Abstract
Three systems of additive biomass models were developed and the effects of tree components, tree sizes, and tree growing regions on the carbon concentration were analyzed for Mongolian oak (Quercus mongolica Fisch. ex Ledeb.) in the natural forests of Northeastern China. The nonlinear seemingly unrelated regression (NSUR) method was used to fit each of the three systems simultaneously; namely, aggregated model systems with no parameter restriction (AMS0), aggregated model systems with one parameter restriction (AMS1), and aggregated model systems with three parameter restrictions (AMS3). A unique weighting function for each biomass model was applied to address the heteroscedasticity issue. The systems assertively guarantee the additivity property, in which, the summation of the respective predicted tree components (i.e., root, stem, branch, and foliage) will match the prediction of subtotals (i.e., crown and aboveground) and total biomass. Using one-, two-, and three-predictor combinations (i.e., D (diameter at breast height), D and H (total height), and D, H, and CL (crown length)) as the general model underlying formats, three systems of additive biomass model were developed. Our results indicate that (1) all of the aggregated model systems performed well and the differences between the systems were relatively small; (2) the rank order of the three systems based on an array of statistics are as follows: AMS0 > AMS1 > AMS3; (3) the carbon concentration significantly varied depending on the types of tree tissues and growing regions; (4) the regional respective component carbon concentration and regional weighted mean carbon concentration multiplied by observed biomass value appeared to be the best approach to calculate carbon stock.
Highlights
On a global scale, forests are known as one of the most dominant terrestrial ecosystems, covering13 billion hectares or approximately 30.6% of the Earth’s total land area [1]
The highest carbon concentration of tree components for all regions was found in foliage, whereas the lowest carbon concentration was varied between roots and branch
We found that the inclusion of H into the aggregative systems improved the performances and model fit significantly for the stem, aboveground, and total biomass models, while adding CL into the aggregative model system only gave a slight improvement for the model fitting of the foliage and crown biomass models
Summary
Forests are known as one of the most dominant terrestrial ecosystems, covering13 billion hectares or approximately 30.6% of the Earth’s total land area [1]. Forests contribute in the storage of a large amount of biomass and play an important role in maintaining atmospheric carbon balance on Earth [2]. Researchers have been facing a challenge for decades to enhance the accuracy of tree biomass and carbon estimations, which are crucial for understanding climate change, nutrient cycling, forest productivity, forest health, etc. Developing allometric biomass equations is acknowledged as a greater alternative means to estimate forest biomass [6,7,8]. A large number of allometric biomass models have been established for more than several hundreds of tree species in different ecological and geographical regions worldwide [9,10,11,12,13]
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