article · Ecology and Evolutionary Biology
Developing accurate forest biomass models is essential for estimating carbon storage and assessing ecosystem services. In the Benishangul-Gumuz region of western Ethiopia, research tested and validated allometric equations and biomass expansion factors across six dominant tree species representing about 69 percent of the total basal area in Combretum-Terminalia woodlands. Using destructive sampling data from 67 standing trees across 40 plots, log-transformed models demonstrated high predictive accuracy, with coefficients of determination consistently exceeding 96 percent. For five species, a model relying solely on diameter at breast height provided the best fit, while Syzygium guineense required tree height to attain maximum precision. Biomass expansion factors were statistically uniform across the examined taxa, averaging 2.077. These validated models provide tailored mathematical tools to improve the precision of regional carbon accounting and enhance woodland conservation strategies.
Accurate measurement of forest biomass is vital for tracking carbon stocks and designing climate change mitigation initiatives. Because regional tree species differ in growth forms, standard general equations often miscalculate carbon reserves. Establishing tailored, validated equations for dominant woodland species enables more dependable carbon reporting, improves environmental monitoring, and supports sustainable forest management and conservation planning in dry woodland ecosystems.
The developed models provide applied tools ready for immediate integration into regional carbon accounting frameworks, forestry monitoring protocols, and carbon credit verification programmes. Potential users include forestry management bodies, conservation organisations, and carbon market project developers operating in dry woodland regions. Because the mathematical models and expansion factors are already tested and validated against field-measured data, they represent an applied, practical asset directly usable for forest auditing and environmental reporting.
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Biomass models play a crucial role in accurately estimating carbon storage potential of forests and evaluate the contribution of forest ecosystem services. However, an allometric model which is specifically tailored to diverse tree species in Ethiopia is currently lacking. Therefore, establishing species-specific allometric models and determining the biomass expansion factor for dry woodland ecosystems is essential for comprehending the role in mitigating climate change impacts. This study tested and validated diffrent allometric models on six dominant tree species in the Combretum-Terminalia woodlands of the Benishangul-Gumuz region, which collectively account for approximately 69% of the total basal area. The study applied an explanatory research design method and data were collected through systematic sampling across 40 plots, involving the destructive sampling of 67 representative standing trees. The Allometric models were developed and tested using log-transformed data to satisfy the assumptions of linear regression and ensure statistical rigor. This is done by applying log-transformed data to develop the models to ensure high statistical accuracy. The models performance was validated using key metrics such as R 2 , RMSE, and Model Efficiency (EF) to ensure the reliability of the carbon storage and biomass estimations. The study demonstrated that aboveground biomass for all six species is highly predictable using species-specific allometric equations with coefficients of determination (R 2 ) consistently exceeding 96%. The DBH-based model (M1) was the best fit for five species, notably achieving the highest R 2 (0.985) for Terminalia laxiflora and the most stable performance for Lonchocarpus fruticosa (EF = 0.953). In contrast, Syzygium guineense required the inclusion of height (M2) to reach the study's highest precision (EF = 0.992, MAPE = 3.42%), while Combretum hartmannianum showed the greatest individual variability (EF = 69%). Biomass Expansion Factors (BEF) were statistically uniform across all taxa (P = 0.482), with a collective mean of 2.077 ± 0.343. These findings conclude that while DBH is a robust primary predictor for most woodland species, species-specific architecture particularly the vertical growth of Syzygium guineense and the high absolute residuals in Pterocarpus lucens necessitates tailored models and correction factors to ensure accurate regional carbon accounting and forest management in the Combretum-Terminalia woodlands. Utilizing these models (M1 & M2) can enhance the accuracy of biomass estimations, leading to more effective management practices and conservation strategies via biomass and carbon estimation.
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DOI: 10.11648/j.eeb.20261103.12
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