| Records |
| Author |
van Lingen, H.J.; Plugge, C.M.; Fadel, J.G.; Kebreab, E.; Bannink, A.; Dijkstra, J. |
| 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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Edition |
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| ISSN |
1932-6203 |
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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. |
| Title |
Modeling Greenhouse Gas Emissions from Enteric Fermentation |
Type |
Book Chapter |
| Year |
2016 |
Publication |
Advances in Agricultural Systems |
Abbreviated Journal |
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| Volume |
6 |
Issue |
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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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Kebreab, E. |
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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 |
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| Series Volume |
Advances in Agricultural Systems (6) |
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| Notes |
LiveM, ftnotmacsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
5032 |
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| Author |
Kipling, R.P.; Topp, C.F.E.; Bannink, A.; Bartley, D.J.; Blanco-Penedo, I.; Cortignani, R.; del Prado, A.; Dono, G.; Faverdin, P.; Graux, A.-I.; Hutchings, N.J.; Lauwers, L.; Gulzari, S.O.; Reidsma, P.; Rolinski, S.; Ruiz-Ramos, M.; Sandars, D.L.; Sandor, R.; Schoenhart, M.; Seddaiu, G.; van Middelkoop, J.; Shrestha, S.; Weindl, I.; Eory, V. |
| Title |
To what extent is climate change adaptation a novel challenge for agricultural modellers |
Type |
Journal Article |
| Year |
2019 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
| Volume |
120 |
Issue |
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Pages |
Unsp 104492 |
| Keywords |
Adaptation; Agricultural modelling; Climate change; Research challenges; greenhouse-gas emissions; farm-level adaptation; land-use; food; security; adapting agriculture; livestock production; decision-making; change impacts; dairy farms; crop |
| Abstract |
Modelling is key to adapting agriculture to climate change (CC), facilitating evaluation of the impacts and efficacy of adaptation measures, and the design of optimal strategies. Although there are many challenges to modelling agricultural CC adaptation, it is unclear whether these are novel or, whether adaptation merely adds new motivations to old challenges. Here, qualitative analysis of modellers’ views revealed three categories of challenge: Content, Use, and Capacity. Triangulation of findings with reviews of agricultural modelling and Climate Change Risk Assessment was then used to highlight challenges specific to modelling adaptation. These were refined through literature review, focussing attention on how the progressive nature of CC affects the role and impact of modelling. Specific challenges identified were: Scope of adaptations modelled, Information on future adaptation, Collaboration to tackle novel challenges, Optimisation under progressive change with thresholds, and Responsibility given the sensitivity of future outcomes to initial choices under progressive change. |
| Address |
2020-02-14 |
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| Language |
English |
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Series Issue |
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Edition |
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| ISSN |
1364-8152 |
ISBN |
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Medium |
Article |
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| Notes |
LiveM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
5223 |
| Permanent link to this record |
