Abstract
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.
Highlights
IntroductionThe contribution of forest ecosystems has been widely recognized for conserving and enhancing carbon sinks and reducing global warming [1–4]
Accurate estimates of biomass is limited for many sites and species due to the lack of specific allometric equations, the most common methods used for biomass estimations [7]
We evaluated an alternative approach using weighted nonlinear modelling to develop the models for Belowground root biomass (BGB), as this approach is considered better at correcting for bias when transforming biomass estimates from logarithmic equations back to the arithmetic scale [42]
Summary
The contribution of forest ecosystems has been widely recognized for conserving and enhancing carbon sinks and reducing global warming [1–4]. Plantation forests comprise 3% of the world’s total forest area [5], they play an important role in climate change mitigation through their capacity to absorb and store carbon [6], where plantation forests are established on previously cleared land. Accurate estimation of forest biomass is important, as it provides data on ecosystem productivity, nutrient flows, and their contribution to the global carbon cycle [3]. Accurate estimates of biomass is limited for many sites and species due to the lack of specific allometric equations, the most common methods used for biomass estimations [7]. A robust regression for carbon sequestration is needed to provide plantation owners with confidence and allow trading of carbon credits
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