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022 _a0168-1923
022 _a1873-2240 (Online)
024 8 _ahttps://doi.org/10.1016/j.agrformet.2025.110697
040 _aMX-TxCIM
041 _aeng
100 1 _aWallach, D.
_91769
245 1 0 _aWhy is there so much variability in crop multi-model studies?
260 _aAmsterdam (Netherlands) :
_bElsevier B.V.,
_c2025.
500 _aPeer review
500 _aOpen Access
520 _aIt has become common to compare crop model results in multi-model simulation experiments. In general, one observes a large variability in such studies, which reduces the confidence one can have in such models. It is important to understand the causes of this variability as a first step toward reducing it. For a given data set, the variability in a multi-model study can arise from uncertainty in model structure or in parameter values for a given structure. Previous studies have made assumptions about the origin of parameter uncertainty, and then quantified its contribution, generally finding that parameter uncertainty is less important than structure uncertainty. However, those studies do not take account of the full parameter variability in multi-model studies. Here we propose estimating parameter uncertainty based on open-call multi-model ensembles where the same structure is used by more than one modeling group. The variability in such a case is due to the full variability of parameters among modeling groups. Then structure and parameter contributions can be estimated using random effects analysis of variance. Based on three multi-model studies for simulating wheat phenology, it is found that the contribution of parameter uncertainty to total uncertainty is, on average, more than twice as large as the uncertainty from structure. A second estimate, based on a comparison of two different calibration approaches for multiple models leads to a very similar result. We conclude that improvement of crop models requires as much attention to parameters as to model structure.
546 _aText in English
591 _aRettie, F.M. : No CIMMYT Affiliation
597 _dNational Key Research and Development Program
_dUnited States Department of Agriculture (USDA)
_dNatural Science Foundation of China (NSFC)
_dPriority Academic Program Development of Jiangsu Higher Education Institutions (
_dDeutsche Forschungsgemeinschaft (DFG)
_dResearch Council of Finland
_dBundesministerium für Bildung und Forschung (BMBF)
_dMinistero dell'Agricoltura, della Sovranità alimentare e delle Foreste (MASAF)
_dMinistry of Education, Youth and Sports (MEYS)
_dCommonwealth Scientific and Industrial Research (CSIRO)
650 7 _aCrop modelling
_2AGROVOC
_92623
650 7 _aStructures
_2AGROVOC
_918463
650 7 _aParameters
_2AGROVOC
_939696
700 1 _aPalosuo, T.
_92657
700 1 _aMielenz, H.
_939697
700 1 _aBuis, S.
_92634
700 1 _aThorburn, P.J.
_91786
700 1 _aAsseng, S.
_91568
700 1 _aDumont, B.
_91582
700 1 _aFerrise, R.
_91585
700 1 _aGayler, S.
_91775
700 1 _aGhahramani, A.
_929877
700 1 _aHarrison, M.T.
_939698
700 1 _aHochman, Z.
_94037
700 1 _aHoogenboom, G.
_94150
700 0 _aMingxia Huang
_939699
700 0 _aQi Jing
_939700
700 1 _aJustes, E.
_917277
700 1 _aKersebaum, K.C.
_91776
700 1 _aLaunay, M.
_939701
700 1 _aLewan, E.
_939702
700 0 _aKe Liu
_939703
700 0 _aQunying Luo
_91605
700 1 _aRettie, F.M.
_8001714439
_gSustainable Agrifood Systems
_926536
700 1 _aNendel, C.
_92654
700 1 _aPadovan, G.
_918325
700 1 _aOlesen, J.E.
_91780
700 1 _aPullens, J.W.M.
_929562
700 0 _aQian, B.
_918564
700 1 _aSeserman, D.M.
_939704
700 1 _aShelia, V
_98595
700 1 _aSouissi, A.
_917241
700 1 _aSpecka, X.
_939705
700 0 _aJing Wang
_91646
700 1 _aWeber, T.K.D.
_926537
700 1 _aWeihermüller, L.
_939706
700 1 _aSeidel, S.J.
_939707
773 0 _tAgricultural and Forest Meteorology
_gv. 372, art. 110697
_dAmsterdam (Netherlands) : Elsevier, 2025.
_x0168-1923
_wG444454
942 _cJA
_n0
_2ddc
999 _c69070
_d69062