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Scientia Silvae Sinicae ›› 2014, Vol. 50 ›› Issue (3): 69-75.doi: 10.11707/j.1001-7488.20140310

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Tree-Height Growth Model for Chinese Fir Plantation Based on Bayesian Method

Zhang Xiongqing, Zhang Jianguo, Duan Aiguo   

  1. Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration Research Institute of Forestry, CAF Beijing 100091
  • Received:2013-05-21 Revised:2013-08-29 Online:2014-03-25 Published:2014-04-16
  • Contact: 张建国

Abstract:

Chinese fir (Cunninghamia lanceolata), a special tree species in China, is one of the most important fast-growing tree species for timber production in southern China. Tree height is an important variable, not only reflecting the site index, but also estimating tree volume, and biomass. It is critical for exploring height-growth law of Chinese fir to develop tree-height growth model. Based on the periodic data of the Chinese fir in Jiangxi Province, tree-height growth model was developed. Bayesian method and classical method (nonlinear least squares method) were used to estimate the height growth mode. In the Bayesian framework, non-informative prior and informative were also introduced. Results showed that the model reliability using Bayesian method was better than classical method, and the informative prior was slightly better than non-informative prior. That is because that the uncertainty of tree-height growth results in low reliability using classical method. In contrast, relevant prior knowledge about the data can be incorporated into Bayesian analyses whereas classical methods ignore the relevant prior knowledge, and the parameters using Bayesian method are treated as random variables, which is a very different assumption from that of classical method, which treats parameters as fixed values. It provides a new method for estimating forest growth model of Chinese fir plantation.

Key words: Bayesian method, informative prior, non-informative prior, tree-height growth, Chinese fir plantation

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