General allometric equation for estimating aboveground biomass of Acacia-Commiphora forest, southern Ethiopia

Abstract 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 MRV activities. The aim of the study is to develop site & species-specific biomass estimation models and validate and evaluate the existing generic models developed for pan-tropical forest and newly developed allometric models. Total of 150 individual sample trees was harvested from each diameter class (proportionally) for the site-specific 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. The root mean square error (RMSE) was used for measuring model bias, precision and accuracy. The coefficient of determination (R²& adj-R²), the Akaike Information Criterion (AIC) and the Schwarz Bayesian information Criterion (SBC) was employed to select most appropriate models. The results showed that a best fit-model for above-ground biomass of tree components (stem, branch and foliage) was developed. AGBStem= exp {-1.8296 + 0.4814 Ln (ρD²H) + 0.1751 Ln (CW) + 0.4059 Ln (DSH₃₀)} AGBBranch= exp {-131.6 + 15.0013 Ln (ρD²H) + 13.176 Ln (CW) + 21.8506 Ln (DSH₃₀)} AGBFoliage= exp {-0.9496 + 0.5282 Ln (DSH₃₀) + 2.3492 Ln (ρ) + 0.4286 Ln (CW)} AGBTotal= exp {-1.8245 + 1.4358 Ln (DSH₃₀) + 1.9921 Ln (ρ) + 0.6154 Ln (CW)} The results of this study demonstrate that the development of local models derived from an appropriate sample of representative species can greatly improve the estimation of AGBTotal.