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
Abstract
Phytophthora cactorum is a plant pathogenic oomycete that causes crown rot in strawberry leading to significant economic losses every year. To invade the host, P. cactorum secretes an arsenal of effectors that can manipulate host physiology and impair its defense system promoting infection. A transcriptome analysis was conducted on a susceptible wild strawberry genotype (Fragaria vesca) 48 hours post inoculation with P. cactorum to identify effectors expressed during the early infection stage. The analysis revealed 4,668 P. cactorum genes expressed during infection of F. vesca. A total of 539 secreted proteins encoded by transcripts were identified, including 120 carbohydrate-active enzymes, 40 RXLRs, 23 proteolytic enzymes, nine elicitins, seven cysteine rich proteins, seven necrosis inducing proteins and 216 hypothetical proteins with unknown function. Twenty of the 40 RXLR effector candidates were transiently expressed in Nicotiana benthamiana using agroinfiltration and five previously unreported RXLR effector genes (Pc741, Pc8318, Pc10890, Pc20813, and Pc22290) triggered cell death when transiently expressed. The identified cell death inducing RXLR effectors showed 31–66% identity to known RXLR effectors in different Phytophthora species having roles in pathogenicity including both activation and suppression of defense response in the host. Furthermore, homology analysis revealed that these cell death inducing RXLR effectors were highly conserved (82 - 100% identity) across 23 different strains of P. cactorum originating from apple or strawberry.
Authors
Geir-Harald Strand Eva Solbjørg Flo Heggem Linda Aune-Lundberg Agata Hościło Adam WaśniewskiAbstract
Land cover maps are frequently produced via the classification of satellite imagery. There is a need for a practicable and automated approach for the generalization of these land cover classification results into scalable, digital maps while minimizing information loss. We demonstrate a method where a land cover raster map produced using the classification of Sentinel 2 imagery was generalized to obtain a simpler, more readable land cover map. A replicable procedure following a formal generalization framework was applied. The result of the initial land cover classification was separated into binary layers representing each land cover class. Each binary layer was simplified via structural generalization. The resulting images were merged to create a new, simplified land cover map. This map was enriched by adding statistical information from the original land cover classification result, describing the internal land cover distribution inside each polygon. This enrichment preserved the original statistical information from the classified image and provided an environment for more complex cartography and analysis. The overall accuracy of the generalized map was compared to the accuracy of the original, classified land cover. The accuracy of the land cover classification in the two products was not significantly different, showing that the accuracy did not deteriorate because of the generalization.
Authors
Jian LiuAbstract
No abstract has been registered
Authors
Shelemia Nyamuryekung'eAbstract
On the Ground: -Precision livestock management through sensor technology using the Internet of Things offers enhanced surveillance and monitoring of the ranching operations. -At the ranch scale, the integration of sensor technology, including on-animal sensors, environmental monitoring equipment, and remote sensing can shift livestock operations from a solely reactive, traditional, knowledge-based approach toward a proactive, data-driven, decision-making process. -Leveraging data from sensors at the ranch scale can address logistical challenges and create efficiency in decision-making processes concerning resource management.
Authors
Kari-Anne Kallerud Lyng Hanne Møller Klaus Mittenzwei Ivar Pettersen Jakob Vesterlund Olsen Hanne Fjerdingby OlsenAbstract
The food system significantly impacts the environment and society. This study examined a shift from a continuation of the current trend (policy as usual scenario) towards a biomass value hierarchy scenario, which focused on optimizing land and biomass use and rethinking the role of livestock production. The biomass value hierarchy was based on circular economy principles, the waste hierarchy, and national self-sufficiency, which eliminated feed import and redistributed protein sources in the diet. A Multi-Criteria Decisions Analysis (MCDA) framework was used to assess the two scenarios across four sustainability dimensions: environmental, social, economic and policy. Environmental and social impacts were analysed using life cycle assessment methodology, while economic and policy implications were explored using partial equilibrium modelling, with the Norwegian food system as a case study. The results for the environmental dimension indicated that, compared to the policy as usual scenario, the biomass value hierarchy reduced environmental impacts by 8% to 18% across the indicators, including climate change, acidification, particulate matter, terrestrial eutrophication and occupation of arable land. Social impacts also improved in categories with the highest social risks, such as equal opportunities for workers, health and safety for farmers, cultural heritage, food security, fair competition, and promoting social responsibility. Contrarily, indicators within the economic dimension revealed reduced profitability, and results within the policy dimension showed a considerable increase in required subsidies, border measures and governmental restrictions on consumption. The study findings indicate that an environmentally and socially sustainable food system is feasible but requires significant political and economic support. Additionally, the study highlights the value of using MCDA when combining different research methods in cross-disciplinary assessments. These results underscore the need for a societal debate on acceptable levels of political intervention and the role of consumers and taxpayers in shaping the future food system.
Abstract
On European golf courses, small lightweight robotic mowers have recently been introduced for fairway and rough mowing. In this study, turfgrass quality and the coverage of broadleaf weeds in three cool-season grasses were compared in response to robotic and traditional fairway mowing. Experiments with pure swards of red fescue (Festuca rubra L.), colonial bentgrass (Agrostis capillaris L.), and Kentucky bluegrass (Poa pratensis L.) were carried out at NIBIO Landvik, Norway, to evaluate differences between lightweight robotic mowing and reel mowing. In a mixture of the three species turfgrass quality and the coverage of broadleaf weeds were compared in response to robotic and reel mowing at yearly fertilizer levels from 0 to 120 kg N ha−1. The results showed that both robotic and reel mowing were found to provide high turfgrass quality, while lower coverage of broadleaf weeds (predominantly white clover [Trifolium repens L.]) was found with robotic mowing independent of grass species. In the mixed stand, higher turfgrass quality was found with robotic mowing regardless of N rate, but N rates above 60 kg ha−1 year−1 were necessary to keep the coverage of white clover in fall on an acceptable low level. Our results suggest that robotic mowing can decrease the spread of white clover at a fairway mowing height of 15 mm, but more research is needed to clarify at which mowing heights, mowing frequencies, and fertilizer levels we can get the best competitiveness against broadleaf weeds on fairways with robotic mowing.
Abstract
No abstract has been registered
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No abstract has been registered
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No abstract has been registered
Abstract
Recurrent climate-driven disturbances impact on the health of European forests that reacted with increased tree dieback and mortality over the course of the last four decades. There is therefore large interest in predicting and understanding the fate and survival of forests under climate change. Forest conditions are monitored within the pan-European ICP Forests programme (UN-ECE International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests) since the 1980s, with tree crown defoliation being the most widely used parameter. Defoliation is not a cause-specific indicator of tree health and vitality, and there is a need to connect defoliation levels with the physiological functioning of trees. The physiological responses connected to tree crown defoliation are species-specific and concern, among others, water relations, photosynthesis and carbon metabolism, growth, and mineral nutrients of leaves. The indicators to measure physiological variables in forest monitoring programs must be easy to apply in the field with current state-of-the-art technologies, be replicable, inexpensive, time efficient and regulated by ad hoc protocols. The ultimate purpose is to provide data to feed process-based models to predict mortality and threats in forests due to climate change. This study reviews the problems and perspectives connected to the realization of a systematic assessment of physiological variables and proposes a set of indicators suitable for future application in forest monitoring programs.