2019
Forfattere
Sofie Hellsten Tommy Dalgaard Katri Rankinen Kjetil Tørseth Lars Bakken Marianne Bechmann Airi Kulmala Filip Moldan Stina Olofsson Kristoffer Piil Kajsa Pira Eila TurtolaSammendrag
During the past twenty years, the Nordic countries (Denmark, Sweden, Finland and Norway) have introduced a range of measures to reduce losses of nitrogen (N) to air and to aquatic environment by leaching and runoff. However, the agricultural sector is still an important N source to the environment, and projections indicate relatively small emission reductions in the coming years. The four Nordic countries have different priorities and strategies regarding agricultural N flows and mitigation measures, and therefore they are facing different challenges and barriers. In Norway farm subsidies are used to encourage measures, but these are mainly focused on phosphorus (P). In contrast, Denmark targets N and uses control regulations to reduce losses. In Sweden and Finland, both voluntary actions combined with subsidies help to mitigate both N and P. The aim of this study was to compare the present situation pertaining to agricultural N in the Nordic countries as well as to provide recommendations for policy instruments to achieve cost effective abatement of reactive N from agriculture in the Nordic countries, and to provide guidance to other countries. To further reduce N losses from agriculture, the four countries will have to continue to take different routes. In particular, some countries will need new actions if 2020 and 2030 National Emissions Ceilings Directive (NECD) targets are to be met. Many options are possible, including voluntary action, regulation, taxation and subsidies, but the difficulty is finding the right balance between these policy options for each country. The governments in the Nordic countries should put more attention to the NECD and consult with relevant stakeholders, researchers and farmer's associations on which measures to prioritize to achieve these goals on time. It is important to pick remaining low hanging fruits through use of the most cost effective mitigation measures. We suggest that N application rate and its timing should be in accordance with the crop need and carrying capacity of environmental recipients. Also, the choice of application technology can further reduce the risk of N losses into air and waters. This may require more region-specific solutions and knowledge-based support with tailored information in combination with further targeted subsidies or regulations.
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Rhodiola rosea is a highly valued herbal medicinal plant. It is growing wild in most parts of Norway and mountainous areas around the world. The marker compounds are salidroside, cinnamyl alcohol, glycosides (rosine, rosavine, rosarine), flavonoids (rhodionin, rhodiosin, rhodiolin) and terpens (Galambosi 1999), where the rosavins are unique to R. rosea. In Norway, germplasm collections of R. rosea are maintained by NIBIO; at Apelsvoll in Southern Norway, consisting of 97 different clones. The ranges in content of secondary metabolites in the collection are for rosavin 2.90-85.95 mg g-1, salidroside 0.03-12.85 mg g-1, rosin 0.08-4.75 mg g-1, tyrosol 0.04-2.15 mg g-1 and cinnamyl alcohol 0.02-1.18 mg g-1. A number of different studies have been performed on how biotic and abiotic factors affects the yield of the roots as well the content in metabolites. We find that the flowering of the plant is dependent on cool temperatures during dormancy and thus climatic changes may affect the plant development as well as the production of metabolites. Studies performed in Norway as well as between European countries shows that geographical location affects the content of metabolites and here also variation in clones are a player. In the present presentation results from these and more studies will be presented. Also comprising results on the effect of white-, blue- and red light on the growth and chemical composition of greenhouse grown plants.
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Tatsiana EspevigSammendrag
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Krishna Reddy Kakumanu Gurava Reddy Kotapati Udaya Sekhar Nagothu Palanisami Kuppanan Suresh Reddy KallamSammendrag
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Muhammad Azher Bhatti Thomas Williams David Laurence Hopkins Leif Jarle Asheim Geir Steinheim Michael Campbell Lars Olav Eik Peter Charles Wynn Tormod ÅdnøySammendrag
Norway is the largest sheep meat producer among Nordic countries with more than 1.3 million lambs and sheep slaughtered in 2017. The sheep industry is limited by the need for in-house feeding during the winter months. In summer, Norwegian sheep are mainly kept on rangeland pastures, with sufficient feed for almost double the current sheep population. Lambs are slaughtered over a three- to four-month period from September to December with a peak in September–October, providing a surplus of lamb, much of which is subsequently frozen, followed by eight months during which fresh produce is in limited supply. Norwegian consumers eat an average of 5.4 kg of sheep meat per person per year, much of which is purchased as a frozen product. The Muslim (4.2% of the population) preference for year-round halal meat, with an increased demand on the eve of the Muslim meat festival (Eid al-Adha), has the potential to boost demand, particularly in Oslo. This paper provides an overview of the Norwegian sheep farming system, the current market value chains, and the potential to meet the demand for halal meat in Norway (specifically during the Muslim meat festival—Eid al-Adha) to the advantage of both consumers and sheep farmers.
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Advances in techniques for automated classification of point cloud data introduce great opportunities for many new and existing applications. However, with a limited number of labelled points, automated classification by a machine learning model is prone to overfitting and poor generalization. The present paper addresses this problem by inducing controlled noise (on a trained model) generated by invoking conditional random field similarity penalties using nearby features. The method is called Atrous XCRF and works by forcing a trained model to respect the similarity penalties provided by unlabeled data. In a benchmark study carried out using the ISPRS 3D labeling dataset, our technique achieves 85.0% in term of overall accuracy, and 71.1% in term of F1 score. The result is on par with the current best model for the benchmark dataset and has the highest value in term of F1 score. Additionally, transfer learning using the Bergen 2018 dataset, without model retraining, was also performed. Even though our proposal provides a consistent 3% improvement in term of accuracy, more work still needs to be done to alleviate the generalization problem on the domain adaptation and the transfer learning field.
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Pia Heltoft Trygve S. Aamlid Karin Juul Hesselsøe Karin Normann Bjarni Hannesson Kemeng XiaoSammendrag
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