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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.

2024

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Potato is an important part of the traditional Norwegian diet, and the crop faces several challenges with respect to pests and diseases, as well as the increasingly challenging changes in climate. Genome editing may provide tools to improve the resilience of Norwegian potato cultivars to new climate challenges. We have altered the skin colour of two potato cultivars, ‘Desirée’ and ‘Nansen’ from red to yellow, as a proof-of-concept for the use of CRISPR/Cas9 in a Norwegian cultivar. Our method has involved the use of protoplasts and we have grown the regenerants for three successive clonal tuber generations to evaluate the stability of the edited plants over time and under varying temperature conditions in contained rooms in a greenhouse. We found that the protoplast method is well suited to achieving CRISPR/Cas9 applications. The results show that the yellow skin is consistent over the three generations of tuber propagation. We found some suspected somaclonal variation in the protoplast regenerants. Some of the variation which we observed under high temperatures (up to nearly 40ºC) during the second growth cycle, disappeared when cultivated under lower temperatures in the third cultivation cycle.

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Controlling creeping perennial weeds is challenging throughout all farming systems. The present study distinguished and explored three different methods to control them non-chemically: disturbance with inversion, disturbance without inversion, and competition. Focusing on Cirsium arvense, Elymus repens, and Sonchus arvensis, we conducted a field study (2019–2021) at three northern European sites in Germany, Finland, and Norway. We investigated the effects of the control methods ploughing (inversion disturbance), root cutting (non-inversion disturbance), and cover crops (competition) alone. Root cutting was conducted using a prototype machine developed by “Kverneland”. Eight treatments were tested in factorial designs adapted for each site. Control methods were applied solely and combined. Response variables after treatments were aboveground weed biomass and grain yield of spring cereals. The control method of ploughing was most effective in reducing weed biomass compared to root cutting or cover crops. However, compared to the untreated control, a pronounced additive effect of root cutting and cover crops occurred, reducing weed biomass (−57.5%) similar to ploughing (−66%). Pooled over sites, the response was species-specific, with each species showing a distinct reaction to both control methods. C. arvense was most susceptible to root cutting, followed by E. repens, while S. arvensis showed no susceptibility. Crop yield losses were prevented compared to untreated plots by ploughing (+60.57%) and root cutting (+30%), but not by cover crops. We conclude that the combination of non-inversion disturbance and competition is a promising strategy to reduce the reliance on herbicides or inversion tillage in the management of perennial weeds.

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Context In high-latitude regions, variable weather conditions during the growing season and in winter cause considerable variation in forage grass productivity. Tools for predicting grassland status and yield, such as field measurements, satellite image analysis and process-based simulation models, can be combined in decision support for grassland management. Here, we calibrated and validated the BASic GRAssland (BASGRA) model against dry matter and Leaf area index data from temporary grasslands in northern Norway. Objective The objective of this study was to compare the performance of model versions calibrated against i) only region-specific ground data, ii) both region-specific ground and Sentinel-2 satellite data and, iii) field trial data from other regions. Methods Ground and satellite sensed data including biomass dry matter, leaf area index, and autumn and spring ground cover from 2020 to 2022 were acquired from 13 non-permanent grassland fields at four locations. These data were input to BASGRA calibrations together with soil and daily weather data, and information about cutting and nitrogen fertilizer application regimes. The effect of the winter season was taken into account in simulations by initiating the simulations either in autumn or in early spring. Results Within datasets, initiating the model in spring resulted in higher dry matter prediction accuracy (normalised RMSE 22.3–54.0 %) than initiating the model in autumn (normalised RMSE 41.1–93.4 %). Regional specific calibrations resulted in more accurate biomass predictions than calibrations from other regions while using satellite sensing data in addition to ground data resulted in only minor changes in biomass prediction accuracy. Conclusion All regional calibrations against data from northern Norway changed model parameter values and improved dry matter prediction accuracy compared with the reference calibration parameter values. Including satellite-sensed data in addition to ground data in calibrations did not further increase prediction accuracy compared with using only ground data. Implications Our findings show that regional data from farmers’ fields can substantially improve the performance of the BASGRA model compared to using controlled field trial data from other regions. This emphasises the need to account for regional diversity in non-permanent grassland when estimating grassland production potential and stress impact across geographic regions. Further use of satellite data in grassland model calibrations would probably benefit from more detailed assessments of the effect of grass growth characteristics and light and cloud conditions on estimates of grassland leaf area index and biomass from remote sensing.

