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.
2022
Authors
Haldis KismulAbstract
No abstract has been registered
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No abstract has been registered
Authors
Lisa M. Schüler Juline Walter Hidehiko Kato Hirono Suzuki Christopher Jonathan Hulatt Ralf Rautenberger Sofia Navalho Benjamin Schmid Joao Varela Kiron Viswanath Peter Simon Claus SchulzeAbstract
Phototrophic microalgae use light to produce biomass and high-value compounds, such as pigments and polyunsaturated fatty acids (PUFA), for food and feed. These biomolecules can be induced by flashing light during the final growth stage. We tested different exposure times (1–6 days) of flashing light (f = 0.5, 5, 50 Hz; duty cycle = 0.05) on biomass, pigment and fatty acid productivity in Diacronema lutheri and Tetraselmis striata. A three-day exposure to low-frequency (5 Hz) flashing light successfully increased the production of fucoxanthin, diatoxanthin, eicosapentaenoic (EPA) and docosahexaenoic (DHA) acids in D. lutheri up to 4.6-fold and of lutein, zeaxanthin and EPA in T. striata up to 1.3-fold compared to that of continuous light. Biomass productivity declined 2-fold for D. lutheri and remained similar for T. striata compared to that of continuous light. Thus, short-term treatments of flashing light may be promising for industrial algal production to increase biomass value.
Authors
Calle Niemi Agnes Mols Mortensen Ralf Rautenberger Böris Sanna Christina A Matsson András Gorzsás Francesco G. GentiliAbstract
Seaweed is considered a potentially sustainable source of protein for human consumption, and rapid, accurate methods for determining seaweed protein contents are needed. Seaweeds contain substances which interfere with common protein estimation methods however. The present study compares the Lowry and BCA protein assays and protein determination by N-ratios to more novel spectroscopic methods. Linear regression of the height or the integrated area under the Amide II band of diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) was used to predict seaweed protein with good prediction performance. Partial least squares regression (PLSR) was performed on both DRIFTS and near-infrared (NIR) spectra, with even higher prediction accuracy. Spectroscopy performed similar to or better than the calculated N-ratio of 4.14 for protein prediction. These spectral prediction methods require minimal sample preparation and chemical use, and are easy to perform, making them environmentally sustainable and economically viable for rapid estimation of seaweed protein.
Abstract
No abstract has been registered
Abstract
Many forestry roles have changed from being manual tasks with a high physical workload to being a machine operator task with a high mental workload. Automation can support a decrease in mental fatigue by removing tasks that are repetitive and monotonous for the operators. Cable yarding presents an ideal opportunity for early adoption of automation technology; specifically the carriage movement along a defined corridor. A Valentini V-850 cable yarder was used in an Italian harvesting setting, in order to gauge the ergonomic benefit of carriage control automation. The study showed that automating yarder carriage movements improved the ergonomic situation of the workers directly involved in the related primary tasks. However, the caveat is that improving one work task may negatively affect the other work tasks, and therefore introducing automation to a worksite must be done after considering all impacts on the whole system. Practitioner summary: Automation decreased the winch operator’s mental workload while improving overall productivity. At the same time, the mental and physiological workload of the operator tasked with bucking were slightly increased. Ideally, winch automation should be coupled with bucking mechanisation to balance the intervention and boost both operator well-being and productivity.
Authors
Jakob GeipelAbstract
No abstract has been registered
Authors
Hilde HallandAbstract
Sustainability is proposed as a solution to the many negative consequences of modern agriculture. However, although science and policy have aimed for sustainability for more than two decades, it seems that we are not making enough progress. This is due to the complexities of the sustainability concept and that we need to better understand how we can create change. In seeing sustainability as a learning process, this thesis aims to understand how to enhance farm sustainability in Arctic Norway. This is achieved by combining four research rationales: stakeholders’ perspectives, sustainability assessments, sustainability learning, and participatory approaches. I use a case study strategy involving farms in Arctic Norway. By applying a multimethod qualitative approach, I explore the topic through three empirical papers wherein stakeholder participation plays a prominent role. By discussing the findings, I conceptualize farm sustainability as a long-term and multilevel learning process. To achieve farm sustainability, several steps must be aligned: there must be a purpose for the process, various stakeholders must take part, we must know what to learn, a transdisciplinary methodology must be used, and the process should be flexible. In addition, the process must be embedded in the very way of farming. The relevance of these findings is that farm sustainability must be aligned with change toward improved sustainability in society at large. Context plays a major role in what, why, and how we can learn, as well as in who we can learn with. Therefore, farm sustainability as a learning process must be translated to fit the empirical context. This thesis contributes to theory development in the field of agricultural sustainability. Furthermore, it deepens our understanding of how values and context influence farm sustainability, demonstrates the relevance of combining sustainability assessments with a learning process, and broadens our understanding of sustainability learning in agriculture. In combining ‘sustainability as a theory’ and ‘sustainability as a practice’, lies the key to farm sustainability in Arctic Norway.
Abstract
Commissioned by the Norwegian Environment Agency, this report presents methodologies for estimating annual numbers of animals and enteric methane emissions for pigs. The methodologies are designed for the Norwegian national inventory of GHG emissions (NIR) and are dynamic, reflecting the effects of progress in genetics and management of the pork production. The data sources for the proposed methodologies are the register for deliveries of carcasses to Norwegian slaughterhouses available from Statistics Norway, and the Norwegian litter recording system (Ingris) of the Norwegian meat and poultry research centre (Animalia).
Authors
Stine Samsonstuen Helge Bonesmo Bente Aspeholen Åby Eli Gjerlaug-Enger Erland Kjesbu Magne Bergfjord Rune Okstad Svein Skøien Tony BarmanAbstract
Through the joint project Climate Smart Agriculture, the agricultural sector in Norway have successfully implemented the whole-farm models HolosNor models as farm advisory tools for milk, beef, pig, sheep, poultry, and crop production. The HolosNor modes are empirical models based on the methodology of the Intergovernmental Panel on Climate Change with modifications to Norwegian conditions. The models estimate direct emissions of methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2) from on-farm livestock production and includes indirect emissions of N2O and CO2 associated with inputs used on the farm in addition to including soil carbon balance through the ICBM model. The digital GHG Calculator automatically collects data from sources the farmer already uses for farm management, such as herd recording systems, manure planning systems, farm accounts, concentrate invoice, dairy, slaughterhouse, in addition to site-specific soil and weather data. Based on the collected data, both total emissions from the production and emission intensities for the different products are estimated. The emission intensities are shown by source relative to a reference group consisting of farms with the same type of production and production volume. Using the GHG Calculator, the farmers have the unique opportunity to have tailor-made mitigation plans to reduce emissions from the farm trough certified climate advisors. Participation and results from the GHG Calculator will be presented in addition to experiences from implementation of a GHG model as a farm advisory tool for commercial farms.