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.
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.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
05 May 2022
Methods for assessment of models |
| 05 May 2022
| 05 May 2022
Nested leave-two-out cross-validation for the optimal crop yield model selection
Sorbonne Université, Observatoire de Paris, Université PSL, CNRS, LERMA, 75014 Paris, France
Filipe Aires
Sorbonne Université, Observatoire de Paris, Université PSL, CNRS, LERMA, 75014 Paris, France
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Cited
21 citations as recorded by crossref.
- Parameterizations of human activities in paleoclimate modeling: Current status and future perspectives X. Zhang et al.
- Enhancing Alfalfa Biomass Prediction: An Innovative Framework Using Remote Sensing Data M. Lucero et al.
- Assessment and Modeling of Green Roof System Hydrological Effectiveness in Runoff Control: A Case Study in Dublin M. Gholamnia et al.
- Contactless biometric verification from in-air signatures using deep siamese networks S. Salturk et al.
- Development of ANN-Based Algorithm to Estimate Wintertime Sea Ice Temperature Profile Over the Arctic Ocean S. Baek et al.
- Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model T. Dinh et al.
- Climate change impacts on Robusta coffee production in Vietnam L. Anh Dinh et al.
- Assessment of Evapotranspiration–Yield Relationships in Northern China Tea Plantations: A Basis for Crop Water Productivity Improvement Q. Liu et al.
- Phenotyping maize stay green traits via in situ leaf hyperspectral reflectance sensing H. Elsharawy et al.
- Random forest machine learning for maize yield and agronomic efficiency prediction in Ghana E. Asamoah et al.
- Statistical Analysis of the Weather Impact on Robusta Coffee Yield in Vietnam T. Dinh et al.
- Speech biomarkers predict amyloid status in cognitively unimpaired adults P. Gabirondo et al.
- Modeling wheat development under extreme weather with WOFOST-EW v1 J. Zheng et al.
- AsiaRiceYield4km: seasonal rice yield in Asia from 1995 to 2015 H. Wu et al.
- The Use of Agricultural Databases for Crop Modeling: A Scoping Review T. Mthembu et al.
- Assessing NDVI, Climate, and Management to Predict Winter Wheat Yields at Field Scale in Kansas, USA R. Maranhão et al.
- Machine Learning Predicts Drivers of Biochar-Diazotrophic Bacteria in Enhancing Brachiaria Growth and Soil Quality T. da Silva et al.
- In-Season Corn Yield Prediction Using Satellite-Derived Solar-Induced Chlorophyll Fluorescence and Machine Learning Algorithms M. Yaakov et al.
- Virtual Sensor for Estimating the Strain-Hardening Rate of Austenitic Stainless Steels Using a Machine Learning Approach J. Contreras-Fortes et al.
- Leveraging temporal variability in global sensitivity analysis of the Daisy soil-plant-atmosphere model L. Delhez et al.
- Mapping annual 30-m paddy rice yield for different cropping systems in mainland Southeast Asia from 2001 to 2021 S. Huan et al.
21 citations as recorded by crossref.
- Parameterizations of human activities in paleoclimate modeling: Current status and future perspectives X. Zhang et al.
- Enhancing Alfalfa Biomass Prediction: An Innovative Framework Using Remote Sensing Data M. Lucero et al.
- Assessment and Modeling of Green Roof System Hydrological Effectiveness in Runoff Control: A Case Study in Dublin M. Gholamnia et al.
- Contactless biometric verification from in-air signatures using deep siamese networks S. Salturk et al.
- Development of ANN-Based Algorithm to Estimate Wintertime Sea Ice Temperature Profile Over the Arctic Ocean S. Baek et al.
- Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model T. Dinh et al.
- Climate change impacts on Robusta coffee production in Vietnam L. Anh Dinh et al.
- Assessment of Evapotranspiration–Yield Relationships in Northern China Tea Plantations: A Basis for Crop Water Productivity Improvement Q. Liu et al.
- Phenotyping maize stay green traits via in situ leaf hyperspectral reflectance sensing H. Elsharawy et al.
- Random forest machine learning for maize yield and agronomic efficiency prediction in Ghana E. Asamoah et al.
- Statistical Analysis of the Weather Impact on Robusta Coffee Yield in Vietnam T. Dinh et al.
- Speech biomarkers predict amyloid status in cognitively unimpaired adults P. Gabirondo et al.
- Modeling wheat development under extreme weather with WOFOST-EW v1 J. Zheng et al.
- AsiaRiceYield4km: seasonal rice yield in Asia from 1995 to 2015 H. Wu et al.
- The Use of Agricultural Databases for Crop Modeling: A Scoping Review T. Mthembu et al.
- Assessing NDVI, Climate, and Management to Predict Winter Wheat Yields at Field Scale in Kansas, USA R. Maranhão et al.
- Machine Learning Predicts Drivers of Biochar-Diazotrophic Bacteria in Enhancing Brachiaria Growth and Soil Quality T. da Silva et al.
- In-Season Corn Yield Prediction Using Satellite-Derived Solar-Induced Chlorophyll Fluorescence and Machine Learning Algorithms M. Yaakov et al.
- Virtual Sensor for Estimating the Strain-Hardening Rate of Austenitic Stainless Steels Using a Machine Learning Approach J. Contreras-Fortes et al.
- Leveraging temporal variability in global sensitivity analysis of the Daisy soil-plant-atmosphere model L. Delhez et al.
- Mapping annual 30-m paddy rice yield for different cropping systems in mainland Southeast Asia from 2001 to 2021 S. Huan et al.
Saved (final revised paper)
Latest update: 04 May 2026
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.
We proposed the leave-two-out method (i.e. one particular implementation of the nested...
