Title: A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration
Authors: MAKOWSKI DavidASSENG SentholdEWERT FrankBASSU SimonaDURAND Jean LouisLI T.MARTRE PierreADAM MyriamAGGARWAL PramodANGULO C.BARON C.BASSO BrunoBERTUZZI PatrickBIERNATH ChristianBOOGAARD HendrikBOOTE K.BOUMAN B.BREGAGLIO SimoneBRISSON N.BUIS S.CAMMARANO DavideCHALLINOR AndrewCONFALONIERI RobertoCONIJN J.COORBELS M.DERYNG D.DE SANCTIS GIACOMODOLTRA JordiFUMOTO T.GAYDON D.GAYLER SebastianGOLDBERG R.GRANT R.GRASSINI P.HATFIELD J.HASEGAWA T.HENG L.HOEK S.HOOKER J.HUNT L.INGWERSEN J.IZAURRALDE CesarJONGSCHAAP R.JONES J.KEMANIAN A.KERSEBAUM ChristianKIM S.LIZASO J.i.MARCAIDA M.MÜLLER ChristophNAKAGAWA H.NARESH KUMAR SooraNENDEL ClaasO’LEARY GarryOLESEN J.e.ORIOL T.OSBORNE T.PALOSUO TaruPRAVIA M.PRIESACK EckartRIPOCHE DominiqueROSENZWEIG C.RUANE AlexRUGET F.SAU F.SEMENOV MikhailSHCHERBAK IuriiSINGH BSINGH Z.SOO H.STEDUTO P.STÖCKLE ClaudioSTRATONOVITCH PierreSTRECK ThiloSUPIT IwanTANG L.TAO FuluTEIXEIRA E.THORBURN PeterTIMLIN D.TRAVASSO M.ROTTER Reimund P.WAHA KatharinaWALLACH DanielWHITE JeffreyWILKENS P.WILLIAMS J.WOLF JoostYIN X.YOSHIDA H.ZHANG Z.ZHU Y.
Citation: AGRICULTURAL AND FOREST METEOROLOGY vol. 214-215 p. 483–493
Publisher: ELSEVIER SCIENCE BV
Publication Year: 2015
JRC N°: JRC97869
ISSN: 0168-1923
URI: http://www.sciencedirect.com/science/article/pii/S0168192315007194
http://publications.jrc.ec.europa.eu/repository/handle/JRC97869
DOI: 10.1016/j.agrformet.2015.09.013
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

Files in This Item:
There are no files associated with this item.


Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated.