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Query: "author" (Mitja Skudnik) .

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Common preferences of European small-scale forest owners towards contract-based management
Artti Juutinen, Elena Haeler, R. Jandl, Katharina Kuhlmey, Mikko Kurttila, Raisa Mäkipää, Tähti Pohjanmies, Lydia Rosenkranz, Mitja Skudnik, Matevž Triplat, Anne Tolvanen, Urša Vilhar, Kerstin Westin, Silvio Schueler, 2022, original scientific article

Abstract: The societal demands on forest management are becoming increasingly diverse, which will be reflected in decisions made by forest owners. We examined the willingness of private forest owners in Austria, Finland, Germany, Slovenia, and Sweden to participate in a contract-based payment scheme in which they were asked to apply a specific management strategy to promote either timber production or environmental goals. The preferences for the contract-based management and associated consequences in terms of profitability, biodiversity, carbon stock, and climate change-induced damages were addressed within a choice experiment. A majority of respondents across all countries agreed to participate in a payment scheme to promote environmental goals, while schemes purely targeted to increase wood production were found less attractive. Forest owners liked improvements in profitability and environmental attributes and disliked deterioration of these attributes. Differences among countries were found in the level of expected contract payments, and commonalities were found with respect to preferences towards environmental goals, including biodiversity and carbon stocks. Hence, new policies to target European forest subsidy to promote the provision of environmental goals would likely be acceptable.
Keywords: choice experiment, ecosystem services, forest policy, incentives, private forest owners
Published in DiRROS: 29.09.2022; Views: 41; Downloads: 10
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Artificial neural networks as an alternative method to nonlinear mixed-effects models for tree height predictions
Mitja Skudnik, Jernej Jevšenak, 2022, original scientific article

Abstract: Tree heights are one of the most important aspects of forest mensuration, but data are often unavailable due to costly and time-consuming field measurements. Therefore, various types of models have been developed for the imputation of tree heights for unmeasured trees, with mixed-effects models being one of the most commonly applied approaches. The disadvantage here is the need of sufficient sample size per tree species for each plot, which is often not met, especially in mixed forests. To avoid this limitation, we used principal component analysis (PCA) for the grouping of similar plots based on the most relevant site descriptors. Next, we compared mixed-effects models with height-diameter models based on artificial neural networks (ANN). In terms of root mean square error (RMSE), mixed-effects models provided the most accurate tree height predictions at the plot level, especially for tree species with a smaller number of tree height measurements. When plots were grouped using the PCA and the number of observations per category increased, ANN predictions improved and became more accurate than those provided by mixed-effects models. The performance of ANN also increased when the competition index was included as an additional explanatory variable. Our results show that in the pursuit of the most accurate modelling approach for tree height predictions, ANN should be seriously considered, especially when the number of tree measurements and their distribution is sufficient.
Keywords: height-diameter models, national forest inventory, permanent sample plot, mixed forests, model comparison, principal component analysis
Published in DiRROS: 08.06.2022; Views: 135; Downloads: 55
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