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.
2024
Forfattere
Anne Linn HykkerudSammendrag
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Sammendrag
Det er nærmest blitt en folkebevegelse å redde bier og humler. 90 prosent av leveområdene til villbiene er nemlig blitt borte de siste 100 årene. Men hvordan lage en blomstereng som faktisk gir biene et godt liv?
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Forfattere
Morten Rese Gijs van Erven Romy J. Veersma Gry Alfredsen Vincent Eijsink Mirjam A. Kabel Tina Rise TuvengSammendrag
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Forfattere
Morten Rese Gijs van Erven Romy J. Veersma Gry Alfredsen Vincent Eijsink Mirjam A. Kabel Tina Rise TuvengSammendrag
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Sammendrag
The total phenolic content and antiradical activity in vitro varied significantly among the fruit mesocarps samples extracts of seven plum cultivars. It shows the influence of the cultivar factor on the quantitative composition of phenolic compounds and antiradical activity in vitro of P. domestica fruit mesocarps samples extracts. The highest total phenolic content and the strongest antiradical activ ity in vitro was determined in the fruit mesocarps samples extracts of the cultivar 'Čačanska Najbolja' (bred in Serbia). The fruit mesocarps from this cultivar could be valuable for the future researches – determination of the qualitative and quantitative composition of the individual phenolic compounds.
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Sammendrag
Context Traditional critical nitrogen (N) dilution curve (CNDC) construction for N nutrition index (NNI) determination has limitations for in-season crop N diagnosis and recommendation under diverse on-farm conditions. Objectives This study was conducted to (i) develop a new rice (Oryza sativa L.) critical N concentration (Nc) determination approach using vegetation index-based CNDCs; and (ii) develop an N recommendation strategy with this new Nc determination approach and evaluate its reliability and practicality. Methods Five years of plot and on-farm experiments involving three japonica rice varieties were conducted at fourteen sites in Qixing Farm, Northeast China. Two machine learning (ML) methods, random forest (RF) and extended gradient boosting (XGBoost) regression, were used to fuse multi-source data including genotype, environment, management, growth stage, normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) from portable active canopy sensor RapidSCAN. The CNDC was established using NDVI and NDRE instead of aboveground biomass (AGB) measured by destructive sampling. A new in-season N diagnosis and recommendation strategy was further developed using direct and indirect NNI prediction using multi-source data fusion and ML models. Results The new CNDC based on NDVI or NDRE explained 94−96 % of Nc variability in the evaluation dataset when it was coupled with environmental and agronomic factors using ML models. The ML-based PNC and NNI prediction models explained 85 % and 21–36 % more variability over simple regression models using NDVI or NDRE in the evaluation dataset, respectively. The new in-season N diagnosis strategy using the NDVI and NDRE-based CNDCs and plant N concentration (PNC) predicted with RF model and multi-source data fusion performed slightly better than direct NNI prediction, explaining 7 % more of NNI variability and achieving 89 % of the areal agreement for N diagnosis across all evaluation experiments. Integrating this new N management strategy into the precision rice management system (as ML_PRM) increased yield, N use efficiency (NUE) and economic benefits over farmer’s practice (FP) by 7–15 %, 11–71 % and 4–16 % (161–596 $ ha−1), respectively, and increased NUE by 11–26 % and economic benefits by 8–97 $ ha−1 than regional optimum rice management (RORM) under rice N surplus status under on-farm conditions. Conclusions In-season rice N status diagnosis can be improved using NDVI- and NDRE-based CNDC and PNC predicted by ML modeling with multi-source data fusion. Implications The active canopy sensor- and ML-based in-season N diagnosis and management strategy is more practical for applications under diverse on-farm conditions and has the potential to improve rice yield and ecological and economic benefits.
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Forfattere
Charles D. Minsavage-Davis G. Matt Davies Siri Vatsø Haugum Pål Thorvaldsen Liv Guri Velle Vigdis VandvikSammendrag
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