| 魏安超，熊河先，胥 辉，李 超，闾妍宇，张 博，冷 燕，欧光龙.基于树干解析的高山松天然林单木木材生物量生长模型[J].广东农业科学,2017,44(1):66-75 |
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基于树干解析的高山松天然林单木木材生物量生长模型 |
| Construction of individual wood biomass growth models for Pinus densata natural forests based on stem analysis |
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| DOI：10.16768/j.issn.1004-874X.2017.01.010 |
| 中文关键词: 木材生物量生长 生物量生长率 非线性混合效应模型 树干解析 高山松 |
| 英文关键词: wood biomass growth biomass growth rate nonlinear mixed model stem analysis Pinus densata |
| 基金项目:国家林业公益性行业科研专项（2014040309）；西南林业大学云南省重点学科（林学）项目；国家自
然科学基金（31560209） |
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| 摘要点击次数: 2539 |
| 全文下载次数: 1130 |
| 中文摘要: |
| 以云南省香格里拉市两块典型样地内的10 株高山松样木为研究对象，基于树干解析测定和计
算其单木木材生物量生长及木材生物量生长率，采用非线性混合效应模型技术，分别考虑了样地效应和样木
效应，将所有不同随机参数组合的模型进行拟合并分析模型的方差和协方差结构，构建其生物量生长及生物
量生长率混合效应模型。结果表明：考虑样地效应、样木效应作为随机效应的单水平混合效应模型和两水平
混合效应模型均提高了模型的拟合精度，其中考虑两水平随机效应的混合效应模型具有最佳的拟合表现，具
有最低的AIC 和BIC 值。考虑两水平混合效应在生物量生长量及生物量生长率模型构建中预估精度最高，分
别达93.05% 和89.83%；考虑样木效应的混合效应模型次之，分别为88.34% 和88.74%；考虑样地效应的混
合模型预估精度均最低，分别为83.99% 和67.27%；而一般回归模型的预估精度仅87.00% 和87.11%。 |
| 英文摘要: |
| Taking 10 Pinus densata sampling trees at two plots located in Shangri-La city of Yunnan province as
the research object，we measured and calculated single wood biomass growth and wood biomass growth rates based on
stem analysis. Considering random effect of the plot effect and tree effect，the biomass growth and growth rate models
were constructed by nonlinear mixed effect model technology，and all the different random parameter combinations
were fitted and the variance and covariance structures of the models were analyzed. The results showed that，
considering random effect of plot effect and tree effect model as the single-level mixed effect model and two-level
mixed effect model，the fitting precision of the models was improved，especially two-level mixed effect model had
the best fitting performance with the lowest values for AIC and BIC. Both biomass growth and growth rates two-level
mixed effect models had the highest prediction accuracy，and the values reached 93.05% and 89.83%；the secondbest
ones were the mixed effect model considering the random effect of tree effect，and the prediction accuracies were
88.34% and 88.74%，respectively；the prediction accuracies of models considering the plot effect were 83.99% and
67.27%，respectively；and the prediction accuracies of ordinary models were 87.00% and 87.11%，respectively. |
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