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Title: A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration

item MAKOWSKI, D - Institut National De La Recherche Agronomique (INRA)
item ASSENG, S - University Of Florida
item EWERT, F - University Of Bonn
item BASSU, S - Institut National De La Recherche Agronomique (INRA)
item DURAND, J - Institut National De La Recherche Agronomique (INRA)
item LI, T - Institut National De La Recherche Agronomique (INRA)
item MARTE, P - Institut National De La Recherche Agronomique (INRA)
item ADAM, M - Centro De Cooperation Internationale En Recherche Agronomique Pour Le Development (CIRAD)
item AGGARWAL, P - University Of Copenhagen
item ANGULO, C - University Of Bonn
item Hatfield, Jerry
item Timlin, Dennis
item White, Jeffrey

Submitted to: Agriculture Forest Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/20/2015
Publication Date: 12/15/2015
Publication URL:
Citation: Makowski, D., Asseng, S., Ewert, F., Bassu, S., Durand, J.L., Li, T., Marte, P., Adam, M., Aggarwal, P.K., Angulo, C., Hatfield, J.L., Timlin, D.J., White, J.W. 2015. A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration. Agriculture Forest Meteorology. 217:82-83.

Interpretive Summary: Changes in the climate will affect the temperature and carbon dioxide conditions surrounding plants. The exact impact of these changes on plant growth and productivity throughout the remainder of this century can only be determined through the use of crop simulation models. In this study we utilized a suite of wheat, maize, and rice models to determine the effects of changes in temperature and carbon dioxide expected under climate change. Using both statistical and process models provide insights into how climate change will affect future productivity of these crops and the increasing temperatures will offset the positive impacts of increasing carbon dioxide. These results help promote an improved understanding of how future environments will affect grain production. These results are of value to researchers and policymakers.

Technical Abstract: Ensembles of process-based crop models are now commonly used to simulate crop growth and development for climate scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of deterministically simulated crop yield data. Such datasets potentially provide new information, but it is sometimes difficult to summarize them in a useful way due to their structural complexity. Another issue is that it is not straightforward to compare crops and to extrapolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical model is that it handles the interpolation between temperature and/or CO2 concentration levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without re-running the original complex crop models. One limitation of such statistical approach is that temporal and spatial extrapolation beyond the original simulations would not be valid. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and used to analyze variability in the yield response to [CO2] and temperature. Based on our results, we show that, for wheat, a [CO2] increase higher than +117 ppm is likely to outweigh the negative effect of a temperature increase of +2°C in the considered sites. Required levels of [CO2] increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than elevated [CO2], are largest for rice, and need to be considered in climate change impact assessments.