Abstract

The Chinese fir (Cunninghamia lanceolata) is the largest tree species used for afforestation in China. The purpose of this study was to explore the effects of site quality, stand density, and tree species composition on the growth and yield of mixed Chinese fir forests and to build prediction models for their stand average DBH (diameter at breast height), average height, and volume. Using 430 plots of mixed Chinese fir forests in the Fujian Province of China, the optimal base models for predicting stand average DBH, average height, and volume were selected from the Schumacher, Korf, Logistic, Mitscherlich, and Richards equations. On this basis, the site class index (SCI), stand density index (SDI), and tree species composition coefficient (TSCC) were introduced to improve the model’s performance, and the applicability of the different models was evaluated. The optimal base models for the average DBH, average height, and stand volume of mixed Chinese fir forests all used the Richards equation. The best fitting effect was obtained when the SCI was introduced into parameter a in the average height model, while the inclusion of the TSCC did not improve the model significantly. The fitting effects of the average DBH and stand volume models were both best in the form of y=a1SCIa2[1−exp⁡(−b1SDIb2)t]c when the SCI and SDI were introduced. When the TSCC was further included, the fitting effects of the stand average DBH and volume models were significantly improved, with their R2 increased by 47.47% and 58.45%, respectively, compared to the base models. The optimal models developed in this study showed good applicability; the residuals were small and distributed uniformly. We found that the SCI had an impact on the maximum values of the stand average DBH, average height, and volume; the SDI was closely related to the growth rate of the diameter and volume, while the TSCC influenced the maximum values of the stand average DBH and volume. The model system established in this study can provide a reference for the harvest prediction and mixing ratio optimization of mixed Chinese fir forests.

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