Modeling Dominant Height Growth in Planted Pinus pinea Stands in Northwest of Tunisia
Modeling Dominant Height Growth in Planted Pinus pinea Stands in Northwest of Tunisia
复制标题
对突尼斯西北部种植的松林的主要高度生长进行建模
DOI：
10.1155/2012/902381
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发表时间：
2012
影响因子：
--
通讯作者：
Pique Miriam
中科院分区：
文献类型：
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作者：
S. Tahar;Palahí Marc;G. Salah;Bonet José Antonio;A. Youssef;Pique Miriam
以突尼斯西北部的樟子松人工林为研究对象，采用Log-Logistic、Bertalanffy-Richards和Lundqvist-Korf三种基本模型推导出6个广义代数差分方程，建立了樟子松人工林地位指数模型。为了确保模型参数估计值的基本年龄不变性，使用了虚拟变量方法。从干分析的数据，与Carmean的方法校正，用于建模。为了考虑到纵向数据的固有自相关性，使用了二阶连续时间自回归误差结构，该结构允许将模型应用于不规则间隔的不平衡数据。使用基于模型的生物现实性的定性分析以及基于模型准确性的数值和图形分析来评估候选模型的性能。地位指数预测的相对误差被用来选择30年作为最佳参考年龄。基于分析，广义代数差分方程（GADA）来自Lundqvist-Korf的基础模型实现了生物和统计约束之间的最佳折衷，产生最适当的地位指数曲线。这是一个多态模型与站点依赖的渐近线。因此，推荐该模型用于突尼斯西北部松树人工林的高生长预测和立地分类。
Six generalized algebraic difference equations (GADAs) derived from the base models of log-logistic, Bertalanffy-Richards, and Lundqvist-Korf were used to develop site index model for Pinus pinea plantations in north-west of Tunisia. To assure the base-age invariance of the model parameter estimates, a dummy variable approach was used. Data from stem analysis, corrected with Carmean's method, were used for modelling. To take into account the inherent autocorrelation of the longitudinal data, a second-order continuous-time autoregressive error structure was used, which allows the models to be applied to irregularly spaced, unbalanced data. Both a qualitative analysis based on the biological realism of the models and numerical and graphical analyses based on the accuracy of the models as well were used to evaluate the performance of candidate models. The relative error in site index predictions was used to select 30 years as the best reference age. Based on the analysis, a generalized algebraic difference equation (GADA) derived from the base model of Lundqvist-Korf realized the best compromise between biological and statistical constraints, producing the most adequate site index curves. It is a polymorphic model with site-dependent asymptotes. This model is therefore recommended for height growth prediction and site classification of Pinus pinea plantations in north-west of Tunisia.