Validation of the Rotstand model for simulating Heterobasidion annosum root rot in Picea abies stands

Validation of the Rotstand model for simulating Heterobasidion annosum root rot in Picea abies stands

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Article ID: iaor20114744
Volume: 261
Issue: 11
Start Page Number: 1841
End Page Number: 1851
Publication Date: Jun 2011
Journal: Forest Ecology and Management
Authors: ,
Keywords: simulation: applications
Abstract:

Rotstand is a computer model that can simulate the development of a forest stand together with the root rot disease caused by Heterobasidion annosum s.l. on several tree species in Europe. We evaluated the accuracy of Rostand model for its use in Sweden by using data from two field experiments of Norway spruce (Picea abies) in which the long term outcome of stump protection methods had been evaluated (14 and 20 plots, respectively). One of the experiments included artificial infection of the stumps that enabled an almost complete parameterization of the model. Artificially infected plots were used for assessing the loss of precision of using average parameter values vs. plot‐specific values. Average values obtained from artificially infected plots were used for validating the model on plots subjected to natural infection. Rotstand proved to be able to predict plots with a large variation of decay development (20–90% of stems with decay) as early as 15 years after infection. The parameter controlling the inoculum expansion within the tree root system appeared as a major factor affecting the accuracy of the predictions. Expansion of decay centres of artificially and naturally infected plots in Southern Sweden was not significantly different from natural infected plots in the rest of the country, opening the possibility of using a single average value all over Sweden. By using an expansion rate of 0.20myear‐1, Rotstand gave unbiased predictions of decay development 15 years after infection with a relative error of 38.4%. When using average parameter values, Rotstand tended to underestimate plots showing more than 50% of decay 15 years after infection; however, when simulating those plots beyond the last decay assessment, these errors were predicted to disappear.

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