Using the process-based stand model ANAFORE including bayesian optimisation to predict wood quality and quantity and their uncertainty in Slovenian Beech

Gaby Deckmyn and Boštjan Mali and Hojka Kraigher and Niko Torelli and Maarten Op de Beeck and Reinhart J. M. Ceulemans (2009) Using the process-based stand model ANAFORE including bayesian optimisation to predict wood quality and quantity and their uncertainty in Slovenian Beech. Silva Fennica (3). pp. 523-533. ISSN 0037-5330

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Abstract

The purpose of this study was to expand an existing semi-mechanistic forest model, ANAFORE (ANAlysing Forest Ecosystems), to allow for the prediction of log quality and the accompanying uncertainty as influenced by climate and management. The forest stand is described as consisting of trees of different cohorts, either of the same or of different species (deciduous or coniferous). In addition to photosynthesis, transpiration, total growth and yield, the model simulates the daily evolution in vessel biomass and radius, parenchyma and branch development. From these data early and latewood biomass, wood tissue composition, knot formation and density are calculated. The new version presented here, includes the description of log quality, including red heart formation of beeches. A Bayesian optimisation routine for the species parameters was added to the stand model. From a given range of input parameters (prior), the model calculates an optimised range for the parameters (posterior) based on given output data, as well as an uncertainty on the predicted values. A case study was performed for Slovenian beech forests to illustrate the main model functioning and more in particular the simulation of the wood quality. The results indicate that the ANAFORE model is a useful tool for analyzing wood quality development and forest ecosystem functioning in response to management, climate and stand characteristics. However, the Bayesian optimization showed that the remaining uncertainty on the input parameters for the chosen stand was very large, due to the large number of input parameters in comparison to the limited stand data.

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