE (1.47–5.67%) across all species. The findings indicated a mean C stock of 121.6 Mg C ha−1 in the plantations studied. Our models provide valuable insights for forest management and improve the accuracy of AGB and C stock estimations in the study area and similar ecosystems. The estimated C stocks can serve as a benchmark for assessing future C dynamics.
Allometric models for estimating fuelwood and above-ground wood dry mass of pruned Faidherbia albida trees in northern Cameroon
In Cameroon’s Sudano-Sahelian zone, rising fuelwood demand is jeopardising Faidherbia albida agroforestry parklands. Faced with these alarming pressures and ongoing climate challenges, evaluating sustainable management practices and the contribution of F. albida agroforestry parklands to climate change mitigation has become a pressing priority. In these agro-ecosystems, total and rotational crown pruning are common practices, traditionally aimed at fuelwood and livestock feed collection. However, its impact on the trade-off between leaf removal and pod production remains poorly documented, particularly regarding long-term fuelwood yield potential. This research aims to develop tools for estimating the above-ground wood dry mass of F. albida components in the context of their use as renewable fuelwood and their carbon storage potential. Allometric equations for the different tree components were developed using data from the most recent pruning event (2023) of F. albida trees that had undergone three complete crown-pruning cycles over the past two decades. The predictors used to estimate fuelwood and above-ground wood dry mass were diameter at breast height (D, in cm), crown area (Ca, in m²), and total tree height (H, in m). The best-performing equations for each tree components relied on structurally meaningful combinations of these variables: (i) D²Ca, capturing the combined influence of stem size and crown development for estimating fuelwood obtained after pruning (DWpr, R²adj = 0.84) and the total above-ground wood dry mass of F. albida (TDWb, R²adj = 0.85), corresponding to the sum of DWpr and the post-pruning wood dry mass remaining after pruning (DWUpr); and (ii) D, reflecting the effect of stem diameter for predicting DWUpr, (R²adj = 0.76). The best crown area-only models for predicting DWpr and TDWb showed strong explanatory power, with adjusted R² values of 0.82 and 0.64, respectively. The integration of structural characteristics visible from space, such as the tree crown area, provides promising prospects for large-scale bio-mass mapping using satellite images and remote sensing methods.
Impacts of landscape restoration and long-term land use change on ecosystem services: Evidence from a case study in Ethiopia
Land degradation threatens food security, water resources, and biodiversity by eroding soil health and productivity. In Ethiopia, restoration initiatives like exclosures are widely implemented, yet most studies fail to integrate plot-level ecological evidence with landscape-scale land use and land cover (LULC) dynamics. Furthermore, few studies combine these biophysical trends with long-term economic valuation of ecosystem services (ES) and local knowledge to quantify ES trade-offs. Using the Sire-Dodota district of Ethiopia, as a case study, we (i) evaluated the localized ecological performances of communal exclosures relative to LULC-driven ecosystem service (ES) changes, (ii) quantified spatial and temporal (22 years) LULC impacts on ecosystem service values (ESVs), and (iii) assessed community perceptions of restoration outcomes. We combined vegetation inventories, spatial analysis, household surveys, focus group discussions, and value transfer methods across plot and landscape scales. Our results reveal a clear socio-ecological paradox across scales. At the plot-scale, community-managed exclosures significantly enhanced biomass accumulation and biodiversity, establishing a robust empirical baseline for ecological recovery. At the landscape scale, however, LULC-based ESVs increased over 22 years, driven by gains of US$80.6 million from agricultural lands and US$1.3 million from forest lands, alongside a US$3.4 million decline in shrubland ESVs. These increases are potential values rather than realized ecosystem service flows, masking systemic losses in regulating and supporting services as short-term agricultural expansion dominates valuation outcomes. Social data from 393 households strongly corroborate these patterns. While communities consistently recognized localized ecological recovery, they favored agroforestry integration over static exclosures to offset high opportunity costs of restricted access. Together, these findings caution against reliance on uncontextualized macro-scale economic indicators and underscore the need for integrated, multifunctional landscape strategies that align ecological recovery with livelihood needs. We, therefore, recommend establishing a national, open-access ESV meta-database linked to Ethiopia’s Land Degradation Neutrality (LDN) targets, alongside future multi-decadal monitoring of below-ground carbon and soil biodiversity, and the cost-benefit performance of blended finance restoration models.
GHG emissions from recent peat forest disturbances: A driver-specific analysis across Indonesia, Peru, and DRC
Consistent data on tropical peat forest disturbances and associated greenhouse gas (GHG) emissions remain limited. We quantified and compared driver-specific GHG emissions from aboveground biomass (AGB) loss, peat decomposition and fire from peat forest disturbances in Indonesia, Peru, and Democratic Republic of Congo (DRC) over 2020-2021. Direct drivers were identified through visual interpretation of Planet (4.77 m) and Sentinel-2A (10 m) imagery for 1000 randomly sampled disturbance events per country. We estimated CO2 emissions from AGB loss using pre-disturbance data from the Climate Change Initiative Biomass product. Emissions (CO2, CH4 and N2O) from peat decomposition were calculated using driver-specific emission factors. We computed emissions from fire applying refined values for prescribed peat fires. Large-scale agriculture was the primary driver of emissions in Indonesia (48%), followed by smallholder agriculture (26%), and grassland (10%). Conversely, smallholder agriculture was the dominant driver of emissions in Peru (59%) and DRC (94%). Logging contributed to emissions across all countries, while mining, road development, and river meandering were relevant mainly in Indonesia and Peru; windstorms played a minor role in Peru. Fire accounted for half of emissions and was predominantly linked to agriculture (80% in Indonesia, 75% in Peru, and 95% in DRC). Overall, CO2 was the dominant GHG (72%), followed by CH4 (20%) and N2O (8%). Across countries, combined emissions from AGB loss, peat decomposition and fire in the disturbance year (2020) exceeded post-disturbance peat decomposition emissions in 2021 by 19-20 times. Nevertheless, cumulative peat decomposition over two decades will eventually reach the massive emissions released from all sources in the disturbance year. Our analysis provides the first systematic cross-country comparison of driver-specific GHG emissions in tropical peat forests, underscoring the major role of fire associated with agricultural expansion and the long-term importance of post-disturbance peat decomposition. These findings offer key insights to inform (inter) national emission reduction policies and strengthen reporting of peat forest emissions under frameworks such as the Paris Agreement.
