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
Svenja B. KroegerAbstract
Road ecology plays a vital role in Norway, where infrastructure development intersects with diverse and sensitive ecosystems. Despite significant efforts to integrate ecological considerations into transportation planning and implementation, numerous challenges remain. The Norwegian Institute for Bioeconomy Research (NIBIO), a leading public research institute, is among the key stakeholders dedicated to understanding and mitigating the ecological impacts of road infrastructure in Norway. This presentation highlights NIBIO’s major contributions to road ecology, introduces significant national initiatives and objectives, and reflects on the pressing challenges we aim to address in the coming years. We underscore the importance of both national and international collaborations and seek to engage with potential partners who share our commitment to advancing transportation ecology. Our overarching aim is to develop a more resilient and ecologically sustainable transportation network by enhancing data integration, refining the design and implementation of mitigation measures, and fostering collaborations that drive innovation. By working together, we can transform past challenges into future achievements, striving to harmonize infrastructure development with the conservation of Norway’s unique and valuable natural landscapes and beyond.
Authors
Michel VerheulAbstract
Energy-efficient greenhouse production for emission-free food cultivation Michel J. Verheul discusses the advancements in energy-efficient and emission-free greenhouse production in Norway, focusing on the innovative methods developed by researchers at the Norwegian Institute of Bioeconomy Research (NIBIO). As the world faces the dual challenges of climate change and food security, the need for sustainable, year-round food production has never been greater. In Norway, where only about 3% of land is arable and the climate restricts traditional agriculture, greenhouses offer a promising solution. However, conventional greenhouse production is energy-intensive and often reliant on fossil fuels, leading to significant CO2 emissions. A new wave of innovation – pioneered by Norwegian researchers and industry partners – aims to change this, making emission-free, energy-efficient greenhouse production both possible and profitable.
Abstract
The simultaneous improvement of crop yield and nitrogen (N) use efficiency (NUE) can be potentially achieved through precision N management. In this study, crop modeling, remote sensing, and machine learning were combined to develop and assess a precision N recommendation strategy (MSM-PNM) for maize (Zea mays L.) based on twenty-eight site-years of field experiments conducted in Northeast China involving various N rates and planting densities. For the MSM-PNM strategy, a crop growth model was used at the eight-leaf stage of maize to initially predict the in-season optimal side-dressing N rate (EOSN) by combining current season’s available weather data prior to side-dressing with weather data from previous years for the remaining growing period. Maize N status was then estimated using machine learning models based on data collected with an active canopy sensor along with weather, soil, and management information. Finally, the crop model predicted EOSN was adjusted using the estimated maize N status. The prediction accuracy of EOSN (R2 = 0.70, root mean squared error (RMSE) = 18.60 kg ha−1) based on this integrated MSM-PNM strategy was higher than using crop model only (R2 = 0.65, RMSE = 20.10 kg ha−1). The precision maize management system based on the integrated MSM-PNM strategy decreased N rates by 6–16 % and increased NUE by 8–18 % over farmer practice applying 250 kg N ha−1 as basal N fertilizer without side-dress N application and regional optimal management practice applying split N at fixed rate and timing, while maintaining high grain yield and marginal net return. It is concluded that this new integrated precision N management strategy combining the advantages of crop modeling, remote sensing, and machine learning can significantly increase maize NUE while maintaining high crop yield, thus contributing to food security and agricultural sustainability.
