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
Yi Zhang Yun Zhao Yijing Feng Yating Yu Yeqing Li Jian Li Zhonghao Ren Shuo Chen Lu Feng Junting Pan Hongjun Zhou Yongming HanAbstract
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.
Authors
Nina Johansen Arne Stensvand Aruppillai Suthaparan Annichen Smith Eriksen Pål Johan From Ellen Altenborg Runa Gidske Thomas Petillon Carl Emil ØyriAbstract
No abstract has been registered
Authors
Jian LiuAbstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Vibeke Lind Angela Dagmar Schwarm Marcello Mele Alice Cappucci Giulia Foggi Özge Sizmaz Eleni Tsiplakou Alberto Stanislao Atzori Joni Van Mullem Nico PeirenAbstract
The aim of this chapter is to summarize dietary measures to mitigate methane at animal level. The chapter briefly summarizes methane measurement techniques. The focus is on the mitigation potential studied in vivo, but when such data were not available, in vitro measurements were included. The chapter covers main dietary ingredients such as forage quality, inclusion of concentrate, grazing management and inclusion of primary (e.g. lipids) and secondary (e.g. tannins) plant compounds as well as chemical inhibitors (e.g. 3-NOP) to the diet. This chapter can be used as a guidance on what to use, at which concentrations in the diets levels (farmers) and how to quantify the effect (researchers).
Authors
Darius KviklysAbstract
No abstract has been registered
Authors
Tomasz Leszek WoznickiAbstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Taavi Riit Michelle Cleary Kalev Adamson Mimmi Blomquist Daiva Burokienė Diana Marčiulynienė Jonàs Oliva Anna Poimala Miguel Angel Redondo Gunn Strømeng Venche Talgø Leho Tedersoo Iben Margrete Thomsen Anne Uimari Johanna Witzell Rein DrenkhanAbstract
This study aimed to determine the differences and drivers of oomycete diversity and community composition in alder- and birch-dominated park and natural forest soils of the Fennoscandian and Baltic countries of Estonia, Finland, Lithuania, Norway, and Sweden. For this, we sequenced libraries of PCR products generated from the DNA of 111 soil samples collected across a climate gradient using oomycete-specific primers on a PacBio high-throughput sequencing platform. We found that oomycete communities are most affected by temperature seasonality, annual mean temperature, and mean temperature of the warmest quarter. Differences in composition were partly explained by the higher diversity of Saprolegniales in Sweden and Norway, as both total oomycete and Saprolegniales richness decreased significantly at higher longitudes, potentially indicating the preference of this group of oomycetes for a more temperate maritime climate. None of the evaluated climatic variables significantly affected the richness of Pythiales or Peronosporales. Interestingly, the relative abundance and richness of Pythiales was higher at urban sites compared to forest sites, whereas the opposite was true for Saprolegniales. Additionally, this is the first report of Phytophthora gallica and P. plurivora in Estonia. Our results indicate that the composition of oomycetes in soils is strongly influenced by climatic factors, and, therefore, changes in climate conditions associated with global warming may have the potential to significantly alter the distribution range of these microbes, which comprise many important pathogens of plants.