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.
2025
Forfattere
Nicole Anderson Hannah M. Rivedal Seth J. DormanSammendrag
Epidemiology and management of aphid-transmitted yellow dwarf viruses (YDVs) have received international attention in small grains, but research regarding YDVs in grass seed production is limited. An integrated pest management program is needed to reduce the impact of YDVs in grass seed crops that are grown for more than one year. The objectives of this work were to: 1) survey commercial grass seed production fields to determine spatiotemporal virus composition, 2) evaluate the effects of nitrogen (N) fertiliser rate, and the timing and frequency of foliar insecticide applications on aphid abundance, YDV disease incidence, and seed yield in two perennial ryegrass cultivars, and 3) develop high-throughput phenotyping methods to screen cultivars for host plant resistance. To determine the incidence and diversity of YDVs, perennial ryegrass (n=20) and tall fescue (n=30) seed fields in Oregon were surveyed in 2021-2022. In 82% of fields, a Luteovirus-type YDV was detected, and 65% had detection of a Polerovirus-type YDV. In small-plot field trials conducted from 2021 to 2024, high N rates increased YDV incidence in perennial ryegrass. Seed yield was greatest for the less susceptible cultivar when protected with one insecticide treatment per season. A higher-than-recommended N rate did not increase seed yield across treatment combinations in first-year stands but did increase seed yields in second and third-year stands when YDV infection was >50%. Phenotyping methods were evaluated to assess potential host-plant resistance to YDVs using perennial ryegrass cultivars (n=27) with highthroughput automated video tracking for aphid behaviours that may confer resistance, and compared to traditional phenotyping methods. Several cultivars showed potential tolerance to YDVs. This research provides new knowledge of the spatial composition of aphid-transmitted YDVs, integrated pest management guidelines, and high-throughput methods for breeding programs to develop cultivars that are resistant to YDVs.
Forfattere
Svenja B. KroegerSammendrag
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.
Forfattere
Michel VerheulSammendrag
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.
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Randi Berland FrøsethSammendrag
Resultater fra Capture-prosjektet
Sammendrag
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.
Forfattere
Xiaokai Chen Yuxin Miao Krzysztof Kusnierek Fenling Li Chao Wang Botai Shi Fei Wu Qingrui Chang Kang YuSammendrag
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.
Sammendrag
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.
Forfattere
Annbjørg KristoffersenSammendrag
Det er ikke registrert sammendrag
Forfattere
Annbjørg KristoffersenSammendrag
Det er ikke registrert sammendrag