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
Sammendrag
Background Bioenergy plays a key role in the transition to a sustainable economy in Europe, but its own sustainability is being questioned. We study the experiences of Sweden, Finland, Denmark and Norway, to find out whether the forest-based bioenergy chains developed in the four countries have led to unsustainable outcomes and how the countries manage the sustainability risks. Data were collected from a diversity of sources including interviews, statistical databases, the scientific literature, government planning documents and legislation. Results Sustainability risks of deforestation, degradation of forests, reduced carbon pools in forests, expensive biopower and heat, resource competition, and lack of acceptance at the local level are considered. The experience of the four countries shows that the sustainability risks can to a high degree be managed with voluntary measures without resorting to prescriptive measures. It is possible to add to the carbon pools of forests along with higher harvest volumes if the risks are well managed. There is, however, a marginal trade-off between harvest volume and carbon pools. Economic sustainability risks may be more challenging than ecological risks because the competitiveness order of renewable energy technologies has been reversed in the last decade. The risk of resource competition harming other sectors in the economy was found to be small and manageable but requires continuous monitoring. Local communities acting as bioenergy communities have been agents of change behind the most expansive bioenergy chains. A fear of non-local actors reaping the economic gains involved in bioenergy chains was found to be one of the risks to the trust and acceptance necessary for local communities to act as bioenergy communities. Conclusions The Nordic experience shows that it has been possible to manage the sustainability risks examined in this paper to an extent avoiding unsustainable outcomes. Sustainability risks have been managed by developing an institutional framework involving laws, regulations, standards and community commitments. Particularly on the local level, bioenergy chains should be developed with stakeholder involvement in development and use, in order to safeguard the legitimacy of bioenergy development and reconcile tensions between the global quest for a climate neutral economy and the local quest for an economically viable community. Keywords: Bioenergy, Sustainability, Risk assessment, Risk management, Nordic countries
Sammendrag
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Forfattere
Wenche DramstadSammendrag
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Forfattere
Matthew J. Kauffman Francesca Cagnacci Simon Chamaillé-Jammes Mark Hebblewhite J. Grant C. Hopcraft Jerod A. Merkle Thomas Mueller Atle Mysterud Wibke Erika Brigitta Peters Christiane Roettger Alethea Steingisser James E. Meacham Kasahun Abera Jan Adamczewski Ellen O. Aikens Hattie Bartlam-Brooks Emily Bennitt Joel Berger Charlotte Boyd Steeve D. Côté Lucie Isabelle Debeffe Andrea S. Dekrout Nandintsetseg Dejid Emiliano Donadio Luthando Dziba William F. Fagan Claude Fischer Stefano Focardi John M. Fryxell Richard W. S. Fynn Chris Geremia Benito A. González Anne Gunn Elie Gurarie Marco Dietmar Heurich Jodi Hilty Mark A. Hurley Aran Johnson Kyle Joly Petra Kaczensky Corinne J. Kendall Pavel Kochkarev Leonid Kolpaschikov Rafal Kowalczyk Frank van Langevelde Binbin V. Li Anne Loison Alex L. Lobora Tinaapi H. Madiri David Mallon Erling Meisingset Christer Moe Rolandsen Erling Johan Solberg Olav StrandSammendrag
Migration of ungulates (hooved mammals) is a fundamental ecological process that promotes abundant herds, whose effects cascade up and down terrestrial food webs. Migratory ungulates provide the prey base that maintains large carnivore and scavenger populations and underpins terrestrial biodiversity (fig. S1). When ungulates move in large aggregations, their hooves, feces, and urine create conditions that facilitate distinct biotic communities. The migrations of ungulates have sustained humans for thousands of years, forming tight cultural links among Indigenous people and local communities. Yet ungulate migrations are disappearing at an alarming rate (1). Efforts by wildlife managers and conservationists are thwarted by a singular challenge: Most ungulate migrations have never been mapped in sufficient detail to guide effective conservation. Without a strategic and collaborative effort, many of the world’s great migrations will continue to be truncated, severed, or lost in the coming decades. Fortunately, a combination of animal tracking datasets, historical records, and local and Indigenous knowledge can form the basis for a global atlas of migrations, designed to support conservation action and policy at local, national, and international levels.
Sammendrag
The size and location of agricultural fields that are in active use and the type of use during the growing season are among the vital information that is needed for the careful planning and forecasting of agricultural production at national and regional scales. In areas where such data are not readily available, an independent seasonal monitoring method is needed. Remote sensing is a widely used tool to map land use types, although there are some limitations that can partly be circumvented by using, among others, multiple observations, careful feature selection and appropriate analysis methods. Here, we used Sentinel-2 satellite image time series (SITS) over the land area of Norway to map three agricultural land use classes: cereal crops, fodder crops (grass) and unused areas. The Multilayer Perceptron (MLP) and two variants of the Convolutional Neural Network (CNN), are implemented on SITS data of four different temporal resolutions. These enabled us to compare twelve model-dataset combinations to identify the model-dataset combination that results in the most accurate predictions. The CNN is implemented in the spectral and temporal dimensions instead of the conventional spatial dimension. Rather than using existing deep learning architectures, an autotuning procedure is implemented so that the model hyperparameters are empirically optimized during the training. The results obtained on held-out test data show that up to 94% overall accuracy and 90% Cohen’s Kappa can be obtained when the 2D CNN is applied on the SITS data with a temporal resolution of 7 days. This is closely followed by the 1D CNN on the same dataset. However, the latter performs better than the former in predicting data outside the training set. It is further observed that cereal is predicted with the highest accuracy, followed by grass. Predicting the unused areas has been found to be difficult as there is no distinct surface condition that is common for all unused areas.
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Forfattere
Kjersti Holt HanssenSammendrag
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Sammendrag
https://www.nationen.no/motkultur/debatt/markedshagedyrking-er-en-spennende-utvikling-i-landbruket/