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马尾松种源枝叶、树干生物量的变异及其预测 被引量:2

Variations of the Branch-Leaf-Trunk Biomass of Massonia Provenances and Their Prediction
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摘要 调查了马尾松种源年平均枝叶鲜重(W_(b1)、茎干鲜重(W_s)、轮盘数(B_n)、树高(H)、和胸径(D_(1·3))等性状。用逐步回归及多元多项式回归方程建立了预报模型,包括:(1)W_s=3.6849-0.9627H-0.3554H^2-1.4685D_(1·3)+1.1484H·D_(1·3)-0.1582D_(1·3)~2;(2)W_s=2.9480+777.6013V;(3)W_(b1)=-26.0355+16.6132H-1.2334H^2-6.1979D(1·3)-0.2227D_(1·3)+13.7858B_n+6.4683B_n^-+1.3876H·D_(1·3)-11.1294H·B—n+3.0664D_(1·3)B_n等三个模型,拟合优度高达94%以上。方差分析的结果表明:马尾松种源在枝叶、茎干生物量上的差异是极显著的,从中选择8~14个枝叶和茎干生物量高的种源,4年生后,每年每公顷可望产枝叶薪材25 500kg,茎干薪材10 200kg,平均遗传增益分别为93.88%和85.23%。马尾松年平均抽梢的轮盘数是个高度遗传的性状,遗传力高达0.97以上。遗传份量占总变异的91%以上。云开山地,南岭南部种源年平均抽梢2~3次,南岭大部及博平岭种源一年可抽梢1~2次,其余种源一年只抽一次梢。 The characteristics of yearly averaged weights of fresh branches, leaves (Wb1) and trunk (Ws), the whorl numbers (Bn), tree height(H)and breast-height diameter (D1.3 ) from different Massonia provenances have been investigated and mathematical models for their prediction have also been established (about 94% of perfect fitness ) as follows: (1)Ws=3.6849-0.9627H-0.3554H2-1.4685D1.3+1.1484HD1.3-0.1582D21.3; (2)Ws = 2.9480+777. 6013V; (3)Wb1=-26.0355+16.6132H-1.2334H2-6.1979D1.3-0.2227D21.3+13.7853BR The results of the variance analysis showed great differences in biomass of branches, leaves and trunk among these different provenances, 8-14 selected provenances with higher production of branches, leaves and trunk are expected to produce 1700kg of branches-leaves firewood and 680kg trunk firewood after 4-year's growing, with averaged genetic gain being 93.887% and 85.23%, respectively. The whole number of yearly averaged shcoting of Massonia is as large as 0.97. The genetic part accounts for 91% or more. The Massonia from yunkai mountains and southern Nanling Ranges yearly shoots 2-3 times, those from the most part of southern Nanling Ranges and Bopingling areas 1-2 times, but the rest shoot only one time.
出处 《福建林学院学报》 CSCD 1993年第3期261-266,共6页 Journal of Fujian College of Forestry
关键词 马尾松 种源 生物量 遗传变异 预报 massonia, provenance test, biomass, heredity variation,prediction model
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