Establishment of a volume prediction model for nodes based on scar size

This study took 49 artificial Pinus koraiensis with a total of 1 207 knot data from 29 standard plots in the Linkou Forestry Bureau and the Dongjingcheng Forestry Bureau in Heilongjiang province as the research object, and a knot volume prediction model was constructed based on their relevant data. Through analyzing the distribution law of the internal knot volume of the artificial Korean pine trunks, it achieved non-destructive evaluation of wood quality and provided a reference for the scientific and rational utilization of wood.

Firstly, we collected relevant data through analysis and reviewed relevant literature to establish a basic model. When constructing a mixed model, introduced random effects at the sample tree and sample plot levels, and selected the best fitting mixed model by comparing evaluation metrics such as AIC and BIC; When constructing a two-level nested mixed model, sample trees were nested at the sample plot level, and the optimal two-level nested mixed model was obtained based on the fitting evaluation index.

Among the three models, the fitting accuracy of the single-level mixed model and the two-level nested mixed model was higher than that of the basic model. The AIC, BIC, and other evaluation indicators showed that the two-level nested mixed model had a better fitting effect on the attributes of knots than the single-level mixed effect model. The test results showed that the prediction accuracy of the basic model was greater than 90%, and the prediction accuracy of both hybrid models was above 98%, indicating that the constructed model could provide good predictions for nodule volume.

Based on the two-level nested mixed model, not only can non-destructive estimation of knot volume be achieved, but also the distribution law of knot volume inside the artificial P. koraiensis trunk can be more accurately reflected. Realize non-destructive evaluation of wood quality, provide reference for scientific and rational utilization of wood, and thus improve economic benefits.