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
Monica Sanden Eirill Ager-Wick Johanna Eva Bodin Nur Duale Anne-Marthe Ganes Jevnaker Kristian Prydz Volha Shapaval Ville Erling Sipinen Tage ThorstensenAbstract
The Norwegian Scientific Committee for Food and Environment (VKM) has assessed an application for approval of the genetically modified maize DAS1131 for food and feed uses, import and processing in the EU. In accordance with an assignment specified by the Norwegian Food Safety Authority (NFSA) and the Norwegian Environment Agency (NEA), VKM assesses whether genetically modified organisms (GMOs) intended for the European market can pose risks to human or animal health, or the environment in Norway. VKM assesses the scientific documentation regarding GMO applications seeking approval for use of GMOs as food and feed, processing, or cultivation. The EU Regulation 1829/2003/EC (Regulation) covers living GMOs that fall under the Norwegian Gene Technology Act, as well as processed food and feed from GMOs (dead material) that fall under the Norwegian Food Act. The regulation is currently not part of the EEA agreement or implemented in Norwegian law. Norway conducts its own assessments of GMO applications in preparation for the possible implementation of the Regulation. In accordance with the assignment by NFSA and NEA, VKM assesses GMO applications during scientific hearings initiated by the European Food Safety Authority (EFSA), as well as after EFSA has published its own risk assessment of a GMO, up until EU member countries vote for or against approval in the EU Commission. The assignment is divided into three stages. (link) Genetically modified maize DAS1131 DAS1131 is a genetically modified maize developed by Agrobacterium tumefaciens -mediated transformation. Maize DAS1131 plants contain the transgenes cry1Da2 and dgt-28 epsps which encode the protein Cry1Da2 and the enzyme DGT-28 EPSPS, respectively. Cry1Da2 provides resistance to certain susceptible Lepidopteran (order of butterflies and moths) pests and the enzyme DGT-28 EPSPS provides tolerance to glyphosate-based herbicides. VKM has assessed the documentation in application GMFF-2021-1530 and EFSA's scientific opinion on genetically modified maize DAS1131. VKM concludes that the applicant's scientific documentation for the genetically modified maize DAS1131 is satisfactory for risk assessment, and in accordance with EFSA guidelines for risk assessment of genetically modified plants for food or feed uses. The genetic modifications in maize DAS1131do not indicate an increased health or environmental risk in Norway compared with EU countries. EFSA's risk assessment is therefore sufficient also for Norwegian conditions. As no specific Norwegian conditions have been identified regarding properties of the genetically modified maize DAS1131, VKM's GMO panel has not performed a complete risk assessment of the maize. About the assignment: In stage 1, VKM shall assess the health and environmental risks of the genetically modified organism and derived products in connection with the EFSA scientific hearing of GMO applications. VKM shall review the scientific documentation that the applicant has submitted and possibly provide comments to EFSA. VKM must also consider: i) whether there are specific Norwegian conditions that could give other risks in Norway than those mentioned in the application, ii) whether the Norwegian diet presents a different health risk for the Norwegian population should the GMO be approved, compared to the European population, and iii) risks associated with co-existence with conventional and/or ecologic production of plants for GMOs seeking approval for cultivation. Relevant measures to ensure co-existence must also be considered. In stage 2, VKM shall assess whether comments from Norway have been satisfactorily answered by EFSA. In addition, VKM shall assess whether comments from other countries imply need for further follow-up. (...)
Abstract
No abstract has been registered
Authors
David Chludil Curt Almqvist Mats Berlin Arne Steffenrem Steven E. McKeand Jiří Korecký Jan Stejskal Jaroslav Čepl Fikret Isik Debojyoti Chakraborty Silvio Schueler Torsten Pook Christi Sagariya Milan LstibůrekAbstract
No abstract has been registered
Authors
Sean P Healey Zhiqiang Yang Angela M Erb Ryan Bright Grant M Domke Tracey S Frescino Crystal B SchaafAbstract
No abstract has been registered
Abstract
Accurately predicting whether pedestrians will cross in front of an autonomous vehicle is essential for ensuring safe and comfortable maneuvers. However, developing models for this task remains challenging due to the limited availability of diverse datasets containing both crossing (C) and non-crossing (NC) scenarios. Therefore, we propose a procedure that leverages synthetic videos with C/NC labels and an untrained model whose architecture is designed for C/NC prediction to automatically produce C/NC labels for a set of real-world videos. Thus, this procedure performs a synth-to-real unsupervised domain adaptation for C/NC prediction, so we term it S2R-UDA-CP. To assess the effectiveness of S2R-UDA-CP in self-labeling, we utilize two state-of-the-art models, PedGNN and ST-CrossingPose, and we rely on the publicly-available PedSynth dataset, which consists of synthetic videos with C/NC labels. Notably, once the real-world videos are self-labeled, they can be used to train models different from those used in S2R-UDA-CP. These models are designed to operate onboard a vehicle, whereas S2R-UDA-CP is an offline procedure. To evaluate the quality of the C/NC labels generated by S2R-UDA-CP, we also employ PedGraph+ (another literature referent) as it is not used in S2R-UDA-CP. Overall, the results show that training models to predict C/NC using videos labeled by S2R-UDA-CP achieves performance even better than models trained on human-labeled data. Our study also highlights different discrepancies between automatic and human labeling. To the best of our knowledge, this is the first study to evaluate synth-to-real self-labeling for C/NC prediction.
