Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2021
Authors
Elisabeth Pötzelsberger Martin M. Gossner Ludwig Beenken Anna Gazda Michal Petr Tiina Ylioja Nicola La Porta Dimitrios N. Avtzis Elodie Bay Maarten De Groot Rein Drenkhan Mihai-Leonard Duduman Rasmus Enderle Margarita Georgieva Ari Hietala Björn Hoppe Herve Jactel Kristjan Jarni Srđan Keren Zsolt Keseru Marcin Koprowski Andrej Kormuťák María Josefa Lombardero Aljona Lukjanova Vitas Marozas Edurad Mauri Maria Cristina Monteverdi Per Holm Nygaard Nikica Ogris Nicolai Olenici Christophe Orazio Bernhard Perny Glória Pinto Michael Power Radoslaw Puchalka Hans Peter Ravn Ignacio Sevillano Sophie Stroheker Paul Taylor Panagiotis Tsopelas Josef Urban Kaljo Voolma Marjana Westergren Johanna Witzell Olga Zborovska Milica ZlatkovicAbstract
For non-native tree species with an origin outside of Europe a detailed compilation of enemy species including the severity of their attack is lacking up to now. We collected information on native and non-native species attacking non-native trees, i.e. type, extent and time of first observation of damage for 23 important non-native trees in 27 European countries. Our database includes about 2300 synthesised attack records (synthesised per biotic threat, tree and country) from over 800 species. Insects (49%) and fungi (45%) are the main observed biotic threats, but also arachnids, bacteria including phytoplasmas, mammals, nematodes, plants and viruses have been recorded. This information will be valuable to identify patterns and drivers of attacks, and trees with a lower current health risk to be considered for planting. In addition, our database will provide a baseline to which future impacts on non-native tree species could be compared with and thus will allow to analyse temporal trends of impacts.
Abstract
No abstract has been registered
Abstract
Leaf blotch diseases (LBD), such as Septoria nodorum bloch (Parastagnospora nodorum), Septoria tritici blotch (Zymoseptoria tritici) and Tan spot (Pyrenophora tritici-repentis) can cause severe yield losses (up to 50%) in Norwegian spring wheat (Triticum aestivum) and are mainly controlled by fungicide applications. A forecasting model to predict disease risk can be an important tool to optimize disease control. The association between specific weather variables and the development of LBD differs between wheat growth stages. In this study, a mathematical model to estimate phenological development of spring wheat was derived based on sowing date, air temperature and photoperiod. Weather factors associated with LBD severity were then identified for selected phenological growth stages by a correlation study of LBD severity data (17 years). Although information regarding host resistance and previous crop were added to the identified weather factors, two purely weather-based risk prediction models (CART, classification and regression tree algorithm) and one black box model (KNN, based on K nearest neighbor algorithm) were most accurate to predict moderate to high LBD severity (>5% infection). The predictive accuracy of these models (76–83%) was compared to that of two existing models used in Norway and Denmark (60 and 61% accuracy, respectively). The newly developed models performed better than the existing models, but still had the tendency to overestimate disease risk. Specificity of the new models varied between 49 and 74% compared to 40 and 37% for the existing models. These new models are promising decision tools to improve integrated LBD management of spring wheat in Norway.
Abstract
No abstract has been registered
Authors
Ingunn Øvsthus Theresa Weigl Hanne Larsen Erlend Indergård Siv Fagertun Remberg Jorunn BørveAbstract
No abstract has been registered
Abstract
Late blight caused by Phytophthora infestans is a serious, worldwide disease on potato (Solanum tuberosum). Phytophthora infestans normally reproduces in a clonal manner, but in some areas, as the Nordic Countries, sexual reproduction has become the major determinant of the population structure. To improve the late blight forecasting in Norway, the process-based Nærstad model was developed. The model includes the structure of the underlying processes in the disease development, including spore production, spore release, spore survival and infection of P. infestans. It needs hourly weather records of air temperature, precipitation, relative humidity, leaf wetness and global radiation. The model contained 19 uncertain parameters, and from a sensitivity analysis, 12 were detected as weakly sensitive to model outputs and fixed to a nominal value within their prior boundaries. The remaining seven parameters were detected as more sensitive to model outputs and were parameterized using maximum a'posteriori (MAP) estimates, calculated through Bayesian calibration. The model was developed based on literature combined with field data of daily observed number of lesions on trap plants of the Bintje cultivar (late blight susceptible) at Ås during the seasons 2006-2008 and 2010-2011. It was further tested on daily observed number of lesions on trap plants of the cultivars Bintje, Saturna (medium susceptible) and Peik (medium resistant) at Ås during the seasons 2012-2015. For all three cultivars, the Nærstad model improved with a higher model accuracy compared to the existing HOSPO-model and the Førsund rules that both have shown relatively good correlation with blight development in field evaluations in Norway. The best accuracy was found for Bintje (0.83) closely followed by Saturna (0.79), whereas a much lower accuracy was detected for Peik (0.66).
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Rapporten dokumenterer utvalgte eksempler på bruk av stordata (engelsk: big data) teknologi og metode i NIBIO. Det første eksemplet er knyttet til oppdatering av arealressurskartet AR5, hvor det undersøkes om stordata-tilnærming kan benyttes for å identifisere lokaliteter der kartet må oppdateres. De neste eksemplene er hentet fra fagområdet plantehelse og tar for seg mulighetene for å bruke stordata-metode for å bedre prediksjonsmodeller og gjenkjenning av for skadegjørere.
Authors
Arne Verstraeten Elena Gottardini Nicolas Bruffaerts Fabiana Cristofolini Elena Vanguelova Johan Neirynck Gerrit Genouw Bruno de Vos Peter Waldner Anne Thimonier Anita Nussbaumer Mathias Neumann Sue Benham Pasi Rautio Liisa Ukonmaanaho Päivi Merilä Annika Saarto Jukka Reiniharju Peter Roskams Geert Sioen Nathalie Cools Nicholas Clarke Volkmar Timmermann Hans-Peter Dietrich Manuel Nicolas Maria Schmitt Katrin Meusburger Silvio Schüler Anna Kowalska Idalia Kasprzyk Katarzyna Kluska Łukasz Grewling Joanna Święta-Musznicka Małgorzata Latałowa Marcelina Zimny Małgorzata Malkiewicz Lars Vesterdal Miklós Manninger Donat Magyar Hugues Titeux Gunilla Pihl-Karlsson Marco FerrettiAbstract
No abstract has been registered