Slovenian Foresty Institute

Ljubljana, Slovenia

Slovenian Foresty Institute

Ljubljana, Slovenia
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Rauch P.,University of Natural Resources and Life Sciences, Vienna | Wolfsmayr U.J.,University of Natural Resources and Life Sciences, Vienna | Borz S.A.,Transilvania University of Brasov | Triplat M.,Slovenian Foresty Institute | And 11 more authors.
Forest Policy and Economics | Year: 2015

The objective of this study is to assess drivers and barriers to primary forest fuel (PFF) supply in the wide-stretched South East Europe (SEE) countries and to develop strategies to improve PFF supply involving dozens of stakeholders from different SEE countries. SWOT (strengths, weaknesses, opportunities and threats) analyses were used to evaluate country supply chains. Based on those a regional SWOT analysis was compiled and strategies were developed and evaluated in a participative decision process. Results show that strategies for increasing biomass utilisation are of high relevance in all participating countries. Additionally, strategies for knowledge dissemination are also important. The evaluated regional strategies for the forest fuel sector examined have great potential to improve cooperation, increase efficiency and strengthen competitiveness of PFF based bioenergy production. © 2015.


Vanguelova E.I.,Center for Ecosystems | Bonifacio E.,University of Turin | De Vos B.,Research Institute for Nature and Forest INBO | Hoosbeek M.R.,Wageningen University | And 13 more authors.
Environmental Monitoring and Assessment | Year: 2016

Spatially explicit knowledge of recent and past soil organic carbon (SOC) stocks in forests will improve our understanding of the effect of human- and non-human-induced changes on forest C fluxes. For SOC accounting, a minimum detectable difference must be defined in order to adequately determine temporal changes and spatial differences in SOC. This requires sufficiently detailed data to predict SOC stocks at appropriate scales within the required accuracy so that only significant changes are accounted for. When designing sampling campaigns, taking into account factors influencing SOC spatial and temporal distribution (such as soil type, topography, climate and vegetation) are needed to optimise sampling depths and numbers of samples, thereby ensuring that samples accurately reflect the distribution of SOC at a site. Furthermore, the appropriate scales related to the research question need to be defined: profile, plot, forests, catchment, national or wider. Scaling up SOC stocks from point sample to landscape unit is challenging, and thus requires reliable baseline data. Knowledge of the associated uncertainties related to SOC measures at each particular scale and how to reduce them is crucial for assessing SOC stocks with the highest possible accuracy at each scale. This review identifies where potential sources of errors and uncertainties related to forest SOC stock estimation occur at five different scales—sample, profile, plot, landscape/regional and European. Recommendations are also provided on how to reduce forest SOC uncertainties and increase efficiency of SOC assessment at each scale. © 2016, Crown Copyright as represented by: Forest Research, Forestry Commission, UK.


PubMed | NO 1431 As, Copenhagen University, Research Institute for Nature and Forest INBO, Center for Ecosystems and 11 more.
Type: Journal Article | Journal: Environmental monitoring and assessment | Year: 2016

Spatially explicit knowledge of recent and past soil organic carbon (SOC) stocks in forests will improve our understanding of the effect of human- and non-human-induced changes on forest C fluxes. For SOC accounting, a minimum detectable difference must be defined in order to adequately determine temporal changes and spatial differences in SOC. This requires sufficiently detailed data to predict SOC stocks at appropriate scales within the required accuracy so that only significant changes are accounted for. When designing sampling campaigns, taking into account factors influencing SOC spatial and temporal distribution (such as soil type, topography, climate and vegetation) are needed to optimise sampling depths and numbers of samples, thereby ensuring that samples accurately reflect the distribution of SOC at a site. Furthermore, the appropriate scales related to the research question need to be defined: profile, plot, forests, catchment, national or wider. Scaling up SOC stocks from point sample to landscape unit is challenging, and thus requires reliable baseline data. Knowledge of the associated uncertainties related to SOC measures at each particular scale and how to reduce them is crucial for assessing SOC stocks with the highest possible accuracy at each scale. This review identifies where potential sources of errors and uncertainties related to forest SOC stock estimation occur at five different scales-sample, profile, plot, landscape/regional and European. Recommendations are also provided on how to reduce forest SOC uncertainties and increase efficiency of SOC assessment at each scale.

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