39 citations as recorded by crossref.

1. Improved representation of plant functional types and physiology in the Joint UK Land Environment Simulator (JULES v4.2) using plant trait information A. Harper et al. 10.5194/gmd-9-2415-2016
2. A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa R. Maidment et al. 10.1038/sdata.2017.63
3. Integrating machine learning and environmental variables to constrain uncertainty in crop yield change projections under climate change L. Li et al. 10.1016/j.eja.2023.126917
4. The impacts of data constraints on the predictive performance of a general process-based crop model (PeakN-crop v1.0) S. Caldararu et al. 10.5194/gmd-10-1679-2017
5. Human–water interface in hydrological modelling: current status and future directions Y. Wada et al. 10.5194/hess-21-4169-2017
6. Integrating Plant Science and Crop Modeling: Assessment of the Impact of Climate Change on Soybean and Maize Production N. Fodor et al. 10.1093/pcp/pcx141
7. Estimating sowing and harvest dates based on the Asian summer monsoon C. Mathison et al. 10.5194/esd-9-563-2018
8. Temporal variability in the impacts of particulate matter on crop yields on the North China Plain M. Wolffe et al. 10.1016/j.scitotenv.2021.145135
9. Soybean-BioCro: a semi-mechanistic model of soybean growth M. Matthews et al. 10.1093/insilicoplants/diab032
10. CO2 fertilization of crops offsets yield losses due to future surface ozone damage and climate change F. Leung et al. 10.1088/1748-9326/ac7246
11. Towards a multiscale crop modelling framework for climate change adaptation assessment B. Peng et al. 10.1038/s41477-020-0625-3
12. Developing a process-based and remote sensing driven crop yield model for maize (PRYM–Maize) and its validation over the Northeast China Plain S. ZHANG et al. 10.1016/S2095-3119(20)63293-2
13. Improve the Performance of the Noah‐MP‐Crop Model by Jointly Assimilating Soil Moisture and Vegetation Phenology Data T. Xu et al. 10.1029/2020MS002394
14. JULES-GL7: the Global Land configuration of the Joint UK Land Environment Simulator version 7.0 and 7.2 A. Wiltshire et al. 10.5194/gmd-13-483-2020
15. Impacts of Surface Ozone Pollution on Global Crop Yields: Comparing Different Ozone Exposure Metrics and Incorporating Co-effects of CO2 A. Tai et al. 10.3389/fsufs.2021.534616
16. JULES-BE: representation of bioenergy crops and harvesting in the Joint UK Land Environment Simulator vn5.1 E. Littleton et al. 10.5194/gmd-13-1123-2020
17. The Land Variational Ensemble Data Assimilation Framework: LAVENDAR v1.0.0 E. Pinnington et al. 10.5194/gmd-13-55-2020
18. The Impact of Crop Rotation and Spatially Varying Crop Parameters in the E3SM Land Model (ELMv2) E. Sinha et al. 10.1029/2022JG007187
19. Sources of interannual yield variability in JULES-crop and implications for forcing with seasonal weather forecasts K. Williams & P. Falloon 10.5194/gmd-8-3987-2015
20. Shift in the Temporal Trend of Boundary Layer Height in China Using Long‐Term (1979–2016) Radiosonde Data J. Guo et al. 10.1029/2019GL082666
21. Improving maize growth processes in the community land model: Implementation and evaluation B. Peng et al. 10.1016/j.agrformet.2017.11.012
22. Disentangling the separate and confounding effects of temperature and precipitation on global maize yield using machine learning, statistical and process crop models X. Yin et al. 10.1088/1748-9326/ac5716
23. The GGCMI Phase 2 experiment: global gridded crop model simulations under uniform changes in CO<sub>2</sub>, temperature, water, and nitrogen levels (protocol version 1.0) J. Franke et al. 10.5194/gmd-13-2315-2020
24. Farmland Carbon and Water Exchange and Its Response to Environmental Factors in Arid Northwest China X. Zheng et al. 10.3390/land12111988
25. Evaluation of JULES-crop performance against site observations of irrigated maize from Mead, Nebraska K. Williams et al. 10.5194/gmd-10-1291-2017
26. Assimilation of remote sensing into crop growth models: Current status and perspectives J. Huang et al. 10.1016/j.agrformet.2019.06.008
27. Calibrating soybean parameters in JULES 5.0 from the US-Ne2/3 FLUXNET sites and the SoyFACE-O<sub>3</sub> experiment F. Leung et al. 10.5194/gmd-13-6201-2020
28. Remote Sensing-Based Quantification of the Summer Maize Yield Gap Induced by Suboptimum Sowing Dates over North China Plain S. Zhang et al. 10.3390/rs13183582
29. Cocoa plant productivity in West Africa under climate change: a modelling and experimental study E. Black et al. 10.1088/1748-9326/abc3f3
30. A call to action for global research on the implications of waterlogging for wheat growth and yield R. Nóia Júnior et al. 10.1016/j.agwat.2023.108334
31. Modeling Perennial Bioenergy Crops in the E3SM Land Model (ELMv2) E. Sinha et al. 10.1029/2022MS003171
32. Global glacier volume projections under high-end climate change scenarios S. Shannon et al. 10.5194/tc-13-325-2019
33. A land surface model combined with a crop growth model for paddy rice (MATCRO-Rice v. 1) – Part 2: Model validation Y. Masutomi et al. 10.5194/gmd-9-4155-2016
34. Improving the representation of cropland sites in the Community Land Model (CLM) version 5.0 T. Boas et al. 10.5194/gmd-14-573-2021
35. The importance of model structure and soil data detail on the simulations of crop growth and water use: A case study for sugarcane M. dos Santos Vianna et al. 10.1016/j.agwat.2024.108938
36. The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO<sub>2</sub>, temperature, water, and nitrogen (version 1.0) J. Franke et al. 10.5194/gmd-13-3995-2020
37. New ozone–nitrogen model shows early senescence onset is the primary cause of ozone-induced reduction in grain quality of wheat J. Cook et al. 10.5194/bg-21-4809-2024
38. Implementation of sequential cropping into JULESvn5.2 land-surface model C. Mathison et al. 10.5194/gmd-14-437-2021
39. A land surface model combined with a crop growth model for paddy rice (MATCRO-Rice v. 1) – Part 1: Model description Y. Masutomi et al. 10.5194/gmd-9-4133-2016