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
Abirami Ramu Ganesan Mohan Kannan Durairaj Karthick Rajan Arti A. Pillay Munisamy Shanmugam Palanivel Sathishkumar Johan Johansen Brijesh K. TiwariSammendrag
Phycoerythrin (PE) is a photosensitive red pigment from phycobiliprotein family predominantly present in the red algae. The concentration of PE depends on photon flux density (PFD) and the quality of light absorbed by the algae tissue. This necessitates robust techniques to extract PE from the embedded cell-wall matrix of the algal frond. Similarly, PE is sensitive to various factors which influence its stability and purity of PE. The PE is extracted from Red algae through different extraction techniques. This review explores an integrative approach of fractionating PE for the scaling-up process and commercialization. The mechanism for stabilizing PE pigment in food was critically evaluated for further retaining this pigment within the food system. The challenges and possibilities of employing efficient extraction for industrial adoption are meticulously estimated. The techniques involved in the sustainable way of extracting PE pigments improved at a laboratory scale in the past decade. Although, the complexity of industrial-scale biorefining was found to be a bottleneck. The extraction of PE using benign chemicals would be safe for food applications to promote health benefits. The precise selection of encapsulation technique with enhanced sensitivity and selectivity of the membrane would bring better stability of PE in the food matrix.
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
This report describes some of the most unique and characteristic natural features and interdependencies between species and nature types at the Vega world heritage site, and how these habitats are intertwined with the continuity of human intervention. Foreseeable threats and change scenarios are then presented and discussed for the key natural features in Vega.
Sammendrag
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Sammendrag
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Sammendrag
Midt-Troms Grønt A/S dyrker økologiske jordbær i tunell ved Rossfjordstraumen i Finnsnes kommune og opplever i enkelte år store skader på avling, planter og infrastruktur fra vånd. I prosjektet har en sett på tiltak for å motvirke slike skader. Tiltakene som er vurdert er inngjerding, skjøtsel av nærområder og bruk av lydsignal. Stor nedgang i våndbestanden i området i forsøksåret innebar få skader og lite press fra vånd mot jordbærfeltet, slik at det ikke var mulig å påvise noen effekt av tiltakene.
Forfattere
Thomas Holm Carlsen Sven Emil Hinderaker Geir Arnesen Elin Karin Plathe Kristin S. JohansenSammendrag
NIBIO har i samarbeid med Sállir Natur kartlagt fem verneområder i Troms og Finnmark i 2021 etter kartleggingsmetodikken Natur i Norge (NiN). Rapporten oppsummerer forhold som kommer dårlig frem i kartobjekter og egenskapsdata som har blitt registret og rapportert via NiNapp. Rapporten inneholder generelle faglige vurderinger, eventuelle observerte forvatningsrelevante problemstillinger, praktiske utfordringer i felt, eventuell usikkerhet knyttet til kartleggingsenheter og viser noen utvalgte bilder for verneområdene.
Sammendrag
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Forfattere
Jørund Johansen Liv Hatleli Gilpin Stein Harald Hjeltnes Bjørn Arild Hatteland Stein Joar Hegland Markus A. K. Sydenham Anders NielsenSammendrag
Det er ikke registrert sammendrag
Sammendrag
The remote sensing of the biophysical and biochemical parameters of crops facilitates the preparation of application maps for variable-rate nitrogen fertilization. According to comparative studies of machine learning algorithms, Gaussian process regression (GPR) can outperform more popular methods in the prediction of crop status from hyperspectral data. The present study evaluates GPR model accuracy in the context of spring wheat dry matter, nitrogen content, and nitrogen uptake estimation. Models with the squared exponential covariance function were trained on images from two hyperspectral cameras (a frenchFabry–Pérot interferometer camera and a push-broom scanner). The most accurate predictions were obtained for nitrogen uptake (R2=0.75–0.85, RPDP=2.0–2.6). Modifications of the basic workflow were then evaluated: the removal of soil pixels from the images prior to the training, data fusion with apparent soil electrical conductivity measurements, and replacing the Euclidean distance in the GPR covariance function with the spectral angle distance. Of these, the data fusion improved the performance while predicting nitrogen uptake and nitrogen content. The estimation accuracy of the latter parameter varied considerably across the two hyperspectral cameras. Satisfactory nitrogen content predictions (R2>0.8, RPDP>2.4) were obtained only in the data-fusion scenario, and only with a high spectral resolution push-broom device capable of capturing longer wavelengths, up to 1000 nm, while the full-frame camera spectral limit was 790 nm. The prediction performance and uncertainty metrics indicated the suitability of the models for precision agriculture applications. Moreover, the spatial patterns that emerged in the generated crop parameter maps accurately reflected the fertilization levels applied across the experimental area as well as the background variation of the abiotic growth conditions, further corroborating this conclusion.