A practical and cost-effective destructive sampling method for estimating above and belowground biomass of Corymbia citriodora subspecies variegata grown in plantations is described. The methodology includes details on selecting sample trees, weighing tree components in the field, excavating root systems and obtaining fresh weights and laboratory analyses of components to determine oven dry weights. The development of these sampling procedures is a basic step towards successful, consistent collection of biomass data in 18-20 years old plantation forests. This methodology was developed for eucalypt hardwood timber plantations in Queensland, Australia. However, these procedures can be applied to plantations elsewhere as well as to trees in native forest environments with minor modifications. The methodology developed for field sampling of the tree components and the derivation of allometric relationships for predicting individual tree biomass (above and belowground) highlighted the following: Accurate quantification of above and belowground biomass of eucalypts. Description of measured variables for developing allometric relationships. Integration of field and laboratory measurements to streamline data collection.

Spotted gum (Corymbia citriodora spp. variegata; CCV) has been widely planted, has a wide natural distribution, and is the most important commercially harvested hardwood species in Queensland, Australia. It has a great capacity to sequester carbon, thus reducing the impact of CO2 emissions on climate. Belowground root biomass (BGB) plays an important role as a carbon sink in terrestrial ecosystems. To explore the potential of biomass and carbon accumulation belowground, we developed and validated models for CCV plantations in Queensland. The roots of twenty-three individual trees (size range 11.8–42.0 cm diameter at breast height) from three sites were excavated to a 1-m depth and were weighed to obtain BGB. Weighted nonlinear regression models were most reliable for estimating BGB. To evaluate the candidate models, the data set was cross-validated with 70% of the data used for training and 30% of the data used for testing. The cross-validation process was repeated 23 times and the validation of the models were averaged over 23 iterations. The best model for predicting spotted gum BGB was based on a single parameter, with the diameter at breast height (D) as an independent variable. The best equation BGB = 0.02933 × D2.5805 had an adjusted R2 of 0.854 and a mean absolute percentage error of 0.090%. This equation was tested against published BGB equations; the findings from this are discussed. Our equation is recommended to allow improved estimates of BGB for this species.

Accurate equations are critical for estimating biomass and carbon accumulation for forest carbon projects, bioenergy, and other inventories. Allometric equations can provide a reliable and accurate method for estimating and predicting biomass and carbon sequestration. Cross-validatory assessments are also essential to evaluate the prediction ability of the selected model with satisfactory accuracy. We destructively sampled and weighed 52 sample trees, ranging from 11.8 to 42.0 cm in diameter at breast height from three plantations in Queensland to determine biomass. Weighted nonlinear models were used to explore the influence of different variables using two datasets: the first dataset (52 trees) included diameter at breast height (D), height (H) and wood density (ρ); and the second dataset (40 trees) also included crown diameter (CD) and crown volume (CV). Cross validation of independent data showed that using D alone proved to be the best performing model, with the lowest values of AIC = 434.4, bias = −2.2% and MAPE = 7.2%. Adding H and ρ improved the adjusted. R2 (Δ adj. R2 from 0.099 to 0.135) but did not improve AIC, bias and MAPE. Using the single variable of CV to estimate aboveground biomass (AGB) was better than CD, with smaller AIC and MAPE less than 2.3%. We demonstrated that the allometric equations developed and validated during this study provide reasonable estimates of Corymbia citriodora subspecies variegata (spotted gum) biomass. This equation could be used to estimate AGB and carbon in similar spotted gum plantations. In the context of global forest AGB estimations and monitoring, the CV variable could allow prediction of aboveground biomass using remote sensing datasets.