Related Experiment Video
Updated: May 7, 2026
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
Developing mixed-effects aboveground biomass model using biotic and abiotic variables for moso bamboo in China
Xiao Zhou1, Xuan Zhang1, Ram P Sharma2
1International Center for Bamboo and Rattan, Key Laboratory of National Forestry and Grassland Administration, Beijing 100102, China; National Location Observation and Research Station of the Bamboo Forest Ecosystem in Yixing, National Forestry and Grassland Administration, Yixing 214200, China.
Abstract:
Moso bamboo forests, which are distributed across southern China, have played the important roles in the global carbon cycle and contribute significantly to mitigating the impacts of climate change. Previous studies on moso bamboo have focused mainly on the impacts of climate change and competition intensity on aboveground biomass (AGB), ignoring the effects of soil and topographic factors on the allometric relationships between biomass and bamboo structural variables and carbon allocation to different geographical regions. This study applied a mixed-effects modeling approach to analyze AGB data acquired from the destructive sampling and measurements of 306 moso bamboo individuals in southern China. The power function was used to develop a two-level (growth region [province]- and elevation-levels) mixed-effects AGB model with diameter of breast height (DBH), topography, climate, and soil characteristics used as predictor variables. Variations of AGB caused by differences of growth regions and elevations were described by random components in the model. The results showed that environmental heterogeneity across growth regions led to substantial differences in the scaling exponent of the power function. Incorporating DBH, height (H), the de Martonne aridity index, sine of the slope combined with the natural logarithm of elevation, sand, and rock fragment (RF) (soil) into the AGB model significantly improved its prediction performance. Increasing DBH, H, soil RF, and precipitation and reducing soil sand fraction to a certain extent showed beneficial effects on moso bamboo AGB accumulation. The model reveals strong relationships between AGB and bamboo structural variables, topography, climate, and soil factors. The model will be useful for developing bamboo forest management strategies in line with the environmental changes, and can offer a novel approach for carbon accounting in the context of climate change.
