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
Yi Zhang Linhui Li Zhonghao Ren Yating Yu Yeqing Li Junting Pan Yanjuan Lu Lu Feng Weijin Zhang Yongming HanAbstract
The parameters from full-scale biogas plants are highly nonlinear and imbalanced, resulting in low prediction accuracy when using traditional machine learning algorithms. In this study, a hybrid extreme learning machine (ELM) model was proposed to improve prediction accuracy by solving imbalanced data. The results showed that the best ELM model had a good prediction for validation data (R2 = 0.972), and the model was developed into the software (prediction error of 2.15 %). Furthermore, two parameters within a certain range (feed volume (FV) = 23–45 m3 and total volatile fatty acids of anaerobic digestion (TVFAAD) = 1750–3000 mg/L) were identified as the most important characteristics that positively affected biogas production. This study combines machine learning with data-balancing techniques and optimization algorithms to achieve accurate predictions of plant biogas production at various loads.
Authors
Jeanne Portier Florian Zellweger Jürgen Zell Iciar Alberdi Asensio Michal Bosela Johannes Breidenbach Vladimír Šebeň Rafael O. Wüest Brigitte RohnerAbstract
To understand the state and trends in biodiversity beyond the scope of monitoring programs, biodiversity indicators must be comparable across inventories. Species richness (SR) is one of the most widely used biodiversity indicators. However, as SR increases with the size of the area sampled, inventories using different plot sizes are hardly comparable. This study aims at producing a methodological framework that enables SR comparisons across plot-based inventories with differing plot sizes. We used National Forest Inventory (NFI) data from Norway, Slovakia, Spain, and Switzerland to build sample-based rarefaction curves by randomly incrementally aggregating plots, representing the relationship between SR and sampled area. As aggregated plots can be far apart and subject to different environmental conditions, we estimated the amount of environmental heterogeneity (EH) introduced in the aggregation process. By correcting for this EH, we produced adjusted rarefaction curves mimicking the sampling of environmentally homogeneous forest stands, thus reducing the effect of plot size and enabling reliable SR comparisons between inventories. Models were built using the Conway–Maxell–Poisson distribution to account for the underdispersed SR data. Our method successfully corrected for the EH introduced during the aggregation process in all countries, with better performances in Norway and Switzerland. We further found that SR comparisons across countries based on the country-specific NFI plot sizes are misleading, and that our approach offers an opportunity to harmonize pan-European SR monitoring. Our method provides reliable and comparable SR estimates for inventories that use different plot sizes. Our approach can be applied to any plot-based inventory and count data other than SR, thus allowing a more comprehensive assessment of biodiversity across various scales and ecosystems.
Authors
Pia Heltoft Thomsen Gudni Thorvaldsson Anne Mette Dahl Jensen Tatsiana Espevig Karin Juul Hesselsøe Wendy Marie Waalen Torben K. Petersen Trond Olav Pettersen Jan Tangsveen Per Sørensen Tanja Gneist Bjarni Hannesson Kristine Sundsdal Trygve S. AamlidAbstract
No abstract has been registered
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Authors
Linn VassvikAbstract
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The diversity and abundance of pollinating insects is declining on a global scale and urgent action is needed. This is a brief film about the importance of pollinators, what is being done in Norway to counteract pollinator decline, and how you can help. Together, we can make a difference.
Authors
Milica Fotirić Akšić Milica Nešović Ivanka Ćirić Živoslav Tešić Lato Pezo Tomislav Tosti Uroš Gašić Biljana Dojčinović Biljana Lončar Mekjell MelandAbstract
Using modern analytical techniques, a comprehensive study of the chemical composition of fruits from apple cultivars grown in Western Norway during 2019 and 2020 was done. Metals, sugars, organic acids, antioxidant tests, and polyphenol content have been observed. In all investigated samples, the most dominant sugars were glucose, fructose, and sucrose. Among 11 tested organic acids, the dominant was malic acid, followed by citric and maleic acid. The most common metal was potassium, followed by magnesium and zinc. The quantification of polyphenols showed that among the 11 quantified polyphenols, chlorogenic acid, quercetin 3-O-rhamnoside, quercetin 3-O-glucoside, quercetin, and phlorizin were the most abundant. A detailed study of the polyphenolic profile of nine investigated apple samples provided 30 identified polyphenolic compounds from the class of hydroxybenzoic and hydroxycinnamic acids, flavonoids, and dihydrochalcones. In addition to the identified 3-O-caffeoylquinic acid, its two isomers of 5-O-caffeoylquinic acid and three esters were also found. Present polyphenols of the tested apples provided significant data on the quality of Norwegian apples, and they contribute to the distinguishing of these apple samples.
Authors
Jiunn Luh Tan Nina Trandem Rostislav Zemek Zhibo Hamborg Bijaya Sapotka Dag-Ragnar Blystad Jana FránováAbstract
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No abstract has been registered