Publikasjoner
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.
2025
Forfattere
Isabell EischeidSammendrag
Det er ikke registrert sammendrag
Forfattere
Gunhild Bødtker Claire Coutris Eva Marie-Louise Denison Åsa Frostegård Erik J. Joner Bjørn Tore Lunestad Elisabeth Henie Madslien Kaare Magne Nielsen Pål TrosvikSammendrag
In this self-tasking scoping review, VKM will map research about the environmental impacts of biodegradable plastics, including biodegradation rates and material persistence in different environments and geographical regions, the influence on microbial ecology and activity, and ecotoxicological effects of materials and associated chemical substances. Related to this is also research associated with the development of methodology, standards, environmental risk assessment, life cycle impact analyses, material sources and properties of biodegradable plastics and products. The aim is to 1) determine the extent of evidence summarised in reviews and original research papers within this emerging research area and 2) map the evidence according to the materials and chemicals studied, types of environments and geographical regions covered, the hypotheses addressed, the type of endpoints assessed and the reported key findings. Systematic literature searches will be performed to identify the summarised evidence, applying APRIO to develop a tailored search protocol that addresses the multi- and cross-disciplinary nature of the research area. We will select and map the identified publications applying Rayyan and sort them into three categories based on their main scientific focus and aim of study: 1) material properties and application, 2) biodegradation and microbial ecology, and 3) ecotoxicology. There will be no geographical restrictions on the search and study selection, but in the data charting process we will highlight findings relevant to Norway and other Nordic countries. The current project adheres to the “Preferred Reporting Items for Systematic Reviews and MetaAnalyses extension for Scoping Reviews (PRISMA-ScR) Checklist” for protocol development and reporting. We will address uncertainties associated with research studies applying EFSA guidelines and their generic list of common types of uncertainty affecting scientific studies and assessments.
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Blandingsbestand av gran og furu kan bidra til å opprettholde og til og med øke produktiviteten i barskogbestand, spesielt på midlere boniteter og under utfordrende klimaforhold. Det er en av konklusjonene til artikkelforfatterne som har studert produktivitet og motstandsdyktighet i norske barblandingsskoger.
Forfattere
Hilde Margrethe HelgesenSammendrag
Det er ikke registrert sammendrag
Forfattere
Hilde Margrethe HelgesenSammendrag
Det er ikke registrert sammendrag
Forfattere
Aksel Granhus Ulrika Jansson Kjersti Holt Hanssen Jørund Rolstad Ignacio Sevillano Ken Olaf StoraunetSammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Heikki Korpunen Yrjö Nuutinen Paula Jylhä Lars Eliasson Aksel Granhus Juha Laitila Stephan Hoffmann Timo MuhonenSammendrag
• Overall forest management objectives and stand properties set the requirements and possibilities for harvesting in continuous cover forestry (CCF). • Harvester and forwarder operators play a key role in successful CCF harvesting, as both productivity and quality of work are essential factors in harvesting operations. • Optimal stand conditions improve work productivity on selection harvesting sites; harvested stem volume correlates well with work productivity in cutting, and density of remaining trees does not significantly reduce work productivity in forwarding. • Carefully executed group cutting and shelterwood harvesting can reduce the number of damaged remaining trees, which is beneficial for future tree generations. • Research-based information is needed about work productivity in harvesting, damage caused by harvesting, and optimisation of strip road and forest road networks for CCF.
Sammendrag
1. Field-based vegetation mapping is important for environmental assessments.Often, the area covered by a species is estimated visually within a reference frame.However, such assessments are prone to observer bias and a large variability. 2. We developed a deep learning pipeline relying on YOLOv8 models to segmentspecies and estimate the percentage cover (%) of Vaccinium myrtillus (blueberry)and Vaccinium vitis-idaea (lingonberry), two key understory species in borealforests. We used 138 nadir and downward-looking images of the forest floorcaptured in correspondence with 50 × 50 cm vegetation sub-plots assessedwithin National Forest Inventory (NFI) plots. First, we trained a bounding-boxframe detection model to crop the image to the same area assessed in the field.Second, we trained an instance segmentation model to classify species. Third,we flattened the class values into a semantic raster and estimated the species-specific cover by pixel counting. 3. We evaluated our method against an independent test set of 156 images andfound a root mean squared error (RMSE) of 8.82% for blueberry and 3.49% forlingonberry and no substantial systematic errors. An additional comparison withocular estimation by various field workers for the same plots showed that themodel estimates were within the range of estimates by field workers 8 out of 9times for blueberry and 7 out of 9 times for lingonberry. 4. The developed method shows promise in reducing observer bias and variabilityin vegetation surveys, thereby improving their consistency while significantlyreducing the time needed for species-specific coverage estimation. This isparticularly beneficial for repeated measurements and monitoring vegetationcover dynamics. However, as the method relies on RGB data, it is limited toestimating the percentage of visible species that are not obscured by others.Expanding the method to include a broader range of cover classes (e.g. grasses,rocks, logs) or species could automate the capture of crucial information