180 citations as recorded by crossref.

1. Changing occurrence of crop water surplus or deficit and the impact of irrigation: An analysis highlighting consequences for rice production in Bangladesh M. Mondol et al. 10.1016/j.agwat.2022.107695
2. Global-to-local-to-global interactions and climate change U. Baldos et al. 10.1088/1748-9326/acc95c
3. Noah‐MP‐Crop: Introducing dynamic crop growth in the Noah‐MP land surface model X. Liu et al. 10.1002/2016JD025597
4. Performance analysis of regional AquaCrop (v6.1) biomass and surface soil moisture simulations using satellite and in situ observations S. de Roos et al. 10.5194/gmd-14-7309-2021
5. Modelling fertiliser significance in three major crops B. Parkes et al. 10.1016/j.eja.2017.06.012
6. Heterogeneous impacts of excessive wetness on maize yields in China: Evidence from statistical yields and process-based crop models W. Liu et al. 10.1016/j.agrformet.2022.109205
7. Analysis on Heat Characteristics for Summer Maize Cropping in a Semi-Arid Region Z. Wang et al. 10.3390/agronomy12061435
8. The impact of climate change on Brazil's agriculture M. Zilli et al. 10.1016/j.scitotenv.2020.139384
9. A review of global gridded cropping system data products K. Kim et al. 10.1088/1748-9326/ac20f4
10. Mapping the Global-Scale Maize Drought Risk Under Climate Change Based on the GEPIC-Vulnerability-Risk Model Y. Yin et al. 10.1007/s13753-021-00349-3
11. Historical and projected impacts of climate change and technology on soybean yield in China S. Guo et al. 10.1016/j.agsy.2022.103522
12. Focus on agriculture and forestry benefits of reducing climate change impacts J. Martinich et al. 10.1088/1748-9326/aa6f23
13. Automatic data matching for geospatial models: a new paradigm for geospatial data and models sharing Y. Zhu & J. Yang 10.1080/19475683.2019.1670735
14. Sensitivity of soybean planting date to wet season onset in Mato Grosso, Brazil, and implications under climate change M. Zhang et al. 10.1007/s10584-021-03223-9
15. Supporting sustainability initiatives through biometeorology education and training M. Allen et al. 10.1007/s00484-017-1408-z
16. Simulating Agriculture in the Community Land Model Version 5 D. Lombardozzi et al. 10.1029/2019JG005529
17. Evaluation of global gridded crop models (GGCMs) for the simulation of major grain crop yields in China Z. Li et al. 10.2166/nh.2022.087
18. US Maize Data Reveals Adaptation to Heat and Water Stress T. Thomas 10.2139/ssrn.2741261
19. Prescreening-Based Subset Selection for Improving Predictions of Earth System Models With Application to Regional Prediction of Red Tide A. Elshall et al. 10.3389/feart.2022.786223
20. Aggravation of global maize yield loss risk under various hot and dry scenarios using multiple types of prediction approaches X. Yin et al. 10.1002/joc.8371
21. Recent changes in county-level corn yield variability in the United States from observations and crop models G. Leng 10.1016/j.scitotenv.2017.07.017
22. Representing water scarcity in future agricultural assessments J. Winter et al. 10.1016/j.ancene.2017.05.002
23. Estimating uncertainty in crop model predictions: Current situation and future prospects D. Wallach & P. Thorburn 10.1016/j.eja.2017.06.001
24. Water savings potentials of irrigation systems: global simulation of processes and linkages J. Jägermeyr et al. 10.5194/hess-19-3073-2015
25. Global Phosphorus Losses from Croplands under Future Precipitation Scenarios W. Liu et al. 10.1021/acs.est.0c03978
26. Toward large-scale crop production forecasts for global food security G. Badr et al. 10.1147/JRD.2016.2591698
27. Direct and indirect effects of CO2 increase on crop yield in West Africa B. Sultan et al. 10.1002/joc.5960
28. Modelling adaptation and transformative adaptation in cropping systems: recent advances and future directions A. Farrell et al. 10.1016/j.cosust.2023.101265
29. Progress in modelling agricultural impacts of and adaptations to climate change R. Rötter et al. 10.1016/j.pbi.2018.05.009
30. Spatial frameworks for robust estimation of yield gaps J. Rattalino Edreira et al. 10.1038/s43016-021-00365-y
31. Global historical soybean and wheat yield loss estimates from ozone pollution considering water and temperature as modifying effects B. Schauberger et al. 10.1016/j.agrformet.2018.11.004
