Cumulative continuous predictions for bole and aboveground woody biomass in Eucalyptus globulus plantations in northwestern Spain

• We developed equations for predicting bole and woody aboveground cumulated tree biomass.
• For prediction of bole biomass, conversion from volume proved the best option.
• Biomass can be predicted for any diameter ranges limits by subtraction.
• Heteroscedasticity and correlated errors were taken into account.
• Other variables can also be estimated by considering their relationship with cumulative biomass.

At present there is increasing interest in modelling biomass to estimate carbon sequestration or the availability of forest products for use as bioenergy. The biomass of different tree components can be estimated to provide more detailed information. However, the different components have not been clearly defined. Moreover, the greater the number of components considered, the more difficult it is to fit the system of equations with any guarantee of statistical robustness. To overcome these limitations, we developed a continuous function that predicts cumulative biomass from the stump until any top diameter (including the biomass of branches). We also used two different methods to predict bole biomass: a cumulative continuous biomass function and conversion from volume to biomass by use of a taper equation and average wood density. We used a mixed-effects modelling framework to account for correlated errors in developing the taper equation. We developed a separate equation to estimate the foliar biomass for use in estimating total aboveground tree biomass. The cumulative aboveground woody biomass equation is implicitly additive, and no heteroscedasticity was observed, thereby resolving two of the main modelling goals in the development of biomass equations. For predicting cumulative bole biomass, estimation from volume generated less error, after bias correction, than direct estimation. Moreover, the indirect method also yields useful variables such as volume and height limits. Other variables such as carbon and nutrient contents, calorific power, ash content, etc. can be estimated by multiplying the mean contents by the predicted biomass or, for more accurate predictions, by using equations based on the relative diameter.