Based on a multilevel linear mixed model approach,an individual diameter increment model was developed for fir plantation trees growing in Jiangxi Province.The data set used in this study came from long-term permanent research plots.The database consists of total of 82 counties,365 plots, 5 416 trees and 16 248 observations.The paper chose mixed effects models instead of regression analysis approach because it allows for proper treatment of error terms and correlation in a repeated measures analysis framework.The model was defined as a mixed linear model with parameter random effect of plot,area or plot and area simultaneous.In addition the heteroscedasticity and correlation was taken into account.Mixed model calibration of diameter increment was carried out with the independent data using a different sample of complementary observations.The result showed that the total stand basal area,the diameter of target trees,the ratio of basal area of larger trees to target tree diameter,and altitude were found to be significant predictors.Both the fitting model and the calibrated model mean a substantial improvement compared with the classical approach widely used in forest management.After taking into account reasonable variance function of heteroscedasticity and correlation,the model shows better of goodness of fit than only taking into account parameter random effects.This type of modeling methodology shows flexible,precise and accurate. Based on a multilevel linear mixed model approach, an individual diameter increment model was developed for fir plantation trees growing in Jiangxi Province. The data set used in this study came from long-term permanent research plots. The database consists of 82 of total counties, 365 plots, 5 416 trees and 16 248 observations.The paper chose mixed effects models instead of regression analysis approach because it allows for proper treatment of error terms and correlation in a repeated measures analysis framework.The model was defined as a mixed linear model with parameter random effect of plot, area or plot and area simultaneous. In addition the heteroscedasticity and correlation was taken into account. Mixed model calibration of diameter increment was carried out with the independent data using a different sample of complementary observations. The result showed that the total stand basal area, the diameter of target trees, the ratio of basal area of ​​larger trees to target tree diameter, and alt itude were found to be significant predictors.Both the fitting model and the calibrated model mean a substantial improvement compared with the classical approach widely used in forest management. After taking into account reasonable variance function of heteroscedasticity and correlation, the model shows better of goodness of fit than only taking into account parameter random effects. This type of modeling methodology shows flexible, precise and accurate.