Background: Application of allometric equations for quantifying forests aboveground biomass is a crucial step related to efforts of climate change mitigation. Generalized allometric equations have been applied for estimating biomass and carbon storage of forests. However, adopting a generalized allometric equation to estimate the biomass of different forests generates uncertainty due to environmental variation. Therefore, formulating species-specific allometric equations is important to accurately quantify the biomass. Montane moist forest ecosystem comprises high forest type which is mainly found in the southwestern part of Ethiopia. Yayu Coffee Forest Biosphere Reserve is categorized into Afromontane Rainforest vegetation types in this ecosystem. This study was aimed to formulate species-specific allometric equations for Albizia grandibracteata Tuab. and Trichilia dregeana Sond. using the semidestructive method. Results: Allometric equations in form of power models were developed for each tree species by evaluating the statistical relationships of total aboveground biomass (TAGB) and dendrometric variables. TAGB was regressed against diameter at breast height (D), total height (H), and wood density (ρ) individually and in a combination. The allometric equations were selected based on model performance statistics. Equations with the higher coefficient of determination (adj.R 2), lower residual standard error (RSE), and low Akaike information criterion (AIC) values were found best fitted. Relationships between TAGB and predictive variables were found statistically significant (p ≤ 0.001) for all selected equations. Higher bias was reported related to the application of pan-tropical or generalized allometric equations. Conclusions: Formulating species-specific allometric equations is found important for accurate tree biomass estimation and quantifying the carbon stock. The developed biomass regression models can be applied as a species-specific equation to the montane moist forest ecosystem of southwestern Ethiopia.

Introduction: Quantifying forest biomass requires the application of allometric equations which is a fundamental step. Generalized allometric equations have been applied to quantify aboveground biomass (AGB) of forests. But, adopting generalized allometric equations to quantify AGB of different forests creates uncertainty. Therefore, developing species-and sitespecific allometric equations is essential to accurately quantify the biomass. The study was aimed to develop species-specific allometric equations for Diospyros abyssinica (Hiern) F. White in Yayu Coffee Forest Biosphere Reserve using the Semi-destructive method. The vegetation types of Yayu Coffee Forest Biosphere Reserve is categorized to Moist Evergreen Montane Rainforest of Ethiopia. Results and discussion: Evaluating statistical relationships of AGB against predictor variables, eight allometric equations were formulated. AGB was regressed against trunk diameter (D), total height (H), and wood density (ρ) individually and in combination. Selection of allometric equations was employed using model performance statistics. Equations with a higher coefficient of determination (adjusted R 2), lower residual standard error, and Akaike information criterion (AIC) values were found best-fitted. Relationships of AGB and independent variables were found statistically significant (p < 0.000). Overall, formulating species-and site-specific allometric equations is significant for accurate estimation of forest biomass and carbon stock budget.

Moist tropical forests have a significant role in provisioning and regulating ecosystem services. However, these forests are under threat of deforestation and forest degradation. In Ethiopia, the moist evergreen Afromontane forests have the potential for carbon storage and support a high diversity of plant species. However, it is under severe threat of deforestation and degradation.This investigation was conducted to obtain adequate information on the carbon stock potential of the moist Afromontane forest of southwestern Ethiopia. A comparison of carbon stock was conducted between disturbed and undisturbed forests. A systematic sampling design was applied for recording woody species and soil data. A total of 100 main plots of 400 m2 were laid to record trees and shrubs with a diameter at breast height (DBH) ≥ 5 cm. The soil data were collected from 1 m2 subplots established at the four corners and the center of each main plot. The DBH and height were measured to calculate the aboveground carbon of trees and shrubs with DBH ≥ 5 cm. A total of 68 tree and shrub species belonging to 59 genera and 33 families were recorded. The mean carbon stock density was 203.80 ± 12.38 t·ha–1 (aboveground carbon stock) and 40.76 ± 2.47 t·ha–1 (belowground carbon stock). The highest proportion of aboveground carbon (t·ha–1) (42.34%) was contributed by a few tree individuals with DBH > 70 cm. The soil organic carbon stock (SOCS) (t·ha–1) for the depth of 0–30 cm is ranging from 58.97 to 198.33 across plots; the mean is 117.16 ± 3.15. The carbon stored in the moist Afromontane forest indicates its huge potential for climate change mitigation. Therefore, for the enhancement of forest biodiversity and carbon sequestration effective conservation measure and sound management approach is essential.