57 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. https://doi.org/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. https://doi.org/10.1038/sdata.2017.63
3. Spatializing Net Ecosystem Exchange in the Brazilian Amazon biome using the JULES model and vegetation properties A. Prudente Jr. et al. https://doi.org/10.5194/bg-23-1771-2026
4. Integrating machine learning and environmental variables to constrain uncertainty in crop yield change projections under climate change L. Li et al. https://doi.org/10.1016/j.eja.2023.126917
5. Human–water interface in hydrological modelling: current status and future directions Y. Wada et al. https://doi.org/10.5194/hess-21-4169-2017
6. Estimating sowing and harvest dates based on the Asian summer monsoon C. Mathison et al. https://doi.org/10.5194/esd-9-563-2018
7. Temporal variability in the impacts of particulate matter on crop yields on the North China Plain M. Wolffe et al. https://doi.org/10.1016/j.scitotenv.2021.145135
8. Winter wheat yield responses to extreme heat in the North China Plain: GGCMI-AgMIP model evaluation and a heat-stress phenology improvement in JULES-crop L. You et al. https://doi.org/10.1016/j.agrformet.2026.111404
9. Soybean-BioCro: a semi-mechanistic model of soybean growth M. Matthews et al. https://doi.org/10.1093/insilicoplants/diab032
10. 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. https://doi.org/10.1016/S2095-3119(20)63293-2
11. Improve the Performance of the Noah‐MP‐Crop Model by Jointly Assimilating Soil Moisture and Vegetation Phenology Data T. Xu et al. https://doi.org/10.1029/2020MS002394
12. Development of the DO3SE-Crop model to assess ozone effects on crop phenology, biomass, and yield P. Pande et al. https://doi.org/10.5194/bg-22-181-2025
13. Advances in crop growth modeling: A review of perennial crop and beneficial soil microorganism approaches L. Ech-Chatir et al. https://doi.org/10.1016/j.agwat.2025.109548
14. Advancements in Leaf Area Index Estimation for Maize Using Modeling and Remote Sensing Techniques: A Review K. Bakó et al. https://doi.org/10.3390/agronomy15030519
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. https://doi.org/10.3389/fsufs.2021.534616
16. The Land Variational Ensemble Data Assimilation Framework: LAVENDAR v1.0.0 E. Pinnington et al. https://doi.org/10.5194/gmd-13-55-2020
17. The Impact of Crop Rotation and Spatially Varying Crop Parameters in the E3SM Land Model (ELMv2) E. Sinha et al. https://doi.org/10.1029/2022JG007187
18. Shift in the Temporal Trend of Boundary Layer Height in China Using Long‐Term (1979–2016) Radiosonde Data J. Guo et al. https://doi.org/10.1029/2019GL082666
19. Improving maize growth processes in the community land model: Implementation and evaluation B. Peng et al. https://doi.org/10.1016/j.agrformet.2017.11.012
20. 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. https://doi.org/10.1088/1748-9326/ac5716
21. The GGCMI Phase 2 experiment: global gridded crop model simulations under uniform changes in CO2, temperature, water, and nitrogen levels (protocol version 1.0) J. Franke et al. https://doi.org/10.5194/gmd-13-2315-2020
22. Evaluation of JULES-crop performance against site observations of irrigated maize from Mead, Nebraska K. Williams et al. https://doi.org/10.5194/gmd-10-1291-2017
23. Assimilation of remote sensing into crop growth models: Current status and perspectives J. Huang et al. https://doi.org/10.1016/j.agrformet.2019.06.008
24. Cocoa plant productivity in West Africa under climate change: a modelling and experimental study E. Black et al. https://doi.org/10.1088/1748-9326/abc3f3
25. Modeling Perennial Bioenergy Crops in the E3SM Land Model (ELMv2) E. Sinha et al. https://doi.org/10.1029/2022MS003171
26. T&C-CROP: representing mechanistic crop growth with a terrestrial biosphere model (T&C, v1.5) – model formulation and validation J. Buckley Paules et al. https://doi.org/10.5194/gmd-18-1287-2025
27. Development of the global maize yield model MATCRO-Maize version 1.0 M. Nagata et al. https://doi.org/10.5194/gmd-18-8927-2025
28. 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. https://doi.org/10.5194/bg-21-4809-2024
29. 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. https://doi.org/10.5194/gmd-9-4133-2016
