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Liu, B.; Asseng, S.; Müller, C.; Ewert, F.; Elliott, J.; Lobell, D. B.; Martre, P.; Ruane, A. C.; Wallach, D.; Jones, J. W.; Rosenzweig, C.; Aggarwal, P. K.; Alderman, P. D.; Anothai, J.; Basso, B.; Biernath, C.; Cammarano, D.; Challinor, A.; Deryng, D.; Sanctis, G. D.; Doltra, J.; Fereres, E.; Folberth, C.; Garcia-Vila, M.; Gayler, S.; Hoogenboom, G.; Hunt, L. A.; Izaurralde, R. C.; Jabloun, M.; Jones, C. D.; Kersebaum, K. C.; Kimball, B. A.; Koehler, A.-K.; Kumar, S. N.; Nendel, C.; O’Leary, G. J.; Olesen, J. E.; Ottman, M. J.; Palosuo, T.; Prasad, P. V. V.; Priesack, E.; Pugh, T. A. M.; Reynolds, M.; Rezaei, E. E.; Rötter, R. P.; Schmid, E.; Semenov, M. A.; Shcherbak, I.; Stehfest, E.; Stöckle, C. O.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Thorburn, P.; Waha, K.; Wall, G. W.; Wang, E.; White, J. W.; Wolf, J.; Zhao, Z.; Zhu, Y. |
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Title |
Similar estimates of temperature impacts on global wheat yield by three independent methods |
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Journal Article |
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2016 |
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Nature Climate Change |
Abbreviated Journal |
Nat. Clim. Change |
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6 |
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12 |
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1130-1136 |
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1758-678x |
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article |
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CropM |
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MA @ admin @ |
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4965 |
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Grosz, B.; Dechow, R.; Hoffmann, H.; Zhao, G.; Constantin, J.; Raynal, H.; Wallach, D.; Coucheney, E.; Lewan, E.; Eckersten, H.; Specka, X.; Kersebaum, K.-C.; Nendel, C.; Kuhnert, M.; Yeluripati, J.; Kiese, R.; Haas, E.; Klatt, S.; Teixeira, E.; Bindi, M.; Trombi, G.; Moriondo, M.; Doro, L.; Roggero, P.P.; Zhao, Z.; Wang, E.; Vanuytrecht, E.; Tao, F.; Rötter, R.; Cammarano, D.; Asseng, S.; Weihermüller, L.; Siebert, S.; Gaiser, T.; Ewert, F |
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The implication of input data aggregation on upscaling of soil organic carbon changes |
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Conference Article |
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2015 |
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MACSUR Science Conference |
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MACSUR Science Conference, 2015-04-08 to 2015-04-10, Reading, United Kingdom |
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MA @ admin @ |
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5038 |
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Wang, E.; Martre, P.; Zhao, Z.; Ewert, F.; Maiorano, A.; Rötter, R.P.; Kimball, B.A.; Ottman, M.J.; Wall, G.W.; White, J.W.; Reynolds, M.P.; Alderman, P.D.; Aggarwal, P.K.; Anothai, J.; Basso, B.; Biernath, C.; Cammarano, D.; Challinor, A.J.; De Sanctis, G.; Doltra, J.; Fereres, E.; Garcia-Vila, M.; Gayler, S.; Hoogenboom, G.; Hunt, L.A.; Izaurralde, R.C.; Jabloun, M.; Jones, C.D.; Kersebaum, K.C.; Koehler, A.-K.; Liu, L.; Müller, C.; Naresh Kumar, S.; Nendel, C.; O’Leary, G.; Olesen, J.E.; Palosuo, T.; Priesack, E.; Eyshi Rezaei, E.; Ripoche, D.; Ruane, A.C.; Semenov, M.A.; Shcherbak, I.; Stöckle, C.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Thorburn, P.; Waha, K.; Wallach, D.; Wang, Z.; Wolf, J.; Zhu, Y.; Asseng, S. |
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Title |
The uncertainty of crop yield projections is reduced by improved temperature response functions |
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Journal Article |
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2017 |
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Nature Plants |
Abbreviated Journal |
Nature Plants |
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3 |
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17102 |
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Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections. Erratum: doi: 10.1038/nplants.2017.125 |
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2017-08-28 |
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CropM, ft_macsur |
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MA @ admin @ |
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5173 |
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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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Uncertainties in Scaling up Crop Models for Large Area Climate-change Impact Assessments |
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2015 |
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261-277 |
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CropM; |
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Imperial College Press |
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London |
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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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MA @ admin @ |
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2427 |
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Cammarano, D.; Rivington, M.; Matthews, K.; B,; Bellocchi, G. |
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Estimates of crop responses to climate change with quantified ranges of uncertainty |
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2015 |
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FACCE MACSUR Reports |
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6 |
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D-C4.1.3 |
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In estimating responses of crops to future climate realisations, it is necessary to understand and differentiate between the sources of uncertainty in climate models and how these lead to errors in estimating the past climate and biases in future projections, and how these affect crop model estimates. This paper investigates the complexities in using climate model projections representing different spatial scales within climate change impacts and adaptation studies. This is illustrated by simulating spring barley with three crop models run using site-specific observed, original (50•50 km) and bias corrected downscaled (site-specific) hindcast (1960-1990) weather data from the HadRM3 Regional Climate Model (RCM). Original and bias corrected downscaled weather data were evaluated against the observed data. The comparisons made between the crop models were in the light of lessons learned from this data evaluation. Though the bias correction downscaling method improved the match between observed and hindcast data, this did not always translate into better matching of crop models estimates. At four sites the original HadRM3 data produced near identical mean simulated yield values as from the observed weather data, despite differences in the weather data, giving a situation of ‘right results for the wrong reasons’. This was likely due to compensating errors in the input weather data and non-linearity in crop models processes, making interpretation of results problematic. Overall, bias correction downscaling improved the quality of simulated outputs. Understanding how biases in climate data manifest themselves in crop models gives greater confidence in the utility of the estimates produced using downscaled future climate projections. The results indicate implications on how future projections of climate change impacts are interpreted. Fundamentally, considerable care is required in determining the impact weather data sources have in climate change impact and adaptation studies, whether from individual models or ensembles. No Label |
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MA @ admin @ |
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2098 |
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