88 citations as recorded by crossref.

1. Recent changes in county-level maize production in the United States: Spatial-temporal patterns, climatic drivers and the implications for crop modelling G. Leng et al. https://doi.org/10.1016/j.scitotenv.2019.06.026
2. Modeling the impact of agricultural land use and management on US carbon budgets B. Drewniak et al. https://doi.org/10.5194/bg-12-2119-2015
3. Quantifying carbon budget, crop yields and their responses to environmental variability using the ecosys model for U.S. Midwestern agroecosystems W. Zhou et al. https://doi.org/10.1016/j.agrformet.2021.108521
4. Crop planting date matters: Estimation methods and effect on future yields L. Dobor et al. https://doi.org/10.1016/j.agrformet.2016.03.023
5. Is potential cultivated land expanding or shrinking in the dryland of China? Spatiotemporal evaluation based on remote sensing and SVM L. Cai et al. https://doi.org/10.1016/j.landusepol.2021.105871
6. Do maize models capture the impacts of heat and drought stresses on yield? Using algorithm ensembles to identify successful approaches Z. Jin et al. https://doi.org/10.1111/gcb.13376
7. The Potential Utility of Satellite Soil Moisture Retrievals for Detecting Irrigation Patterns in China X. Zhang et al. https://doi.org/10.3390/w10111505
8. 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
9. Full Implementation of Matrix Approach to Biogeochemistry Module of CLM5 X. Lu et al. https://doi.org/10.1029/2020MS002105
10. The Cycles agroecosystem model: Fundamentals, testing, and applications A. Kemanian et al. https://doi.org/10.1016/j.compag.2024.109510
11. Improving the representation of hydrologic processes in Earth System Models M. Clark et al. https://doi.org/10.1002/2015WR017096
12. Modeling the interinfluence of fertilizer-induced NH3 emission, nitrogen deposition, and aerosol radiative effects using modified CESM2 K. Fung et al. https://doi.org/10.5194/bg-19-1635-2022
13. Evaluation of CLM-Crop for maize growth simulation over Northeast China M. Sheng et al. https://doi.org/10.1016/j.ecolmodel.2018.03.005
14. Evapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets R. Wartenburger et al. https://doi.org/10.1088/1748-9326/aac4bb
15. On inclusion of water resource management in Earth system models – Part 2: Representation of water supply and allocation and opportunities for improved modeling A. Nazemi & H. Wheater https://doi.org/10.5194/hess-19-63-2015
16. Validation of Spring Wheat Responses to Elevated CO2, Irrigation, and Nitrogen Fertilization in the Community Land Model 4.5 Y. Lu & B. Kimball https://doi.org/10.1029/2020EA001088
17. Modelling cropping periods of grain crops at the global scale S. Minoli et al. https://doi.org/10.1016/j.gloplacha.2018.12.013
18. Incorporating dynamic crop growth processes and management practices into a terrestrial biosphere model for simulating crop production in the United States: Toward a unified modeling framework Y. You et al. https://doi.org/10.1016/j.agrformet.2022.109144
19. Joint Modeling of Crop and Irrigation in the central United States Using the Noah‐MP Land Surface Model Z. Zhang et al. https://doi.org/10.1029/2020MS002159
20. Global change pressures on soils from land use and management P. Smith et al. https://doi.org/10.1111/gcb.13068
21. Simulating county‐level crop yields in the Conterminous United States using the Community Land Model: The effects of optimizing irrigation and fertilization G. Leng et al. https://doi.org/10.1002/2016MS000645
22. 100 Years of Progress in Applied Meteorology. Part III: Additional Applications S. Haupt et al. https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0012.1
23. Projection of 21st century irrigation water requirement across the Lower Mississippi Alluvial Valley J. Yang et al. https://doi.org/10.1016/j.agwat.2019.02.033
24. Initial Land Use/Cover Distribution Substantially Affects Global Carbon and Local Temperature Projections in the Integrated Earth System Model A. Di Vittorio et al. https://doi.org/10.1029/2019GB006383
25. Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications C. Müller et al. https://doi.org/10.5194/gmd-10-1403-2017
26. Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles S. Tan et al. https://doi.org/10.3390/agriculture14112058
27. Cross-scale evaluation of dynamic crop growth in WRF and Noah-MP-Crop T. Partridge et al. https://doi.org/10.1016/j.agrformet.2020.108217
28. Crop physiology calibration in the CLM I. Bilionis et al. https://doi.org/10.5194/gmd-8-1071-2015