Tree diversity and soil fertility interactions drive carbon storage in degraded cocoa landscapes of Côte d’Ivoire
Introduction: Understanding the ecological functioning of degraded cocoa landscapes is critical for restoring productivity and ecosystem services in West Africa. This study investigated how climate, geography, and land-use systems interact to shape tree diversity, soil fertility, and aboveground carbon (AGC) stocks across six cocoa-producing sites in Côte d’Ivoire.
Methods: Field inventories and soil sampling to 50 cm depth were conducted across gradients of climate (1,150–1,650 mm rainfall) and land-use systems (fallow, cocoa agroforestry, and monocropping). Mixed effects ANOVA, regression, and redundancy analyses were applied to test three hypotheses linking environmental gradients, tree diversity, and soil–carbon relationships.
Results: Land-use system had the strongest effect on tree diversity (P< 0.0001), with fallows supporting significantly higher richness (7.2 ± 0.51) and Shannon diversity (1.7 ± 0.05) than agroforestry or monocrop systems. Climate and site nested within climate also significantly influenced species abundance and evenness. Soil fertility indicators were primarily controlled by climate and geographic site, with significant effects on pH, exchangeable bases, and structural stability. Interactions between climate and land-use significantly affected soil pH, organic carbon, calcium, and cation exchange capacity. Stepwise regression identified aluminum, potassium, boron, and magnesium as key soil predictors of AGC (R² = 0.14; P< 0.0001), while species richness was the only diversity metric significantly associated with AGC (R² = 0.12; P< 0.0001).
Discussion: These results highlight the dominant role of species richness over evenness and abundance in sustaining soil fertility and carbon storage, emphasizing biodiversity conservation as a cornerstone of cocoa landscape restoration.
A scalable, annual aboveground biomass product for monitoring carbon impacts of ecosystem restoration projects
Restoring natural ecosystems has the potential to remove billions of tons of CO2 annually through the end of the century, but rigorously measuring the climate impacts of restoration activities on the ground remains elusive. Ecosystem restoration interventions across hundreds or thousands of smallholder properties require robust above-ground biomass (AGB) products at high spatial (deca-metric: 10–30 m) resolution for annual monitoring, reporting, and verification (MRV). In addition to ongoing monitoring, historical AGB time series across the region are also necessary. Historical maps are for example needed for eligibility checks and the selection of appropriate counterfactuals, i.e., to establish a dynamic performance benchmark. We present a novel AGB product based on a recently developed foundation model leveraging progress in self-supervised learning (SSL) techniques from multi-spectral Earth Observation (EO) time series. The foundation model is non-contrastive and condenses all available spectral observations acquired within a year into a few, orthogonal and highly informative representations at 10 m (for Sentinel-2) and 30 m (for Landsat 7/8). Combined with spatially sparse Global Ecosystem Dynamics Investigation (GEDI) full-waveform measurements at two relative heights (RH95 and RH10), but otherwise without any further fine-tuning, we are able to estimate forest biomass with an RMSE of <25 Mg/ha, when validated against 38 in-situ AGB measurement sites across a range of agroforestry (cacao and oil palm) and restoration age classes. Compared to five openly available datasets – most of them not available at annual time steps – our approach reduces the RMSE by 15–55%. We demonstrate the scalability of our approach, by producing annual AGB maps covering the entire state of Para, Brazil, for the years 2013 to 2024. The approach is computationally efficient, fully self-supervised without relying on contrastive samples, and can therefore be scaled to global coverage, even under conditions of high cloudiness.
Séquestration du carbone et provision d’autres services écosystémiques des parcs agroforestiers à karité au Burkina Faso [Carbon sequestration and other ecosystem services provided by shea-tree agroforestry parks in Burkina Faso]
Shea-tree agroforestry parks play a crucial role in climate mitigation and ecosystem services in West Africa, yet their carbon sequestration potential remains poorly documented. This study assesses the contribution of shea-tree (Vitellaria paradoxa) agroforestry parks in Burkina Faso, using household surveys and field inventories across three phytogeographical sectors. Findings indicate that over 89% of surveyed households have shea trees in their fields, which provide fuelwood, timber, improve soil fertility, and help control erosion. Tree density varies from 32 to 45 individuals per hectare, with an average height of 7.93 meters and a diameter at breast height (DBH) of 31.9 cm. Above-ground biomass estimates range from 15.5 to 42.8 Mg/ha, averaging 25.7 Mg/ha or 12.8 Mg of carbon per hectare. The study highlights the significant carbon sequestration potential of these agroforestry systems, though benefits vary based on tree DBH and regional factors. Notably, all surveyed trees had a DBH greater than 24 cm, indicating a lack of young trees and an urgent need for regeneration through assisted natural regeneration, planting, or a combination of both.