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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 site-specific 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 R2), 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

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 site-specific 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 R2), 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. [ABSTRACT FROM AUTHOR]