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
Petter Öhrn Mats Berlin Jan-Olov Weslien Malin Elfstrand Paal Krokene Anna Maria Jönsson Audrius MenkisAbstract
Background and aims Drought weakens tree defenses, predisposing Norway spruce (Picea abies) to spruce bark beetle (Ips typographus) attack. The extreme 2018 summer drought in Sweden triggered an unprecedented bark beetle outbreak. Our objective was to quantify how weather, soil moisture, and tree provenance influence Norway spruce defense capacity to a necrotrophic beetle-associated pathogen. Methods Trees at three sites in Sweden were inoculated with the phytopathogenic fungus Leptographium europhioides on four occasions during each of the 2019 and 2021 growing seasons. At each site, we inoculated spruce provenances of Swedish or East European origin, with early and late spring bud burst, respectively. Tree defense capacity, expressed as the extent of necrotic lesion formation following fungal inoculation, was used as a proxy for resistance to bark beetle attack. Results Spruce defense capacity (i.e. lesion size) differed with water availability (both precipitation and soil moisture conditions) but not with the timing of spring bud burst. There were within-season differences, with trees having less efficient defenses (producing larger lesions) in the early season (June). On intermediate soil moisture sites, lesions were larger in 2019 than in 2021. In both years, there was a significant negative correlation between lesion size and water availability in the autumn of the previous year. Conclusion Spruce defense capacity varied with local environmental conditions but not with provenance phenology. Variations between study years reflected the sensitivity of spruce defenses to climatic variability and the partial recovery of tree resistance 3 years after the 2018 drought.
Authors
Ingunn Berget Atle Wehn Hegnes Mari Øvrum Gaarder Valerie Lengard Almli Geir Wæhler GustavsenAbstract
No abstract has been registered
Authors
Marta Vergarechea C Antón-Fernández J.U Jepsen Ole Petter Laksforsmo Vindstad Nicolas Cattaneo J.J Camarero Rasmus AstrupAbstract
No abstract has been registered
Abstract
Viroids are the smallest known nucleic acid‐based infectious agents of plants and consist of single‐stranded, circular, non‐coding RNAs that can cause significant crop diseases. The potato spindle tuber viroid (PSTVd), a model Pospiviroidae member, severely impacts Solanaceous hosts like potato and tomato, causing substantial yield reductions. Its 359‐nucleotide, rod‐like genome, with five functional domains, mediates nuclear replication, systemic movement via plasmodesmata and phloem, and evasion of host RNA silencing. High mutation rates generate diverse quasi‐species, enhancing adaptability. Recent multi‐omics studies reveal PSTVd reprogramming of host transcriptomes, epigenomes, and metabolomes, disrupting defence, hormone signalling, and photosynthesis. Within the plant holobiont, PSTVd modulates interactions with viruses, notably via RNA‐directed DNA methylation, and may affect rhizosphere microbial communities indirectly via changes in host physiology, an area that remains poorly resolved. This review synthesises advances in PSTVd structure, infection mechanisms, and holobiont interactions, highlighting its role in uncovering RNA‐mediated pathogenesis principles. Key knowledge gaps persist regarding host factors facilitating systemic spread and interactions with other organisms, such as microbial communities. Ongoing PSTVd research is essential to address this gap and guide strategies for viroid‐resistant crops and sustainable control.
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.
