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Background: Most of the biomass equations were developed using sample trees collected mainly from pan-tropical and tropical regions that may over-or underestimate biomass. Site-specific models would improve the accuracy of the biomass estimates and enhance the country's measurement, reporting, and verification activities. The aim of the study is to develop site-specific biomass estimation models and validate and evaluate the existing generic models developed for pan-tropical forest and newly developed allometric models. Total of 140 trees was harvested from each diameter class biomass model development. Data was analyzed using SAS procedures. All relevant statistical tests (normality, multicollinearity, and heteroscedasticity) were performed. Data was transformed to logarithmic functions and multiple linear regression techniques were used to develop model to estimate aboveground biomass (AGB). The root mean square error (RMSE) was used for measuring model bias, precision, and accuracy. The coefficient of determination (R 2 and adjusted adj-R 2), the Akaike Information Criterion (AIC) and the Schwarz Bayesian information Criterion was employed to select most appropriate models. Results: For the general total AGB models, adj-R 2 ranged from 0. 71 to 0. 85, and model 9 with diameter at stump height at 10 cm (DSH 10), ρ and crown width (CW) as predictor variables, performed best according to RMSE and AIC. For the merchantable stem models, adj-R 2 varied from 0. 73 to 0. 82, and model 8) with combination of ρ, diameter at breast height and height (H), CW and DSH 10 as predictor variables, was best in terms of RMSE and AIC. The results showed that a best-fit model for above-ground biomass of tree components was developed. AGB Stem = exp -1. 8296 + 0. 4814 natural logarithm (Ln) (ρD 2 H) + 0. 1751 Ln (CW) + 0. 4059 Ln (DSH 30) AGB Branch = exp -131. 6 + 15. 0013 Ln (ρD 2 H) + 13. 176 Ln (CW) + 21. 8506 Ln (DSH 30) AGB Foliage
Amanuel et al. (Mon,) studied this question.