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【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】 The objective of this study was to analyze the occurrence, formation and distribution of nodules during the growth of Erythrophleum fordii. At the same time, through multiple regression analysis, multiple regression models were established by selecting the key factors and establishing the influence of judgments. 【Method】 The 30-year-old germplasm was used as research object, and the formation and distribution characteristics of its knot were studied by tree trunk analysis. 【Result】 Compared with the geographical position, the aspect was the most important factor that affected the branching distribution. The section with 2.0 ~ 8.0 m trunk height had the most nodes, which was the highest part of timber utilization rate and the knot distribution Seriously affecting the utilization value of grid wood. The diameters of branches forming branches less than 60 ° were larger than 2.5 cm. The larger the diameter, the larger the length of knot, the greater the span of nodes was in xylem. The first to 15 years It is the peak of branching formation. The branch off and wound healing are concentrated in the 16th to 25th year. The most dead section is formed during the 11th to 20th years, which is the key period for controlling the formation of dead knot. Through stepwise regression analysis, three key factors of branch diameter (BD), branch angle (IA) and branch age (YB) were screened out and multivariate regression was established with the distance from the knot point to the healing point Model: 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). 【Conclusion】 The model can be used to predict the effect of wood branching on the wood.