Aboveground tree biomass prediction options for the Dry Afromontane forests in south-central Ethiopia

Abstract

Biomass of trees may be predicted either directly applying allometric models or indirectly from volume and biomass expansion factors (BEFs). For the Dry Afromontane forests, the second largest biomass pool in Ethiopia, such methods are not devised and properly documented. The main objective of this study was to explore different aboveground tree biomass prediction options based on destructively sampled tree biomass data. We explored the direct method by means of 1) new mixed-species general biomass models developed in the present study, and 2) some previously developed models including the pan-tropical models, and the indirect method by means of 3) volume and BEFs. From two sites in south-central Ethiopia, based on information from systematic sample plot inventories, 63 trees from 30 different species that contributed about 87% to the total forest basal area, were destructively sampled. Weighted nonlinear regression was applied to fit new models and their performance was assessed using root mean squared error (RMSE, %), mean prediction error (MPE, %) and pseudo-R2 based on leave-one-out-cross-validation. Previously developed models and the indirect method were also evaluated by means of RMSE and MPE. The new general total biomass models performed well with pseudo-R2 ranging between 0.87 and 0.96 and are presented along with covariance matrices for the parameter estimates enabling error propagation in biomass estimation. Most previously developed models resulted in significant MPEs up to 78%, while the best pan-tropical model performed much better with an MPE of about 7%. The indirect method also showed poor performance with MPEs ranging between 5% and 30%. Generally, the new models are accurate and flexible, thus, preferred over all previously developed models and the indirect method for application. However, their application to Dry Afromontane forests outside the study sites should be made only after thoroughly evaluating growing conditions and species composition. The results are step forward to enhance decisions made towards sustainable forest management including the REDD+ implementation for Dry Afromontane forests in Ethiopia.