32. 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
33. Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios C. Müller et al. 10.1088/1748-9326/abd8fc
34. Spatio-temporal downscaling of gridded crop model yield estimates based on machine learning C. Folberth et al. 10.1016/j.agrformet.2018.09.021
35. Evaluation of global gridded crop models in simulating sugarcane yield in China D. Yin et al. 10.1016/j.aosl.2023.100329
36. Simulation of Staple Crop Yields for Determination of Regional Impacts of Climate Change: A Case Study in Chonnam Province, Republic of Korea J. Choi et al. 10.3390/agronomy11122544
37. Aligning the harvesting year in global gridded crop model simulations with that in census reports is pivotal to national-level model performance evaluations for rice T. Iizumi et al. 10.1016/j.eja.2021.126367
38. Irrigation in the Earth system S. McDermid et al. 10.1038/s43017-023-00438-5
39. Dissecting the nonlinear response of maize yield to high temperature stress with model‐data integration P. Zhu et al. 10.1111/gcb.14632
40. Implementation of a dynamic specific leaf area (SLA) into a land surface model (LSM) incorporated crop-growth model Z. Li et al. 10.1016/j.compag.2023.108238
41. Africa Would Need to Import More Maize in the Future Even Under 1.5°C Warming Scenario R. Zhai et al. 10.1029/2020EF001574
42. Global patterns of crop yield stability under additional nutrient and water inputs C. Müller et al. 10.1371/journal.pone.0198748
43. Global food self-sufficiency in the 21st century under sustainable intensification of agriculture A. Beltran-Peña et al. 10.1088/1748-9326/ab9388
44. Climate and management interaction cause diverse crop phenology trends E. Eyshi Rezaei et al. 10.1016/j.agrformet.2016.11.003
45. Climate change impact and potential adaptation strategies under alternate realizations of climate scenarios for three major crops in Europe M. Donatelli et al. 10.1088/1748-9326/10/7/075005
46. The optimization of model ensemble composition and size can enhance the robustness of crop yield projections L. Li et al. 10.1038/s43247-023-01016-9
47. Phosphorus in agricultural soils: drivers of its distribution at the global scale B. Ringeval et al. 10.1111/gcb.13618
48. Global Carbon Budget 2016 C. Le Quéré et al. 10.5194/essd-8-605-2016
49. Increased wheat price spikes and larger economic inequality with 2°C global warming T. Zhang et al. 10.1016/j.oneear.2022.07.004
50. Potential impacts of projected warming scenarios on winter wheat in the UK D. Cammarano et al. 10.1017/S0021859621000903
51. Simulated vs. empirical weather responsiveness of crop yields: US evidence and implications for the agricultural impacts of climate change M. Mistry et al. 10.1088/1748-9326/aa788c
52. Estimating the Global Influence of Cover Crops on Ecosystem Service Indicators in Croplands With the LPJ‐GUESS Model J. Ma et al. 10.1029/2022EF003142
53. Untangling irrigation effects on maize water and heat stress alleviation using satellite data P. Zhu & J. Burney 10.5194/hess-26-827-2022
54. Global irrigation contribution to wheat and maize yield X. Wang et al. 10.1038/s41467-021-21498-5
55. Hydrologic and Agricultural Earth Observations and Modeling for the Water-Food Nexus A. McNally et al. 10.3389/fenvs.2019.00023
56. Climate shifts within major agricultural seasons for +1.5 and +2.0 °C worlds: HAPPI projections and AgMIP modeling scenarios A. Ruane et al. 10.1016/j.agrformet.2018.05.013
57. Representing winter wheat in the Community Land Model (version 4.5) Y. Lu et al. 10.5194/gmd-10-1873-2017
58. Projected changes in crop yield mean and variability over West Africa in a world 1.5 K warmer than the pre-industrial era B. Parkes et al. 10.5194/esd-9-119-2018
59. Role of Modelling in International Crop Research: Overview and Some Case Studies M. Reynolds et al. 10.3390/agronomy8120291
60. Assessing the impacts of agricultural managements on soil carbon stocks, nitrogen loss, and crop production – a modelling study in eastern Africa J. Ma et al. 10.5194/bg-19-2145-2022
61. The use of biogeochemical models to evaluate mitigation of greenhouse gas emissions from managed grasslands R. Sándor et al. 10.1016/j.scitotenv.2018.06.020