Abstract
No abstract has been registered
Abstract
This study quantified field-scale nitrogen (N) and phosphorus (P) removal by crop harvests, balances, and use efficiencies in 14 grass fields in the Timebekken catchment. Measurements of grass yields, nutrient concentrations, manure composition, and soil properties across multiple fields and farms were combined with survey data. Results showed large variation across farms and fields in day matter yield, nutrient inputs, removals, balances, and use efficiencies. Annual dry matter yield ranged 6,830–12,800 kg ha-1 (mean 9,010 kg ha-1) in 2024 and 7,480–12,130 kg ha⁻¹ (mean 9,800 kg ha⁻¹) in 2025. In 2024, nutrient inputs as mineral fertilizers and manure ranged 169–362 kg N ha⁻¹ (mean 240 kg ha⁻¹) and 23–57 kg P ha⁻¹ (mean 40 kg ha⁻¹). Corresponding nutrient removal ranged 150–303 kg N ha⁻¹ (mean 220 kg ha⁻¹) and 22–40 kg P ha⁻¹ (mean 29 kg ha⁻¹). Nutrient balances ranged from −111 to +182 kg ha⁻¹ (+14 kg ha⁻¹) for N and from −14 to +35 kg ha⁻¹ (12 kg ha⁻¹) for P. Nutrient use efficiency (input∕removal) ranged 50%–166% (mean 100%) for N and 38%–160% (mean 80%) for P. Overall, results indicate consistent management within farms but clear differences between farms, and therefore substantial potential for improving fertilizer and manure precision while maintaining yields. Phosphorus yield exceeded 27 kg ha-1 in several fields, in some 35 kg ha-1, which are the maximal allowed fertilizer limits from 2033. This substantiates farmers’ concerns about these limits being too low, yet average P inputs still exceeded crop demand. Despite lower topsoil P-AL in 2023 than in 2005, soil P status remained high, likely sustaining yields under stricter P limits. Elevated subsoil P highlights long-term loss risks and the need for targeted mitigation measures in hotspot areas. The study also calls for more monitoring of manure nutrients, yields, and soil P properties.
Authors
Tore Flatlandsmo Berglen Christine Forsetlund Solbakken Hilde Thelle Uggerud Jenny Lovisa Alexandra Jensen Guttorm Christensen Tone Roksvåg AandahlAbstract
Did you know that stairstep moss can be used as a sampler for air pollution? Researchers at NILU have collected this kind of moss on several occasions and examined it for metals and other pollutants.
Authors
Belachew Gizachew ZelekeAbstract
Tropical forests, despite their critical environmental and socio-economic roles, remain highly vulnerable to deforestation, forest degradation, and climate-related disturbances. There is a growing demand for robust and transparent forest monitoring systems, particularly under REDD+, the Paris Agreement’s Enhanced Transparency Framework (ETF), and emerging climate-finance mechanisms. Conventional approaches based on field inventories and traditional remote sensing are often constrained by limited or uneven field data, persistent cloud cover, complex forest conditions, and limited institutional and technical capacity. This review examines how artificial intelligence (AI) and machine learning (ML) are being integrated into remote sensing–based tropical forest monitoring to address these structural constraints. Using a semi-systematic synthesis of peer-reviewed studies, complemented by operational platforms and grey literature, the review assesses AI/ML approaches, remote sensing datasets, and applications relevant to national and large-scale monitoring. Evidence is synthesized across five analytical dimensions: AI/ML model families and workflows, multi-sensor datasets and training resources, operational monitoring platforms, application domains (including deforestation, degradation, and biomass/carbon estimation), and cross-cutting technical, institutional, and governance barriers. The review finds that AI/ML-enabled remote sensing, particularly those combining optical, radar, and LiDAR time series within cloud-based platforms, has substantially improved the automation, scalability, and speed of tropical forest monitoring. However, effective and equitable adoption remains constrained by limitations in training and validation data, dependence on proprietary platforms and data, uneven technical capacity, and unresolved governance and ethical challenges. Emerging solutions, including open and representative training datasets, platform-agnostic processing infrastructures, long-term capacity building, and inclusive data-governance frameworks, are identified as critical enablers of credible and nationally owned AI/ML-enabled forest-monitoring systems. The review highlights that AI/ML can play a transformative role in supporting climate mitigation, biodiversity conservation, and informed decision-making. This potential, however, depends on transparent data governance arrangements, long-term capacity building, and platform-agnostic infrastructures that support national ownership.
Authors
Karen Ane Frøyland Skjennum Jan Mulder Gijsbert Dirk Breedveld Thomas Hartnik Nicolas Estoppey Erlend Grenager SørmoAbstract
No abstract has been registered