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基于贝叶斯网络模型的杉楠近自然改造下杉木大径材出材量的影响机制

江怡航 曾庆伟 刘振华 张建国 张雄清

林业科学2026,Vol.62Issue(4):81-90,10.
林业科学2026,Vol.62Issue(4):81-90,10.DOI:10.11707/j.1001-7488.LYKX20250191

基于贝叶斯网络模型的杉楠近自然改造下杉木大径材出材量的影响机制

Impact mechanism of Large-Diameter Timber Yield of Chinese Fir under Close-to-Nature Transformation from Chinese Fir to Phoebe bournei Based on Bayesian Network Model

江怡航 1曾庆伟 2刘振华 3张建国 4张雄清1

作者信息

  • 1. 中国林业科学研究院林业研究所 国家林业和草原局林木培育重点实验室 林木资源高效生产全国重点实验室 北京 100091||南京林业大学南方现代林业协同创新中心 南京 210037
  • 2. 北京中云伟图科技有限公司 北京 100091
  • 3. 湖南省林业科学院 长沙 410004
  • 4. 中国林业科学研究院林业研究所 国家林业和草原局林木培育重点实验室 林木资源高效生产全国重点实验室 北京 100091
  • 折叠

摘要

Abstract

[Objective]With Bayesian network model,a machine learning method based on probabilistic inference,this study aims to analyze the effects of factors such as growth traits,soil nutrients,and understory vegetation diversity on the yield of large-diameter timber of Chinese fir under close-to-nature transformation from Chinese fir to Phoebe bournei,so as to provide theoretical support for the optimal management of Chinese fir stand and the cultivation of large-diameter timber.[Method]The Chinese fir plantations planted in 2004 in Xishan State-owned Forest Farm in Linwu County,Hunan Province were targeted,and in 2015,the plantations were thined and,then interplanted with P.bournei.Key variables,including retained density of Chinese fir,DBH,dominant height,crown width,soil nutrients(total nitrogen and total phosphorus),and understory vegetation diversity,were selected.By integrating empirical data with expert knowledge,a mechanism model for the influence of Chinese fir large-diameter timber yield was constructed based on the Bayesian network model,and Expectation-Maximization(EM)algorithm was used to learn model,revealing the effects and interactions of different factors on the large-diameter timber yield.[Result]The yield of large-diameter timber of Chinese fir was comprehensively affected by factors such as retained density of Chinese fir,crown width,DBH,dominant height,soil nutrients and understory vegetation diversity.The growth of DBH and the expansion of crown width were the key factors affecting the yield of large-diameter timber of Chinese fir(43.0%),and their influence on the yield was greater than that of the dominant height(2.07%).Suitable retained density of Chinese fir was able to promote the growth of DBH and crown width,so as to improve the yield of large diameter timber.Total phosphorus,as an important nutrient element in soil,had a positive effect on the growth of Chinese fir(1.40%),while the diversity of understory vegetation had little effect on the yield of large-diameter timber,which mainly affected the growth of Chinese fir through indirect ways.The Bayesian network model showed high prediction accuracy(88.9%,AUC=0.916 7)and good interpretability in capturing the complex relationship between multiple factors and predicting the large-diameter timber yield of Chinese fir.[Conclusion]Based on the Bayesian network model,this study reveals the influence mechanism of large-diameter timber yield of Chinese fir under close-to-nature transformation,and proposes that Chinese fir plantations management should focus on the growth of DBH and crown width,optimize stand density and soil phosphorus supply,so as to promote the sustainable improvement of large-diameter timber yield.As a machine learning approach,the Bayesian Network model shows high prediction accuracy and interpretability in revealing the complex relationships among multiple factors such as Chinese fir growth conditions,soil nutrients,and understory vegetation diversity,etc.This study provides a scientific basis for the efficient management of Chinese fir plantations and improvement of large-diameter timber yield,and an efficient and interpretable tool for forest management decision-making.

关键词

杉木/大径材出材量/贝叶斯网络/近自然改造

Key words

Chinese fir/large-diameter timber yield/Bayesian network/close-to-nature silviculture

分类

农业科技

引用本文复制引用

江怡航,曾庆伟,刘振华,张建国,张雄清..基于贝叶斯网络模型的杉楠近自然改造下杉木大径材出材量的影响机制[J].林业科学,2026,62(4):81-90,10.

基金项目

中央级公益性科研院所基本科研业务费专项资金(CAFYBB2024MA004) (CAFYBB2024MA004)

十四五国家重点研发计划课题(2021YFD2201304). (2021YFD2201304)

林业科学

1001-7488

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