Article(id=1322924282162062308, tenantId=1146029695717560320, journalId=1322258771877134531, issueId=1322924153027834207, articleNumber=null, orderNo=null, doi=10.14067/j.cnki.1673-923x.2026.06.009, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1743868800000, receivedDateStr=2025-04-06, revisedDate=1751212800000, revisedDateStr=2025-06-30, acceptedDate=null, acceptedDateStr=null, onlineDate=1793339327612, onlineDateStr=2026-10-30, pubDate=1782316800000, pubDateStr=2026-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793339327612, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793339327612, creator=13701087609, updateTime=1793339327612, updator=13701087609, issue=Issue{id=1322924153027834207, tenantId=1146029695717560320, journalId=1322258771877134531, year='2026', volume='46', issue='6', pageStart='1', pageEnd='230', issueExtLink='null', onlineDate='null', pubDate='1782316800000', pubDateStr='2026-06-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793339296824, creator='13701087609', updateTime=1793344680557, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322946734422360158, tenantId=1146029695717560320, journalId=1322258771877134531, issueId=1322924153027834207, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322946734422360159, tenantId=1146029695717560320, journalId=1322258771877134531, issueId=1322924153027834207, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=91, endPage=102, ext={EN=ArticleExt(id=1322924282359194597, articleId=1322924282162062308, tenantId=1146029695717560320, journalId=1322258771877134531, language=EN, title=A diameter at breast height growth model for dominant Chinese fir trees incorporating site and climatic effects, columnId=null, journalTitle=Journal of Central South University of Forestry & Technology, columnName=null, runingTitle=null, highlight=null, articleAbstract=
【Objective】 To accurately predict the growth dynamics of Chinese fir and construct a dominant tree diameter at breast height (DBH) growth model that integrates site and climatic conditions, and to provide guidance for sustainable management and carbon sequestration assessment of Chinese fir plantations.
【Method】 Utilizing 1 121 DBH-age datasets from 129 Chinese fir plantation plots, topographic, edaphic and climatic factors were incorporated. Significant environmental drivers affecting DBH growth were identified through Quantification Theory I. The optimal theoretical growth equation was selected from six candidate models. Site types and climate types were clustered into site type groups and climate type groups using the k-means algorithm. Nonlinear mixed-effects regression models were developed by incorporating site type, climate type, and their clustered groups as random effects, either individually or in combination.
【Result】 Seven leading factors influencing DBH growth of dominant trees, including altitude, aspect, Climatic moisture deficit and extreme minimum temperature, were screened. Among the six basic models, the Korf model had the best fitting effect, with a determination coefficient R2 of 0.782 0, 75 site types and 173 climate types clustered into 5 site type groups and 7 climate type groups, respectively, and the random effect model R2 with site factor groups and climate factor groups only was 0.822 9 and 0.839 1, respectively, and the random effect model with site and climate factors was R2= 0.854 3 which was significantly better than the other models.
【Conclusion】 The nonlinear mixed-effects model developed in this study, which integrates site- and climate-effects, comprehensively incorporates environmental factors influencing the DBH growth of dominant Chinese fir trees. This model not only provides scientific guidance for predicting the DBH growth of dominant Chinese fir trees, but also establishes a generalizable modeling framework for analyzing and forecasting environmental impacts on the growth of subtropical Chinese fir plantations.
, authors=Xingyi HE
1, Enlin LIU
2, Yong LYU
1, Xiongqing ZHANG
3, Guangyu ZHU
1, authorsList=Xingyi HE, Enlin LIU, Yong LYU, Xiongqing ZHANG, Guangyu ZHU, authorCompany=null, correspAuthors=Guangyu ZHU, authorNote=null, correspAuthorsNote=null, copyrightStatement=© 2026 Journal of Central South University of Forestry & Technology, copyrightOwner=Journal of Central South University of Forestry & Technology, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322924282711516134, articleId=1322924282162062308, tenantId=1146029695717560320, journalId=1322258771877134531, language=CN, title=基于立地和气候效应的杉木优势木胸径生长模型, columnId=1322924153761837409, journalTitle=中南林业科技大学学报, columnName=林学, runingTitle=null, highlight=null, articleAbstract=
【目的】 为了精准预测杉木的生长动态，构建反映立地和气候条件的杉木优势木胸径生长模型，为杉木人工林可持续经营和碳汇功能评估提供指导。
【方法】 以129块杉木人工林样地的1 121组杉木优势木胸径生长数据为研究对象，结合地形、土壤和气候等环境因子，采用数量化方法Ⅰ筛选出影响杉木优势木胸径生长的显著性环境因子；基于树木生长理论方程确定最优基础模型；运用K-means聚类算法将立地类型和气候类型聚类为立地类型组和气候类型组；采用非线性混合效应回归建模法，将立地类型、气候类型、立地类型组和气候类型组分别或同时作为随机效应构建优势木胸径生长模型。
【结果】 筛选出海拔、坡向、哈格里弗斯气候水汽亏损和极端最低气温等7个影响优势木胸径生长的主导因子；6个基础模型中，Korf模型拟合效果最优，确定系数R2为0.782 0；75种立地类型和173种气候类型分别聚类为5组立地类型组和7组气候类型组；在构建的各混合效应模型中，仅含立地类型组和仅含气候类型组的随机效应模型的R2分别为0.822 9和0.839 1，同时包含立地和气候因子的随机效应模型R2为0.854 3，明显优于其余模型。
【结论】 本研究所构建的基于立地和气候效应的非线性混合效应模型综合了影响杉木优势木胸径生长的环境因子，不仅能为预估杉木优势木胸径生长提供科学指导，同时也可为考虑环境因子对亚热带杉木人工林生长的影响和预测提供可推广的建模框架。
, authors=何星谊
1, 刘恩林
2, 吕勇
1, 张雄清
3, 朱光玉
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