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Dirnböck, T.; Bezák, P.; Dullinger, S.; Haberl, H.; Lotze-Campen, H.; Mirtl, M.; Peterseil, J.; Redpath, S.; Singh, S.J.; Travis, J.; Wijdeven, S.M.J. |
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Title |
Critical Scales for Long-Term Socio-ecological Biodiversity Research |
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Book Chapter |
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Year |
2013 |
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Pages |
123-138 |
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TradeM |
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Publisher |
Springer |
Place of Publication |
Dordrecht |
Editor |
Singh, S.J.; Haberl, H.; Chertow, M.; Mirtl, M.; Schmid, M. |
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Long Term Socio-Ecological Research |
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Human-environment interactions (2) |
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Call Number |
MA @ admin @ |
Serial |
2390 |
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Author |
Sinabell, F.; Heinschink, K.; Tribl, C. |
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Title |
Explicit cost accounting for analyses on climate change adaptation, mitigation and ecosystem service provision in agriculture |
Type |
Conference Article |
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Year |
2016 |
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Sauvage, S.; Sánchez-Pérez, J.M.; Rizzoli, A.E. |
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8th International Congress on Environmental Modelling and Software |
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8th International Congress on Environmental Modelling and Software, 2016-07-10 to 2016-07-14, Toulouse, France |
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Notes |
TradeM |
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no |
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Call Number |
MA @ admin @ |
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5031 |
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Author |
Ewert, F.; van Bussel, L.G.J.; Zhao, G.; Hoffmann, H.; Gaiser, T.; Specka, X.; Nendel, C.; Kersebaum, K.-C.; Sosa, C.; Lewan, E.; Yeluripati, J.; Kuhnert, M.; Tao, F.; Rötter, R.P.; Constantin, J.; Raynal, H.; Wallach, D.; Teixeira, E.; Grosz, B.; Bach, M.; Doro, L.; Roggero, P.P.; Zhao, Z.; Wang, E.; Kiese, R.; Haas, E.; Eckersten, H.; Trombi, G.; Bindi, M.; Klein, C.; Biernath, C.; Heinlein, F.; Priesack, E.; Cammarano, D.; Asseng, S.; Elliott, J.; Glotter, M.; Basso, B.; Baigorria, G.A.; Romero, C.C.; Moriondo, M. |
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Title |
Uncertainties in Scaling up Crop Models for Large Area Climate-change Impact Assessments |
Type |
Book Chapter |
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Year |
2015 |
Publication |
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Pages |
261-277 |
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Keywords |
CropM; |
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Imperial College Press |
Place of Publication |
London |
Editor |
Rosenzweig, C.; Hillel, D. |
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Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP) Integrated Crop and Economic Assessments — Joint Publication with American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America (In 2 Parts) |
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ICP Series on Climate Change Impacts, Adaptation, |
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Call Number |
MA @ admin @ |
Serial |
2427 |
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Author |
Kebreab, E.; Tedeschi, L.; Dijkstra, J.; Ellis, J.L.; Bannink, A.; France, J. |
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Title |
Modeling Greenhouse Gas Emissions from Enteric Fermentation |
Type |
Book Chapter |
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Year |
2016 |
Publication |
Advances in Agricultural Systems |
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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 |
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Series Volume |
Advances in Agricultural Systems (6) |
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Notes |
LiveM, ftnotmacsur |
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no |
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Call Number |
MA @ admin @ |
Serial |
5032 |
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Author |
Boote, K.J.; Porter, C.; Jones, J.W.; Thorburn, P.J.; Kersebaum, K.C.; Hoogenboom, G.; White, J.W.; Hatfield, J.L. |
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Title |
Sentinel site data for crop model improvement – definition and characterization |
Type |
Conference Article |
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Year |
2015 |
Publication |
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Abbreviated Journal |
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Volume |
Advances in Agricultural Systems Modeling (7) |
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Pages |
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Keywords |
CropM; |
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Publisher |
ASA, CSSA, and SSSA |
Place of Publication |
Madison, WI |
Editor |
Hatfield, J.L.; Fleisher, D. |
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Notes |
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no |
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Call Number |
MA @ admin @ |
Serial |
2338 |
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