Anneli Jalkanen. (2001). The probability of moose damage at the stand level in southern Finland. Silva Fennica vol. 35 no. 2 article id 593. https://doi.org/10.14214/sf.593

Keywords: National Forest Inventory; Alces alces; multilevel logistic regression; risk management

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The probability of moose damage was studied in sapling stands and young thinning stands in southern Finland. Data from the eighth National Forest Inventory in 1986–92 were used for modelling. The frequency of damage was highest at the height of two to five meters and at the age of ten to twenty years (at the time of measurement). Moose preferred aspen stands the most and least preferred Norway spruce stands. Scots pine and silver birch were also susceptible to damage. Logistic regression models were developed for predicting the probability that moose damage is the most important damaging agent in a forest stand. The best predictive variables were the age and dominant species of the stand. Variables describing the site were significant as cluster averages, possibly characterizing the area as a food source (fertility and organic soil), as well as the lack of shelter (wall stand). When sample plot, cluster and municipality levels were compared, it was found that most of the unexplained variance was at the cluster level. To improve the model, more information should be obtained from that level. The regression coefficients for aspen as supplementary species, and for pine as dominant species, had significant variance from cluster to cluster (area to area). It was also shown that the occurrence of aspen is closely connected to the occurrence of moose damage in pine sapling stands.

Jalkanen, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland E-mail: anneli.jalkanen@metla.fi

Category : Review article

article id 9984, category Review article

Christoph Kogler, Peter Rauch. (2018). Discrete event simulation of multimodal and unimodal transportation in the wood supply chain: a literature review. Silva Fennica vol. 52 no. 4 article id 9984. https://doi.org/10.14214/sf.9984

Keywords: logistics; supply chain management; forest products industry; decision support systems; validation and verification of simulation models; resilient risk management

Highlights: Focus on discrete event simulation, wood supply chain and multimodal transport; Analyses of 12 review articles and a core of 32 research papers, complemented by 48 related ones; Research focus from unimodal to multimodal transport to build efficient, resilient, green and socially sustainable supply chains; Development of robust risk management considering supply risks, demand risks and external risks is needed.

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This review systematically analyses and classifies research and review papers focusing on discrete event simulation applied to wood transport, and therefore illustrates the development of the research area from 1997 until 2017. Discrete event simulation allows complex supply chain models to be mapped in a straightforward manner to study supply chain dynamics, test alternative strategies, communicate findings and facilitate understanding of various stakeholders. The presented analyses confirm that discrete event simulation is well-suited for analyzing interconnected wood supply chain transportation issues on an operational and tactical level. Transport is the connective link between interrelated system components of the forest products industry. Therefore, a survey on transport logistics allows to analyze the significance of entire supply chain management considerations to improve the overall performance and not only one part in isolation. Thus far, research focuses mainly on biomass, unimodal truck transport and terminal operations. Common shortcomings identified include rough explanations of simulation models and sparse details provided about the verification and validation processes. Research gaps exist concerning simulations of entire, resilient and multimodal wood supply chains as well as supply and demand risks. Further studies should expand upon the few initial attempts to combine various simulation methods with optimization.

Kogler, Institute of Production and Logistics, Department of Economics and Social Sciences, University of Natural Resources and Life Sciences, Vienna, Feistmantelstrasse 4, A-1180 Vienna, Austria https://orcid.org/0000-0001-8811-152X E-mail: christoph.kogler@boku.ac.at
Rauch, Institute of Production and Logistics, Department of Economics and Social Sciences, University of Natural Resources and Life Sciences, Vienna, Feistmantelstrasse 4, A-1180 Vienna, Austria http://orcid.org/0000-0002-5812-4415 E-mail: peter.rauch@boku.ac.at

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