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
Forfattere
Eva Narten HøbergSammendrag
FNs klimapanel sier at alle land skal produsere mat på egne ressurser. Natur- og kulturverdier representerer disse ressursene. Det ligger utallige muligheter for å utvikle og skreddersy matopplevelser som bidrar til å nå bærekraftsmålene. Hvilke muligheter er dette, og hvordan kan aktører langs verdikjeden fra jord og fjord til bord synliggjøre sitt samfunnsbidrag? Miljøutfordringene verden står overfor styrker oppmerksomheten omkring bruk av natur- og kulturverdier i opplevelsesproduksjonen. Det er derfor en voksende trend å tilby lokale råvarer og lokale spesialiteter. Dette gir muligheter for økt verdiskaping gjennom bærekraftig matproduksjon, foredling og servering. Hvis mulighetene fullt ut skal la seg realisere, kreves tverrfaglig kompetanseheving langs verdikjeden fra jord og fjord til bord. Seminaret gir en oversikt over aktuelle tema langs verdikjeden der utvikling av bærekraftige matopplevelser bidrar positivt – både for miljøet, gjesten og næringene.
Forfattere
Eva Narten HøbergSammendrag
Det er ikke registrert sammendrag
Forfattere
Eva Narten HøbergSammendrag
Det er ikke registrert sammendrag
Forfattere
Eva Narten HøbergSammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Eva Narten HøbergSammendrag
Det er ikke registrert sammendrag
Forfattere
Eva Narten HøbergSammendrag
Det er ikke registrert sammendrag
Forfattere
Ingunn Øvsthus Kristian Thorup-Kristensen Randi Seljåsen Hugh Riley Peter Dörsch Tor Arvid BrelandSammendrag
Mechanistic models are useful tools for understanding and taking account of the complex, dynamic processes such as carbon (C) and nitrogen (N) turnover in soil and crop growth. In this study, the EU-Rotate_N model was first calibrated with measured C and N mineralization from nine potential fertilizer resources decomposing at controlled soil temperature and moisture. The materials included seaweeds, wastes from the food industry, food waste anaerobically digested for biogas production, and animal manure. Then the model’s ability to predict soil and crop data in a field trial with broccoli and potato was evaluated. Except for seaweed, up to 68% of added C and 54–86% of added N was mineralized within 60 days under controlled conditions. The organic resources fell into three groups: seaweed, high-N industrial wastes, and materials with high initial content of mineral N. EU-Rotate_N was successfully calibrated for the materials of industrial origin, whereas seaweeds, anaerobically digested food waste and sheep manure were challenging. The model satisfactorily predicted dry matter (DM) and N contents (root mean square; RMSE: 0.11–0.32) of the above-ground part of broccoli fertilized with anaerobically digested food waste, shrimp shell pellets, sheep manure and mineral fertilizers but not algal meal. After adjusting critical %N for optimum growth, potato DM and N contents were also predicted quite well (RMSE: 0.08–0.44). In conclusion, the model can be used as a learning and decision support tool when using organic materials as N fertilizer, preferably in combination with other models and information from the literature.
Sammendrag
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.
Forfattere
Paal KrokeneSammendrag
Det er ikke registrert sammendrag