Authors
Daniel Moreno-Fernández Johannes Breidenbach Isabel Cañellas Gherardo Chirici Giovanni D’amico Marco Ferretti Francesca Giannetti Stefano Puliti Sebastian Schnell Ross Shackleton Mitja Skudnik Iciar AlberdiAbstract
Forest biodiversity is a multifaceted term encompassing tree and shrub diversity and the diversity of other life forms such as animals or fungi. Extensive forest monitoring networks such as National Forest Inventories or the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forest plots have implemented biodiversity-monitoring protocols to satisfy increasing information demands. However, these protocols often evaluate biodiversity through potential biodiversity indicators (e.g., stand structure and deadwood), which may not provide sufficient information on other aspects of the current forest biodiversity status. In this study, we present the forest biodiversity monitoring results and lessons from a cross-country study to support large-scale monitoring systems. We developed, evaluated, and discussed harmonized protocols, mainly focused on birds and mammals, which extend beyond the traditional features captured in large-scale forest inventories. We leverage information from 30 intensively monitored plots established in six European countries to achieve these goals. The protocols were helpful in recording data that could be used to reproduce biodiversity-related attributes such as measures of forest structure, regeneration, deadwood features, and bird and mammal diversity. Specifically, field data on trees was used to describe structural features of forests such as stand composition and forest complexity. In contrast, composition and regeneration data provided helpful information for other biodiversity indicators. Data gathering to monitor bird and mammal diversity requires revisiting the plots, which involves greater economic investment and human effort. Once the bird and mammal data have been collected, advanced algorithms could facilitate and enhance the efficiency of the analyses. To optimize the monitoring efficiency, we recommend including these two new biodiversity assessments in a subset of extensive survey plots. Furthermore, using standard guidelines for these new assessments across all countries would facilitate the comparison and reporting of statistical data.
Authors
Shaohui Zhang Poul Erik Lærke Mathias Neumann Andersen Junxiang Peng Esben Øster Mortensen Johannes Wilhelmus Maria Pullens Sheng Wang Klaus Steenberg Larsen Davide Cammarano Uffe Jørgensen Kiril ManevskiAbstract
No abstract has been registered
Abstract
Environmental observation networks, such as AmeriFlux, are foundational for monitoring ecosystem response to climate change, management practices, and natural disturbances; however, their effectiveness depends on their representativeness for the regions or continents. We proposed an empirical, time series approach to quantify the similarity of ecosystem fluxes across AmeriFlux sites. We extracted the diel and seasonal characteristics (i.e., amplitudes, phases) from carbon dioxide, water vapor, energy, and momentum fluxes, which reflect the effects of climate, plant phenology, and ecophysiology on the observations, and explored the potential aggregations of AmeriFlux sites through hierarchical clustering. While net radiation and temperature showed latitudinal clustering as expected, flux variables revealed a more uneven clustering with many small (number of sites < 5), unique groups and a few large (> 100) to intermediate (15–70) groups, highlighting the significant ecological regulations of ecosystem fluxes. Many identified unique groups were from under-sampled ecoregions and biome types of the International Geosphere-Biosphere Programme (IGBP), with distinct flux dynamics compared to the rest of the network. At the finer spatial scale, local topography, disturbance, management, edaphic, and hydrological regimes further enlarge the difference in flux dynamics within the groups. Nonetheless, our clustering approach is a data-driven method to interpret the AmeriFlux network, informing future cross-site syntheses, upscaling, and model-data benchmarking research. Finally, we highlighted the unique and underrepresented sites in the AmeriFlux network, which were found mainly in Hawaii and Latin America, mountains, and at under- sampled IGBP types (e.g., urban, open water), motivating the incorporation of new/unregistered sites from these groups.
Authors
Katharina HobrakAbstract
No abstract has been registered
Authors
Arne Verstraeten Andreas Schmitz Bernd Ahrends Nicholas Clarke Wim de Vries Karin Hansen Char Hilgers Carmen Iacoban Tamara Jakovljevic Per Erik Karlsson Till Kirchner Aldo Marchetto Henning Meesenburg Gunilla Pihl Karlsson Anne-Katrin Prescher Anne Thimonier Peter WaldnerAbstract
No abstract has been registered