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|Title:||A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration|
|Authors:||MAKOWSKI David; ASSENG Senthold; EWERT Frank; BASSU Simona; DURAND Jean Louis; LI T.; MARTRE Pierre; ADAM Myriam; AGGARWAL Pramod; ANGULO C.; BARON C.; BASSO Bruno; BERTUZZI Patrick; BIERNATH Christian; BOOGAARD Hendrik; BOOTE K.; BOUMAN B.; BREGAGLIO Simone; BRISSON N.; BUIS S.; CAMMARANO Davide; CHALLINOR Andrew; CONFALONIERI Roberto; CONIJN J.; COORBELS M.; DERYNG D.; DE SANCTIS GIACOMO; DOLTRA Jordi; FUMOTO T.; GAYDON D.; GAYLER Sebastian; GOLDBERG R.; GRANT R.; GRASSINI P.; HATFIELD J.; HASEGAWA T.; HENG L.; HOEK S.; HOOKER J.; HUNT L.; INGWERSEN J.; IZAURRALDE Cesar; JONGSCHAAP R.; JONES J.; KEMANIAN A.; KERSEBAUM Christian; KIM S.; LIZASO J.i.; MARCAIDA M.; MÜLLER Christoph; NAKAGAWA H.; NARESH KUMAR Soora; NENDEL Claas; O’LEARY Garry; OLESEN J.e.; ORIOL T.; OSBORNE T.; PALOSUO Taru; PRAVIA M.; PRIESACK Eckart; RIPOCHE Dominique; ROSENZWEIG C.; RUANE Alex; RUGET F.; SAU F.; SEMENOV Mikhail; SHCHERBAK Iurii; SINGH B; SINGH Z.; SOO H.; STEDUTO P.; STÖCKLE Claudio; STRATONOVITCH Pierre; STRECK Thilo; SUPIT Iwan; TANG L.; TAO Fulu; TEIXEIRA E.; THORBURN Peter; TIMLIN D.; TRAVASSO M.; ROTTER Reimund P.; WAHA Katharina; WALLACH Daniel; WHITE Jeffrey; WILKENS P.; WILLIAMS J.; WOLF Joost; YIN X.; YOSHIDA H.; ZHANG Z.; ZHU Y.|
|Citation:||AGRICULTURAL AND FOREST METEOROLOGY vol. 214-215 p. 483–493|
|Publisher:||ELSEVIER SCIENCE BV|
|Type:||Articles in periodicals and books|
|Abstract:||Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate 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 models is that they can interpolate between temperature levels and between CO2concentration 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. 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 are then used to analyze the variability of the yield response to [CO2] and temperature.Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effect of a temperature increase of +2◦C in the considered sites. Compared to wheat, 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 with elevated [CO2].|
|JRC Directorate:||Sustainable Resources|
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