考虑样地效应的人工杨树立木可加性生物量模型构建

Construction of additive biomass model of planted poplar trees considering plot effect

“迎春5号”杨树(Populus nigra×P.simonii)作为速生丰产林的重要树种之一,其生物量的准确估算有重要意义。根据胸径(DBH)和树高(H)对杨树各组分生物量的影响,本文分别构建两类似乎不相关回归(SUR)模型和引入了样地随机效应的SUR混合效应(SURM)模型(SUR1和SURM1:以DBH为自变量;SUR^(2)和SURM2:以DBH和H为自变量),对比分析了SURM模型在实际应用中的预测精度。结果表明:加入H后,对于干、枝、叶生物量模拟效果的改善较为明显(SUR模型R^(2)提高了0.1%~1.6%,SURM提高了0.6%~7.0%),对于皮和根改善较差。考虑样地水平随机效应后,枝、叶和根拟合效果改善较为明显,SURM模型各组分R^(2)均提高了5.7%~16.5%,干和皮拟合改善效果较小。此外,当预测生物量时,使用样地水平的混合效应模型SURM1要优于使用两个树木变量的SUR^(2)模型,SURM1模型各组分拟合效果均优于SUR^(2)模型(R^(2)提高了0.2%~9.9%),SURM模型对于迎春5号杨树各组分生物量拟合和预测效果均比SUR模型精度更高,在实际中使用SURM模型进行生物量预测时,考虑到生物量数据获取的难度,最终确定随机抽取4棵树用于随机效应的校正。本文所提出的两类SURM模型适用于不同的数据类型,可为准确评估迎春5号杨树生物量提供基础支撑。

Populus nigra×P.simonii is one of the important tree species with the characteristics of fast-growing and high-yield.The accurate estimation of its biomass is of great significance.Based on diameter at breast height(DBH)and tree height(H),we constructed two seemingly unrelated regression models(SUR)and seemingly un-related mixed-effects models(SURM)introduced random effects of plots,respectively(SUR1 and SURM1:DBH as the independent variable;SUR2 and SURM2:DBH and H as independent variables),and analyzed the predic-tion accuracy of SURM models.The results showed that after adding H to the model,the simulation effect on the biomass of trunk,branch,and leaf was significantly improved(R^(2) in the SUR model increased by 0.1%-1.6%,and R in SURM increased by 0.6%-7.0%),but the improvement on bark and root was poor.After considering the random effects at the plot level,the ftting effect of branch,leaf,and root biomass in the model was significantly improved,with the R^(2) of each component in the SURM model increased by 5.7%-16.5%,while the improvement effect of trunk and bark biomass simulation was small.In addition,the mixed-effects model using the plot level(SURM1)performed better than using two variables(SUR2)to predict biomass,and the fitting effect of each com-ponent of the SURMI model was better than the SUR2 model(R?increased by 0.2%-9.9%).The SURM model was more accurate for the biomass fitting and prediction of each component than the SUR model.In practice,when using the SURM model for biomass prediction,considering the difficulty of obtaining biomass data,it was deter-mined that four trees were randomly selected for the correction of random effects.The two types of SURM models proposed here were applicable to different data types,which can provide basic support for accurately assessing the biomass of Populus nigra ×P.simoni.