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Nelson, G.C.; van der Mensbrugghe, D.; Ahammad, H.; Blanc, E.; Calvin, K.; Hasegawa, T.; Havlik, P.; Heyhoe, E.; Kyle, P.; Lotze-Campen, H.; von Lampe, M.; Mason, d’C., Daniel; van Meijl, H.; Müller, C.; Reilly, J.; Robertson, R.; Sands, R.D.; Schmitz, C.; Tabeau, A.; Takahashi, K.; Valin, H.; Willenbockel, D. |
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Title |
Agriculture and climate change in global scenarios: why don’t the models agree |
Type |
Journal Article |
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Year |
2014 |
Publication |
Agricultural Economics |
Abbreviated Journal |
Agric. Econ. |
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Volume |
45 |
Issue |
1 |
Pages |
85-85 |
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Keywords |
climate change impacts; economic models of agriculture; scenarios; system model; demand; cmip5 |
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Abstract |
Agriculture is unique among economic sectors in the nature of impacts from climate change. The production activity that transforms inputs into agricultural outputs involves direct use of weather inputs (temperature, solar radiation available to the plant, and precipitation). Previous studies of the impacts of climate change on agriculture have reported substantial differences in outcomes such as prices, production, and trade arising from differences in model inputs and model specification. This article presents climate change results and underlying determinants from a model comparison exercise with 10 of the leading global economic models that include significant representation of agriculture. By harmonizing key drivers that include climate change effects, differences in model outcomes were reduced. The particular choice of climate change drivers for this comparison activity results in large and negative productivity effects. All models respond with higher prices. Producer behavior differs by model with some emphasizing area response and others yield response. Demand response is least important. The differences reflect both differences in model specification and perspectives on the future. The results from this study highlight the need to more fully compare the deep model parameters, to generate a call for a combination of econometric and validation studies to narrow the degree of uncertainty and variability in these parameters and to move to Monte Carlo type simulations to better map the contours of economic uncertainty. |
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2016-10-31 |
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English |
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0169-5150 |
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CropM, TradeM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
4796 |
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Author |
Nelson, G.C.; van der Mensbrugghe, D.; Ahammad, H.; Blanc, E.; Calvin, K.; Hasegawa, T.; Havlik, P.; Heyhoe, E.; Kyle, P.; Lotze-Campen, H.; von Lampe, M.; Mason, d’C., Daniel; van Meijl, H.; Müller, C.; Reilly, J.; Robertson, R.; Sands, R.D.; Schmitz, C.; Tabeau, A.; Takahashi, K.; Valin, H.; Willenbockel, D. |
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Title |
Agriculture and climate change in global scenarios: why don’t the models agree |
Type |
Journal Article |
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Year |
2014 |
Publication |
Agricultural Economics |
Abbreviated Journal |
Agric. Econ. |
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Volume |
45 |
Issue |
1 |
Pages |
85-101 |
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Keywords |
climate change impacts; economic models of agriculture; scenarios; system model; demand; CMIP5 |
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Abstract |
Agriculture is unique among economic sectors in the nature of impacts from climate change. The production activity that transforms inputs into agricultural outputs involves direct use of weather inputs (temperature, solar radiation available to the plant, and precipitation). Previous studies of the impacts of climate change on agriculture have reported substantial differences in outcomes such as prices, production, and trade arising from differences in model inputs and model specification. This article presents climate change results and underlying determinants from a model comparison exercise with 10 of the leading global economic models that include significant representation of agriculture. By harmonizing key drivers that include climate change effects, differences in model outcomes