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.
2026
Authors
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 WitingAbstract
No abstract has been registered
Authors
Xiaoyu Xu Yuang Cao Suli Zhi Jiahua Liu Cheryl Marie Cordeiro Erik Sindhøj Han Wang Keqiang ZhangAbstract
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.
Authors
Yuang Cao Zhuowu Li Xiaoyu Xu Cheryl Marie Cordeiro Jiahua Liu Keqiang Zhang Lianzhu Du Suli ZhiAbstract
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.
Abstract
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.
Authors
Stephan Hoffmann Mostafa Hoseini Moritz Wingartz Mahmoud Rajabi Helle Ross Gobakken Rasmus AstrupAbstract
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 -
Abstract
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.
Abstract
Energy-efficient greenhouse climate control is important in high-latitude regions, where heating demand is high and minimizing environmental impacts is increasingly necessary. In this study, a novel centralized environmental control system (ECS) was implemented in a semi-closed tomato greenhouse under Norwegian conditions. The ECS integrates heating, cooling, dehumidification, and heat recovery through air-to water heat exchangers, a heat pump, and thermal energy storage system to support climate control and energy management. The ECS was monitored across three tomato production experiments conducted during summer and winter seasons, and its operational performance was evaluated based on greenhouse climate, tomato yield, and energy use. The experiments included variations in temperature setpoints and cooling capacity. The ECS maintained greenhouse climate that was suitable for tomato production across all experiments. Changes in temperature setpoints and cooling capacity affected ECS electricity consumption and influenced the balance between recovered heat and boiler heating, while having limited effects on tomato yield. The results indicate the potential of centralized ECS technology to sustain tomato production while reducing reliance on fossil-energy, supporting the transition towards energy-efficient and emission-free smart greenhouse production.
Abstract
Greenhouse tomato production at high latitudes requires substantial inputs of supplemental lighting, heating and climate control. (Semi-) closed greenhouses can improve heat, water and CO₂ retention, but require additional electricity, climate control system capacities and investment. Crop productivity and resource use must therefore be evaluated jointly. This paper presents the EFREE-Green systems framework for integrating local production conditions, greenhouse environmental control, crop physiological responses, resource flows, and economic and environmental performance. The framework is implemented in two experimental greenhouse compartments operating at a semi-commercial scale at NIBIO Særheim, Norway, connected to a centralized environmental control system (ECS). Measurements at leaf, canopy and greenhouse scale link environmental control with crop carbon gain, biomass partitioning, marketable yield and resource use. Illustrative observations demonstrate that crop responses to supplemental lighting depend on interactions among light availability, CO₂ supply and climate management. Experimental measurements, modelling, techno-economic assessment and life-cycle assessment are combined to evaluate crop productivity, energy efficiency, resource recovery, production costs and greenhouse gas emissions. The framework is transferable to other climatic and production settings, but optimal technologies, capacities and control strategies remain location-specific.
Abstract
No abstract has been registered
Authors
Nicolas CattaneoAbstract
No abstract has been registered