Individual tree basal area growth model for oak in Jingning with dummy variables

The oak species within the natural arboreal forests of Jingning She Autonomous County, Zhejiang Province, were selected as the research subjects to investigate the appropriateness of incorporating forest stand spatial structure into individual tree basal area growth models of oak forests and its potential to enhance the accuracy of growth prediction.

Based on the continuous forest resource inventory data from Zhejiang Province in 2014 and 2019, integrating forest stand spatial structure factors such as the Hegyi competition index, complete mingling, and aggregation index. By employing the entropy method, a comprehensive spatial structure Index (S) is constructed to fully reflect the spatial distribution and competitive relationships of trees. Utilizing the upper exclusion method, the S is categorized into three distinct levels and introduced as a dummy variable into four commonly applied theoretical growth equations, namely Schumacher, Johnson-Schumacher, Gompertz and Logistic. This integration establishes individual tree basal area growth models inclusive of the S dummy variable. Subsequently, a comparative analysis is conducted with the fundamental growth models that do not incorporate the S dummy variable to assess the influence of spatial structure on growth prediction accuracy.

1) S exerts a significantly positive influence on the growth of breast-height basal area; 2) incorporating S as a dummy variable into the four foundational growth models enhances the models’ fitting accuracy and predictive precision; 3) among all the models tested, the basal area growth model that integrates S as a dummy variable, based on the Johnson-Schumacher model, demonstrates the highest predictive accuracy.

Incorporating stand spatial structure into individual tree basal area growth models is not only suitable for application but also improves the accuracy of tree growth predictions. Holding substantial theoretical and practical significance for forest management and ecological conservation.