Geoadditive Bayesian models for forestry defoliation data: a case study

M Musio, Nicole H Augustin, K von Wilpert

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

We analyze forestry defoliation data from the Emission Impact and Forest Nutrition (IWE) survey, which was carried out in Baden-Württemberg, Germany in the year 1988. The survey contains information on individual trees such as the degree of defoliation, age, species and measurements on nutrients in the needles, as well as information on tree locations such as soil and geographical characteristics. Our goal is to find suitable predictors for tree defoliation from the above information, and to find a set of models which can explain the underlying biological and environmental processes. To model the spatial correlation in the data and possible nonlinear effects of the covariates we use a geoadditive hierarchical Bayesian model. Posterior inference and model comparison are computationally assessed via Markov Chain Monte Carlo (MCMC) techniques and deviance information criterion (DIC) respectively.
Original languageEnglish
Pages (from-to)630-642
Number of pages13
JournalEnvironmetrics
Volume19
Issue number6
Early online date5 Feb 2008
DOIs
Publication statusPublished - Sep 2008

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Defoliation
Forestry
defoliation
Bayesian Model
forestry
Deviance Information Criterion
Hierarchical Bayesian Model
Model Comparison
Nutrition
Monte Carlo Techniques
Nonlinear Effects
Spatial Correlation
geographical characteristics
Markov Chain Monte Carlo
Nutrients
Soil
Covariates
Predictors
Markov chain
nutrition

Cite this

Geoadditive Bayesian models for forestry defoliation data: a case study. / Musio, M; Augustin, Nicole H; von Wilpert, K.

In: Environmetrics, Vol. 19, No. 6, 09.2008, p. 630-642.

Research output: Contribution to journalArticle

Musio, M ; Augustin, Nicole H ; von Wilpert, K. / Geoadditive Bayesian models for forestry defoliation data: a case study. In: Environmetrics. 2008 ; Vol. 19, No. 6. pp. 630-642.
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