Authors
Xiaokai Chen Yuxin Miao Krzysztof Kusnierek Fenling Li Chao Wang Botai Shi Fei Wu Qingrui Chang Kang YuAbstract
Timely and accurate monitoring of crop nitrogen (N) status is essential for precision agriculture. UAV-based hyperspectral remote sensing offers high-resolution data for estimating plant nitrogen concentration (PNC), but its cost and complexity limit large-scale application. This study compares the performance of UAV hyperspectral data (S185 sensor) with simulated multispectral data from DJI Phantom 4 Multispectral (P4M), PlanetScope (PS), and Sentinel-2A (S2) in estimating winter wheat PNC. Spectral data were collected across six growth stages over two seasons and resampled to match the spectral characteristics of the three multispectral sensors. Three variable selection strategies (one-dimensional (1D) spectral reflectance, optimized two-dimensional (2D), and three-dimensional (3D) spectral indices) were combined with Random Forest Regression (RFR), Support Vector Machine Regression (SVMR), and Partial Least Squares Regression (PLSR) to build PNC prediction models. Results showed that, while hyperspectral data yielded slightly higher accuracy, optimized multispectral indices, particularly from PS and S2, achieved comparable performance. Among models, SVM and RFR showed consistent effectiveness across strategies. These findings highlight the potential of low-cost multispectral platforms for practical crop N monitoring. Future work should validate these models using real satellite imagery and explore multi-source data fusion with advanced learning algorithms.
Abstract
Interpreting multi-component 1H NMR spectra is difficult due to peak overlap, concentration variability, and low-abundance signals. We cast mixture identification as a single-pass multi-label task. A compact CNN–Transformer (“Hybrid”) model was trained end-to-end on domain-informed and realistically simulated spectra derived from a 13-component flavor library; the model requires no real mixtures for training. On 16 real formulations, the Hybrid attains micro-F1 = 0.990 and exact-match (subset) accuracy = 0.875, outperforming CNN-only and Transformer-only ablations, while remaining efficient (~0.47 M parameters; ~0.68 ms on GPU, V100). The approach supports abstention and shows robustness to simulated outsiders. Although the evaluation set was small, and the macro-ECE (per-class, 15 bins) was inflated by sparse classes (≈0.70), the micro-averaged Brier is low (0.0179), and temperature scaling had negligible effect (T ≈ 1.0), indicating the good overall probability quality. The pipeline is readily extensible to larger libraries and adjacent applications in food authenticity and targeted metabolomics. Classical chemometric baselines trained on simulation failed to transfer to real measurements (subset accuracy 0.00), while the Hybrid model maintained strong performance.
Authors
Ivan M De-la-Cruz Femke Batsleer Dries Bonte Carolina Diller José Luis Izquierdo Sonja Still Sonia Osorio David Posé Aurora de la Rosa Martijn L. Vandegehuchte Anne Muola Timo Hytönen Johan A StenbergAbstract
Climate change creates novel environmental conditions that plant species must adapt to. Since plants are finely tuned to the seasonality of their environments, shifts in their phenology serve as some of the most compelling evidence of climate change’s impact. Understanding how key fitness-related phenological traits, such as flowering onset, respond to novel environments is crucial for assessing species’ plasticity and/or adaptive potential under climate change. Here, we investigated the onset of flowering in Fragaria vesca (woodland strawberry; Rosaceae) by translocating genotypes between four sites along a south–north gradient in Europe, encompassing its entire latitudinal distribution range with varying temperatures, precipitation patterns, and photoperiods. At each site, we included a reduced precipitation treatment using rainout shelters to simulate drought conditions and assess their impact on flowering onset. Our findings revealed that southern and central European genotypes exhibited a delayed onset of flowering when translocated to the northernmost site. In contrast, no difference among genotypes was found in the onset of flowering when grown in more southerly sites. Reduced precipitation accelerated flowering across several sites and all genotypes, irrespective of their latitudinal origin. Overall, northern European genotypes showed a greater capacity to adjust their onset of flowering in response to the different photoperiods and temperatures across the latitudinal gradient compared to southern European genotypes, suggesting that they may be more resilient to shifting environmental conditions. Differences in phenotypic plasticity among genotypes translocated to higher versus lower latitudes highlight the role of photoperiod in evaluating a species’ capacity to cope with climate change.
