Objective To improve regional site index model, the site index model with the random effects of site factors was developed.

Method Based on the 360 samples with dominant height-age of Chinese fir in the hilly and plain area of Hunan Province, the quantification method I was used to select the site factors affecting the dominant height growth (P<0.05). The 8 commonly used models were used to develop the basic site index model, as well as the models considering the random effects of site factors and their combinations. The evaluation statistics including AIC, BIC, Log-likelihood and R² were used to select the optimal random effect model. In addition, K-means clustering was used to divide site type groups for model applications.

Results1) The site factors including altitude, slope, aspect and soil type had significant impact on dominant height growth based on the quantitative method I. And soil type was the most important factor, following by altitude, slope aspect, and Slope. 2) The fitting accuracy of the 8 candidate basic models was low (R²=0.4243~0.5644). M4 (R²=0.5644) was selected the best basic model for developing the polymorphic site index curve. 3) Considering the influence of the site effect on the site index, the nonlinear mixed effects models with different site factors and their combination of random effects were developed. The mixed effects model with the random effects of site type performed the best (R²=0.8089). 4) The site types were divided into 11 site groups. The mixed model containing the site type groups improved the modeling accuracy (R²=0.8117).

Conclusion The site index model with site mixed effect can significantly improve the site modeling accuracy of regional complex site types, and provide a reference and basis for regional forest site quality evaluation.