Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Geosci. Model Dev., 10, 1679-1701, 2017
https://doi.org/10.5194/gmd-10-1679-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
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 et al.
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Interactive discussionStatus: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version      Supplement - Supplement
 
SC1: 'Review', Daniel wallach, 15 Nov 2016 Printer-friendly Version 
RC1: 'Referee Report', Daniel Wallach, 23 Nov 2016 Printer-friendly Version 
AC1: 'Response to Review 1 and proposed changes to manuscript', Matthew Smith, 30 Jan 2017 Printer-friendly Version 
 
RC2: 'comments', Anonymous Referee #2, 03 Jan 2017 Printer-friendly Version 
AC2: 'Response to Review 2 and proposed changes to manuscript', Matthew Smith, 30 Jan 2017 Printer-friendly Version 
 
AC3: 'Final response', Matthew Smith, 02 Feb 2017 Printer-friendly Version 
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
CC BY 4.0
Publications Copernicus
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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.
We developed a new general model for predicting the growth and development of annual crops to...
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