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
Sammendrag
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Forfattere
Eva Narten HøbergSammendrag
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Forfattere
Eva Narten HøbergSammendrag
«Vi vil vise fram hva vi har før vi mister det.» Dette er motivasjonen til søstrene Rakel og Rebekka Nystabakk, som står bak filmen SAU. Rakel og Rebekka har vokst opp på en gård i Nordland, nærmere bestemt Engan i Sørfold. Det var mange bruk i bygda på den tida, og da snakker vi om 80-90-tallet. Gjennom foredraget ser vi nærmere på hva som har skjedd siden da, og også gjøre oss noen tanker om HVORFOR. Hvordan står det egentlig til med den nordnorske sauen? Hva kjennetegner driftsformen? Hva blir konsekvensene hvis sauebrukene i bygdene forsvinner helt? Er vi villige til å sikre at vi også i framtida har beitedyr i den nordnorske utmarka?
Sammendrag
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Forfattere
Lise GrøvaSammendrag
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Forfattere
Lise GrøvaSammendrag
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Forfattere
Lise GrøvaSammendrag
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Forfattere
Emil Sandström Tove Ortman Christine A Watson Jan Bengtsson Clara Gustafsson Göran BergkvistSammendrag
One of the major challenges facing agricultural and food systems today is the loss of agrobiodiversity. Considering the current impasse of preventing the worldwide loss of crop diversity, this paper highlights the possibility for a radical reorientation of current legal seed frameworks that could provide more space for alternative seed systems to evolve which centre on norms that support on-farm agrobiodiversity. Understanding the underlying norms that shape seed commons are important, since norms both delimit and contribute to what ultimately will constitute the seeds and who will ultimately have access to the seeds and thus to the extent to which agrobiodiversity is upheld and supported. This paper applies a commoning approach to explore the underpinning norms of a Swedish seed commons initiative and discusses the potential for furthering agrobiodiversity in the context of wider legal and authoritative discourses on seed enclosure. The paper shows how the seed commoning system is shaped and protected by a particular set of farming norms, which allows for sharing seeds among those who adhere to the norms but excludes those who will not. The paper further illustrates how farmers have been able to navigate fragile legal and economic pathways to collectively organize around landrace seeds, which function as an epistemic farming community, that maintain landraces from the past and shape new landraces for the present, adapted to diverse agro-ecological environments for low-input agriculture. The paper reveals how the ascribed norms to the seed commons in combination with the current seed laws set a certain limit to the extent to which agrobiodiversity is upheld and supported and discusses why prescriptions of “getting institutions right” for seed governance are difficult at best, when considering the shifting socio-nature of seeds. To further increase agrobiodiversity, the paper suggests future seed laws are redirected to the sustenance of a proliferation of protected seed commoning systems that can supply locally adapted plant material for diverse groups of farmers and farming systems.
Sammendrag
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Forfattere
Fernanda Leiva Florent Abdelghafour Muath K Alsheikh Nina Elisabeth Nagy Jahn Davik Aakash ChawadeSammendrag
Common scab (CS) is a major bacterial disease causing lesions on potato tubers, degrading their appearance and reducing their market value. To accurately grade scab-infected potato tubers, this study introduces “ScabyNet”, an image processing approach combining color-morphology analysis with deep learning techniques. ScabyNet estimates tuber quality traits and accurately detects and quantifies CS severity levels from color images. It is presented as a standalone application with a graphical user interface comprising two main modules. One module identifies and separates tubers on images and estimates quality-related morphological features. In addition, it enables the extraction of tubers as standard tiles for the deep-learning module. The deep-learning module detects and quantifies the scab infection into five severity classes related to the relative infected area. The analysis was performed on a dataset of 7154 images of individual tiles collected from field and glasshouse experiments. Combining the two modules yields essential parameters for quality and disease inspection. The first module simplifies imaging by replacing the region proposal step of instance segmentation networks. Furthermore, the approach is an operational tool for an affordable phenotyping system that selects scab-resistant genotypes while maintaining their market standards.