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The Norwegian Committee for Food and Environment (VKM) has performed a preliminary assessment of an application for authorization for the genetically modified maize event DP202216 in the EAA. The scope of the application includes all uses of maize DP202216 and sub-combinations independently of their origin equivalent to the uses of any other maize grain and forage. The assessment was performed in connection with EFSAs (European Food Safety Authorities) public hearing of application EFSA-GMO-NL-2019-159, on request from the Norwegian Food Safety Authority and the Norwegian Environment Agency. The assessment of maize DP202216 is based on information provided by the applicant in the application EFSA-GMO-NL-2019-159, and relevant peer-reviewed scientific literature. Maize DP202216 has the potential to enhanced grain yield, and provides tolerance to glufosinate-ammonium herbicides. Authorisation process for genetically modified organisms Through the EEA Agreement, the EU Directive 2001/18/EC on deliberate release into the environment of genetically modified organisms is implemented in Norwegian law. Norway is therefore affiliated with the GMO authorisation process in the EU. In the EU, both GMOs and derived products are regulated by the Directive and Regulation 1829/2003/EC. The Regulation concerns genetically modified food and feed and is currently not a part of the EEA Agreement. In preparation for a legal implementation of the Regulation in Norwegian law, Norway follows the EU proceedings for GMO applications. When a company seeks approval of a genetically modified organism, the application is submitted to the national competent authority of an EU Member State, which forwards the application to EFSA. EFSA then submits the application for a public hearing across all EEA countries. VKM conducts its own review of the application and sends its comments to EFSA. EFSA then completes their scientific opinion based on information from the applicant, comments from EEA member countries and independent literature. The scientific opinion is then issued to the European Commission. VKM submitted their comments on application EFSA-GMO-NL-2019-159 to EFSA before the deadline January 3, 2020.

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In this study, 200 Norwegian dairy farms were analyzed over three years to compare greenhouse gas emissions, nitrogen (N) intensity, gross margin, and land use occupation between organically and conventionally managed farms. Conventionally managed farm groups were constructed based on propensity matching, selecting the closest counterparts to organically managed farms (n=15). These groups, each containing 15 farms, were differentiated by an increasing number of matching variables. The first group was matched based on geographical location, milk quota, and milking cow units. In the second match, the proportion of milking cows in the total cattle herd was added, and in the third, the ratio of milk delivered to milk produced and concentrate usage per dairy cow were included. The analysis showed that the conventionally managed farms (n=185) had higher greenhouse gas emissions (1.42 vs 0.98 kg CO2 per 2.78 MJ of edible energy from milk and meat, calculated as GWP100-AR4) and higher N intensity (6.9 vs 5.0 kg N input per kg N output) compared to the organic farms (N=15). When comparing emissions per kg of energy-corrected milk (ECM) delivered, conventional farms also emitted more CO2 (1.07 vs 0.8 kg CO2 per kg ECM). Furthermore, conventionally managed farms showed lower gross margins both in terms of NOK per 2.78 MJ edible energy delivered (5.8 vs 6.5 NOK) and per milking cow unit (30 100 vs 34 400 NOK), and they used less land (2.9 vs 3.6 m² per 2.78 MJ edible energy delivered) compared to organic farms. No differences were observed among the three conventionally managed groups in terms of emissions, N intensity, land use occupation, and gross margin.

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In many species, polymorphic genomic inversions underlie complex phenotypic polymorphisms and facilitate local adaptation in the face of gene flow. Multiple polymorphic inversions can co-occur in a genome, but the prevalence, evolutionary significance, and limits to complexity of genomic inversion landscapes remain poorly understood. Here, we examine genome-wide genetic variation in one of Europe's most destructive forest pests, the spruce bark beetle Ips typographus, scan for polymorphic inversions, and test whether inversions are associated with key traits in this species. We analyzed 240 individuals from 18 populations across the species' European range and, using a whole-genome resequencing approach, identified 27 polymorphic inversions covering ∼28% of the genome. The inversions vary in size and in levels of intra-inversion recombination, are highly polymorphic across the species range, and often overlap, forming a complex genomic architecture. We found no support for mechanisms such as directional selection, overdominance, and associative overdominance that are often invoked to explain the presence of large inversion polymorphisms in the genome. This suggests that inversions are either neutral or maintained by the combined action of multiple evolutionary forces. We also found that inversions are enriched in odorant receptor genes encoding elements of recognition pathways for host plants, mates, and symbiotic fungi. Our results indicate that the genome of this major forest pest of growing social, political, and economic importance harbors one of the most complex inversion landscapes described to date and raise questions about the limits of intraspecific genomic architecture complexity.

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Monitoring surface albedo at a fine spatial resolution in forests can enrich process understanding and benefit ecosystem modeling and climate-oriented forest management. Direct estimation of surface albedo using 10 m reflectance imagery from Sentinel-2 is a promising research avenue to this extent, although questions remain regarding the representativeness of the underlying model of surface reflectance anisotropy originating from coarser-resolution imagery (e.g., MODIS). Here, using Fennoscandia (Norway, Sweden, Finland) as a case region, we test the hypothesis that systematic stratification of the forested landscape into similar species compositions and physical structures prior to the step of carrying out angular bin regressions can lead to improved albedo estimation accuracy of direct estimation algorithms. We find that such stratification does not lead to statistically meaningful improvement over stratification based on conventional land cover classification, suggesting that factors other than forest structure (e.g., soils, understory vegetation) may be equally important in explaining within-forest variations in surface reflectance anisotropy. Nevertheless, for Sentinel-2-based direct estimation based on conventional forest classification, we document total-sky surface albedo errors (RMSE) during snow-free and snow-covered conditions of 0.015 (15 %) and 0.037 (21 %), respectively, which align with those of the coarser spatial resolution products in current operation.