中南林业科技大学学报2026，Vol.46Issue(3)：45-55,11.DOI:10.14067/j.cnki.1673-923x.2026.03.005
不同修枝强度下的长白落叶松节子属性模型构建
Modeling knot characteristics of Larix olgensis under different pruning intensities
摘要
Abstract
[Objective]This study aimed to investigate the effects of different pruning intensities on knot size in young Larix olgensis plantations.[Method]Thirty young L.olgensis trees from the Mengjiagang forest farm in Heilongjiang Province were selected as sample trees and subjected to four pruning intensities(0%,20%,30%,and 40%).Knot dissection data were collected,and mixed-effect models for knot diameter(KD),sound knot length(SKL),and loose knot length(LKL)were constructed with pruning intensity as a fixed effect.These models were compared with baseline models to evaluate the effects of pruning on knot attributes.[Result]Compared with baseline models,the mixed-effect models performed better in terms of RMSE and MAE,with R2 values of 0.724,0.826,and 0.308 for KD,SKL,and LKL,respectively,prediction accuracy exceeded 94%,indicating that mixed-effect models provided reliable predictions of knot properties.Artificial pruning significantly reduced knot influence in L.olgensis.Under moderate pruning(30%),SKL increased by 0.46 cm compared with unpruned trees,while KD and LKL were reduced by 0.14 cm and 0.31 cm,respectively.This suggests that moderate pruning promotes healthy branch growth while effectively reducing the formation of loose knots.[Conclusion]This study confirmed the reliability of mixed-effect models in predicting knot attributes,demonstrating their ability to simulate the effects of pruning intensity on knot development.Among different pruning intensities,moderate pruning(30%)showed the best performance in controlling knot size,reducing LKL while maintaining stem stability.Under the study conditions,it represents a reasonable pruning strategy.关键词
长白落叶松人工林/混合效应模型/人工修枝/节子Key words
Larix olgensis plantation/mixed-effects model/artificial pruning/knot分类
农业科技引用本文复制引用
李彤,贾炜玮,李泽霖,赵国强..不同修枝强度下的长白落叶松节子属性模型构建[J].中南林业科技大学学报,2026,46(3):45-55,11.基金项目
国家重点研发计划项目(2023YFD2200802). （2023YFD2200802）
