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

• Published in: Journal of ecology and environment 2024, Vol.48 (48), p.196-206
• Main Authors: Wondimagegn Amanuel, Chala Tadesse, Moges Molla, Desalegn Getinet, Zenebe Mekonnen
• Format: Article
• Language: Korean
• ISSN: 2287-8327; 2288-1220

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 (R2 and adjusted [adj]-R2), 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-R2 ranged from 0.71 to 0.85, and model 9 with diameter at stump height at 10 cm (DSH10), ρ and crown width (CW) as predictor variables, performed best according to RMSE and AIC. For the merchantable stem models, adj-R2 varied from 0.73 to 0.82, and model 8) with combination of ρ, diameter at breast height and height (H), CW and DSH10 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. AGBStem = exp {-1.8296 + 0.4814 natural logarithm (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)} Conclusions: The results demonstrated that the development of local models derived from an appropriate sample of representative species can greatly improve the estimation of total AGB.