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.
2023
Authors
Holger Lange Jaana Bäck Georg Jocher Natascha Kljun Anne Klosterhalfen Alexander Knohl Natalia Kowalska Adam Kristensson Corinna Rebmann Teresa Saura-Yera Alberto VilagrosaAbstract
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.
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Yi Zhang Zhangmu Jing Yijing Feng Shuo Chen Yeqing Li Yongming Han Lu Feng Junting Pan Mahmoud Mazarji Hongjun Zhou Xiaonan Wang Chunming XuAbstract
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.
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Simon BergAbstract
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Johannes BreidenbachAbstract
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Philip van Niekerk Gry Alfredsen Targo Kalamees Roja Modaresi Anna Sandak Jonas Niklewski Christian BrischkeAbstract
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Zeinab Qazanfarzadeh Abirami Ramu Ganesan Loredana Mariniello Lorenza Conterno Vignesh KumaravelAbstract
The accumulation of petroleum-based plastics causes economic and environmental concerns which necessitate a comprehensive search for biodegradable packaging materials. Brewer's spent grain (BSG) is an interesting by-product, which is one of the main wastes of beer production in Europe. BSG could offer added value in the food packaging sector owing to the significant amount generated annually, high biomaterials content, and low market value. Herein, the significance of various biorefinery techniques (physical, chemical, and biological) for the extraction of high-value products (such as protein, cellulose, hemicellulose, lignin, and phenolic compounds) from the BSG are comprehensively examined. BSG-derived biodegradable films and coatings for food packaging are critically evaluated. Finally, techno-economics, environmental impacts, energy consumption, regulations, challenges, and prospects are also critically evaluated. The best biorefinery system necessitates a balance between extraction efficiency, energy consumption, environmental impact, tangible upscaling, and operating cost. The mechanical dewatering of BSG before extraction, including the physical pretreatments, utilization of green solvents, the integration of the solvent recovery system, and the combination of two or more biorefinery techniques could reduce the energy requirements, greenhouse gas emissions, and increase the recovery yield of biomaterials. Cellulose, lignin, xylitol, and arabinoxylan are recommended as the most promising components from BSG for food packaging applications.