摘要
【目的】为研究节子空间分布特征以及节子属性对干材质量的影响。【方法】以河南省洛宁县地区楸树人工林为研究对象,采用树干解析和节子剖析技术,获取了7株解析木和355个节子数据。本研究分析了节子的空间分布特征,通过逐步回归分析,筛选主导因子建立预测节子面积的多元线性回归模型。同时通过主成分分析,筛选出评判干材质量的关键节子指标。【结果】1)水平方向上,楸树节子数量主要分布于树干南向,占比59.4%,数量高于北向(占比40.6%);垂直方向上,节子数量在树干高度10.0 m以下分布居多,占比61.4%;节子平均直径随着树干高度增加而增大,在树干高度7.6~10.0 m处达到最大,为4.2 cm,随后节子平均直径逐渐降低。2)通过逐步回归分析,筛选出节子周长(KP)、节子直径(KD)、节子半径(KR)和节子角度(KI)等4个影响节子面积的主导因子,并建立节子面积的多元线性回归模型:Y_(KA)=-62.357+3.123X_(KP)+5.829X_(KD)-4.969X_(KR)+0.633X_(KI)(F=258.798,R^(2)=0.877,P<0.01)。3)节子面积与节子周长、节子半径、节子直径以及节子长度等呈正相关,与节子角度呈负相关。节子周长与节子直径、节子半径以及节子长度呈正相关,与节子角度呈负相关。4)通过主成分分析,筛选出节子面积、节子周长、节子长度和节子宽度4个主要指标,可对楸树干材质量进行综合评价。【结论】可通过节子周长、节子直径、节子半径和节子角度对节子面积大小进行预测,利用节子面积模型来预测楸树节子对干材质量的影响情况。
【Objective】In order to study the spatial distribution characteristics of knots and the effect of knot attributes on wood quality.【Method】7 sample trees and 355 knots data of Catalpa bungei plantation were obtained using stem analysis and knot analysis techniques in Luoning County,Henan Province.In this study,we analyzed the spatial distribution characteristics of knots.We selected the dominant factors by stepwise regression analysis,and established the multiple linear regression model to predict the knot area.At the same time,we selected the key knot indexes to evaluate wood quality through the principal component analysis.【Result】1)In the horizontal direction,the number of Catalpa bungei knots mainly distributed in the south direction,accounting for 59.4%,which was higher than that in the north direction(accounting for 40.6%);In the vertical direction,the number of knots was mostly below 10.0 m,accounting for 61.4%;The average diameter of knots increased with the increase of tree height,and reached the maximum at the height of 7.6-10.0 m,which was 4.2 cm,and then the average diameter of the knots decreased gradually.2)Through stepwise regression analysis,we selected four dominant factors,such as knot perimeter(KP),knot diameter(KD),knot radius(KR)and knot angle(KI)to establish a multiple linear regression model of knot area:Y_(KA)=-62.357+3.123X_(KP)+5.829X_(KD)-4.969X_(KR)+0.633X_(KI)(F=258.798,R^(2)=0.877,P<0.01).3)Knot area was positively correlated with knot perimeter,knot radius,knot diameter and knot length,but negatively correlated with knot angle.The knot perimeter was positively correlated with the knot diameter,knot radius and knot length,but negatively correlated with the knot angle.4)Four main indexes were screened out including knot area,knot perimeter,knot length and knot width to evaluate wood quality of Catalpa bungei through the principal component analysis.【Conclusion】The knot area could be predicted by knot perimeter,knot diameter,knot radius and knot angle,and the knot area model could predict the effect of knot on wood quality of Catalpa bungei.
作者
关追追
卢奇锋
何双玉
邱权
麻文俊
苏艳
王军辉
李吉跃
何茜
GUAN Zhuizhui;LU Qifeng;HE Shuangyu;QIU Quan;MA Wenjun;SU Yan;WANG Junhui;LI Jiyue;HE Qian(College of Forestry and Landscape Architecture,South China Agricultural University,Guangdong Key Laboratory for Innovative Development and Utilization of Forest Plant Germplasm,Guangzhou 510642,Guangdong,China;Research Institute of Forestry,Chinese Academy of Forestry,Key Laboratory of Tree Breeding and Cultivation,State Forestry Administration,Beijing 100091,China)
出处
《中南林业科技大学学报》
CAS
CSCD
北大核心
2021年第10期20-28,共9页
Journal of Central South University of Forestry & Technology
基金
国家重点研发计划项目(2017YFD060060404)。
关键词
楸树
节子
空间分布
节子面积
预测模型
干材质量
Catalpa bungei
knot
spatial distribution
knot area
prediction model
wood quality
