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.
2026
Sammendrag
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Sammendrag
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Forfattere
Sebastian EiterSammendrag
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Forfattere
Ingrid Nesheim Julia Szulecka Anne-Grete Buseth Blankenberg Natalja Čerkasova Rozalija Cvejić Joana Eichenberger Caroline Enge Raimonds Ernšteins Marie Anne Eurie Forio Petr Fučík Marek Giełczewski Agota Horel Kinga Farkas-Iványi Ilona Kása Piroska Kassai Gregor Kramberger Dominika Krzeminska Tatenda Lemann Peter Molnar Federica Monaco Michael Strauch Brigitta Szabó Felix WitingSammendrag
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Forfattere
Xiaoyu Xu Yuang Cao Suli Zhi Jiahua Liu Cheryl Marie Cordeiro Erik Sindhøj Han Wang Keqiang ZhangSammendrag
Microalgae exhibit unique advantages in ARG removal, yet their growth and efficacy are often constrained by complex organic matter and microorganisms in wastewater. To address this issue, this study employed chemical pretreatment to synergistically enhance microalgal treatment and, for the first time, developed a novel coupled process to tackle ARGs in livestock wastewater. The results indicate that low-chlorine (1 mg/L) pretreatment combined with the indigenous filamentous alga (S2) significantly removed pollutants (TN: 81.50%, COD: 70.71%) and reduced the total abundance of ARGs by 81.73%. The core mechanism lies in low-chlorine pretreatment shaping a mutually beneficial algae-bacteria system, which achieves efficient ARG control by altering the host bacterial. The study formalized the operating condition with a multi-objective desirability index combining nutrient removal, ARG reduction, and algal growth, which identified 1 mg/L as the overall optimum. The combined treatment process at a low chlorine dosage demonstrated both high efficiency and feasibility, providing an innovative strategy for livestock wastewater treatment.
Forfattere
Yuang Cao Zhuowu Li Xiaoyu Xu Cheryl Marie Cordeiro Jiahua Liu Keqiang Zhang Lianzhu Du Suli ZhiSammendrag
The spread of antibiotic resistance genes (ARGs) in livestock and poultry wastewater poses a serious threat to the ecological environment and public health. This study compared the effects of biochar (BC), ferrous sulfate (FS), ferrous sulfate-modified biochar (FC), a physical mixture of ferrous sulfate and biochar (F_C), and sulfuric acid (HS) on ARG dynamics and nitrogen metabolism during the 60-day storage and fermentation of pig manure slurry. The results showed that single treatments (BC or FS) had limited ARG-removal efficiency. Compared with the control, the F_C treatment maintained higher total nitrogen (TN) levels (up to 2.42 mg/g in F_C3) while contributing to ARG reduction; however, its ARG-removal performance was not consistently superior to that of all other treatments. Although HS inhibited some ARGs, strong acidification altered the microbial community structure and may have disrupted ecological stability. Metagenomic analysis revealed that multidrug, peptide, and glycopeptide ARGs were dominant (approximately 80%) and were significantly positively correlated with key nitrogen-metabolism genes (e.g., nxrAB and nasAB, p < 0.01), suggesting a link between nitrogen cycling and ARG dissemination. Overall, the physical mixing of biochar and ferrous sulfate shows potential as a practical strategy for jointly regulating ARG dynamics and nitrogen transformation during pig manure slurry storage and fermentation, but further optimisation and validation are needed before field-scale application.
Sammendrag
Collection, processing and provision of comprehensive geometric information of forest roads is decisive for its technical classification to facilitate sustainable timber supply chains. An automized classification system based on the mobile proximal sensor platform RoadSens was developed, applied and validated through a case study approach in Eastern Norway. Six sample roads of various vegetation stages were surveyed through RoadSens and complemented through sampled total station measurements for validation purposes. The determined geometric parameters road slope, curvature and width were used for technical classification following the national forest road standard. Road width was identified as the main constraint in meeting the standard, resulting in a general downgrading of the sampled roads according to its technical class. The results showed a root mean square error (RMSE) ranging from ±0.53 to 1.50 m (12–33%) depending on the road and vegetation stage compared to the validation data. Despite these accuracy constraints, the application case study already indicates a general need for improvement of road data acquisition and updating of associated databases. The study underscores that, despite the challenges and limitations, there is a clear need for an automated sensing and classification system, which offers a cost-effective alternative to manual surveying and requires less specialized expertise.
Forfattere
Stephan Hoffmann Mostafa Hoseini Moritz Wingartz Mahmoud Rajabi Helle Ross Gobakken Rasmus AstrupSammendrag
A functional and low-impact forest road network is essential for sustainable forest management, yet maintaining such infrastructure is costly and requires monitoring tools that are reliable and simple enough for operational use. We present an automated approach to detect, map, and evaluate forest road surface deterioration, designed to support end-users, including those with limited road expertise, to indicate required maintenance actions. The system relies on data collected by the vehicle-mounted near-field sensor platform RoadSens, which integrates stereo camera imagery with GNSS-based geo-referencing to capture detailed road surface information. Collected data are processed within a monitoring and scheduling environment using a YOLOv8 object detection model trained on nearly 14,000 annotated images. The model identifies six key deterioration features: potholes, wheel ruts, gullies, washboards, stones, and vegetation. These detections are used to locate maintenance-relevant features and classify road segments into three deterioration levels based on coverage thresholds, which are then visualized through a traffic-light system. A case study on a forest road in southern Norway demonstrated the system’s ability to detect and classify maintenance needs. While performance was strong for more uniform features such as vegetation, irregular structures like wheel ruts proved more challenging, occasionally leading to misclassification of actual maintenance requirements. Nevertheless, the findings confirm the technical feasibility of integrating object detection models into data-driven forest road maintenance scheduling. Future improvements will require larger and more diverse training datasets, as well as classification frameworks tailored to local conditions and specific road-user needs.309671 -
Sammendrag
Time and motion studies in forest operations benefit from video-based analysis, but manual annotation is time consuming. This pilot study aims to reduce analysis time by developing a deep-learning framework that classifies dashcam video into four work elements: crane out, cutting and processing, driving, and processing. Using a 3D ResNet-50 (PyTorchVideo) trained on manually annotated clips, the model achieved validation F1 = 0.88 and precision = 0.90, showing that spatiotemporal CNNs can capture rele-vant motion and appearance cues in forest environments. Overfitting indicates that more diverse data and better class balance are needed, but the approach shows clear potential to scale automated work-element monitoring and efficiency analysis.
Forfattere
Pia Heltoft ThomsenSammendrag
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