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小叶杨胸径和树高生长进程的连续性和阶段性定量研究 被引量:2

THE QUANTITATIVE RESEARCH ON STAGE AND CONTINUITY OF Poplus simonii DBH AND HEIGHT GROWTH PROCESS
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摘要 应用logistic模型和有序样品聚类法对小叶杨胸径和树高生长进程的连续性和阶段性进行定量研究,将胸径和树高2个表观生长过程科学地划分为前慢期、速生期和后慢期3个阶段,实现了胸径和树高二维有序样品的聚类,不仅反映出小叶杨生长进程的综合节奏性,更为龄组划分提供了依据。对小叶杨而言,所获如下结论值得重视:(1)径生长开始进入速生期时,完成了幼龄林阶段;(2)高生长速生期结束时完成了中龄林阶段,此时恰好是径生长速率最大的时期;(3)径生长速生期结束时,基本完成了近熟林阶段。除小叶杨外,文中提供的方法尚可供其它树种生长进程的连续性和阶段性定量研究中参考。再有,该文还以logistic模型为基础,以时间因子为媒介,推导出1个能准确地描述小叶杨D-H关系的新模型--logistic衍生模型。 The application of logistic model and the ordinal data clustering for the quantitative research on stage and continuity of Populus simonii DBH and height growth process,the two apparent growth process of diameter and height were scientifically classified as front slow period,fast growding period and the back slow period,achieved DBH and tree height of the two-dimensional ordered samples of the cluster,not only reflects the growth process in integrated rhythmic of Populus simonii,but providing the basis for age groups classification.For Populus simonii,the following conclusions obtained noteworthy:(1) diameter growth began to enter the fast growing period,the young forest stage completed;(2) The completion of high growth in fast growing period is the end stage of middle-aged forests,this time just is the period of the diameter largest growth rate;(3) As the end of the diameter growth in fast-growing period,the nearly mature forest stage completed.In addition to Populus simonii,the method provided by the article is still available for the reference of other tree species in the growth process of stage and continuity for the quantitative study.Besides,the article is based on logistic model,the time factor for the media,derived a new accurate model description of the D-H relationship of Populus simonii-logistic derivative model.
出处 《内蒙古农业大学学报(自然科学版)》 CAS 北大核心 2011年第2期38-41,共4页 Journal of Inner Mongolia Agricultural University(Natural Science Edition)
关键词 小叶杨 生长规律 龄组划分 LOGISTIC模型 有序样品聚类 logistic衍生模型 Populus simonii growth law age groups classification logistic model ordinal data clustering logistic derivative model
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