Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2022
Sammendrag
The visual impacts of landscape change are important for how people perceive landscapes and whether they consider changes to be positive or negative. Landscape photographs and photographs of landscape elements may capture information about the visual qualities of landscapes and can also be used to illustrate, and even to quantify, how these visual qualities change over time. We developed a methodology for a monitoring scheme, based on taking photographs from exactly the same locations at different points in time. We tested two methods: one where fieldworkers chose freely the location and direction of photographs, and one where photo locations and four out of five directions were predefined. We found that the method using predefined locations provided a representative sample of the visual qualities present in the landscape and was relatively person-independent but missed rare landscape components. The method using free selection of photo locations and directions captured rarities, but the content of the photos varied from photographer to photographer. Considering the strengths and weaknesses of the two approaches, we recommend a method that combines aspects of both when establishing a monitoring scheme based on repeat photography, with predefined locations to ensure that the entire area is covered, and additional freely chosen photo locations to capture special subject matter that would otherwise be missed.
Forfattere
Axel Mithöfer Michael Riemann Corine Alexis Faehn Anna Mrazova Laura JaakolaSammendrag
Global warming is predicted to change the growth conditions for plants and crops in regions at high latitudes (>60° N), including the Arctic. This will be accompanied by alterations in the composition of natural plant and pest communities, as herbivorous arthropods will invade these regions as well. Interactions between previously non-overlapping species may occur and cause new challenges to herbivore attack. However, plants growing at high latitudes experience less herbivory compared to plants grown at lower latitudes. We hypothesize that this finding is due to a gradient of constitutive chemical defense towards the Northern regions. We further hypothesize that higher level of defensive compounds is mediated by higher level of the defense-related phytohormone jasmonate. Because its biosynthesis is light dependent, Arctic summer day light conditions can promote jasmonate accumulation and, hence, downstream physiological responses. A pilot study with bilberry (Vaccinium myrtillus) plants grown under different light regimes supports the hypothesis.
Forfattere
Kemeng Xiao Karin Juul Hesselsøe Christer Magnusson Tatsiana Espevig Trond Olav Pettersen Trygve S. AamlidSammendrag
Det er ikke registrert sammendrag
Sammendrag
Plant selection for rain gardens along streets and roads in cold climates can be complicated, as the plants are subjected to combined stresses including periodic inundation, de-icing salts, road dust, splashes of water from the road, freezing and thawing of soil, and periods with ice cover during the winter. The purpose of this study was to identify species suited to grow in these conditions and determine their optimal placement within roadside rain gardens. Thirty-one herbaceous perennial species and cultivars were planted in real-scale rain gardens in a street in Drammen (Norway) with supplemental irrigation, and their progress was recorded during the following three growing seasons. The study highlights considerable differences between species’ adaptation to roadside rain gardens in cold climates, especially closest to the road. Some candidate species/cultivars had a high survival rate in all rain garden positions and were developed well. These were: Amsonia tabernaemontana, Baptisia australis, Calamagrostis × acutiflora ‘Overdam’, Hemerocallis ‘Camden Gold Dollar’, Hemerocallis ‘Sovereign’, Hemerocallis lilioasphodelus, Hosta ‘Sum & Substance’, Iris pseudacorus and Liatris spicata ‘Floristan Weiss’. Other species/cultivars appeared to adapt only to certain parts of the rain garden or had medium tolerance. These were: Calamagrostis brachytricha, Carex muskingumensis, Eurybia × herveyi ‘Twilight’, Hakonechloa macra, Hosta ‘Francee’, Hosta ‘Striptease’, Liatris spicata ‘Alba’, Lythrum salicaria ‘Ziegeunerblut’, Molinia caerulea ‘Moorhexe’, Molinia caerulea ‘Overdam’, and Sesleria autumnalis. Species/cultivars that showed high mortality and poor development at all rain garden positions should be avoided in roadside cold climate rain gardens. These include Amsonia orientalis, Aster incisus ‘Madiva’, Astilbe chinensis var. tacquettii ‘Purpurlanze’, Chelone obliqua, Dryopteris filix-mas, Eurybia divaricata, Geranium ‘Rozanne’, Helenium ‘Pumilum Magnificum’, Luzula sylvatica, Polygonatum multiflorum and Veronicastrum virginicum ‘Apollo’. The study also found considerable differences between cultivars within the same species, especially for Hosta cvv. and Liatris spicata. Further investigations are needed to identify the cultivars with the best adaption to roadside rain gardens in cold climates.
Forfattere
Yi Zhang Linhui Li Zhonghao Ren Yating Yu Yeqing Li Junting Pan Yanjuan Lu Lu Feng Weijin Zhang Yongming HanSammendrag
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.
Forfattere
Arne SteffenremSammendrag
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Forfattere
Gunda ThömingSammendrag
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Sammendrag
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Forfattere
Michel VerheulSammendrag
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Forfattere
Inger Sundheim FløistadSammendrag
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