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
Abstract
No abstract has been registered
Authors
Inger Martinussen Mathias Amundsen Aksel Granhus Antje Gonera Marius Hauglin Anne Linn Hykkerud Laura Jaakola Mikko Kurttila Jari Miina Rainer Peltola Gesine Schmidt Josefine Skaret Baoru Yang Kjersti AabyAbstract
Almost 95% of the area in Norway is wilderness and 38% of the land area is covered by woods. These areas are abundant in valuable renewable resources, including wild berries. In our neighbouring countries, Sweden and Finland, wild berries are already a big industry. At the same time, on the market the Norwegian wild berries are almost non-existent and berries are left unexploited. Lingonberry (Vaccinium vitis-idaea) is one of the most abundant and economically important wild berries in the Nordic countries. Nevertheless, lingonberry has a large untapped potential due to its unique health effects and potential for increased value creation. It is estimated that 111,500 t of lingonberry are produced in the Norwegian woods. Norway is a long and diverse country with a range of climatic conditions. Adaptations to different conditions can give differences in both yield and quality of wild berries. Yields vary enormously from year to year and among different locations. A steady supply, predictable volumes and high quality are vital for successful commercialization of wild berries. To increase the utilization of berries, there is a need for increased knowledge regarding availability and quality variation of the berries. In addition, the Norwegian market suffers from high labour costs and cannot compete in product price. Innovative solutions and new knowledge on quality aspects can open possibilities for value creation. Toward achieving this goal, we have created a project called “WildBerries”, the main objective of which is to produce research-based knowledge that will create the basis for increased commercial utilization of Norwegian wild berries.
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
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
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Abstract
No abstract has been registered