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
Michael Altgen Michael Fröba Julius Gurr Andreas Krause Martin Ohlmeyer Uta Sazama Wim Willems Martin NopensSammendrag
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Sammendrag
The European spruce bark beetle Ips typographus and the North American spruce beetle Dendroctonus rufipennis cause high mortality of spruces on their native continents. Both species have been inadvertently transported beyond their native ranges. With similar climates and the presence of congeneric spruce hosts in Europe and North America, there is a risk that one or both bark beetle species become established into the non-native continent. There are many challenges that an introduced population of bark beetles would face, but an important prerequisite for establishment is the presence of suitable host trees. We tested the suitability of non-native versus native hosts by exposing cut bolts of Norway spruce (Picea abies), black spruce (Picea mariana) and white spruce (Picea glauca) to beetle attacks in the field in Norway and Canada. We quantified attack density, brood density and reproductive success of I. typographus and D. rufipennis in the three host species. We found that I. typographus attacked white and black spruce at comparable densities to its native host, Norway spruce, and with similar reproductive success in all three host species. In contrast, D. rufipennis strongly preferred to attack white spruce (a native host) but performed better in the novel Norway spruce host than it did in black spruce, a suboptimal native host. Our results suggest that I. typographus will find abundant and highly suitable hosts in North America, while D. rufipennis in Europe may experience reduced reproductive success in Norway spruce.
Forfattere
Håvard SteinshamnSammendrag
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Forfattere
Håvard SteinshamnSammendrag
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Sammendrag
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Sammendrag
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Forfattere
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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Sammendrag
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Sammendrag
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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.