李亚麒, 孙继伟, 李建华, 李伟, 陈诗, 许玉兰, 蔡年辉. 不同等级云南松幼苗生物量估测模型[J]. 云南大学学报(自然科学版), 2019, 41(5): 1073-1082. doi: 10.7540/j.ynu.20190024
引用本文: 李亚麒, 孙继伟, 李建华, 李伟, 陈诗, 许玉兰, 蔡年辉. 不同等级云南松幼苗生物量估测模型[J]. 云南大学学报(自然科学版), 2019, 41(5): 1073-1082. doi: 10.7540/j.ynu.20190024
LI Ya-qi, SUN Ji-wei, LI Jian-hua, LI Wei, CHEN Shi, XU Yu-lan, CAI Nian-hui. Biomass estimation models of Pinus yunnanensis Franch. seedlings of different classes[J]. Journal of Yunnan University: Natural Sciences Edition, 2019, 41(5): 1073-1082. DOI: 10.7540/j.ynu.20190024
Citation: LI Ya-qi, SUN Ji-wei, LI Jian-hua, LI Wei, CHEN Shi, XU Yu-lan, CAI Nian-hui. Biomass estimation models of Pinus yunnanensis Franch. seedlings of different classes[J]. Journal of Yunnan University: Natural Sciences Edition, 2019, 41(5): 1073-1082. DOI: 10.7540/j.ynu.20190024

不同等级云南松幼苗生物量估测模型

Biomass estimation models of Pinus yunnanensis Franch. seedlings of different classes

  • 摘要: 为了更简便、准确地估测云南松幼苗生物量,以幼苗的地径(D)、苗高(H)、地径与苗高的乘积(DH)、地径平方与苗高的乘积(D2H)为自变量,建立6个不同等级云南松幼苗各器官及单株生物量估测模型. 通过相关系数(R)、估计值的标准误(SEE)及回归检验显著水平(P<0.05)筛选最优生物量估测模型. 结果表明:不同等级云南松幼苗生物量最优估测模型多为幂函数,二次方程与三次方程次之;生物量最优模型最佳变量多为D2H,其中Ⅰ、Ⅵ级苗木生物量的最佳估测变量为D2H,Ⅱ、Ⅲ级苗木生物量的最佳估测变量为DH,Ⅳ级苗木生物量的最佳估测变量为DHD2H,Ⅴ级苗木生物量的最佳估测变量为DD2H. 通过实测值与估计值的相关检验,3种最优模型均具有较好的预估精度. 估算模型可较好地用于估测不同等级云南松幼苗生物量,不同等级幼苗选取的自变量和最优方程并不完全相同. 因此,不能用同一种变量对6个不同等级苗木构建形式上相对统一的方程.

     

    Abstract: To estimate the biomass of Pinus yunnanensis Franch. Seedlings, the estimation models of biomass of different organs and individual plant for six different classes of P. yunnanensis seedlings were constructed after the determination of the proper independent variables, which were ground diameter (D), height (H), ground diameter and height (DH) or the square of the diameter multiplied by height (D2H). The optimal model was selected according to the largest correlation coefficient (R), the smallest standard error of estimates (SEE) and significance level (P<0.05). The results showed that the optimal biomass models for different classes of P. yunnanensis seedlings were mostly power function, quadratic and cubic equations, and the optimal variable was D2H in most cases. The optimal variable of class Ⅰ and Ⅵ was D2H, it usually was DH for class Ⅱ and Ⅲ, DH and D2H for class Ⅳ, and D and D2H for class Ⅴ. All the three estimation models displayed good and precise estimation of the seedlings biomass, yet for different classes the independent variables and optimal equations were not completely identical. Therefore, it was impossible to construct a relatively uniform equation for the six different classes of seedlings with same variables.

     

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