Developing biomass estimation models for above-ground compartments in Eucalyptus dunnii and Corymbia citriodora plantations

Abstract Biomass has been widely studied in terms of ecosystem ecology, timber production profitability, bioenergy (biofuels) and greenhouse gas emission reduction mechanisms. However, uncertainty in biomass estimation is still a current concern. In this study, direct and indirect methods were used to develop species-specific biomass estimation models (BEMs) for stem, bark, branch and crown compartments in 16-year old plantations of Eucalyptus dunnii and Corymbia citriodora. A total of 93 trees were destructively sampled. An analysis of covariance (ANCOVA) assessed the effect of species on biomass prediction. Our results indicated that equations developed by using parameters or predictors such as diameter at breast height (DBH), height (H), wood density (p) and branch diameter were generally significant (p   0.84). After a rigorous process that included testing hypotheses, checking diagnostic statistics, assessing model coefficients and model functionality, the most suitable stem BEMs corresponded to those ones derived from the compound variable DBH2Hp. The most reliable branch and crown BEMs used DBH and branch diameter respectively as single variable (simple linear models). Bark BEMs differ between species as DBH was the best predictor for E. dunnii whilst the compound variable DBH H predicted better for C. citriodora. The BEMs with multiple predictors, and in particular polynomial models, produced wider confidence intervals, unreliable coefficients, multicollinearity and higher proportion of outliers and leverage points. In conclusion, appropriate model diagnosis can reduce pitfalls and ensure selection of valid BEMs.