It is a scientific problem to be solved urgently in the process of forest management that how to realize area compatibility between whole-stand growth model and an individual-tree model and improve the prediction accuracy. In this paper, data from 105 permanent sample plots of Larix principis
plantation were used to develop both whole-stand growth model and individual-tree model. In a first step, using Gauss-Newton algorithm, a whole-stand growth model and an individual-tree model were established. In a second step, the single tree survival probability equation was fitted based on the logistic equation in different forms. Finally, the best combinations obtained in each step were compared. Regarding the disaggregation of predicted stand density, the approach based on considering the intercept of the logistic function for tree survival as a specific parameter of each sample plot and optimizing its value produced the best results. The results showed that the prediction model of stand density, stand basal area and individual basal area had a good predictive effect, and can explain more than 90% of the variance in the constraint parameter methods. In the decomposition method, survival probability of single trees and stand density were predicted based on the logistic equation. The area under the ROC curve obtained by the test was 0.906, which indicated that the equation could predict the survival probability of forest trees. In combination forecasting methods, the combination forecasting method had the best effect using different levels of optimal model. When predicting the stand density and basal area, the combination forecasting equation had the highest accuracy, and the stand level model was the second, and the accuracy of the single tree level model was the lowest. The combined forecasting method can predict the stand density, tree survival, stand basal area and tree basal area. The method improves the prediction accuracy of the model, and provides a reference for the prediction of stand growth, dynamic change of spatial structure and management effect evaluation.