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Related Experiment Video

Updated: Aug 7, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Constructing two-level nonlinear mixed-effects crown width models for Moso bamboo in China.

Xiao Zhou1,2, Zhen Li1,2, Liyang Liu1,2

  • 1International Center for Bamboo and Rattan, Key Laboratory of National Forestry and Grassland Administration, Beijing, China.

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|March 13, 2023
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Summary

A new nonlinear mixed-effects model accurately predicts bamboo crown width (CW) using easily measured variables. The model, calibrated with data from Phyllostachys pubescens, offers efficient forest management and carbon estimation.

Keywords:
bamboo forest managementgrowth functionrandom effectsampling strategyvariance-stabilizing function

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Area of Science:

  • Forestry Science
  • Ecological Modeling
  • Quantitative Biology

Background:

  • Bamboo crown width (CW) is crucial for assessing forest health, productivity, and carbon sequestration.
  • Accurate CW estimation is vital for effective bamboo forest management and carbon inventory.

Purpose of the Study:

  • To develop and validate a robust nonlinear mixed-effects (NLME) model for predicting bamboo crown width (CW).
  • To identify optimal sampling strategies for calibrating the NLME CW model.
  • To explore the relationship between CW and key bamboo structural variables.

Main Methods:

  • Fitted eight growth functions to Phyllostachys pubescens data, selecting the logistic function for the NLME model.
  • Incorporated forest block and sample plot-level random effects into the NLME model.
  • Evaluated four bamboo selection methods and eight sample sizes for model calibration, using DBH, HCB, MDBH, and H as predictors.

Main Results:

  • The logistic function-based NLME model demonstrated superior fit statistics (Max R², min RMSE, TRE).
  • CW showed positive correlations with height to crown base (HCB) and diameter at breast height (DBH), and a negative correlation with height (H).
  • Using the two smallest bamboo poles per plot for random effects estimation offered the best balance of cost, efficiency, and accuracy.

Conclusions:

  • The developed NLME CW model provides a reliable tool for estimating bamboo forest characteristics.
  • The model can aid in optimizing bamboo forest management practices and carbon stock assessments.
  • The findings highlight the importance of considering hierarchical structures (forest block, sample plot) in ecological modeling.