基于线性混合效应的红松人工林枝条生物量模型

基于黑龙江省孟家岗林场60株红松解析木3643个枝条生物量的实测数据,利用全部子回归技术建立了枝条生物量模型(枝、叶和枝总生物量模型),最终选择lnw=k₁+k₂lnL_b+k₃lnD_b为枝条生物量最优基础模型.利用SAS 9.3统计软件的PROC MIXED模块建立枝条生物量混合模型,并采用AIC、BIC、对数似然值和似然比等统计指标评价不同模型的拟合效果.结果表明: 红松解析木的叶和枝总生物量混合模型以k₁、k₂、k₃作为随机效应参数的拟合效果最好,而枝生物量混合模型以k₁、k₂作为随机效应参数的拟合效果最好.最后将枝条生物量最优基础模型与最优混合模型进行模型检验.混合模型各项指标优于基础模型,能有效地提高模型的预估精度,并且通过方差协方差结构校正随机参数来反映树木之间的差异.

Linear mixed modeling of branch biomass for Korean pine plantation.

Based on the measurement of 3643 branch biomass samples of 60 Korean pine (Pinus koraiensis) trees from Mengjiagang Forest Farm, Heilongjiang Province, all subset regressions techniques were used to develop the branch biomass model (branch, foliage, and total biomass mo-dels). The optimal base model of branch biomass was developed as lnw=k1+k2lnLb+k3lnDb. Then, linear mixed models were developed based on PROC MIXED of SAS 9.3 software, and evaluated with AIC, BIC, Log Likelihood and Likelihood ratio tests. The results showed that the foliage and total biomass models with parameters k1, k2 and k3 as mixed effects showed the best performance. The branch biomass model with parameters k1 and k2 as mixed effects showed the best performance. Finally, we evaluated the optimal base model and the mixed model of branch biomass. Model validation confirmed that the mixed model was better than the optimal base model. The mixed model with random parameters could not only provide more accurate and precise prediction, but also showed the individual difference based on variance covariance structure.