基于混合效应模型的人工红松节子属性

基于黑龙江省孟家岗林场60株人工红松1534个节子数据,利用SAS软件中的NLMIXED和GLIMMIX模块构建人工红松节子属性因子(基径、健全节长度、死亡年龄、角度)的混合效应预测模型.采用赤池信息准则(AIC)、贝叶斯信息准则(BIC)、对数似然值(-2LL)和似然比检验(LRT)评价指标对所构建模型的精度进行比较.结果表明:考虑树木效应的混合模型模拟精度均高于传统回归模型.含有b_1、b_2随机参数组合的节子基径模型是最优混合效应模型;含有b_1、b_3随机参数组合的节子健全节长度模型是最优混合效应模型;含有节子基径随机参数的广义线性混合模型为节子死亡年龄的最优模型;含有截距、节子基径、健全节长度3种随机效应参数组合的广义线性混合模型为节子角度的最优模型.混合效应模型比传统回归模型更能有效地描述节子属性.红松是东北主要的用材树种,利用节子属性预测模型结合合理的整枝方案可以提高木材质量.

Knot attributes of Korean pine plantation based on mixed effect model.

Based on 1534 knot data from 60 sample trees in a Korean pine plantation in Mengjiagang Forest Farm, Heilongjiang Province, China, mixed effect model of knot attributefactors (knot diameter, sound knot length, year of death of knot and knot angle) of Korean pine plantation was established using NLMIXED and GLIMMIX procedures of SAS software. The prediction accuracy of models was compared using evaluation statistics, such as Akaike information criterion (AIC), Bayesian information criterion (BIC), -2Log likelihood(-2LL), and likelihood ratio test (LRT). Results showed that all of the mixed effect models that considered tree effect performed better than conventional fixed-effect models. For knot diameter models, the model with random parameter combination of b₁, b₂ had the best performance. For sound knot length models, the model with random parameter combination of b₁, b₃ had the best performance. For the models of year of death of knot, the model with random variables of knot diameter was proved to be the optimal generalized linear mixed model. For the models of knot angle, the model with randomvariables of intercept, knot diameter, sound knot length was proved to be the optimal generalized linear mixed model. Mixed effect model was more effective than conventional fixed-effect model for describing knot attributes. The combination of knot attributes models and reasonable prunning schemes could improve timber quality of Korean pine which is one of the main commercial tree species in Northeast China.