Objective This study aimed to analyze the aboveground biomass allocation patterns varying with tree size, and to establish additive allometric biomass equations for accurate estimation of biomass and carbon sequestration of loblolly pine plantations in Ji’an Region, Jiangxi Province of eastern China.
Method A total of 35 trees covering different diameter classes were harvested and measured for wood (inside bark), bark, branch, and foliage biomass in the Wugongshan Forest Farm of Anfu County of Jiangxi Province. The share of biomass allocated to different components and its variation trend with tree size were assessed by calculating the biomass fractions. The best biomass model for each component was determined by testing the DBH (D) and height (H) as predictors. A seemingly unrelated regression method with a unique weighting function for each equation was applied to establish additive systems of biomass models and to overcome the model heteroscedasticity. The predictive ability was validated by the leave-one-out cross-validation method.
With the increase of tree size, stem and branch biomass ratios increased and the ratios for bark and foliage decreased. Diameter proved to be the most important predictor for each biomass component, the model coefficients were extremely significant with good fitting results ( R_\textadj^2
ranged from 0.91 to 0.97). Adding tree height can help to improve the model fit and performance for wood and bark, but it is not conducive to the improvement of branch and leaf models, and the coefficient for tree height is non-significant. Compared with the previously published biomass models of loblolly pine, the models developed in this study performed well and had a small bias with total relative error (TRE) basically within ±1%.
Conclusion Tree size is the main factor affecting biomass allocation, and trunk accounted for most of the aboveground biomass, followed by branch and foliage. Wood and bark biomass models are best fitted with D2H, while models with D alone perform best for branch and foliage biomass. The developed additive biomass models based on the most suitable predictors show high prediction accuracy, indicating that the models can be applied in biomass estimation of loblolly pine plantations in Ji’an Region, Jiangxi Province of eastern China.