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
Sammendrag
Local food is increasing in popularity. The more local, the better. However, selling local food is much more demanding than many may be aware of. Fortunately, new digital solutions are on the way.
Forfattere
Sebastian EiterSammendrag
Det er ikke registrert sammendrag
Sammendrag
Et regneeksempel for Oslo viser at privat, småskala og ikke-kommersiell dyrking i byer kan øke norsk selvforsyning med poteter, tomater, løk og gulrøtter.
Forfattere
Holger Lange Jaana Bäck Georg Jocher Natascha Kljun Anne Klosterhalfen Alexander Knohl Natalia Kowalska Adam Kristensson Corinna Rebmann Teresa Saura-Yera Alberto VilagrosaSammendrag
Utilizing forest ecosystems to mitigate climate change effects and to preserve biodiversity requires detailed insights into the feedbacks between forest type, climatic and soil conditions, and in particular forest management history and practice. Analysis of long-term observations at the site level, remote sensing proxies and understanding relevant biogeochemical and biophysical processes are key to achieving these insights. In the recently started EU H2020 project “CLimate Mitigation and Bioeconomy pathways for sustainable FORESTry” (CLIMB-FOREST), we address these issues based on intensely monitored sites with flux measurements (ICOS, Fluxnet), other ecosystem research and observation networks (eLTER, National Forest Inventories), remotely sensed observations and process understanding. This presentation outlines the activities of CLIMB-FOREST regarding (1) carbon stocks and fluxes according to stand age, species distribution, management and disturbance history; (2) biophysical effects of forest structure; (3) effects and importance of short-lived climate forcers (e.g. BVOCs) and (4) management and extreme event (drought, fire) impact on SOC and N dynamics. We also outline how the gained knowledge informs scenario runs of the Vegetation and Earth System Model RCA-GUESS in the project.
Sammendrag
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Sammendrag
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Sammendrag
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Sammendrag
Metangassutslipp fra sau, storfe og geit utgjør rundt fire prosent av det totale norske klimagassutslippet. Mange av beregningene som utgjør grunnlaget for dette tallet, er imidlertid basert på utenlandske data, og det er flere forhold som ikke er tatt hensyn til.
Forfattere
Yi Zhang Zhangmu Jing Yijing Feng Shuo Chen Yeqing Li Yongming Han Lu Feng Junting Pan Mahmoud Mazarji Hongjun Zhou Xiaonan Wang Chunming XuSammendrag
Exploring key factors has important guidance for understanding complex anaerobic digestion (AD) systems. This study proposed a multi-layer automated machine learning framework to understand the complex interactions in AD systems and explore key factors at the environmental factor, microorganisms and system levels. The first layer of the framework identified hydraulic residence time (HRT) as the most important environmental factor, with an optimal range of 33–45 d. In the second layer of the framework, Methanocelleus (optimal relative abundance (ORA) = 3.0%) and Candidatus_Caldatribacterium (ORA = 1.7%) were found to be the key archaea and bacteria, respectively. Furthermore, the prediction of key microorganisms based on environmental factors and remaining microbial data showed the essential roles of Methanothermobacter and Acetomicrobium. The third layer for finding the optimal combination of data variables for predicting biogas production demonstrated that combined Archaea genera and environmental factors should be achieved for the most accurate prediction (root mean square error (RMSE) = 84.21). GBM had the best model performance and prediction accuracy among all the built-in models. Based on the optimal GBM model, the analysis at the system level showed that HRT was the most important variable. However the most important microorganism, Methanocelleus, within the appropriate survival range is also essential to achieve optimal biogas production. This research explores key parameters at various levels through automated machine learning techniques, which are expected to provide guidance in understanding the complex architecture of industrial and laboratory AD systems.
Forfattere
Simon BergSammendrag
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