62. Economic shifts in agricultural production and trade due to climate change L. Porfirio et al. 10.1057/s41599-018-0164-y
63. Evaluating the Sensitivity of Agricultural Model Performance to Different Climate Inputs M. Glotter et al. 10.1175/JAMC-D-15-0120.1
64. Uncertainties in estimating global potential yields and their impacts for long-term modeling M. Ollenburger et al. 10.1007/s12571-021-01228-x
65. Processing tomato production is expected to decrease by 2050 due to the projected increase in temperature D. Cammarano et al. 10.1038/s43016-022-00521-y
66. Türkiye’de İklim Değişikliği ile Şeker Pancarı Üretimi Arasındaki İlişkinin İncelenmesi: Zamanla Değişen Simetrik ve Asimetrik Nedensellik Analizi C. DEMİRTAŞ & M. ILIKKAN ÖZGÜR 10.21547/jss.995302
67. Climate analogues suggest limited potential for intensification of production on current croplands under climate change T. Pugh et al. 10.1038/ncomms12608
68. Modeling the Global Sowing and Harvesting Windows of Major Crops Around the Year 2000 T. Iizumi et al. 10.1029/2018MS001477
69. An AgMIP framework for improved agricultural representation in integrated assessment models A. Ruane et al. 10.1088/1748-9326/aa8da6
70. Modelling cropping periods of grain crops at the global scale S. Minoli et al. 10.1016/j.gloplacha.2018.12.013
71. Assessment of Crop Yield in China Simulated by Thirteen Global Gridded Crop Models D. Yin et al. 10.1007/s00376-023-2234-3
72. Towards a multiscale crop modelling framework for climate change adaptation assessment B. Peng et al. 10.1038/s41477-020-0625-3
73. 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. 10.1016/j.scitotenv.2019.06.026
74. Simulating the effect of tillage practices with the global ecosystem model LPJmL (version 5.0-tillage) F. Lutz et al. 10.5194/gmd-12-2419-2019
75. Spatial and temporal uncertainty of crop yield aggregations V. Porwollik et al. 10.1016/j.eja.2016.08.006
76. Multi-wheat-model ensemble responses to interannual climate variability A. Ruane et al. 10.1016/j.envsoft.2016.03.008
77. Biophysical and economic implications for agriculture of +1.5° and +2.0°C global warming using AgMIP Coordinated Global and Regional Assessments A. Ruane et al. 10.3354/cr01520
78. Parameterization-induced uncertainties and impacts of crop management harmonization in a global gridded crop model ensemble C. Folberth et al. 10.1371/journal.pone.0221862
79. The critical benefits of snowpack insulation and snowmelt for winter wheat productivity P. Zhu et al. 10.1038/s41558-022-01327-3
80. A multi-model analysis of teleconnected crop yield variability in a range of cropping systems M. Heino et al. 10.5194/esd-11-113-2020
81. Forecasting Corn Yield With Machine Learning Ensembles M. Shahhosseini et al. 10.3389/fpls.2020.01120
82. Crop yield sensitivity of global major agricultural countries to droughts and the projected changes in the future G. Leng & J. Hall 10.1016/j.scitotenv.2018.10.434
83. Calibration‐induced uncertainty of the EPIC model to estimate climate change impact on global maize yield W. Xiong et al. 10.1002/2016MS000625
84. Global Response Patterns of Major Rainfed Crops to Adaptation by Maintaining Current Growing Periods and Irrigation S. Minoli et al. 10.1029/2018EF001130
85. Extending a first-principles primary production model to predict wheat yields S. Qiao et al. 10.1016/j.agrformet.2020.107932
86. Modelling crop yield and harvest index: the role of carbon assimilation and allocation parameters H. Camargo-Alvarez et al. 10.1007/s40808-022-01625-x
87. 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. 10.1016/j.agrformet.2016.10.016
88. Agriculture's Historic Twin-Challenge Toward Sustainable Water Use and Food Supply for All J. Jägermeyr 10.3389/fsufs.2020.00035
89. Contrasting area and yield responses to extreme climate contributes to climate-resilient rice production in Asia N. Hosokawa et al. 10.1038/s41598-023-33413-7
90. Systemic Risk in Global Agricultural Markets and Trade Liberalization under Climate Change: Synchronized Crop-Yield Change and Agricultural Price Volatility Y. Kunimitsu et al. 10.3390/su122410680
91. Implications of climate mitigation for future agricultural production C. Müller et al. 10.1088/1748-9326/10/12/125004
