Abstract: Based on the stem analysis data of 80 sample trees in dahurian larch (Larix gmelinii) plantations of Daqingchuan Forest Farm, Dailing Forest Bureau in Heilongjiang Province and the Logistic growth model, the NLME procedure of S-PLUS software was adopted to fit the nonlinear tree volume growth models, with consideration of individual tree effect and plot effect, and the evaluation statistics such as AIC, BIC, Log Likelihood, and likelihood ratio test were used to compare the prediction precisions of the models. The results showed that the random effect models with parameters b₁, b₂, and b₃(representing the random parameters for progressive, scale, and shape in Logistic model, respectively) had the best performance when considering individual tree effect, while the models with parameter b₁ had the best performance when considering plot effect. The models considering both individual tree effect and plot effect provided better model fitting than the basic model (Logistic growth model), and the models considering individual tree effect showed more precision, as compared with those considering plot effect. The model validation indicated that random effect models not only showed the mean variation trend of individual tree volume growth, but also showed the differences among the individuals. In addition, the prediction precision of the models could be further improved through the calibration of random parameters.

Key words: individual tree volume growth model, random effect, fixed effect, Larix gmelinii