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
Navneet Kaur Seth J. Dorman Christy Tanner Nicole AndersonAbstract
Tychius picirostris Fabricius (Coleoptera: Curculionidae), the clover seed weevil (CSW), is a major pest of white clover (Trifolium repens L.) seed crops in Oregon, USA, where larval feeding during seed development reduces yield and quality, causing substantial economic losses. For decades, CSW management has relied on bifenthrin (IRAC Group 3A), but since 2017 growers have reported reduced efficacy, prompting concern of resistance. Laboratory bioassays in 2022-2023 confirmed very high resistance to bifenthrin (RR50 = 178-726) and moderate resistance to malathion (Group 1B; RR50 = 7.8-32.8), underscoring the need for alternative chemistries and insecticide resistance management (IRM) guidelines. From 2022 to 2024, on-farm insecticide efficacy trials were conducted in commercial white clover seed fields in western Oregon. Early-season (pre-bloom or PB) application using contact insecticides (malathion, isocycloseram [Group 30], indoxacarb [Group 22]) targeted adults, either alone or in sequence with mid-season (full bloom or FB) application of systemic insecticides (chlorantraniliprole and cyantraniliprole [Group 28]) targeting larvae. Adult abundance was monitored with 20-sweep net samples, and larval densities were estimated from 30 inflorescences per plot extracted with Berlese funnels. Across sites and years, isocycloseram consistently suppressed adult populations, cyantraniliprole reduced larval densities, while indoxacarb showed variable performance. Although seed yields did not differ significantly among treatments, yet efficacy data supported product registration in Oregon and highlighted the value of chemical rotation plans for resistance management. Based on these findings, we recommend discontinuing bifenthrin and adopting an integrated resistance management (IRM) program that applies contact insecticides during spring adult migration when ≥2 weevils per sweep are detected and systemic insecticides during full bloom when ≥3 per 30 inflorescences are observed.
Authors
Mohammed M. Morad Nicole Anderson Thomas G. ChastainAbstract
Orchardgrass (Dactylis glomerata L.) is an important forage seed crop, but unlike other cool-season grass seed crops such as perennial ryegrass and tall fescue , seed yields have not increased over time. Research from the literature suggests that plant growth regulators (PGRs), such as trinexapac-ethyl (TE), and spring nitrogen (N) application increase seed yield in orchardgrass by increasing seed number. However, no research has investigated the effects of PGRs and spring N on orchardgrass seed development. Field trials were conducted in 2018 and 2019 to investigate orchardgrass seed development and the effects of PGR and spring N treatments on this process. Treatments included an untreated control, TE (210 g ai ha-1), spring N (112 kg ha-1), and TE + N. Regression analyses were used to elucidate seed development in three spikelet positions: distal, central, and proximal. In 2018, seed weight increased over growing degree days (GDD) in a bi-phasic segmented pattern from distal and central spikelets, but increases were linear from proximal spikelets. In 2019, seed weight increased in proximal spikelets following a bi-phasic segmented function, and in central spikelets, the seed weight increase was also bi-phasic, except for the TE treatment. Seed growth rate varied among spikelet positions, ranging from 0.22 to 0.34 mg GDD-1 per 100 seeds. The seed growth rate varied among TE and N treatments, ranging from 0.31 to 0.47 mg GDD-1 per 100 seed. The TE + N treatment had the shortest seed filling duration and one of the smallest seed growth rate values, producing low seed weight. The TE + N treatment produced high seed number and seed yield, indicating a reduction in seed abortion or shattering. Seed carbon (C) and N content increased during seed development and peak deposition preceded physiological maturity. There was no effect of TE on deposition of C or N in orchardgrass seed.
Authors
Nicole Anderson Hannah M. Rivedal Seth J. DormanAbstract
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.
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
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