王庆华, 陈强, 刘永刚, 苏俊武, 沈立新, 刘云彩, 毕波, 许彦红, 周筑, 段成波, 杨锐铣, 赵永红, 孙志刚, 孙宏. 基于数量化回归模型的秃杉优树选择[J]. 西南林业大学学报, 2017, 37(2): 41-46. DOI: 10.11929/j.issn.2095-1914.2017.02.007
引用本文: 王庆华, 陈强, 刘永刚, 苏俊武, 沈立新, 刘云彩, 毕波, 许彦红, 周筑, 段成波, 杨锐铣, 赵永红, 孙志刚, 孙宏. 基于数量化回归模型的秃杉优树选择[J]. 西南林业大学学报, 2017, 37(2): 41-46. DOI: 10.11929/j.issn.2095-1914.2017.02.007
shu
Qinghua Wang, Qiang Chen, Yonggang Liu, Junwu Su, Lixin Shen, Yuncai Liu, Bo Bi, Yanhong Xu, Zhu Zhou, Chengbo Duan, Ruixian Yang, Yonghong Zhao, Zhigang Sun, Hong Sun. Selection of Superior Trees of Taiwania flousiana Based on the Multiple Entry Quantity Model[J]. Journal of Southwest Forestry University, 2017, 37(2): 41-46. DOI: 10.11929/j.issn.2095-1914.2017.02.007
Citation: Qinghua Wang, Qiang Chen, Yonggang Liu, Junwu Su, Lixin Shen, Yuncai Liu, Bo Bi, Yanhong Xu, Zhu Zhou, Chengbo Duan, Ruixian Yang, Yonghong Zhao, Zhigang Sun, Hong Sun. Selection of Superior Trees of Taiwania flousiana Based on the Multiple Entry Quantity Model[J]. Journal of Southwest Forestry University, 2017, 37(2): 41-46. DOI: 10.11929/j.issn.2095-1914.2017.02.007
shu

基于数量化回归模型的秃杉优树选择

Selection of Superior Trees of Taiwania flousiana Based on the Multiple Entry Quantity Model

    摘要
  • 摘要: 对秃杉分布区13个县(市)的纯林、混交林、散生木、孤立木进行调查，选出236株作为初选优树，并观测这些候选优树的立地因子、生长性状等指标。经相关性分析，选择单株材积作为优树复选的主要指标。采用数量化回归的方法，建立经度、纬度、海拔、坡度、黑土层厚度、树龄6个数量因子和坡向、坡位、坡形、基岩、土壤类型、起源、立木类型7个定性因子与秃杉单株材积的回归方程，其复相关系数为0.799。秃杉单株材积实测值与理论值的差值(Ii)代表基因型值，其频率分布成正态分布。以Ii与差值平均值I ±标准差δ相比较作为划分优树等级的依据，秃杉以候选优树70%的入选率统计。差值Ii ≥I+0.3δ，即Ii ≥1.644 6为Ⅰ级优树；I+0.3δ> Ii> I - 0.3δ，即1.644 6> Ii> -1.644 6为Ⅱ级优树；Ii ≤I - 0.3δ，即Ii ≤-1.644 6为Ⅲ级优树(一般林木)，Ⅲ级优树淘汰不选。用此标准对秃杉236株野外初选优树进行复选，Ⅰ级优树54株，占候选优树的22.88%；Ⅱ级优树114株，占候选优树的48.31%；Ⅰ、Ⅱ级优树预估遗传增益达20.82%。

     

    Abstract: The site factors and growth traits of 236 candidate Taiwania flousiana trees of pure forest, mingled forest, scattered wood and isolated wood in 13 counties were measured. The correlation analysis of candidate trees showed that volume could be used as the main indexes for selection of superior trees of T. flousiana. The regression equation with individual volume was established by using 6 quantitative factors including longitude, latitude, altitude, slope, the depth of black soil layer, ages of trees and 7 qualitative factors including slope aspect, slope position, slope shape, bedrock, soil type, origin, tree type. Its multiple correlation coefficient was 0.799. There was a difference between theoretical and realistic volume, the frequency of difference was in normal distribution. The superior tree selection was based on the comparison of Ii and the mean value of difference I ± standard deviation δ. The superior tree selection standards with 70% selective ratio were as follows:Class Ⅰ superior tree, Ii ≥ I + 0.3δ, that is Ii ≥ 1.644 6. Class Ⅱ superior tree, I + 0.3δ > Ii > I - 0.3δ, that is 1.644 6 > Ii > -1.644 6. Class Ⅲ superior tree (normal tree), Ii ≤ I - 0.3δ, that is Ii ≤ -1.644 6. 54 strains of class Ⅰ superior trees and 114 strains of class Ⅱ superior trees were selected by the standards. They accounted for 22.88% and 48.31% in all candidate superior trees respectively. The genetic gain of the class Ⅰ and class Ⅱ superior trees reached 20.82%.

     

    • HTML全文
    • 参考文献(20)
    • 相关文章
    • 施引文献
  • 资源附件(0)

/

返回文章
返回