were reduced. The particular choice of climate change drivers for this comparison activity results in large and negative productivity effects. All models respond with higher prices. Producer behavior differs by model with some emphasizing area response and others yield response. Demand response is least important. The differences reflect both differences in model specification and perspectives on the future. The results from this study highlight the need to more fully compare the deep model parameters, to generate a call for a combination of econometric and validation studies to narrow the degree of uncertainty and variability in these parameters and to move to Monte Carlo type simulations to better map the contours of economic uncertainty. |
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0169-5150 |
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CropM, TradeM, ft_macsur |
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MA @ admin @ |
Serial |
4536 |
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Nendel, C.; Thorburn, P.; Melzer, D.; Cerri, C.E.P.; Claessens, L.; Aggarwal, P.K.; Adam, M.; Angulo, C.; Asseng, S.; Baron, C.; Basso, B.; Bassu, S.; Bertuzzi, P.; Biernath, C.; Boogaard, H.; Boote, K.J.; Brisson, N.; Cammarano, D.; Conijn, S.; Corbeels, M.; Deryng, D.; Sanctis, G.D.; Doltra, J.; Durand, J.L.; Ewert, F.; Gayler, S.; Goldberg, R.; Grant, R.; Grassini, P.; Heng, L.; Hoek, S.B.; Hooker, J.A.U.-, L.A.H.; Ingwersen, J.; Izaurralde, C.; Jongschaap, R.; Kemanian, A.; Kersebaum, K.C.; Lizaso, J.; Makowski, D.; Martre, P.; Müller, C.; Kim, S.H.; Kumar, S.N.; O’Leary, G.; Olesen, J.E.; Osborne, T.; Palosuo, T.; Pravia, M.V.; Priesack, E.; Ripoche, D.A.U.-, R.P.R.; Sau, F.; Semenov, M.A.; Shcherbak, I.; Steduto, P.; Stöckle, C.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.L.; Teixeira, E.; Timlin, D.; Travasso, M.; Waha, K.; Wallach, D.; White, J.W.; Wolf, J. |
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Title |
Soil nitrogen mineralisation simulated by crop models across different environments and the consequences for model improvement |
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Conference Article |
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Year |
2016 |
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Berlin (Germany) |
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International Crop Modelling Symposium iCROPM 2016, 2016-05-15 to 2016-05-17, Berlin, Germany |
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Call Number |
MA @ admin @ |
Serial |
4903 |
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Author |
Piontek, F.; Müller, C.; Pugh, T.A.; Clark, D.B.; Deryng, D.; Elliott, J.; Colón González, F.J.; Flörke, M.; Folberth, C.; Franssen, W.; Frieler, K.; Friend, A.D.; Gosling, S.N.; Hemming, D.; Khabarov, N.; Kim, H.; Lomas, M.R.; Masaki, Y.; Mengel, M.; Morse, A.; Neumann, K.; Nishina, K.; Ostberg, S.; Pavlick, R.; Ruane, A.C.; Schewe, J.; Schmid, E.; Stacke, T.; Tang, Q.; Tessler, Z.D.; Tompkins, A.M.; Warszawski, L.; Wisser, D.; Schellnhuber, H.J. |
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Title |
Multisectoral climate impact hotspots in a warming world |
Type |
Journal Article |
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Year |
2014 |
Publication |
Proceedings of the National Academy of Sciences of the United States of America |
Abbreviated Journal |
Proc. Natl. Acad. Sci. U. S. A. |
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Volume |
111 |
Issue |
9 |
Pages |
3233-3238 |
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Keywords |
Agriculture/statistics & numerical data; Computer Simulation; Conservation of Natural Resources/*methods; Ecosystem; *Environment; Geography; Global Warming/economics/*statistics & numerical data; Humans; Malaria/epidemiology; *Models, Theoretical; *Public Policy; Temperature; Water Supply/statistics & numerical data; Isi-mip; coinciding pressures; differential climate impacts |
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Abstract |
