Articles | Volume 10, issue 4
https://doi.org/10.5194/gmd-10-1679-2017
https://doi.org/10.5194/gmd-10-1679-2017
Model evaluation paper
 | 
20 Apr 2017
Model evaluation paper |  | 20 Apr 2017

The impacts of data constraints on the predictive performance of a general process-based crop model (PeakN-crop v1.0)

Silvia Caldararu, Drew W. Purves, and Matthew J. Smith

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Matthew Smith on behalf of the Authors (01 Feb 2017)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (01 Feb 2017) by Christoph Müller
RR by Anonymous Referee #2 (21 Feb 2017)
RR by Daniel wallach (13 Mar 2017)
ED: Publish subject to minor revisions (Editor review) (17 Mar 2017) by Christoph Müller
AR by Matthew Smith on behalf of the Authors (24 Mar 2017)  Author's response   Manuscript 
ED: Publish subject to technical corrections (27 Mar 2017) by Christoph Müller
AR by Matthew Smith on behalf of the Authors (27 Mar 2017)  Author's response   Manuscript 
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Short summary
We developed a new general model for predicting the growth and development of annual crops to help improve food security worldwide. We explore how accurately such a model can predict wheat and maize crop growth in Europe and the US when we use commonly used public datasets to calibrate the model. Satellite measurements of crop greenness and ground measurements of carbon dioxide exchange improve prediction accuracy substantially, whereas regional measurements of crop yields are less important.