Abstract
Background: Accurate biomass estimation has critical effects on quantifying carbon stocks and sequestration rates, and above-ground biomass (AGB) growth models are a key component of tree biomass estimation. The study objective was to develop a growth model for AGB of an individual tree by combining competition factors and site quality using a mixed-effect model.
 Methods: The AGB of 128 sampling trees was investigated for Simao pine (Pinus kesiya var. langbianensis) at three typical sites near Pu’er City of Yunnan Province, China. Richards’ Equation was used for the basic growth model (BM) of the AGB, and a mixed-effect model with random effect of site quality (MEM) based on BM and a mixed-effect model with fixed effect of competition factors (MEMC) based on MEM were built using S-plus.
 Results: Both mixed-effect models are significantly better than the basic model in fitting and predicting the individual tree AGB growth for Simao pine, but the MEM is better than the MEMC. Moreover, the mixed-effect model with competition factors and site quality is the optimal estimation model due to its highest prediction precision (P=86.08%) as well as the lowest absolute average relative error (RMA=54.34%) and average relative error (EE =6.45%).
 Conclusion: A model including site quality and competition factors can be used to improve the tree AGB growth estimation for the individual tree AGB growth of Simao pine.
Highlights
Forests are important components of terrestrial ecosystems, and they play vital roles in the global carbon balance (Woodwell et al 1978; Talhelm et al 2014; Sleeter et al 2018)
The mixed-effect model is listed in Equation 15
Our study found that mixed-effect models, including the random effects of site quality, are better than basic models, and incorporating random effects can improve the biomass growth model for Simao pine
Summary
Forests are important components of terrestrial ecosystems, and they play vital roles in the global carbon balance (Woodwell et al 1978; Talhelm et al 2014; Sleeter et al 2018). Accurate estimates of tree biomass are critical for quantifying carbon stocks and fluxes as well as the potential climate mitigation benefits of forest management and for assessing climate change impacts on forest ecosystems (Temesgen et al 2015; Cosmo et al 2016). Many studies on tree-level biomass models have already been developed, the existing Equations are generally not good enough and need improvement (Temesgen et al 2015). Accurate biomass estimation has critical effects on quantifying carbon stocks and sequestration rates, and aboveground biomass (AGB) growth models are a key component of tree biomass estimation. The study objective was to develop a growth model for AGB of an individual tree by combining competition factors and site quality using a mixed-effect model
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