Abstract
No abstract has been registered
Abstract
Climate change and human activities are prone to cause the shrinkage of lakes and soil salinization in arid areas, thereby affecting regional ecological security. Biodiversity conservation and ecological restoration in shrinking lake areas have attracted more attention. We have investigated the changes in soil organic carbon (SOC) content and microbial community diversity under different vegetation restoration measures, such as the species of Carex, Salicornia, Tamarisk, reed, and grass restoration in the lakeshore of Dalinor lake in Inner Mongolia. Results showed that the soil pH and water-soluble salt content are relatively high in the Carex and Salicornia restoration areas compared to the bare land, and the changes in SOC and TN content are not significant. Still, the contents of AP (available phosphorus) and AK (available potassium) are significantly increased. For the Tamarisk, reed, and grass restoration areas, the level of soil salinization has significantly decreased. At the same time, the contents of SOC and TN are increased by 23.1% and 116.2% compared with the bare land, respectively. With the different vegetation restoration measures, soil microbial biomass carbon (MBC) content was, on average, 62.4% higher than that of bare land. The high-throughput sequencing data showed that different vegetation restoration measures have significantly changed the composition of soil bacterial communities, the alpha diversity indices of Chao1 and Shannon increased by 73.6% and 19.7%, respectively, and the abundance of microbial species related to soil carbon and nitrogen cycling also showed an enrichment trend. Taken together, our study, built on the joint efforts of Chinese and Norwegian partners, has provided valuable information for the future adaptive management of climate change risks and biodiversity conservation related to the shrinkage of lakes in arid areas.
Authors
Fride Høistad ScheiAbstract
No abstract has been registered
Authors
Alexandro B. Leverkus María Medina Alba Lazaro-Gonzalez Laura Levy Olivia Lorente-Casalini Marino P. Reyes Martín Enrique Andivia Alessandro Bizzarri Henrik Böhlenius Vito E. Cambria Claudia Cocozza Branislav Cvjetkovic Giovanbattista de Dato Lenka Ehrenbergerova Peter Ferus Lorena Gomez-Aparicio Arndt Hampe Kjersti Holt Hanssen Marcin Jakubowski Ivona Kerkez Jankovic Marcin Klisz Wojciech Kowalkowski Klaus Kremer Jelena Lazarevic Dagnija Lazdina Emanuele Lingua Magnus Löf Manuel E. Lucas-Borja Adrian Łukowski Paula Maia Paola Mairota Alberto Maltoni Barbara Mariotti Raffaella Marzano Lucia Mondanelli Antonio Montagnoli Maria Cristina Monteverdi Francisco B. Navarro Reyes Juan A. Oliet Maria S. Patrício Zoran Poduska Vladan Popovic Radosław Puchałka Piotr Robakowski Piotr Sewerniak Carmen Urena-Lara Pedro Villar-Salvador Johanna Witzell Nicolo Anselmetto Inger Sundheim FløistadAbstract
Seedling emergence constitutes a critical recruitment step, and early growth relates to plant competitive ability. Understanding their drivers has implications for forestry and forest ecosystem conservation, restoration, and adaptation to climate change. We seeded 6984 acorns in an experiment with 97 cases at 45 sites in 15 European countries, encompassing 12 oak species. We tested whether the quality of the acorn batch, site-level weather and soil characteristics, year of seeding, and species’ mean specific leaf area (SLA) affected the emergence and early growth of seedlings after the first summer. Germination potential and acorn dry weight, measured under controlled conditions, were positively associated with emergence and early growth. Seedling emergence was negatively associated with the mean monthly temperature and cumulative winter precipitation, and it was higher in the seedling cohort that was spared from the 2021 drought. Additionally, seedling emergence was positively related to soil nutrient concentration and negatively to increasing soil pH, but not to water-holding capacity, and growth was unrelated to soils. Species-level SLA was not related to either response. The four main study species –Quercus cerris, Q. ilex, Q. petraea, and Q. robur– responded similarly to weather but not to soil conditions. We conclude that, at a continental scale, and assuming that species establish within their current distributions, (a) oak seedling emergence and early growth are associated with acorn quality rather than species identity or SLA, (b) they are highest at sites with low winter precipitation and temperature, (c) emergence is reduced in dry years, and d) soil properties play a secondary role at this early recruitment stage.