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Author Köchy, M.; Bishop, J.; Lehtonen, H.; Scollan, N.; Webber, H.; Zimmermann, A.; Bellocchi, G.; Bannink, A.; Biewald, A.; Ferrise, R.; Helming, K.; Kipling, R.P.; Milford, A.; Özkan Gülzari, Ş.; Ruiz-Ramos, M.; Curth-van Middelkoop, J. url  openurl
  Title Challenges and research gaps in the area of integrated climate change risk assessment for European agriculture and food security Type Report
  Year 2017 Publication FACCE MACSUR Reports Abbreviated Journal  
  Volume 10 Issue Pages H0.1-D  
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  Abstract Priorities in addressing research gaps and challenges should follow the order of im­por­tance, which in itself would be a matter of defining goals and metrics of importance, e.g. the extent, impact and likelihood of occurrence. For improving assessments of cli­mate change impacts on agriculture for achieving food security and other sustainable develop­ment goals across the European continent, the most important research gaps and challen­ges appear to be the agreement on goals with a wide range of stakeholders from policy, science, producers and society, better reflection of political and societal prefer­ences in the modelling process, and the reflection of economic decisions in farm manage­ment within models. These and other challenges could be approached in phase 3 of MACSUR.  
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  Call Number MA @ admin @ Serial 4950  
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Author Topp, K.; Eory, V.; Bannink, A.; Bartley, D.J.; Blanco-Penedo, I.; Cortignani, R.; Del Prado, A.; Dono, G.; Faverdin, P.; Graux, A.-I.; Hutchings, N.; Lauwers, L.; Özkan Gülzari, Ş.; Rolinski, S.; Ruiz Ramos, M.; Sandars, D.L.; Sándor, R.; Schoenhart, M.; Seddaiu, G.; van Middelkoop, J.; Weindl, I.; Kipling, R.P. url  openurl
  Title Modelling climate change adaptation in European agriculture: Definitions and Current Modelling Type Report
  Year 2017 Publication FACCE MACSUR Reports Abbreviated Journal  
  Volume 10 Issue Pages L2.3.2-D  
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  Abstract Confidential content, in preparation for a peer-reviewed publication.  
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  Notes LiveM Approved no  
  Call Number MA @ admin @ Serial 4959  
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Author Hutchings, N.; Weindl, I.; Topp, C.F.E.; Snow, V.O.; Rotz, A.; Raynal, H.; Özkan Gülzari, Ş.; Martin, R.; Holzworth, D.P.; Graux, A.-I.; Faverdin, P.; Del Prado, A.; Eckard, R.; Bannink, A. url  openurl
  Title Does collaborative farm-scale modelling address current challenges and future opportunities Type Report
  Year 2017 Publication FACCE MACSUR Reports Abbreviated Journal  
  Volume 10 Issue Pages L1.4-D2  
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  Abstract Resources required increasing, resources available decreasing Farm-scale modellers will need to make strategic decisions Single-owner models May continue with additional resources Risk of ‘succession’ problem Community modelling is an alternative Need to continue building a community of farm modellers  
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  Notes LiveM Approved no  
  Call Number MA @ admin @ Serial 4978  
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Author van Lingen, H.J.; Plugge, C.M.; Fadel, J.G.; Kebreab, E.; Bannink, A.; Dijkstra, J. url  doi
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  Title Correction: Thermodynamic Driving Force of Hydrogen on Rumen Microbial Metabolism: A Theoretical Investigation Type Miscellaneous
  Year 2016 Publication PLoS One Abbreviated Journal PLoS One  
  Volume 11(12) Issue 12 Pages e0168052  
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  Abstract [This corrects the article DOI: 10.1371/journal.pone.0161362.].  
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  ISSN 1932-6203 ISBN Medium  
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  Notes LiveM, ftnotmacsur Approved no  
  Call Number MA @ admin @ Serial 5020  
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Author Kebreab, E.; Tedeschi, L.; Dijkstra, J.; Ellis, J.L.; Bannink, A.; France, J. url  doi
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  Title Modeling Greenhouse Gas Emissions from Enteric Fermentation Type Book Chapter
  Year 2016 Publication Advances in Agricultural Systems Abbreviated Journal  
  Volume 6 Issue Pages 173-196  
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  Abstract Livestock directly contribute to greenhouse gas (GHG) emissions mainly through methane (CH4) and nitrous oxide (N2O) emissions. For cost and practicality reasons, quantification of GHG has been through development of various types of mathematical models. This chapter addresses the utility and limitations of mathematical models used to estimate enteric CH4 emissions from livestock production. Models used in GHG quantification can be broadly classified into either empirical or mechanistic models. Empirical models might be easier to use because they require fewer input variables compared with mechanistic models. However, their applicability in assessing mitigation options such as dietary manipulation may be limited. The major driving variables identified for both types of models include feed intake, lipid and nonstructural carbohydrate content of the feed, and animal variables. Knowledge gaps identified in empirical modeling were that some of the assumptions might not be valid because of geographical location, health status of animals, genetic differences, or production type. In mechanistic modeling, errors related to estimating feed intake, stoichiometry of volatile fatty acid (VFA) production, and acidity of rumen contents are limitations that need further investigation. Model prediction uncertainty was also investigated, and, depending on the intensity and source of the prediction uncertainty, the mathematical model may inaccurately predict the observed values with more or less variability. In conclusion, although there are quantification tools available, global collaboration is required to come to a consensus on quantification protocols. This can be achieved through developing various types of models specific to region, animal, and production type using large global datasets developed through international collaboration.  
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  Publisher Place of Publication Editor Kebreab, E.  
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  Series Editor Series Title Synthesis and Modeling of Greenhouse Gas Emissions and Carbon Storage in Agricultural and Forest Systems to Guide Mitigation and Adaptation Abbreviated Series Title  
  Series Volume Advances in Agricultural Systems (6) Series Issue Edition  
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  Notes LiveM, ftnotmacsur Approved no  
  Call Number MA @ admin @ Serial 5032  
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