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Background: Korean pine (Pinus koraiensis Siebold & Zucc.) is one of the primary plantation conifer
species of economic and ecological importance in northeast China. Forest biomass estimation in the
broader landscape has been receiving attention from researchers and forest managers. The development
of forest stand biomass models is regarded an effective method to estimate forest biomass at large
scales. This study was carried out for developing stand-level biomass models for Korean pine plantations.
Four additive methods were compared: Aggregation 1, Aggregation 2, Adjustment, and Disaggregation.
All the stand biomass additive modeling systems (i.e., total, root, stem, branch, and leaf) included both
stand volume and biomass conversion and expansion factors (BCEFs) as predictors.
Results: The predictive performance of the four additive methods and Constant BCEFs were ranked
as follows: Aggregation 1 > Disaggregation > Adjustment > Aggregation 2 > Constant BCEFs. The
prediction accuracy of the four additive methods was not consistent across the stand volume intervals.
Conclusion: The model based on the Aggregation 1 method was recommended for predicting stand
biomass. However, different additive method should be selected according to the stand volume intervals
of the Korean pine plantations.
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