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
Forfattere
Erling MeisingsetSammendrag
Det er ikke registrert sammendrag
Forfattere
Erling MeisingsetSammendrag
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Forfattere
Erling MeisingsetSammendrag
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Forfattere
Kjersti Holt HanssenSammendrag
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Sammendrag
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Sammendrag
Strawberry powdery mildew, caused by Podosphaera aphanis, can be particularly destructive in glasshouse and plastic tunnel production systems, which generally are constructed of materials that block ultraviolet (UV) solar radiation (about 280 to 400 nm). We compared epidemic progress in replicated plots in open fields and under tunnels constructed of polyethylene, which blocks nearly all solar UV-B, and two formulations of ethylene tetrafluoroethylene (ETFE), one of which contained a UV blocker and another that transmitted nearly 90% of solar UV-B. Disease severity under all plastics was higher than in open-field plots, indicating a generally more favorable environment in containment structures. However, the foliar severity of powdery mildew within the tunnels was inversely related to their UV transmissibility. Among the tunnels tested, incidence of fruit infection was highest under polyethylene and lowest under UV-transmitting ETFE. These effects probably transcend crop, and the blocking of solar UV transmission by glass and certain plastics probably contributes to the widely observed favorability of greenhouse and high-tunnel growing systems for powdery mildew.
Sammendrag
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Sammendrag
Norwegian-grown peas and faba beans are a healthier alternative to meat and dairy products, which are over-consumed in Norway, hence these legumes represent an interesting alternative as food protein source in Norway. However, the environmental impact of these legumes compared to other protein sources has not been studied, in detail. Hence this study, where the environmental impact of this plant protein was analysed and compared to other main protein sources in the Norwegian diet, covers a research gap. The method used was Life Cycle Assessment (LCA) and a large range of impacts was covered. The climate impact for dried grain legumes were 0.55–0.57 kg CO2-eq/kg, The climate impact for dried grain legumes were 0.55–0.57 kg CO2-eq/kg, which is much lower than ruminant meat (19–38 kg CO2-eq/kg), other meat (3.6–4.2 kg CO2-eq/kg), seafood (0.8–22 kg CO2-eq/kg), dairy products (1.2–22 kg CO2-eq/kg products) and cereals (0.66–0.72 kg CO2-eq/kg product). The same trend was found for all impact categories studied. The same pattern was found when comparing the environmental impacts of grain legumes in intermediate and finished products. An evaluation of the nutrient content showed that there is no trade-off between health and environment but the effect of lower protein digestibility and anti-nutritional compounds in legumes remains to be investigated quantitatively. The study indicates that legumes are a more sustainable source of dietary protein than animal protein sources. It is recommended that more research should be done on social and economic sustainability should be done to get at more complete picture of the sustainability of these grain legumes.
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
Mekjell Meland Milica Fotiric Aksic Oddmund Frøynes Almira Konjic Lejla Lasic Naris Pojskic Fuad GasiSammendrag
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