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001010148 1001_ $$00000-0003-3500-8179$$aWallach, Daniel$$b0$$eCorresponding author
001010148 245__ $$aProposal and extensive test of a calibration protocol for crop phenology models
001010148 260__ $$aHeidelberg$$bSpringer$$c2023
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001010148 520__ $$aA major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. We propose an original, detailed approach for calibration of such models, which we refer to as a calibration protocol. The protocol covers all the steps in the calibration workflow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values, and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are “obligatory” parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their “usual” calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and reduced prediction error by 11%.
001010148 536__ $$0G:(DE-HGF)POF4-2173$$a2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217)$$cPOF4-217$$fPOF IV$$x0
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001010148 7001_ $$0P:(DE-HGF)0$$aPalosuo, Taru$$b1
001010148 7001_ $$0P:(DE-HGF)0$$aThorburn, Peter$$b2
001010148 7001_ $$0P:(DE-HGF)0$$aMielenz, Henrike$$b3
001010148 7001_ $$0P:(DE-HGF)0$$aBuis, Samuel$$b4
001010148 7001_ $$0P:(DE-HGF)0$$aHochman, Zvi$$b5
001010148 7001_ $$0P:(DE-HGF)0$$aGourdain, Emmanuelle$$b6
001010148 7001_ $$0P:(DE-HGF)0$$aAndrianasolo, Fety$$b7
001010148 7001_ $$0P:(DE-HGF)0$$aDumont, Benjamin$$b8
001010148 7001_ $$0P:(DE-HGF)0$$aFerrise, Roberto$$b9
001010148 7001_ $$0P:(DE-HGF)0$$aGaiser, Thomas$$b10
001010148 7001_ $$0P:(DE-HGF)0$$aGarcia, Cecile$$b11
001010148 7001_ $$0P:(DE-HGF)0$$aGayler, Sebastian$$b12
001010148 7001_ $$0P:(DE-HGF)0$$aHarrison, Matthew$$b13
001010148 7001_ $$0P:(DE-HGF)0$$aHiremath, Santosh$$b14
001010148 7001_ $$0P:(DE-HGF)0$$aHoran, Heidi$$b15
001010148 7001_ $$0P:(DE-HGF)0$$aHoogenboom, Gerrit$$b16
001010148 7001_ $$0P:(DE-HGF)0$$aJansson, Per-Erik$$b17
001010148 7001_ $$0P:(DE-HGF)0$$aJing, Qi$$b18
001010148 7001_ $$0P:(DE-HGF)0$$aJustes, Eric$$b19
001010148 7001_ $$0P:(DE-HGF)0$$aKersebaum, Kurt-Christian$$b20
001010148 7001_ $$0P:(DE-HGF)0$$aLaunay, Marie$$b21
001010148 7001_ $$0P:(DE-HGF)0$$aLewan, Elisabet$$b22
001010148 7001_ $$0P:(DE-HGF)0$$aLiu, Ke$$b23
001010148 7001_ $$0P:(DE-HGF)0$$aMequanint, Fasil$$b24
001010148 7001_ $$0P:(DE-HGF)0$$aMoriondo, Marco$$b25
001010148 7001_ $$0P:(DE-HGF)0$$aNendel, Claas$$b26
001010148 7001_ $$0P:(DE-HGF)0$$aPadovan, Gloria$$b27
001010148 7001_ $$0P:(DE-HGF)0$$aQian, Budong$$b28
001010148 7001_ $$0P:(DE-HGF)0$$aSchütze, Niels$$b29
001010148 7001_ $$0P:(DE-HGF)0$$aSeserman, Diana-Maria$$b30
001010148 7001_ $$0P:(DE-HGF)0$$aShelia, Vakhtang$$b31
001010148 7001_ $$0P:(DE-HGF)0$$aSouissi, Amir$$b32
001010148 7001_ $$0P:(DE-HGF)0$$aSpecka, Xenia$$b33
001010148 7001_ $$0P:(DE-HGF)0$$aSrivastava, Amit Kumar$$b34
001010148 7001_ $$0P:(DE-HGF)0$$aTrombi, Giacomo$$b35
001010148 7001_ $$0P:(DE-HGF)0$$aWeber, Tobias K. D.$$b36
001010148 7001_ $$0P:(DE-Juel1)129553$$aWeihermüller, Lutz$$b37$$ufzj
001010148 7001_ $$0P:(DE-HGF)0$$aWöhling, Thomas$$b38
001010148 7001_ $$00000-0003-3283-8361$$aSeidel, Sabine J.$$b39
001010148 773__ $$0PERI:(DE-600)2012314-0$$a10.1007/s13593-023-00900-0$$gVol. 43, no. 4, p. 46$$n4$$p46$$tAgronomy for sustainable development$$v43$$x0249-5627$$y2023
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