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EVALUATION OF FOREST STAND GROWTH AND YIELD MODELS.

机译：森林林分生长和产量模型的评估。

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摘要

Six well known stand growth and yield models are modified to incorporate the thinning effect variable. To determine the "best" model from the chosen six, the models are first classified into one of the following categories: (1) Stand Volume Formulae Models which use stand basal area and stand height as variables to estimate stand growth and yield. (2) Age, Site, Density Models which utilize stand parameters like age, site index and some measure of density as estimators. (3) Biomathematical Models which employ von Bertalanffy's concept of growth which treats growth as the net result of constructive and destructive metabolisms taking place in the stand.; The models are then tested by estimating the volume growth rate and yield for young thinned red pine (Pinus resinosa Ait.) plantations, ranging from 10 to 25 years, using data gathered from three-and-four-year remeasurements of 21 thinned permanent sample plots located in Drury forest in southern Ontario.; Comparison of models is carried out in two stages; stage I involves a comparison within each of the three categories while under stage II the "best" three models from stage I are then compared. Selection of the "best" models in both stages is governed by the results of seven criteria used for comparison. As a safeguard against inferences arising from the use of a rare sample, 24 sample data sets are created through random sampling procedure (without replacement).; Knowing the desirable characteristics of the comparison criterion, results for the 24 sample and test data sets are compared and summarized in comparison tables which basically present a visual display of comparison for various criteria. In the final selection if two models seem to qualify as the "best" model, the choice rests with the user.; On the basis of the data used in this study, analyses and comparison tests reveal that Stoate's model from category 1 is the "best" predictor of growth. However, for yield, Stoate's model and Clutter's model from category 2 produce equally accurate estimates. The closeness of the predictive power of Stoate's and Clutter's yield models coupled with the fact that the categories are collapsible calls for development of a "Hybrid" model which embodies the characteristic features of categories 1 and 2. By including age (raised to an undefined power) as an additional variable in Stoate's yield model, a non-linear "Hybrid" yield function is proposed for estimation of yield. Examination of stand basal area growth rate data reveals existence of a reverse logistic relationship between basal area growth rate and stand parameters such as stand basal area, stand age, stand height and product of stand basal area and stand height. This leads to a new third degree basal area growth rate polynomial which produces a standard error of 0.0645 sq.ft./acre/year and accounts for 97% variation in basal area growth rate (on logarithmic scale). Comparison tests show that for the available data set the "Hybrid" model estimates both growth and yield more accurately than the stand models originally investigated.
机译：修改了六个众所周知的林分生长和产量模型，以纳入稀疏效应变量。为了从所选的六个模型中确定“最佳”模型，首先将模型分类为以下类别之一：（1）林分体积公式使用林分基础面积和林分高度作为变量来估算林分生长和产量的模型。 （2）年龄，地点，密度模型，这些模型利用林分参数，例如年龄，地点指数和一些密度度量作为估计量。 （3）采用冯·贝塔兰菲（von Bertalanffy）的生长概念的生物数学模型，该模型将生长视为看台上发生的建设性和破坏性新陈代谢的净结果。然后，通过使用21个稀疏永久样品的三年和四年重新测量收集的数据，通过估算10至25年不等的赤松红松（Pinus resinosa Ait。）人工林的体积增长率和产量，对模型进行测试。位于安大略省南部德鲁里森林中的地块。模型比较分两个阶段进行：第一阶段涉及在三个类别中的每个类别之间的比较，而在第二阶段则对第一阶段的“最佳”三个模型进行比较。在两个阶段中“最佳”模型的选择取决于用于比较的七个标准的结果。为了防止因使用稀有样本而引起的推论，通过随机抽样程序（不进行替换）创建了24个样本数据集。知道比较标准的理想特性后，将24个样本和测试数据集的结果进行比较并汇总到比较表中，这些比较表基本上以可视方式显示了各种标准的比较结果。在最终选择中，如果有两个模型似乎被视为“最佳”模型，则选择权取决于用户。根据本研究中使用的数据，分析和比较测试表明，第1类的Stoate模型是增长的“最佳”预测因子。但是，对于产量，来自第2类的Stoate模型和Clutter模型产生同样准确的估计。 Stoate和Clutter的收益模型的预测能力的接近性以及类别可折叠的事实要求开发“混合”模型，该模型体现了类别1和2的特征。通过包括年龄（提高到不确定的功效） ）作为Stoate收益模型中的一个附加变量，提出了一种非线性“混合”收益函数来估算收益。对林分基础面积增长率数据的检查表明，林分基础面积增长率与林分参数（例如林分基础面积，林分年龄，林分高度和林分基础面积与林分高度的乘积）之间存在逆逻辑关系。这导致了一个新的三阶基础面积增长率多项式，该多项式的标准误差为0.0645平方英尺/英亩/年，占基础面积增长率（对数尺度）的97％。对比测试表明，对于可用数据集，“混合”模型比最初调查的林分模型更准确地估计了生长和产量。

著录项

  • 作者

    SADIQ, RIYAZ AHMED.;

  • 作者单位

    University of Toronto (Canada).;

  • 授予单位 University of Toronto (Canada).;
  • 学科 Agriculture Forestry and Wildlife.
  • 学位 Ph.D.
  • 年度 1981
  • 页码
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 森林生物学;
  • 关键词

  • 入库时间 2022-08-17 11:51:33

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