47 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. Human–water interface in hydrological modelling: current status and future directions Y. Wada et al. 10.5194/hess-21-4169-2017
5. Estimating sowing and harvest dates based on the Asian summer monsoon C. Mathison et al. 10.5194/esd-9-563-2018
6. 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
7. Soybean-BioCro: a semi-mechanistic model of soybean growth M. Matthews et al. 10.1093/insilicoplants/diab032
8. 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
9. 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
10. Development of the DO3SE-Crop model to assess ozone effects on crop phenology, biomass, and yield P. Pande et al. 10.5194/bg-22-181-2025
11. Advancements in Leaf Area Index Estimation for Maize Using Modeling and Remote Sensing Techniques: A Review K. Bakó et al. 10.3390/agronomy15030519
12. 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
13. The Land Variational Ensemble Data Assimilation Framework: LAVENDAR v1.0.0 E. Pinnington et al. 10.5194/gmd-13-55-2020
14. The Impact of Crop Rotation and Spatially Varying Crop Parameters in the E3SM Land Model (ELMv2) E. Sinha et al. 10.1029/2022JG007187
15. 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
16. Improving maize growth processes in the community land model: Implementation and evaluation B. Peng et al. 10.1016/j.agrformet.2017.11.012
17. 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
18. 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. 10.5194/gmd-13-2315-2020
19. Evaluation of JULES-crop performance against site observations of irrigated maize from Mead, Nebraska K. Williams et al. 10.5194/gmd-10-1291-2017
20. Assimilation of remote sensing into crop growth models: Current status and perspectives J. Huang et al. 10.1016/j.agrformet.2019.06.008
21. Cocoa plant productivity in West Africa under climate change: a modelling and experimental study E. Black et al. 10.1088/1748-9326/abc3f3
22. Modeling Perennial Bioenergy Crops in the E3SM Land Model (ELMv2) E. Sinha et al. 10.1029/2022MS003171
23. 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
24. 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
25. Field-scale evaluation of a satellite-based terrestrial biosphere model for estimating crop response to management practices and productivity J. Wang et al. 10.1016/j.isprsjprs.2024.11.008
26. 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
27. 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
28. Advances in regional-scale crop growth and associated process modeling W. Liu et al. 10.1007/s11430-024-1477-2
29. 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
30. Towards a multiscale crop modelling framework for climate change adaptation assessment B. Peng et al. 10.1038/s41477-020-0625-3
31. 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
32. 区域尺度作物生长及伴生过程模型研究进展 文. 刘 et al. 10.1360/SSTe-2024-0018
33. 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
34. 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
35. Farmland Carbon and Water Exchange and Its Response to Environmental Factors in Arid Northwest China X. Zheng et al. 10.3390/land12111988
36. Modelling ozone-induced changes in wheat amino acids and protein quality using a process-based crop model J. Cook et al. 10.5194/bg-22-1035-2025
37. Calibrating soybean parameters in JULES 5.0 from the US-Ne2/3 FLUXNET sites and the SoyFACE-O3 experiment F. Leung et al. 10.5194/gmd-13-6201-2020
38. 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
39. 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
40. Comparing crop growth models across the contiguous USA with a focus on dry and warm spells S. Chevuru et al. 10.1016/j.agwat.2025.109403
41. Global glacier volume projections under high-end climate change scenarios S. Shannon et al. 10.5194/tc-13-325-2019
42. Improving the representation of major Indian crops in the Community Land Model version 5.0 (CLM5) using site-scale crop data K. Reddy et al. 10.5194/gmd-18-763-2025
43. 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
44. 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
45. 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
46. 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. 10.5194/gmd-13-3995-2020
47. Implementation of sequential cropping into JULESvn5.2 land-surface model C. Mathison et al. 10.5194/gmd-14-437-2021