The impacts of global climate change on different aspects of humanity’s diverse life-support systems are complex and often difficult to predict. To facilitate policy decisions on mitigation and adaptation strategies, it is necessary to understand, quantify, and synthesize these climate-change impacts, taking into account their uncertainties. Crucial to these decisions is an understanding of how impacts in different sectors overlap, as overlapping impacts increase exposure, lead to interactions of impacts, and are likely to raise adaptation pressure. As a first step we develop herein a framework to study coinciding impacts and identify regional exposure hotspots. This framework can then be used as a starting point for regional case studies on vulnerability and multifaceted adaptation strategies. We consider impacts related to water, agriculture, ecosystems, and malaria at different levels of global warming. Multisectoral overlap starts to be seen robustly at a mean global warming of 3 °C above the 1980-2010 mean, with 11% of the world population subject to severe impacts in at least two of the four impact sectors at 4 °C. Despite these general conclusions, we find that uncertainty arising from the impact models is considerable, and larger than that from the climate models. In a low probability-high impact worst-case assessment, almost the whole inhabited world is at risk for multisectoral pressures. Hence, there is a pressing need for an increased research effort to develop a more comprehensive understanding of impacts, as well as for the development of policy measures under existing uncertainty. |
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0027-8424 |
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CropM |
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MA @ admin @ |
Serial |
4538 |
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Pirttioja, N.; Carter, T.R.; Fronzek, S.; Bindi, M.; Hoffmann, H.; Palosuo, T.; Ruiz-Ramos, M.; Tao, F.; Trnka, M.; Acutis, M.; Asseng, S.; Baranowski, P.; Basso, B.; Bodin, P.; Buis, S.; Cammarano, D.; Deligios, P.; Destain, M.F.; Dumont, B.; Ewert, F.; Ferrise, R.; François, L.; Gaiser, T.; Hlavinka, P.; Jacquemin, I.; Kersebaum, K.C.; Kollas, C.; Krzyszczak, J.; Lorite, I.J.; Minet, J.; Minguez, M.I.; Montesino-San Martin, M.; Moriondo, M.; Müller, C.; Nendel, C.; Öztürk, I.; Perego, A.; Rodríguez, A.; Ruane, A.C.; Ruget, F.; Sanna, M.; Semenov, M.A.; Slawinski, C.; Stratonovitch, P.; Supit, I.; Waha, K.; Wang, E.; Wu, L.; Zhao, Z.; Rötter, R.P. |
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Title |
Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces |
Type |
Journal Article |
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Year |
2015 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
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Volume |
65 |
Issue |
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Pages |
87-105 |
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Keywords |
climate; crop model; impact response surface; IRS; sensitivity analysis; wheat; yield; climate-change impacts; uncertainty; 21st-century; projections; simulation; growth; region |
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Abstract |
This study explored the utility of the impact response surface (IRS) approach for investigating model ensemble crop yield responses under a large range of changes in climate. IRSs of spring and winter wheat Triticum aestivum yields were constructed from a 26-member ensemble of process-based crop simulation models for sites in Finland, Germany and Spain across a latitudinal transect. The sensitivity of modelled yield to systematic increments of changes in temperature (-2 to +9°C) and precipitation (-50 to +50%) was tested by modifying values of baseline (1981 to 2010) daily weather, with CO2 concentration fixed at 360 ppm. The IRS approach offers an effective method of portraying model behaviour under changing climate as well as advantages for analysing, comparing and presenting results from multi-model ensemble simulations. Though individual model behaviour occasionally departed markedly from the average, ensemble median responses across sites and crop varieties indicated that yields decline with higher temperatures and decreased precipitation and increase with higher precipitation. Across the uncertainty ranges defined for the IRSs, yields were more sensitive to temperature than precipitation changes at the Finnish site while sensitivities were mixed at the German and Spanish sites. Precipitation effects diminished under higher temperature changes. While the bivariate and multi-model characteristics of the analysis impose some limits to interpretation, the IRS approach nonetheless provides additional insights into sensitivities to inter-model and inter-annual variability. Taken together, these sensitivities may help to pinpoint processes such as heat stress, vernalisation or drought effects requiring refinement in future model development. |
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0936-577x 1616-1572 |
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CropM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
4662 |
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