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首页> 外文期刊>New Zealand Journal of Forestry Science >A comparison between traditional ordinary least-squares regression and three methods for enforcing additivity in biomass equations using a sample of Pinus radiata trees
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A comparison between traditional ordinary least-squares regression and three methods for enforcing additivity in biomass equations using a sample of Pinus radiata trees

机译：使用碱辐射树树立的传统普通最小二乘回归和三种方法对生物质方程中的增生性的三种方法

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Background: Additivity has long been recognised as a desirable property of systems of equations to predict the biomass of components and the whole tree. However, most tree biomass studies report biomass equations fitted using traditional ordinary least-squares regression. Therefore, we aimed to develop models to estimate components, subtotals and above-ground total biomass for a Pinus radiata D.Don biomass dataset using traditional linear and nonlinear ordinary leastsquares regressions, and to contrast these equations with the additive procedures of biomass estimation.Methods: A total of 24 ten-year-old trees were felled to assess above-ground biomass. Two broad procedures were implemented for biomass modelling: (a) independent; and (b) additive. For the independent procedure, traditional linear models (LINOLS) with scaled power transformations and y-intercepts and nonlinear power models (NLINOLS) without y-intercepts were compared. The best linear (transformed) models from the independent procedure were further tested in three different additive structures (LINADD1, LINADD2, and LINADD3). All models were evaluated using goodness-of-fit statistics, standard errors of estimates, and residual plots.Results: The LINOLS with scaled power transformations and y-intercepts performed better for all components, subtotals and total above-ground biomass in contrast to NLINOLS that lacked y-intercepts. The additive model (LINADD3) in a joint generalised linear least-squares regression, also called seemingly unrelated regression (SUR), provided the best goodness-of-fit statistics and residual plots for four out of six components (stem, branch, new foliage and old foliage), two out of three subtotals (foliage and crown), and above-ground total biomass compared to other methods. However, bark, cone and bole biomass were better predicted by the LINOLS method.Conclusions: SUR was the best method to predict biomass for the 24-tree dataset because it provided the best goodness-of-fit statistics with unbiased estimates for 7 out of 10 biomass components. This study may assist silviculturists and forest managers to overcome one of the main problems when using biomass equations fitted independently for each tree component, which is that the sum of the biomasses of the predicted tree components does not necessarily add to the total biomass, as the additive biomass models do.
机译：背景：增量长期被认为是方程式的理想性质，以预测成分和整个树的生物量。然而，大多数树生物量研究报告了使用传统普通最小二乘回归的生物质方程。因此，我们旨在使用传统的线性和非线性常见常见性回归来开发用于估计组分，小计和地上总生物质的模型，以估计Pinus radiata D.Don生物量数据集，并将这些方程与生物量估计的添加剂对比。方法：砍伐了24岁的十岁树木来评估地上的生物量。为生物质建模实施了两种广泛的程序：（a）独立; （b）添加剂。对于独立的程序，比较了具有缩放功率变换和Y-截距和非线性电源模型（Nlinols）的传统线性模型（Linols），没有Y-entercepts。来自独立程序的最佳线性（变换）模型在三种不同的添加性结构中进一步测试（Linadd1，Linadd2和Linadd3）。所有模型都是使用拟合统计数据，估计标准误差和剩余地块评估所有型号。结果：具有缩放功率变换和y-entercept的亚丁为所有组件，小计和地上生物量更好地执行与Nlinols相反这缺乏y-拦截。在联合广义的线性最小二乘性回归中的添加剂模型（LinaDD3），也称为看似无关的回归（Sur），为六种组分中的四个（茎，分支，新叶子）提供了最佳的拟合统计和剩余地块。和旧的叶子），三个小计（叶子和冠）中的两个，与其他方法相比，地上总生物量。然而，通过亚麻籽方法更好地预测树皮，锥形和孔生物量。链接：SUR是预测24树数据集的生物量的最佳方法，因为它提供了最佳的健康统计数据，其中7个不偏见的估计10生物量组件。本研究可以帮助造林系统和森林经理克服使用对每个树组分独立安装的生物质方程时的主要问题，这是预测树组分的生物质的总和不一定增加到总生物质的总和添加生物量模型。

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