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NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.

2016

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National and international carbon reporting systems require information on carbon stocks of forests. For this purpose, terrestrial assessment systems such as forest inventory data in combination with carbon estimation methods are often used. In this study we analyze and compare terrestrial carbon estimation methods from 12 European countries. The country-specific methods are applied to five European tree species (Fagus sylvatica L., Quercus robur L., Betula pendula Roth, Picea abies (L.) Karst. and Pinus sylvestris L.), using a standardized theoretically-generated tree dataset. We avoid any bias due to data collection and/or sample design by using this approach. We are then able to demonstrate the conceptual differences in the resulting carbon estimates with regard to the applied country-specific method. In our study we analyze (i) allometric biomass functions, (ii) biomass expansion factors in combination with volume functions and (iii) a combination of both. The results of the analysis show discrepancies in the resulting estimates for total tree carbon and for single tree compartments across the countries analyzed of up to 140 t carbon/ha. After grouping the country-specific approaches by European Forest regions, the deviation within the results in each region is smaller but still remains. This indicates that part of the observed differences can be attributed to varying growing conditions and tree properties throughout Europe. However, the large remaining error is caused by differences in the conceptual approach, different tree allometry, the sample material used for developing the biomass estimation models and the definition of the tree compartments. These issues are currently not addressed and require consideration for reliable and consistent carbon estimates throughout Europe.

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Key message The increment estimation methods of European NFIs were explored by means of 12 essential NFI features. The results indicate various differences among NFIs within the commonly acknowledged methodological frame. The perspectives for harmonisation at the European level are promising. Context The estimation of increment is implemented differently in European National Forest Inventories (NFIs) due to different historical origins of NFIs and sampling designs and field assessments accommodated to country-specific conditions. The aspired harmonisation of increment estimation requires a comparison and an analysis of NFI methods. Aims The objective was to investigate the differences in volume increment estimation methods used in European NFIs. The conducted work shall set a basis for harmonisation at the European level which is needed to improve information on forest resources for various strategic processes. Methods A comprehensive enquiry was conducted during Cost Action FP1001 to explore the methods of increment estimation of 29 European NFIs. The enquiry built upon the preceding Cost Action E43 and was complemented by an analysis of literature to demonstrate the methodological backgrounds. Results The comparison of methods revealed differences concerning the NFI features such as sampling grids, periodicity of assessments, permanent and temporary plots, use of remote sensing, sample tree selection, components of forest growth, forest area changes, sampling thresholds, field measurements, drain assessment, involved models and tree parts included in estimates. Conclusion Increment estimation methods differ considerably among European NFIs. Their harmonisation introduces new issues into the harmonisation process. Recent accomplishments and the increased use of sample-based inventories in Europe make perspectives for harmonised reporting of increment estimation promising.

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Individual tree mortality models based on logistic regression exist for different tree species and countries around the world. We examine two mortality models developed in Norway and two models from Austria for Norway spruce (Picea abies), Scots pine (Pinus sylvestris) and birch (Betula pubescens and Betula pendula) trees. We apply all models with their original coefficients on the Norwegian National Forest Inventory (NNFI) data. The dataset comprises 36,217 spruce, 17,483 pine and 24,418 birch trees. We show the differences in predictions that arise from newly paramete-rized predictor variables and the effect of the original calibration data from different geographic regions. Next we recalibrate the mortality functions with the NNFI data to show the improvements in the predictions and illustrate the impact of the different predictor variables. We apply statistical methods to assess which of the original and recalibrated models best mimic the observed mortality rates of the three species. Finally we provide the new coefficient set for the model functions for spruce, pine and birch in Norway.

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Demand for organic meat is partially driven by consumer perceptions that organic foods are more nutritious than non-organic foods. However, there have been no systematic reviews comparing specifically the nutrient content of organic and conventionally produced meat. In this study, we report results of a meta-analysis based on sixty-seven published studies comparing the composition of organic and non-organic meat products. For many nutritionally relevant compounds (e.g. minerals, antioxidants and most individual fatty acids (FA)), the evidence base was too weak for meaningful meta-analyses. However, significant differences in FA profiles were detected when data from all livestock species were pooled. Concentrations of SFA and MUFA were similar or slightly lower, respectively, in organic compared with conventional meat. Larger differences were detected for total PUFA and n-3 PUFA, which were an estimated 23 (95 % CI 11, 35) % and 47 (95 % CI 10, 84) % higher in organic meat, respectively. However, for these and many other composition parameters, for which meta-analyses found significant differences, heterogeneity was high, and this could be explained by differences between animal species/meat types. Evidence from controlled experimental studies indicates that the high grazing/forage-based diets prescribed under organic farming standards may be the main reason for differences in FA profiles. Further studies are required to enable meta-analyses for a wider range of parameters (e.g. antioxidant, vitamin and mineral concentrations) and to improve both precision and consistency of results for FA profiles for all species. Potential impacts of composition differences on human health are discussed.

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This contribution demonstrates an example of experimental automatic image analysis to detect spores prepared on microscope slides derived from trapping. The application is to monitor aerial spore counts of the entomopathogenic fungus Pandora neoaphidis which may serve as a biological control agent for aphids. Automatic detection of such spores can therefore play a role in plant protection. The present approach for such detection is a modification of traditional manual microscopy of prepared slides, where autonomous image recording precedes computerised image analysis. The purpose of the present image analysis is to support human visual inspection of imagery data – not to replace it. The workflow has three components: • Preparation of slides for microscopy. • Image recording. • Computerised image processing where the initial part is, as usual, segmentation depending on the actual data product. Then comes identification of blobs, calculation of principal axes of blobs, symmetry operations and projection on a three parameter egg shape space.