Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 10, issue 4
Articles | Volume 10, issue 4
https://doi.org/10.5194/gmd-10-1679-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-10-1679-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Articles | Volume 10, issue 4
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

Viewed

Total article views: 3,231 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,260 837 134 3,231 125 143
  • HTML: 2,260
  • PDF: 837
  • XML: 134
  • Total: 3,231
  • BibTeX: 125
  • EndNote: 143
Views and downloads (calculated since 18 Oct 2016)
Cumulative views and downloads (calculated since 18 Oct 2016)

Viewed (geographical distribution)

Total article views: 3,231 (including HTML, PDF, and XML) Thereof 3,021 with geography defined and 210 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 18 Apr 2024
Download
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.
Read more