92. Verifiable soil organic carbon modelling to facilitate regional reporting of cropland carbon change: A test case in the Czech Republic J. Balkovič et al. 10.1016/j.jenvman.2020.111206
93. Climate change impacts on rice yield of a large-scale irrigation scheme in Malaysia A. Houma et al. 10.1016/j.agwat.2021.106908
94. Modelling global impacts of climate variability and trend on maize yield during 1980–2010 X. Yin & G. Leng 10.1002/joc.6792
95. A modelling platform for climate change impact on local and regional crop water requirements S. Masia et al. 10.1016/j.agwat.2021.107005
96. Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0) W. von Bloh et al. 10.5194/gmd-11-2789-2018
97. Exploring the challenges with soil data in regional land use analysis C. Hendriks et al. 10.1016/j.agsy.2016.01.007
98. Climate impacts on global agriculture emerge earlier in new generation of climate and crop models J. Jägermeyr et al. 10.1038/s43016-021-00400-y
99. Increased probability of hot and dry weather extremes during the growing season threatens global crop yields M. Heino et al. 10.1038/s41598-023-29378-2
100. Heat stress may cause a significant reduction of rice yield in China under future climate scenarios Q. Sun et al. 10.1016/j.scitotenv.2021.151746
101. Coupling Process-Based Crop Model and Extreme Climate Indicators with Machine Learning Can Improve the Predictions and Reduce Uncertainties of Global Soybean Yields Q. Sun et al. 10.3390/agriculture12111791
102. The role of cover crops for cropland soil carbon, nitrogen leaching, and agricultural yields – a global simulation study with LPJmL (V. 5.0-tillage-cc) V. Porwollik et al. 10.5194/bg-19-957-2022
103. 100 Years of Progress in Applied Meteorology. Part III: Additional Applications S. Haupt et al. 10.1175/AMSMONOGRAPHS-D-18-0012.1
104. Representing agriculture in Earth System Models: Approaches and priorities for development S. McDermid et al. 10.1002/2016MS000749
105. Observed meteorological drought trends in Bangladesh identified with the Effective Drought Index (EDI) M. Mondol et al. 10.1016/j.agwat.2021.107001
106. Enhancing Maize Yield Simulations in Regional China Using Machine Learning and Multi-Data Resources Y. Zou et al. 10.3390/rs16040701
107. Uncertainties of potentials and recent changes in global yields of major crops resulting from census- and satellite-based yield datasets at multiple resolutions T. Iizumi et al. 10.1371/journal.pone.0203809
108. Varying applicability of four different satellite-derived soil moisture products to global gridded crop model evaluation T. Sakai et al. 10.1016/j.jag.2015.09.011
109. Global investigation of impacts of PET methods on simulating crop-water relations for maize W. Liu et al. 10.1016/j.agrformet.2016.02.017
110. Assessing inter-sectoral climate change risks: the role of ISIMIP C. Rosenzweig et al. 10.1088/1748-9326/12/1/010301
111. The importance of management information and soil moisture representation for simulating tillage effects on N<sub>2</sub>O emissions in LPJmL5.0-tillage F. Lutz et al. 10.5194/gmd-13-3905-2020
112. Potential yield simulated by global gridded crop models: using a process-based emulator to explain their differences B. Ringeval et al. 10.5194/gmd-14-1639-2021
113. A similarity-based automatic data recommendation approach for geographic models Y. Zhu et al. 10.1080/13658816.2017.1300805
114. The impact of future climate change and potential adaptation methods on Maize yields in West Africa B. Parkes et al. 10.1007/s10584-018-2290-3
115. Spatial variations in crop growing seasons pivotal to reproduce global fluctuations in maize and wheat yields J. Jägermeyr & K. Frieler 10.1126/sciadv.aat4517
116. Different uncertainty distribution between high and low latitudes in modelling warming impacts on wheat W. Xiong et al. 10.1038/s43016-019-0004-2
117. Taking climate model evaluation to the next level V. Eyring et al. 10.1038/s41558-018-0355-y
118. Contribution of Remote Sensing on Crop Models: A Review D. Kasampalis et al. 10.3390/jimaging4040052
119. Emergence of robust precipitation changes across crop production areas in the 21st century M. Rojas et al. 10.1073/pnas.1811463116
120. Modeling symbiotic biological nitrogen fixation in grain legumes globally with LPJ-GUESS (v4.0, r10285) J. Ma et al. 10.5194/gmd-15-815-2022
121. Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and process-based models G. Leng & J. Hall 10.1088/1748-9326/ab7b24
122. Emulator-based optimization of APSIM-Sugar using the results of sensitivity analysis performed with the software GEM-SA M. Gunarathna et al. 10.3389/fsufs.2023.1157854
123. The Global Gridded Crop Model Intercomparison phase 1 simulation dataset C. Müller et al. 10.1038/s41597-019-0023-8
124. 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
125. Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions F. Ehrhardt et al. 10.1111/gcb.13965
126. Improving maize growth processes in the community land model: Implementation and evaluation B. Peng et al. 10.1016/j.agrformet.2017.11.012
127. Water resources conservation and nitrogen pollution reduction under global food trade and agricultural intensification W. Liu et al. 10.1016/j.scitotenv.2018.03.306
128. A meta-analysis of crop response patterns to nitrogen limitation for improved model representation V. Seufert et al. 10.1371/journal.pone.0223508
129. Evaluation of crop model prediction and uncertainty using Bayesian parameter estimation and Bayesian model averaging Y. Gao et al. 10.1016/j.agrformet.2021.108686
130. Evaluation of the accuracy of satellite-based rainfed wheat yield dataset over an area with complex geography M. Eini et al. 10.1016/j.jaridenv.2023.104963
131. Future climate impacts on global agricultural yields over the 21st century S. Waldhoff et al. 10.1088/1748-9326/abadcb
132. Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2.0°C assessments C. Rosenzweig et al. 10.1098/rsta.2016.0455
133. Decreases in global beer supply due to extreme drought and heat W. Xie et al. 10.1038/s41477-018-0263-1
134. Maize yield loss risk under droughts in observations and crop models in the United States G. Leng 10.1088/1748-9326/abd500
135. Selection of a representative subset of global climate models that captures the profile of regional changes for integrated climate impacts assessment A. Ruane & S. McDermid 10.1186/s40322-017-0036-4
136. The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water, and nitrogen perturbations W. Liu et al. 10.5194/gmd-16-7203-2023
137. An Integrated Agriculture, Atmosphere, and Hydrology Modeling System for Ecosystem Assessments L. Ran et al. 10.1029/2019MS001708
138. Estimation of Spring Maize Planting Dates in China Using the Environmental Similarity Method M. Sheng et al. 10.3390/agronomy14010097
139. Crop planting date matters: Estimation methods and effect on future yields L. Dobor et al. 10.1016/j.agrformet.2016.03.023
140. How much control do smallholder maize farmers have over yield? M. Cecil et al. 10.1016/j.fcr.2023.109014
141. Quantifying the Limitation to World Cereal Production Due To Soil Phosphorus Status M. Kvakić et al. 10.1002/2017GB005754
142. Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations C. Folberth et al. 10.1038/ncomms11872
143. Carbon uptake by European agricultural land is variable, and in many regions could be increased: Evidence from remote sensing, yield statistics and models of potential productivity M. Neumann & P. Smith 10.1016/j.scitotenv.2018.06.268
144. Soil carbon management in large-scale Earth system modelling: implications for crop yields and nitrogen leaching S. Olin et al. 10.5194/esd-6-745-2015
145. Assessing recall bias and measurement error in high-frequency social data collection for human-environment research A. Bell et al. 10.1007/s11111-019-0314-1
146. A crop yield change emulator for use in GCAM and similar models: Persephone v1.0 A. Snyder et al. 10.5194/gmd-12-1319-2019
147. Field-scale mapping of evaporative stress indicators of crop yield: An application over Mead, NE, USA Y. Yang et al. 10.1016/j.rse.2018.02.020
148. Projecting Exposure to Extreme Climate Impact Events Across Six Event Categories and Three Spatial Scales S. Lange et al. 10.1029/2020EF001616
149. Projection of future drought and its impact on simulated crop yield over South Asia using ensemble machine learning approach F. Prodhan et al. 10.1016/j.scitotenv.2021.151029
150. Evapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets R. Wartenburger et al. 10.1088/1748-9326/aac4bb
