Wallach, DanielMartre, PierreLiu, BingAsseng, SentholdEwert, FrankThorburn, Peter J.Ittersum, Martin K. vanAggarwal, Pramod K.Ahmed, MukhtarBasso, BrunoBiernath, ChristianCammarano, DavideChallinor, Andrew J.Sanctis, Giacomo deDumont, BenjaminEyshi Rezaei, EhsanFereres, EliasFitzgerald, Glenn J.Gao, YGarcía Vila, MargaritaGayler, SebastianGirousse, ChristineHoogenboom, GerritHoran, HeidiIzaurralde, Roberto CésarJones, Curtis D.Kassie, Belay T.Kersebaum, Kurt-ChristianKlein, ChristianKöhler, Ann-KristinMaiorano, AndreaMinoli, SaraMüller, ChristophNaresh Kumar, SooraNendel, ClaasO’Leary, Garry J.Palosuo, TaruPriesack, EckartRipoche, DominiqueRötter, Reimund P.Semenov, Mikhail A.Stöckle, Claudio O.Stratonovitch, PierreStreck, ThiloSupit, IwanTao, FuluWolf, JoostZhang, Zhao2018-09-112018-09-11https://hdl.handle.net/10568/97157Multimodel ensembles improve predictions of crop–environment–management interactions