A Nonlinear Mixed-Effects Model to Predict Stem Cumulative Biomass of Standing Trees

A nonlinear mixed-effects modeling approach was used to model stem cumulative biomass based on logistic model for dahurian larch (Larix gmelinii Rupr.) plantations in northeastern China. The NLME procedure in S-Plus is used to fit the mixed-effects models for stem biomass data. The results showed that logistic model with random parameter b1 could significantly improve the model performance. The fitted mixed effects model was also evaluated using mean error, mean absolute error, mean percent error, and mean absolute percent error. The mixed model was found to predict stem cumulative biomass better than the original model fitted using ordinary least squares based on all errors. The application of mixed stem cumulative biomass model not only showed the mean trends of stem cumulative biomass, but also showed the individual difference based on variance-covariance structure of random parameters.