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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.

2021

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Sammendrag

I dette prosjektet har vi studert sammenhengen mellom aktive støler og øvrig næringsliv i Nordre Buskerud. Kommunene Nesbyen, Nore og Uvdal, Hemsedal, Hol, Gol og Ål inngår i regionen Nordre Buskerud. Vi setter søkelys på samfunnsverdien ved at det støles i regionen og at det produseres felles goder. Det er gjennomført en survey til alle næringsaktører i regionen med søkelys på økt verdiskaping ved å samhandle med aktive stølsbedrifter. Med bakgrunn i spørreundersøkelsen inviterte vi til en digital Workshop med deltakelse fra næringslivet generelt og stølsmiljøet spesielt...

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Sammendrag

Formålet med denne rapporten har vært å undersøke: 1) Hva er viktig for potetdyrkere i drifta deres? 2) Hvordan tar de beslutninger om planteverntiltak? 3) Hvordan bruker og vurderer potetdyrkere VIPS? 4) Hvordan skiller de potetdyrkerne som bruker VIPS seg fra de som ikke bruker VIPS? 5) Hvordan vurderer potetdyrkere rammevilkårene for plantevern? og 6) Hva er viktig for framtida til norsk potetproduksjon?....

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Integrated pest management (IPM) was introduced in the 1960s as a response to increasing pesticide use and has since evolved from being understood mainly as an economic issue to also including environmental and human health considerations. The EU has made IPM mandatory for all farmers through the Sustainable Use of Pesticides Directive (SUD). Using a mixed-methods approach, this paper examines how Norwegian cereal farmers have responded to this requirement. The qualitative results show that most farmers have an understanding of IPM that goes beyond economic considerations only. The quantitative results display that farmers’ intrinsic motivation for IPM changed after introduction of the SUD. There is increased emphasis on using methods other than spraying, producing grain without traces of pesticides, and preventing pesticide resistance. Farmers’ self-reported knowledge of IPM increased, and 41% of farmers stated that they use IPM to a greater extent than before the SUD was introduced. These results demonstrate that mandatory IPM requirements have been a successful strategy for increasing farmers use of IPM in Norway. Clearer IPM provisions and increased intrinsic motivation for IPM among farmers will, however, be important to reduce the risks from pesticides further.

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Accurate assessment of crop nitrogen (N) status and understanding the N demand are considered essential in precision N management. Chlorophyll fluorescence is unsusceptible to confounding signals from underlying bare soil and is closely related to plant photosynthetic activity. Therefore, fluorescence sensing is considered a promising technology for monitoring crop N status, even at an early growth stage. The objectives of this study were to evaluate the potential of using Multiplex® 3, a proximal canopy fluorescence sensor, to detect N status variability and to quantitatively estimate N status indicators at four key growth stages of maize. The sensor measurements were performed at different growth stages, and three different regression methods were compared to estimate plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). The results indicated that the induced differences in maize plant N status were detectable as early as the V6 growth stage. The first method based on simple regression (SR) and the Multiplex sensor indices normalized by growing degree days (GDD) or N sufficiency index (NSI) achieved acceptable estimation accuracy (R2 = 0.73–0.87), showing a good potential of canopy fluorescence sensing for N status estimation. The second method using multiple linear regression (MLR), fluorescence indices and GDDs had the lowest modeling accuracy (R2 = 0.46–0.79). The third tested method used a non-linear regression approach in the form of random forest regression (RFR) based on multiple sensor indices and GDDs. This approach achieved the best estimation accuracy (R2 = 0.84–0.93) and the most accurate diagnostic result.