Based on mixed-effects model and Empirical Best Linear Unbiased Predictor predicting growth profile of height for Chinese fir.

Abstract

Based on the data of 30 sample trees from 15 permanent plots of Chinese fir in the national forest farm of Jiangle, at first we study the best function as the base model with the least square method among five growth profile equations. The nonlinear mixed model was constructed based on the base model and modeling data. We use the R for model fitting. Select the mixed model with the minimum value of AIC, BIC and the maximum value of Loglik as the best model by changing the number of mixed parameters in fitting progress. Using mixed model to predict growth profile of height and studying the characteristics of Empirical Best Linear Unbiased Predictor (EBLUP). Fitting results showed the simulation's precision of Weibull's including three random effect parameters(β1, β2 and β3) was maximal. In the analysis of prediction, prediction accuracy decreased as age interval of observations extended with the same number of previous observations. MSE decreased as the number of previous observations increased. EBLUP prediction could fully predict individual growth process, given that there were multiple previous observations with long-enough age intervals.