29. BioEarth: Envisioning and developing a new regional earth system model to inform natural and agricultural resource management J. Adam et al. https://doi.org/10.1007/s10584-014-1115-2
30. Changes in Wood Biomass and Crop Yields in Response to Projected CO2, O3, Nitrogen Deposition, and Climate D. Lombardozzi et al. https://doi.org/10.1029/2018JG004680
31. Improving Representation of Crop Growth and Yield in the Dynamic Land Ecosystem Model and Its Application to China J. Zhang et al. https://doi.org/10.1029/2017MS001253
32. 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
33. Projecting potential planting dates of global soybean and wheat under future climate change J. Zheng et al. https://doi.org/10.1016/j.indic.2025.100909
34. Irrigation, damming, and streamflow fluctuations of the Yellow River Z. Yin et al. https://doi.org/10.5194/hess-25-1133-2021
35. Modeling Perennial Bioenergy Crops in the E3SM Land Model (ELMv2) E. Sinha et al. https://doi.org/10.1029/2022MS003171
36. Advances in regional-scale crop growth and associated process modeling W. Liu et al. https://doi.org/10.1007/s11430-024-1477-2
37. Towards a multiscale crop modelling framework for climate change adaptation assessment B. Peng et al. https://doi.org/10.1038/s41477-020-0625-3
38. Simulating crop phenology in the Community Land Model and its impact on energy and carbon fluxes M. Chen et al. https://doi.org/10.1002/2014JG002780
39. Impact of environmental changes and land management practices on wheat production in India S. Gahlot et al. https://doi.org/10.5194/esd-11-641-2020
40. Modelling the response of yields and tissue C : N to changes in atmospheric CO2 and N management in the main wheat regions of western Europe S. Olin et al. https://doi.org/10.5194/bg-12-2489-2015
41. Spatial and temporal uncertainty of crop yield aggregations V. Porwollik et al. https://doi.org/10.1016/j.eja.2016.08.006
42. 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
43. Reliable, robust and realistic: the three R's of next-generation land-surface modelling I. Prentice et al. https://doi.org/10.5194/acp-15-5987-2015
44. Simulation of crop yield using the global hydrological model H08 (crp.v1) Z. Ai & N. Hanasaki https://doi.org/10.5194/gmd-16-3275-2023
45. Global evaluation of a new biochar model for supporting climate-smart agriculture W. Ren et al. https://doi.org/10.1007/s42773-026-00609-9
46. Soil carbon management in large-scale Earth system modelling: implications for crop yields and nitrogen leaching S. Olin et al. https://doi.org/10.5194/esd-6-745-2015
47. VIC–CropSyst-v2: A regional-scale modeling platform to simulate the nexus of climate, hydrology, cropping systems, and human decisions K. Malek et al. https://doi.org/10.5194/gmd-10-3059-2017
48. Uncertainties in estimating global potential yields and their impacts for long-term modeling M. Ollenburger et al. https://doi.org/10.1007/s12571-021-01228-x
49. Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO, and NH3 emissions from enhanced rock weathering with croplands M. Val Martin et al. https://doi.org/10.5194/gmd-16-5783-2023
50. United States multi-sector land use and land cover base maps to support human and Earth system models J. Oliver & R. McManamay https://doi.org/10.1038/s41597-025-04713-6
51. Research progress on nitrogen and phosphorus runoff loss models for rice paddy H. Weichen et al. https://doi.org/10.18307/2021.0202
52. The Community Land Model underestimates land-use CO2 emissions by neglecting soil disturbance from cultivation S. Levis et al. https://doi.org/10.5194/gmd-7-613-2014
53. Increased harvested carbon of cropland in China P. Ren et al. https://doi.org/10.1088/1748-9326/ad3b20
54. Accounting for interannual variability in agricultural intensification: The potential of crop selection in Sub-Saharan Africa P. Bodin et al. https://doi.org/10.1016/j.agsy.2016.07.012
55. The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty D. Lawrence et al. https://doi.org/10.1029/2018MS001583
56. Significant impacts of irrigation water sources and methods on modeling irrigation effects in the ACMELand Model G. Leng et al. https://doi.org/10.1002/2016MS000885
57. The Purdue Agro-climatic (PAC) dataset for the U.S. Corn Belt: Development and initial results X. Liu et al. https://doi.org/10.1016/j.crm.2016.10.005
58. 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
59. Increased greenhouse gas emissions intensity of major croplands in China: Implications for food security and climate change mitigation J. Zhang et al. https://doi.org/10.1111/gcb.15290
