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.
2023
Forfattere
Håvard SteinshamnSammendrag
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Julia Le Noë Stefano Manzoni Rose Abramoff Tobias Bölscher Elisa Bruni Rémi Cardinael Philippe Ciais Claire Chenu Hugues Clivot Delphine Derrien Fabien Ferchaud Patricia Garnier Daniel Goll Gwenaëlle Lashermes Manuel Martin Daniel Rasse Frédéric Rees Julien Sainte-Marie Elodie Salmon Marcus Schiedung Josh Schimel William Wieder Samuel Abiven Pierre Barré Lauric Cécillon Bertrand GuenetSammendrag
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Questions Observations in permanent forest vegetation plots in Norway and elsewhere indicate that complex changes have taken place over the period 1988–2020. These observations are summarised in the “climate-induced understorey change (CIUC)” hypothesis, i.e. that the understorey vegetation of old-growth boreal forests in Norway undergoes significant long-term changes and that these changes are consistent with the ongoing climate change as an important driver. Seven testable predictions were derived from the CIUC hypothesis. Location Norway. Methods Vegetation has been monitored in a total of 458 permanently marked plots, each 1 m2, in nine old-growth forest sites dominated by Picea abies at intervals of 5–8 years over the 32-year study period. For each of the 52 combinations of site and year, we obtained response variables for the abundance of single species, abundance and species density of taxonomic–ecological species groups and two size classes of cryptogams, and site species richness. All of these variables were subjected to linear regression modelling with site and year as predictors. Results Mean annual temperature, growing-season length and the number of days with precipitation were higher in the study period than in the preceding ca. 30-year period, resulting in increasingly favourable conditions for bryophyte growth. Site species richness decreased by 13% over the 32-year study period. On average, group abundance of vascular plants decreased by 24% (decrease in forbs: 38%). Patterns of group abundance change differed among cryptogam groups: although peat-moss abundance increased by 39%, the abundance of mosses, hepatics and lichens decreased by 13%, 49% and 67%, respectively. Group abundance of small cryptogams decreased by 61%, whereas a 13% increase was found for large cryptogams. Of 61 single species tested for abundance change, a significant decrease was found for 43 species, whereas a significant increase was found only for 6 species. Conclusions The major patterns of change in species richness, group species density and group abundance observed over the 32-year study period accord with most predictions from the CIUC hypothesis and are interpreted as direct and indirect responses to climate change, partly mediated through changes in the population dynamics of microtine rodents. The more favourable climate for bryophyte growth explains the observed increase for a few large bryophyte species, whereas the decrease observed for small mosses and hepatics is interpreted as an indirect amensalistic effect, brought about by shading and burial in mats of larger species and accelerated by reduced fine-scale disturbance by microtine rodents. Indirect effects of a thicker moss mat most likely drive the vascular plant decline although long-term effects of tree-stand dynamics and former logging cannot be completely ruled out. Our results suggest that the ongoing climate change has extensive, cascading effects on boreal forest ecosystems. The importance of long time-series of permanent vegetation plots for detecting and understanding the effects of climate change on boreal forests is emphasised.
Forfattere
Yi Zhang Yun Zhao Yijing Feng Yating Yu Yeqing Li Jian Li Zhonghao Ren Shuo Chen Lu Feng Junting Pan Hongjun Zhou Yongming HanSammendrag
Industrial-scale garage dry fermentation systems are extremely nonlinear, and traditional machine learning algorithms have low prediction accuracy. Therefore, this study presents a novel intelligent system that employs two automated machine learning (AutoML) algorithms (AutoGluon and H2O) for biogas performance prediction and Shapley additive explanation (SHAP) for interpretable analysis, along with multiobjective particle swarm optimization (MOPSO) for early warning guidance of industrial-scale garage dry fermentation. The stacked ensemble models generated by AutoGluon have the highest prediction accuracy for digester and percolate tank biogas performances. Based on the interpretable analysis, the optimal parameter combinations for the digester and percolate tank were determined in order to maximize biogas production and CH4 content. The optimal conditions for the digester involve maintaining a temperature range of 35–38 °C, implementing a daily spray time of approximately 10 min and a pressure of 1000 Pa, and utilizing a feedstock with high total solids content. Additionally, the percolate tank should be maintained at a temperature range of 35–38 °C, with a liquid level of 1500 mm, a pH range of 8.0–8.1, and a total inorganic carbon concentration greater than 13.8 g/L. The software developed based on the intelligent system was successfully validated in production for prediction and early warning, and MOPSO-recommended guidance was provided. In conclusion, the novel intelligent system described in this study could accurately predict biogas performance in industrial-scale garage dry fermentation and guide operating condition optimization, paving the way for the next generation of intelligent industrial systems.
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The aim of the project is to evaluate and assess measures in lawn care management and at the same time to combine new techniques and alternative products to control diseases such as snow mold (Microdochium nivale) and dollar spots (Sclerotinia homoeocarpa) without or with a greatly reduced use of pesticides. Therefore, the lawn research group of the NIBIO (Norwegian Institute for Bioeconomy Research) started a project on Integrated Pest Management (IPM) with a focus on the most important fungal diseases and insect pests on golf turf. The project is supported by STERF (Scandinavian Turf and Environmental Research Foundation) and the R&A (The Royal and Ancient Golf Club of St. Andrews) as main sponsors, as well as by the German Golf Association, the Netherlands Golf Federation sponsor, the Botaniska Analysgruppen Sweden and the Danish Environmental Protection Agency. The current project aims is to develop new findings with regard to the increasing challenges in dealing with the above-mentioned pests. The two questions to check are: (1) the effectiveness of the “rolling” of greens (dollar spot treatment) and the effectivity of UV-C exposure (snow mold prevention). For this reason, two different attempts were made on a putting green at the golf course Osnabrueck (Bissendorf-Jeggen).
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