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
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
Authors
Ulrika Jansson Asplund Damian Petkovic Karlsen Anne Krag Brysting Rune Halvorsen Håvard Kauserud O. Janne Kjønaas Johan AsplundAbstract
No abstract has been registered
Authors
Ming Yu Sebastian Kepfer-Rojas Yamina Micaela Rosas Teresa Gómez de la Bárcena Inger Kappel Schmidt Per Gundersen Ludovica D'Imperio Carsten W. Mueller Lars VesterdalAbstract
No abstract has been registered
Authors
L. Duncanson P. M. Montesano A. Neuenschwander A. Zarringhalam N. Thomas D. M. Minor M. A. Wulder J. C. White E. Guenther T. Feng V. Leitold S. Hancock J. Armston Stefano Puliti A. I. Mandel S. Shah C. Silva M. Purslow J. Bruening Johannes Breidenbach Erik Næsset Svetlana Saarela N. Hunka J. R. Kellner S. P. Healey D. Schepaschenko J. Wallerman C. S.R. Neigh N. Carvalhais R. DubayahAbstract
No abstract has been registered
Authors
José Luís Trevizan Chiomento Nícolas Alberton Mandelli Ana Júlia Schroeder Ely Matheus Welter Henrique Didó Maria Antônia Lacourt Oliveira Bruna Jirkowski Francisco Wilson Reichert Junior Mateus Possebon Bortoluzzi Rafael Rieder Thomas dos Santos Trentin Anita SønstebyAbstract
No abstract has been registered
Authors
Milica Fotirić Akšić Mirjana Pešić Ilinka Pećinar Mihajlo Jakanovski Danijel Milinčić Aleksandar Kostić Marko Kitanović Uroš Gašić Dragana Dabić Zagorac Dušanka Milojković Opsenica Mekjell MelandAbstract
No abstract has been registered
Authors
Chen‐Jui Yang Tomas Funda Rajiv Chaudhary Zhi‐Qiang Chen Maximiliano Estravis Barcala Harry WuAbstract
Genetic differentiation among populations often varies significantly across the genome due to factors such as selection and recombination, resulting in a heterogeneous genomic landscape. However, variation in low‐differentiation regions—genomic valleys—remains poorly understood. Moreover, most insights into plant genomic landscapes come from flowering plants, while comparable genome‐wide studies in other taxa, such as conifers, remain limited. We analyzed whole‐genome sequencing data from 100 individuals of three pine species— Pinus banksiana , Pinus contorta , and Pinus nigra . We found substantial genome‐wide variation in recombination rates, with intergenic regions exhibiting higher recombination than genic regions, and rates decreasing with increasing distance from genes. Recombination rate was negatively correlated with gene length, driven primarily by intron length, suggesting that long introns in conifers may promote the retention of exceptionally long genes by maintaining low recombination in these regions. Genomic scans further revealed that genomic valleys are maintained through either balancing, background, or parallel selection. Additionally, multiple forms of selection were strongly associated with local recombination rate variation, highlighting the significant role of recombination in shaping patterns of genomic differentiation. Our findings provide new insight into the evolution and maintenance of extremely long genes in conifers. Moreover, the results indicate that allopatric selection in regions of low recombination is a major force structuring genomic variation in these species.