60. Where is the Planetary Boundary for freshwater being exceeded because of livestock farming? G. Leng & J. Hall https://doi.org/10.1016/j.scitotenv.2020.144035
61. Representing winter wheat in the Community Land Model (version 4.5) Y. Lu et al. https://doi.org/10.5194/gmd-10-1873-2017
62. Modest capacity of no-till farming to offset emissions over 21st century M. Graham et al. https://doi.org/10.1088/1748-9326/abe6c6
63. A sub-canopy structure for simulating oil palm in the Community Land Model (CLM-Palm): phenology, allocation and yield Y. Fan et al. https://doi.org/10.5194/gmd-8-3785-2015
64. Representing agriculture in Earth System Models: Approaches and priorities for development S. McDermid et al. https://doi.org/10.1002/2016MS000749
65. Recent changes in county-level corn yield variability in the United States from observations and crop models G. Leng https://doi.org/10.1016/j.scitotenv.2017.07.017
66. Simulating Agriculture in the Community Land Model Version 5 D. Lombardozzi et al. https://doi.org/10.1029/2019JG005529
67. Global Variability of Simulated and Observed Vegetation Growing Season D. Peano et al. https://doi.org/10.1029/2018JG004881
68. ChinaCropSM1 km: a fine 1 km daily soil moisture dataset for dryland wheat and maize across China during 1993–2018 F. Cheng et al. https://doi.org/10.5194/essd-15-395-2023
69. CLMcrop yields and water requirements: avoided impacts by choosing RCP 4.5 over 8.5 S. Levis et al. https://doi.org/10.1007/s10584-016-1654-9
70. Evidence for a weakening strength of temperature-corn yield relation in the United States during 1980–2010 G. Leng https://doi.org/10.1016/j.scitotenv.2017.06.211
71. 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
72. 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
73. Potential yield simulated by global gridded crop models: using a process-based emulator to explain their differences B. Ringeval et al. https://doi.org/10.5194/gmd-14-1639-2021
74. Representation of a two-way coupled irrigation system in the Common Land Model S. Zhang et al. https://doi.org/10.5194/hess-29-3119-2025
75. Simulating Dynamic Roots in the Energy Exascale Earth System Land Model B. Drewniak https://doi.org/10.1029/2018MS001334
76. The Global Gridded Crop Model Intercomparison phase 1 simulation dataset C. Müller et al. https://doi.org/10.1038/s41597-019-0023-8
77. Global patterns of crop yield stability under additional nutrient and water inputs C. Müller et al. https://doi.org/10.1371/journal.pone.0198748
78. Applications of land surface model to economic and environmental-friendly optimization of nitrogen fertilization and irrigation F. Wang et al. https://doi.org/10.1016/j.heliyon.2024.e27549
79. Modeling symbiotic biological nitrogen fixation in grain legumes globally with LPJ-GUESS (v4.0, r10285) J. Ma et al. https://doi.org/10.5194/gmd-15-815-2022
80. How Much Are Planting Dates for Maize Affected by the Climate Trend? Lessons for Scenario Analysis Using Land Surface Models M. Sheng et al. https://doi.org/10.3390/agronomy9060316
81. Impacts and biophysical mechanisms of agricultural water saving on land surface temperature in Northwest China J. Fu et al. https://doi.org/10.1016/j.agwat.2025.109804
82. Using the anomaly forcing Community Land Model (CLM 4.5) for crop yield projections Y. Lu & X. Yang https://doi.org/10.5194/gmd-14-1253-2021
83. Joint structural and physiological control on the interannual variation in productivity in a temperate grassland: A data‐model comparison Z. Hu et al. https://doi.org/10.1111/gcb.14274
84. Integration of prognostic sowing and harvesting schemes to enhance crop dynamic growth simulation in Noah-MP-Crop model F. Wang et al. https://doi.org/10.1016/j.ecoinf.2024.102785
85. Quantifying indirect groundwater-mediated effects of urbanization on agroecosystem productivity using MODFLOW-AgroIBIS (MAGI), a complete critical zone model S. Zipper et al. https://doi.org/10.1016/j.ecolmodel.2017.06.002
86. Comparison of temporal trends from multiple soil moisture data sets and precipitation: The implication of irrigation on regional soil moisture trend J. Qiu et al. https://doi.org/10.1016/j.jag.2015.11.012
87. Crop yield sensitivity of global major agricultural countries to droughts and the projected changes in the future G. Leng & J. Hall https://doi.org/10.1016/j.scitotenv.2018.10.434
88. 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