Huber, P.

Hujala, T.

Kurttila, M.

Vacik, H.

Wolfslehner, B.

Jukuri

1. IntroductionNon-wood forest products (NWFPs), i.e. products of biological origin otherthan wood derived from forests, other wooded land and trees outsideforests (FAO, 1999) are an integral element of sustainable forestmanagement in Europe. In the wake of contemporary international policiesNWFPs are being considered as important means for businessdiversification and income generation, particularly in regions where wood isnot the most profitable product. In the most recent State of Europe´sforests report, the total economic value of NWFPs in the Forest Europeregion accounted for 2,7 mio €. Thus there seems to be high latent potentialto strengthen the economic viability of rural economies.In this context we aim to:• support people who give advice to forest owners (e.g. forest extensionservices, forest owner associations, forestry consultants) to− attract forest owners to engage in new NWFP businesses and− foster the sustainable management of forest resources• support small-scale forest owners as regards the co-production of woodand non-wood forest products in order to− diversify their product portfolio− distribute related socio-economic risks− contribute to biodiversity conservation3. BBN developmentA Bayesian Belief Network approach to assess the potential of non-wood forest products for small-scale forest ownersVacik, H. 1, Huber, P.1,2, Hujala, T. 3, Kurttila,M. 3, Wolfslehner, B. 1,22. MethodIn our application, the BBN is applied in order to shed light on a FMU´spotential to integrate one or more of regionally available NWFPs in its forestmanagement concept.A Bayesian Belief Network (BBN) is a kind of probabilistic graphical modelthat may be applied to a wide range of environmental problems, inter aliadue to the powerful probability theory involved. BBNs are understood asuseful tools to model ecological predictions and to aid resource-management decision-making. They consist of two structural components:i. a causal network (often referred to as the directed acyclic graph), andii. conditional probability tables (that quantify the relations in thenetwork)i. Causal networkii. Conditional probability table(1.) In an initial meeting, led by a domain expert, cognitive mapping wasapplied to identify relevant criteria and their relationships in order todesign a basic structure of the BBN. The suitability of the network wasassessed with regard to data needs for single NWFP species. In thefollowing criteria were refined and the BBN structure adapted within atwo-day workshop by the core development group. Further developmentwas based upon two distinct case studies (i.e. North Karelia in Finland,Styria in Austria). (2.) Currently, the BBN is evaluated against its feasibilityand applicability to be applied to 4 clusters of NWFPs (tree products,mushrooms & truffles, understorey products, animal products). Domainexperts will be consulted in order to fill the conditional probability tables(CPT) for each criteria and each NWFP cluster respectively. Anapproximation approach is used to feed the CPT values into the HUGINsoftware and to reduce the elicitation burden of the experts using the Rpackage. Finally, the approach will be tested on the level of a ForestManagement Unit (FMU) for each case study.1… University of Natural Resources and Life Sciences Vienna (BOKU) 2… European Forest Institute Central-East European Regional Office (EFICEEC) 3… Natural Resources Institute Finland Bio-based Industry and Business (Luke)1.2.Cork(Quercus suber)Chestnut (Castanea sativa)Honey(Apis mellifera)Rowan(Sorbus aucuparia)Stone pine(Pinus cembra)Mushrooms(Boletus pinophilus)Expert knowledgeModel walkthroughCPT calculationThe development process can be clustered in two main steps:1. Structural development2. Parameter estimation and quantitative evaluation

A Bayesian Belief Network approach to assess the potential of non-wood forest products for small-scale forest owners

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