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
Simon BergSammendrag
Root rot (Heterobasidion spp.) causes substantial losses for forest owners due to decreased wood quality in Norway spruce (Picea abies). Containing root rot spread in regeneration can be achieved by planting resistant species around infected stumps. However, detecting rotten trees remains challenging. In this study, ground truth data for root rot was collected by seven contractors by adding assortments for rotten pulpwood and cutoffs, with all energy wood assumed rotten. Root rot occurrence was estimated in two ways: (1) by developing Extreme Gradient Boosting (XGB) models from all data (XGB-only); and (2) trough binary classification for bucking patterns containing only rotten or healthy trees, followed by developing XGB models for remaining trees (combined). XGB models were developed nationwide and for two specific contractors. Classifications showed sensitivity of 83–87% (rot) and specificity of 95–99% (healthy).Whether nationwide, contractor-specific, XGB-only or combined classification was better varied by situation. Compared to prior studies, predictions from harvester data outperformed UAV images in classification but were surpassed by handheld camera images. Despite lower sensitivity compared to previous XGB applications, more rotten trees were detected than when using only energy wood as an indicator. As estimations are almost cost-free, the results may be acceptable.
Sammendrag
The year-to-year variation in the availability of lingonberries (Vaccinium vitis-idaea L.) is a challenge for commercial exploitation. There is also a need to identify the best locations for lingonberry harvesting. Here, we present research that utilized field observations from the Norwegian National Forest Inventory to model and map the association between lingonberry cover and stand characteristics. Additionally, a set of permanent sampling plots were established for annual recording of berry yields in different Norwegian regions, representing variations in slope and forest characteristics. Ultimately, the recorded information on yield from the temporary sample plots were combined with predictions from the cover model, as well as data from remote sensing and climatic data from nearby weather stations (for locations see Figure 1a) to derive: 1) a model for lingonberry yield, and 2) and a yield map covering all forest land in Norway. Variables included in the final berry yield model are main tree species, soil parent material, mean temperature June-August, stand basal area, latitude, slope and distance to coastline (Miina et al., 2024).
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Holger LangeSammendrag
Oversikt over ICOS tårnet på Hurdal samt siste resultater fra i år
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Erik Larnøy Andreas Treu Manon Diraison Mathias Smith Anaël Audouin Peer Thorben Lewandowski Johan BiørnstadSammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Vi beskriver tilnærmingen hvordan man kan bruke dendrometer målinger pluss klimavariabler til å modellere transpirasjon i Hurdal skogen
Sammendrag
An update on the carbon gains and losses at Hurdal
Sammendrag
Det er ikke registrert sammendrag