样本量对云南松幼苗生物量模型构建及预估精度的影响

为探究不同样本量对生物量模型构建及建模精度的影响，以实际调查的20个家系，共615株云南松幼苗为例，通过编写计算机程序建立进行简单随机抽样，构建不同样本量云南松幼苗各器官及单株生物量异速生长方程。利用决定系数（R²）、估计值标准误（SEE）、均方根误差（RMSE）、总相对误差（RS）及平均误差绝对值（MAB），对模型拟合优度与精度进行比较分析。结果表明：幂函数方程可较好地用于估测云南松幼苗生物量；随着样本量的增加模型精度评估指数MAB呈幂函数形式逐渐减小；当样本数量小于200时MAB较为敏感，模型精度较差，样本量大于200时，其精度随样本量逐渐增加，但变化幅度逐步减小并趋于稳定。因此，根据MAB的变化趋势，样本量达到200时可以构建精度较高且稳定模型。

Effect of Sample Size on the Precision of Biomass Model of Pinus yunnanensis Seedlings

A total of 615 Pinus yunnanensis seedlings from 20 families were selected to study the influence of different sample sizes on biomass model construction and modelling accuracy. Different sampling sizes of 20 families of P.yunnanensis were used to establish the allometric equation of each organ and individual biomass of P.yunnanensis seedlings, after a sampling frame was established. The goodness of fit and accuracy of the optimal models were compared by the coefficient determination(R²), standard error of estimated value(SEE), root mean square error(RMSE), total relative error(RS) and mean absolute error(MAB). The allometric equation displayed a good biomass estimate of P.yunnanensis seedlings. With the increase of the sample size, the model precision evaluation index MAB decreases gradually in the form of power function. When the sample size is less than 200, MAB is more sensitive and the modeling accuracy is poor. If the sample size reaches about 200, the accuracy reaches a stable state.