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Heinschink, K.; Lembacher, F.; Sinabell, F.; Trible, C. |
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Title |
Crop production costs in Austria: Comparison of simulated results and farm observations |
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2016 |
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Jahrbuch der ÖGA |
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26 |
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33-34 |
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26. Jahrestagung der Österreichischen Gesellschaft für Agrarökonomie, 2016-09-15 to 2016-09-16, Vienna, Austria |
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MA @ admin @ |
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5025 |
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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 |
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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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TradeM |
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MA @ admin @ |
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5031 |
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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 |
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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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Korhonen, P.; Palosuo, T.; Höglind, M.; Persson, T.; van Oijen, M.; Jego, G.; Virkajärvi, P.; Belanger, G.; Gustavsson, A.M. |
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Intercomparison of models for simulating timothy yield in Northern countries. The multiple roles of grassland in the European bioeconomy |
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2016 |
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Trondheim, Norway |
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General Meeting of the European Grassland Federation |
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26 |
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General Meeting of the European Grassland Federation, 2016-09-04 to 2016-09-08, Trondheim, Norway 26: |
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MA @ admin @ |
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5168 |
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Author |
Jancic, M. |
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Title |
Climate change adaptation in maize production in Serbia |
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Report |
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2016 |
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FACCE MACSUR Reports |
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9 C6 - |
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Sp9-3 |
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Climate change is noticed and well established phenomenon, described as change in the statistical properties of the climate system, considered over long period of time, regardless of cause (Houghton, 1996). This change has been monitored on global (Rosenzweig et al., 1994; Harrison et al., 1995; Wolf et al., 1995; Watson et al., 1996; Downing et al., 2000; Sathaye et al., 1997; Sirotenko et al., 1997) and regional scale (Alexandrov et al., 2002; Lalic et al., 2012; Vučetić, 2011) by researchers, organizations and part of various programmes (IPCC, UNESCO Climate Change Initiative). In a same time, it was analysed change in agroclimatic indices, soil and water balance, crop development and yield, that quantify climate change impact on agricultural production. In recent regional studies and research projects (COST 734, 2008; ADAGIO, 2009), it was estimated and quantified climate change impact on yield and development in crop production of Central and Eastern European countries and Mediterranean region. The research showed a decrease in yield in several major crops, important in national food production and part of economy. A high variability in yield from year to year and decrease in yield was showed for most cereals. |
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MA @ admin @ |
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4856 |
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