30. Field-scale evaluation of a satellite-based terrestrial biosphere model for estimating crop response to management practices and productivity J. Wang et al. https://doi.org/10.1016/j.isprsjprs.2024.11.008
31. Breeding changes water use of winter wheat across Europe D. Behrend et al. https://doi.org/10.1038/s44264-026-00135-y
32. A first calibration of JULES-crop version 7.4 for rice using the novel O3-FACE experiment in China B. Xu et al. https://doi.org/10.5194/gmd-18-7257-2025
33. The impacts of data constraints on the predictive performance of a general process-based crop model (PeakN-crop v1.0) S. Caldararu et al. https://doi.org/10.5194/gmd-10-1679-2017
34. Integrating Plant Science and Crop Modeling: Assessment of the Impact of Climate Change on Soybean and Maize Production N. Fodor et al. https://doi.org/10.1093/pcp/pcx141
35. Advances in regional-scale crop growth and associated process modeling W. Liu et al. https://doi.org/10.1007/s11430-024-1477-2
36. Land use modelling needs to better account for multiple cropping to inform pathways for sustainable agriculture K. Waha et al. https://doi.org/10.1038/s43247-025-02724-0
37. CO2 fertilization of crops offsets yield losses due to future surface ozone damage and climate change F. Leung et al. https://doi.org/10.1088/1748-9326/ac7246
38. Towards a multiscale crop modelling framework for climate change adaptation assessment B. Peng et al. https://doi.org/10.1038/s41477-020-0625-3
39. JULES-GL7: the Global Land configuration of the Joint UK Land Environment Simulator version 7.0 and 7.2 A. Wiltshire et al. https://doi.org/10.5194/gmd-13-483-2020
40. Impacts of surface ozone pollution on wheat production in China from 2005 to 2019: A comparison among different methodologies for ozone-crop relationships J. Mao et al. https://doi.org/10.1016/j.atmosenv.2025.121413
41. 区域尺度作物生长及伴生过程模型研究进展 文. 刘 et al. https://doi.org/10.1360/SSTe-2024-0018
42. JULES-BE: representation of bioenergy crops and harvesting in the Joint UK Land Environment Simulator vn5.1 E. Littleton et al. https://doi.org/10.5194/gmd-13-1123-2020
43. Sources of interannual yield variability in JULES-crop and implications for forcing with seasonal weather forecasts K. Williams & P. Falloon https://doi.org/10.5194/gmd-8-3987-2015
44. Farmland Carbon and Water Exchange and Its Response to Environmental Factors in Arid Northwest China X. Zheng et al. https://doi.org/10.3390/land12111988
45. Modelling ozone-induced changes in wheat amino acids and protein quality using a process-based crop model J. Cook et al. https://doi.org/10.5194/bg-22-1035-2025
46. Calibrating soybean parameters in JULES 5.0 from the US-Ne2/3 FLUXNET sites and the SoyFACE-O3 experiment F. Leung et al. https://doi.org/10.5194/gmd-13-6201-2020
47. Remote Sensing-Based Quantification of the Summer Maize Yield Gap Induced by Suboptimum Sowing Dates over North China Plain S. Zhang et al. https://doi.org/10.3390/rs13183582
48. A call to action for global research on the implications of waterlogging for wheat growth and yield R. Nóia Júnior et al. https://doi.org/10.1016/j.agwat.2023.108334
49. Comparing crop growth models across the contiguous USA with a focus on dry and warm spells S. Chevuru et al. https://doi.org/10.1016/j.agwat.2025.109403
50. Identifying sensitive parameters of global major staple crops in the Noah-MP Crop model: Similarities and differences J. Fang et al. https://doi.org/10.1016/j.eja.2026.128289
51. Global glacier volume projections under high-end climate change scenarios S. Shannon et al. https://doi.org/10.5194/tc-13-325-2019
52. Improving the representation of major Indian crops in the Community Land Model version 5.0 (CLM5) using site-scale crop data N. Kangari et al. https://doi.org/10.5194/gmd-18-763-2025
53. 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. https://doi.org/10.5194/gmd-9-4155-2016
54. Improving the representation of cropland sites in the Community Land Model (CLM) version 5.0 T. Boas et al. https://doi.org/10.5194/gmd-14-573-2021
55. 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. https://doi.org/10.1016/j.agwat.2024.108938
56. The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0) J. Franke et al. https://doi.org/10.5194/gmd-13-3995-2020
57. Implementation of sequential cropping into JULESvn5.2 land-surface model C. Mathison et al. https://doi.org/10.5194/gmd-14-437-2021