Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2025
Authors
Simon BergAbstract
Root rot causes significant losses for Norwegian forestry. Mapping infected stumps and planting rot-resistant species around infected stumps could reduce future impacts. At 20 sites, root rot was mapped by adding specific assortments for rotten logs using a harvester that recorded tree locations with high accuracy. The optimal approach was considered detailed planning of planting a Norway spruce and Scotspine mix, using root rot information at tree positions. The average opportunity cost of business as usual(planting only Norway spruce) for the forest owner was 409 €/ha. Planting only Scots pine and detailed planning with rot information at harvester locations increased opportunity costs to 615–886 €/ha.Considering fertility variations reduced the opportunity cost to 408 €/ha, considering average rot at site level reduced it to 397 €/ha, considering rot information at harvester locations and coarse planning reduced it to 378 €/ha, and considering rot information at tree level and coarse planning reduced it to 268€/ha. The optimal approach is currently impractical, while coarse planning with rot information at tree locations is feasible. Costs for rot registration and multi-species planting, excluded due to high uncertainty, are likely covered by the increase of 141 €/ha in net present value.
Authors
Simon BergAbstract
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.
Abstract
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).
Authors
Holger LangeAbstract
Oversikt over ICOS tårnet på Hurdal samt siste resultater fra i år
Authors
Erik Larnøy Andreas Treu Manon Diraison Mathias Smith Anaël Audouin Peer Thorben Lewandowski Johan BiørnstadAbstract
No abstract has been registered
Abstract
Vi beskriver tilnærmingen hvordan man kan bruke dendrometer målinger pluss klimavariabler til å modellere transpirasjon i Hurdal skogen
Abstract
An update on the carbon gains and losses at Hurdal
Abstract
Yasso07 simuleringer av jordkarbon i Hurdal
Authors
Junbin Zhao Holger Lange Christian Wilhelm Mohr Cornelya Klutsch Simon Weldon Jonathan Rizzi Gunnhild Søgaard Hanna Marika Silvennoinen Teresa Gómez de la BárcenaAbstract
Jordrespirasjonsmålinger på Svanhovd og dens modellering
Authors
Mark Ramsden Berit Nordskog Tor-Einar Skog Dave Skirvin Angelo Marguglio Antonio Caruso Christophe Pradal Lise Jorgensen Mette Sonderskov Nikos Georgantzis Marko Debeljak Jurij Marinko Harm Brinks Bjorn Andersson Ilias Travlos Eleanor Dearlove Neil PaveleyAbstract
Crop protection and pest management are major economic and environmental concerns throughout Europe. The consultation of decision support systems (DSS) to guide decisions relating to Integrated Pest Management (IPM) is one of the key principles of IPM, reducing the ambiguity around potential risks to crop health. ‘Pests’ in this context include invertebrate pests, weeds and pathogens. The impact of DSS can be limited by a lack of awareness of DSS availability, inconsistencies in the user functions of different DSS, regional fragmentation of access, and a lack of transparency of the origin, validity, and benefits of DSS. Failure to address these limitations undermines trust in IPM DSS and leads to a reluctance of farmers and advisors to invest time in consulting multiple DSS sources as part of their agronomic decision toolbox. The EU-funded IPM Decisions project (Grant agreement ID: 817617) addressed these limitations by creating a Europe-wide free-access online platform. The IPM Decisions platform was designed in consultation with farmers, advisors and wider stakeholders to increase access to and uptake of IPM DSS integrated within it. It offers an end-point for IPM researchers and DSS developers to make adapted and novel DSS available to users, and provides a ‘one-stop shop' for farmers and advisors looking to consult free access or paid IPM DSS. Dedicated dashboards within the platform facilitate farm set up, consultation of DSS, comparison of DSS outputs, and adjustment of model parameters for adaption to different pests/regions. The IPM Decisions digital infrastructure enables easy integration of models and data with external platforms, providing a framework for accessing and sharing models and data between researchers and developers. The platform therefore provides both a ready to go user interface for new DSS, as well as the infrastructure to support and connect existing and future user interfaces.