03684naa a2200769 a 450000100080000000500110000800800410001902400270006010000140008724501110010126000090021250000880022152016960030965000100200565000230201565000180203865000100205665000270206665000160209365300100210965300190211965300120213865300080215065300160215865300100217465300260218465300300221065300220224065300160226270000160227870000170229470000140231170000150232570000160234070000190235670000160237570000130239170000140240470000140241870000170243270000170244970000150246670000180248170000150249970000180251470000150253270000170254770000170256470000130258170000190259470000190261370000190263270000270265170000140267870000170269270000160270970000150272570000170274070000150275770000120277270000180278470000120280270000160281470000150283070000130284577300560285810545172018-09-24       2014    bl uuuu     u00u1 u    #d7 a10.1111/gcb.125202DOI1 aBASSU, S.  aHow do various maize crop models vary in their responses to climate change factors?h[electronic resource]  c2014  aArticle history: Received 7 June 2013 and accepted 2 December 2013, published 2014.  aAbstract: Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data forcalibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly 0.5 Mg ha1 per °C. Doubling [CO2] from 360 to 720 lmol mol1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.  aCLIMA  aDIOXIDO DE CARBONO  aINCERTIDUMBRE  aMAÍZ  aMODELOS DE SIMULACIÓN  aTEMPERATURA  aAGMIP  aCARBON DIOXIDE  aCLIMATE  aCO2  aGRAIN YIELD  aMAIZE  aMODEL INTERCOMPARISON  aMODELIZACIÓN DE CULTIVOS  aSIMULATION MODELS  aTEMPERATURE1 aBRISSON, N.1 aDURAND, J.L.1 aBOOTE, K.1 aLIZASO, J.1 aJONES, J.W.1 aROSENZWEIG, C.1 aRUANE, A.C.1 aADAM, M.1 aBARON, C.1 aBASSO, B.1 aBIERNATH, C.1 aBOOGAARD, H.1 aCONIJN, S.1 aCORBEELS, M.L1 aDERYNG, D.1 aSANTIS, G. DE1 aGAYLER, S.1 aGRASSINI, P.1 aHATFIELD, J.1 aHOEK, S.1 aIZAURRALDE, C.1 aJONGSCHAAP, R.1 aKEMANIAN, A.R.1 aKERSEBAUM, C.KIM, S-H.1 aKUMAR, N.1 aMAKOWSKI, D.1 aMÜLLER, C.1 aNENDEL, C.1 aPRIESACK, E.1 aPRAVIA, V.1 aSAU, F.1 aSHCHERBAK, I.1 aTAO, F.1 aTEXEIRA, E.1 aTIMLIN, D.1 aWAHA, K.  tGlobal Change Biology, 2014gv.20(7), p. 2301-2320.