Objective  This paper uses nonlinear regression, mixed effect model, quantile regression and quantile regression combination to construct the crown width model of Larix gmelinii, which provides a reference for the accurate prediction of the crown width of Larix gmelinii in Xiaoxing’an Mountains of northeastern China.

  Method  In this study, the data were collected from sample plot of 60 Larix gmelinii plantations in Mayongshun Forest Farm, the generalized nonlinear model, quantile regression model and mixed effect model were constructed, respectively. 10-fold cross validation was used to compare the prediction. The number of 1 to 8 sample trees were randomly selected from each sample plot to calibrate the two kinds of quantile regression combination models, including QRc-1 (τ = 0.1, 0.5, 0.9) and QRc-2 (τ = 0.3, 0.5, 0.7), and mixed effect model to determine the best sampling scheme for quantile regression combination and mixed effect model, and comparison was carried out and analyzed for different methods.

  Result  (1) The model fitting results showed that the mixed effect model had the best fitting statistics. Median regression was the best quantile regression model. There was little difference between the fitting statistics of median regression and nonlinear regression model, but it was slightly better than nonlinear regression model. (2) The results of sampling calibration showed that, when the number of samples was greater than 2 trees, the order of the models was QRc-2 > the mixed effect model > QRc-1. (3) The significance test of cross validation showed that the best sampling scheme of the two kinds quantile regression combinations was 4 trees, and the best sampling scheme of the mixed effect model was 5 trees.

  Conclusion  In this study, both mixed effect model and quantile regression combination can improve the prediction accuracy of crown width model. Quantile regression combination (τ = 0.3, 0.5, 0.7) is slightly higher than the mixed effect model in the validation statistics when using the best sampling scheme. Due to the sample number of quantile regression combination is less, which saves more time and cost, therefore, the quantile regression combination (τ = 0.3, 0.5, 0.7) of 4 sample trees is selected as the best model to predict the crown width.