Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 9
Articles | Volume 15, issue 9
https://doi.org/10.5194/gmd-15-3519-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-3519-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 9
Methods for assessment of models
 | 
05 May 2022
Methods for assessment of models |  | 05 May 2022

Nested leave-two-out cross-validation for the optimal crop yield model selection

Thi Lan Anh Dinh and Filipe Aires

Viewed

Total article views: 3,744 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,930 726 88 3,744 71 44
  • HTML: 2,930
  • PDF: 726
  • XML: 88
  • Total: 3,744
  • BibTeX: 71
  • EndNote: 44
Views and downloads (calculated since 06 Aug 2021)
Cumulative views and downloads (calculated since 06 Aug 2021)

Viewed (geographical distribution)

Total article views: 3,744 (including HTML, PDF, and XML) Thereof 3,543 with geography defined and 201 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 30 Mar 2025
Download
Short summary
We proposed the leave-two-out method (i.e. one particular implementation of the nested cross-validation) to determine the optimal statistical crop model (using the validation dataset) and estimate its true generalization ability (using the testing dataset). This approach is applied to two examples (robusta coffee in Cu M'gar and grain maize in France). The results suggested that the simple models are more suitable in crop modelling where a limited number of samples is available.
Read more
Share