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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. |
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Does collaborative farm-scale modelling address current challenges and future opportunities |
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2017 |
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FACCE MACSUR Reports |
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10 |
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L1.4-D2 |
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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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LiveM |
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MA @ admin @ |
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4978 |
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van Lingen, H.J.; Plugge, C.M.; Fadel, J.G.; Kebreab, E.; Bannink, A.; Dijkstra, J. |
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Correction: Thermodynamic Driving Force of Hydrogen on Rumen Microbial Metabolism: A Theoretical Investigation |
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Miscellaneous |
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2016 |
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PLoS One |
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PLoS One |
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11(12) |
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12 |
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e0168052 |
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[This corrects the article DOI: 10.1371/journal.pone.0161362.]. |
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1932-6203 |
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LiveM, ftnotmacsur |
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MA @ admin @ |
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5020 |
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Kebreab, E.; Tedeschi, L.; Dijkstra, J.; Ellis, J.L.; Bannink, A.; France, J. |
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Modeling Greenhouse Gas Emissions from Enteric Fermentation |
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Book Chapter |
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2016 |
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Advances in Agricultural Systems |
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6 |
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173-196 |
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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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Kebreab, E. |
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Synthesis and Modeling of Greenhouse Gas Emissions and Carbon Storage in Agricultural and Forest Systems to Guide Mitigation and Adaptation |
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Advances in Agricultural Systems (6) |
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LiveM, ftnotmacsur |
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MA @ admin @ |
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5032 |
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Bannink, A. |
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Application of a Tier 3 approach for estimating enteric fermentation in dairy cows: Advantages and disadvantages |
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2014 |
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International Livestock Modelling and Research Colloquium, Bilbao, Spain, 2014-10-14 to 2014-10-16 |
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2288 |
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Kipling, R.; Scollan, N.; Bannink, A.; van Middelkoop, J. |
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From diversity to strategy: Livestock research for effective policy in a climate change world |
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2016 |
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FACCE MACSUR Reports |
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8 |
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H0.3-D1 |
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policy brief, networking |
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European livestock agriculture is extraordinarily diverse, and so are the challenges it faces. This diversity has contributed to the development of a fragmented set of research communities. As a result, livestock research is often under-represented at policy level, despite its high relevance for the environment and food security. Understanding livestock systems and how they can sustainably adapt to global change requires inputs across research areas, including grasslands, nutrition, health, welfare and ecology. It also requires experimental researchers, modellers and stakeholders to work closely together. Networks and capacity building structures are vital to enable livestock research to meet the challenges of climate change. They need to maintain shared resources and provide non-competitive arenas to share and synthesize results for policy support. ï‚· Long term strategic investment is needed to support such structures. Their leadership requires very different skills to those effective in scientific project coordination. |
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2269 |
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