15 citations as recorded by crossref.

1. Revealing Climate-Induced Patterns in Crop Yields and the Water-Energy-Food-Carbon Nexus: Insights from the Pearl River Basin C. Ye et al. https://doi.org/10.3390/w16243693
2. Sugarcane yield estimation through remote sensing time series and phenology metrics D. Dimov et al. https://doi.org/10.1016/j.atech.2022.100046
3. Evaluation of a Phenology-Dependent Response Method for Estimating Leaf Area Index of Rice Across Climate Gradients B. Lee et al. https://doi.org/10.3390/rs9010020
4. Equality testing for soil grid unit resolutions to polygon unit scales with DNDC modeling of regional SOC pools D. Yu et al. https://doi.org/10.1007/s11769-017-0887-5
5. Consistent negative response of US crops to high temperatures in observations and crop models B. Schauberger et al. https://doi.org/10.1038/ncomms13931
6. ORCHIDEE-CROP (v0), a new process-based agro-land surface model: model description and evaluation over Europe X. Wu et al. https://doi.org/10.5194/gmd-9-857-2016
7. Skilful rainfall forecasts from artificial neural networks with long duration series and single-month optimization J. Abbot & J. Marohasy https://doi.org/10.1016/j.atmosres.2017.07.015
8. A global dataset on phosphorus in agricultural soils B. Ringeval et al. https://doi.org/10.1038/s41597-023-02751-6
9. Integrating global crop models with knowledge graphs: Evolution, comparison, and future directions X. Gong et al. https://doi.org/10.1016/j.agwat.2026.110732
10. Management outweighs climate change on affecting length of rice growing period for early rice and single rice in China during 1991–2012 X. Wang et al. https://doi.org/10.1016/j.agrformet.2016.10.016
11. Crop Parameters for Modeling Sugarcane under Rainfed Conditions in Mexico A. Baez-Gonzalez et al. https://doi.org/10.3390/su9081337
12. Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis P. Casadebaig et al. https://doi.org/10.1371/journal.pone.0146385
13. Sample size, range of parameters and time-dependent effects on global sensitivity analysis in sugarcane modelling R. Pereira et al. https://doi.org/10.1017/S0021859624000030
14. Parameterization-induced uncertainties and impacts of crop management harmonization in a global gridded crop model ensemble C. Folberth et al. https://doi.org/10.1371/journal.pone.0221862
15. The Global Gridded Crop Model Intercomparison: data and modeling protocols for Phase 1 (v1.0) J. Elliott et al. https://doi.org/10.5194/gmd-8-261-2015