Incorporating competition factors in a mixed-effect model with random effects of site quality for individual tree above-ground biomass growth of Pinus kesiya var. langbianensis

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.