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Forests 2016, 7(1), 18; doi:10.3390/f7010018

A Comparison of Hierarchical and Non-Hierarchical Bayesian Approaches for Fitting Allometric Larch (Larix.spp.) Biomass Equations

1
Key Laboratory of Tree Breeding and Cultivation, State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
2
School of Forestry & Landscape of Architecture, Anhui Agricultural University, Hefei 230036, China
3
School of Natural Resources, West Virginia University, Morgantown, WV 26506, USA
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Eric J. Jokela
Received: 9 September 2015 / Revised: 17 November 2015 / Accepted: 18 December 2015 / Published: 11 January 2016
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Abstract

Accurate biomass estimations are important for assessing and monitoring forest carbon storage. Bayesian theory has been widely applied to tree biomass models. Recently, a hierarchical Bayesian approach has received increasing attention for improving biomass models. In this study, tree biomass data were obtained by sampling 310 trees from 209 permanent sample plots from larch plantations in six regions across China. Non-hierarchical and hierarchical Bayesian approaches were used to model allometric biomass equations. We found that the total, root, stem wood, stem bark, branch and foliage biomass model relationships were statistically significant (p-values < 0.001) for both the non-hierarchical and hierarchical Bayesian approaches, but the hierarchical Bayesian approach increased the goodness-of-fit statistics over the non-hierarchical Bayesian approach. The R2 values of the hierarchical approach were higher than those of the non-hierarchical approach by 0.008, 0.018, 0.020, 0.003, 0.088 and 0.116 for the total tree, root, stem wood, stem bark, branch and foliage models, respectively. The hierarchical Bayesian approach significantly improved the accuracy of the biomass model (except for the stem bark) and can reflect regional differences by using random parameters to improve the regional scale model accuracy. View Full-Text
Keywords: larch; non-hierarchical Bayesian approach; hierarchical Bayesian approach; biomass model larch; non-hierarchical Bayesian approach; hierarchical Bayesian approach; biomass model
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Chen, D.; Huang, X.; Sun, X.; Ma, W.; Zhang, S. A Comparison of Hierarchical and Non-Hierarchical Bayesian Approaches for Fitting Allometric Larch (Larix.spp.) Biomass Equations. Forests 2016, 7, 18.

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