ObjectiveForest biomass, the foundation of researching many forestry and ecology problems, is a basic quantity character of the forest ecological system. Thus, accurate measurement of biomass and carbon is very important. Developing biomass models is a major way to biomass estimation. Based on the data of biomass for Populus simonii × P. nigra, we established three additive systems of individual tree biomass equations, i.e., the additive system of biomass equations based on one-variable models, the additive system of biomass equations based on two-variable models, the best additive system of biomass equations based on multiple-variable models. These provided technical and theoretical support for accounting and monitoring the Chinese forest biomass and carbon stock.

MethodThe aggregation system was used to establish the individual tree biomass additive models, and nonlinear seemly unrelated regression was used to estimate the parameters in the additive system of biomass equations. The individual tree biomass model validation was accomplished by Jackknifing technique in this study.

ResultTree biomass models using diameter at breast height (D) as the sole predictor are simple in model structure, and have higher prediction precision. Adding tree height (H) and crown attributes (crown width (CW) and crown length (CL)) as the additional predictors into biomass equations can significantly improve the model fitting and predictive ability, especially for predicting branch, foliage and crown biomass. The model fitting results showed that three additive systems of individual tree biomass equations fitted the data well, of which the adjusted coefficient of determination (R_a²) of biomass additive systems was all above 0.81, the mean relative error (ME) was between -1.0%-10.0%, the mean absolute relative error (MAE) was less than 25%, and all models for total and component biomass had the good prediction precision (more than 85%). Most biomass equations of the additive system based on multiple-variable models produced better model fitting than those of the additive system based D and the additive system based D and H.

ConclusionIn order to estimate biomass model parameters more effectively, the additive property of estimating tree total, sub-totals, and component biomass should be taken into account. Although obtaining crown attributes is costly in terms of labor and time, the additive system of biomass equations based on the best models is very useful in conjunction with individual growth models to accurately predict biomass in response to changes in stand condition. Overall, the biomass models would be suitable for predicting individual tree biomass and carbon of planted Populus simonii × P. nigra.