4.7 Article

A generalized allometric equation to predict foliar dry weight on the basis of trunk diameter for eastern white pine (Pinus strobus L.)

期刊

FOREST ECOLOGY AND MANAGEMENT
卷 255, 期 5-6, 页码 1789-1792

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.foreco.2007.12.001

关键词

allometry; diameter at breast height (dbh); foliar biomass; Pinus strobus; eastern white pine

类别

向作者/读者索取更多资源

Leaf area index is a critical input into computations and models seeking to estimate or predict a whole host of ecophysiological processes, including carbon sequestration and evapotranspirational losses from forest ecosystems. The aim of this study was to derive and validate a generalized allometric equation for eastern white pine (Pinus strobus L.) that would estimate and predict foliar biomass on the basis of trunk diameter at breast height (dbh, 1.37 m above-ground level). Coupled with locally derived specific leaf areas, this generalized equation would be a useful predictor of LAI for input to ecological models and a useful comparator to LAI estimated by remote sensing techniques or plant canopy analyzers. The generalized allometric equation was developed by using site-specific allometric equations from the four provenances of eastern white pine. Nine eastern white pine trees were destructively sampled over a 9-year period to form the validation data set. The generalized allometric equation derived and validated in this work is: y = 0.004x(2.308) wherey = foliar dry weight (kg) and x = dbh (cm) (R-2 = 0.967,p < 0.000). There was strong agreement between the derived generalized allometric equation and the validation data set with a mean absolute error of only 2.60 kg. The allometric equation derived and validated in this study may be used with confidence by foresters, forest ecologists, and other plant scientists seeking estimates of foliar biomass or LAI for use in ecological models in natural and planted eastern white pine forests. (C) 2007 Elsevier B.V. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据