We constructed base model, dummy variable model, and mixture model with three variables including knot diameter, loose knot length, and sound knot length with three typical coniferous species, , , and var. , from the Linkou Forestry Bureau and Mengjiagang forest farm in Heilongjiang Province in 2020. We analyzed the differences in knot properties among different tree species and simplified the modeling work. Firstly, we collected relevant knot property data through the sectioning method based on relevant literature, transformation of the model form and substitution of related variables to conduct a base model. We transformed the species into dummy variables as qualitative factors, and introduced the dummy variable model of the relevant attributes into the base model. We introduced the random effects of sample trees and sample plots when constructing the mixture model. By comparing evaluation indicators, such as Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), the mixture model with the best fitting effect was selected. We selected the optimal universal equation by comparing the fitting accuracy of the base model, dummy variable model and mixture model. The fitting accuracy of the dummy variable model and mixture model was higher than that of the basic model. The evaluation indicators (AIC and BIC) showed that the mixture model had a better fitting effect on knot properties than the dummy variable model. In the model comparison results, of mixture models for sound knot length, the loose knot length, and knot diameter increased by 13.2%, 84.8% and 40.3%, respectively. The predictive accuracy of the three base models for different tree species' knot attributes was above 90%, and both the prediction accuracy of the dummy variable model and mixture model were above 94%, indicating that the constructed models could well predict knot-related properties. From the perspective of tree species, the sound knot length, knot diameter, and loose knot length was in order of var. > > . Fitted results of the dummy variable model and the mixture model were superior to the basic model, with higher accuracy.

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http://dx.doi.org/10.13287/j.1001-9332.202311.001DOI Listing

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