文献类型：期刊文献

中文题名：格木人工林节子的分布特征及预测模型

英文题名：Distribution and statistical analysis of knots in Erythrophleum fordii plantations

作者：郝建[1,2] 蒙明君[1] 黄德卫[1] 韦菊玲[1] 李忠国[1] 唐继新[1] 徐大平[2]

第一作者：郝建

机构：[1]中国林业科学研究院热带林业实验中心;[2]中国林业科学研究院热带林业研究所

年份：2017

卷号：41

期号：3

起止页码：100-104

中文期刊名：南京林业大学学报：自然科学版

收录：CSTPCD;;北大核心:【北大核心2014】；CSCD:【CSCD2017_2018】；

基金：广西自然科学基金青年基金项目(2015GXNSFBA139057);中国林业科学研究院热带林业实验中心主任基金项目(RL2011-04号)

语种：中文

中文关键词：格木;节子;分布特征;预测模型

外文关键词：Erythrophleum fordii ; knot ; distribution characteristics ; predicting model

分类号：S796

摘要：【目的】分析节子在格木(Erythrophleum fordii)生长过程中的发生、形成及分布特征,同时通过逐步回归分析,筛选出关键因子建立评判节子影响的多元回归模型。【方法】以30年生格木作为研究对象,利用树干解析方法对其节子的形成及分布特征进行研究。【结果】与地理方位相比,坡向是影响格木分枝分布的重要因素;树干高度2.0~8.0 m的区段上分布的节子最多,此段是木材利用率最高部分,节子分布严重影响格木的利用价值;分枝角度小于60°的分枝形成节子的直径均大于2.5 cm,直径越大死节长度越大,节子在木质部的跨度越大;第1~15年是格木形成分枝的高峰期,分枝脱落及伤口愈合集中在第16~25年;第11~20年间格木形成死节最多,该时段是控制死节形成的关键时期。通过逐步回归分析,筛选出分枝直径(BD)、分枝角度(IA)和分枝年龄(YB)3个关键因子,并建立了与节子发生点到愈合点距离(RT)的多元回归模型:y_(RT)=1.634 4x_(BD)+0.067 8x_(IA)+0.164 8x_(YB)-1.611 4(F=106.869 7,P=0.000 1)。【结论】可以利用该模型来预测格木分枝形成节子后对木材的影响状况。
[Objective] In order to investigate the formation and distribution characteristics of knots during the process of Erythrophleum fordii growth, and screen out the key factors to establish a multiple regression model to predict the effects on branch wood after forming knot by step wise regression analysis. [ Methods ] Thirty years old E. fordii plantation was researched by using a stem analysis method. [ Results ] The results indicated that compared with geographical location, slope direction was the important factor influencing the distribution of E. fordii branches. Most knots were distributed on the highest utilization region of E. fordii trunks （2.0-8.0 m）, which seriously reduced the wood utilization value. When branch angle was less than 60~, the knot diameter was greater than 2.5 cm. As the branch diameter increased, the length between dead knots and the span of knots in xylem was larger. Peak forming on branches occurred at 1-15 years. Branch wound healing was concentrated at 16-25 years, and most dead knots formed at 11-20 years, which was the critical period for the control of dead knot formation. Diameter of the branch （ BD）, insertion angle of the branch （ IA）, and year of birth of the branch （YB） were selected as key factors to establish a multiple regression model by step wise regression analysis, YRT= 1.634 4XBD＋ 0.067 8x1A＋ 0.164 8 XYB-1.611 4 （F = 106.869 7, P = 0.000 1）. [Conclusions]The model was suitable to predict the effects on branch wood caused by knot formation.