151. Empirical estimation of weather-driven yield shocks using biophysical characteristics for U.S. rainfed and irrigated maize, soybeans, and winter wheat A. Snyder et al. 10.1088/1748-9326/ac15ce
152. Crop productivity changes in 1.5 °C and 2 °C worlds under climate sensitivity uncertainty C. Schleussner et al. 10.1088/1748-9326/aab63b
153. Spatiotemporal patterns of winter wheat phenology and its climatic drivers based on an improved pDSSAT model Y. Luo et al. 10.1007/s11430-020-9821-0
154. Achieving High Crop Yields with Low Nitrogen Emissions in Global Agricultural Input Intensification W. Liu et al. 10.1021/acs.est.8b03610
155. The impact of climate change on barley yield in the Mediterranean basin D. Cammarano et al. 10.1016/j.eja.2019.03.002
156. Worldwide Maize and Soybean Yield Response to Environmental and Management Factors Over the 20th and 21st Centuries T. Lin et al. 10.1029/2021JG006304
157. Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications C. Müller et al. 10.5194/gmd-10-1403-2017
158. Historical simulation of maize water footprints with a new global gridded crop model ACEA O. Mialyk et al. 10.5194/hess-26-923-2022
159. 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
160. Estimating sowing and harvest dates based on the Asian summer monsoon C. Mathison et al. 10.5194/esd-9-563-2018
161. Probabilistic Assessment of Cereal Rye Cover Crop Impacts on Regional Crop Yield and Soil Carbon T. Rai et al. 10.3390/agriculture13010176
162. Observational constraint of process crop models suggests higher risks for global maize yield under climate change X. Yin & G. Leng 10.1088/1748-9326/ac7ac7
163. Consistent negative response of US crops to high temperatures in observations and crop models B. Schauberger et al. 10.1038/ncomms13931
164. A regional nuclear conflict would compromise global food security J. Jägermeyr et al. 10.1073/pnas.1919049117
165. Development of a 10-km resolution global soil profile dataset for crop modeling applications E. Han et al. 10.1016/j.envsoft.2019.05.012
166. Climate change has likely already affected global food production D. Ray et al. 10.1371/journal.pone.0217148
167. Large potential for crop production adaptation depends on available future varieties F. Zabel et al. 10.1111/gcb.15649
168. Future climate change significantly alters interannual wheat yield variability over half of harvested areas W. Liu et al. 10.1088/1748-9326/ac1fbb
169. Field warming experiments shed light on the wheat yield response to temperature in China C. Zhao et al. 10.1038/ncomms13530
170. Timing of rice maturity in China is affected more by transplanting date than by climate change H. Zhao et al. 10.1016/j.agrformet.2015.11.001
171. Impacts of Spatial Zonation Schemes on Yield Potential Estimates at the Regional Scale H. Xu et al. 10.3390/agronomy10050631
172. Climate forcing datasets for agricultural modeling: Merged products for gap-filling and historical climate series estimation A. Ruane et al. 10.1016/j.agrformet.2014.09.016
173. Assessing uncertainty and complexity in regional-scale crop model simulations J. Ramirez-Villegas et al. 10.1016/j.eja.2015.11.021
174. Simulated carbon emissions from land-use change are substantially enhanced by accounting for agricultural management T. Pugh et al. 10.1088/1748-9326/10/12/124008
175. A network-based approach for semi-quantitative knowledge mining and its application to yield variability B. Schauberger et al. 10.1088/1748-9326/11/12/123001
176. Understanding the weather signal in national crop‐yield variability K. Frieler et al. 10.1002/2016EF000525
177. Modelling the response of yields and tissue C : N to changes in atmospheric CO<sub>2</sub> and N management in the main wheat regions of western Europe S. Olin et al. 10.5194/bg-12-2489-2015
178. Rapid aggregation of global gridded crop model outputs to facilitate cross-disciplinary analysis of climate change impacts in agriculture N. Villoria et al. 10.1016/j.envsoft.2015.10.016
179. Climate shocks, food price stability and international trade: evidence from 76 maize markets in 27 net-importing countries B. Chen & N. Villoria 10.1088/1748-9326/aaf07f
180. Genetic Variability and Association of Yield and Yield-Related Traits under Moisture Stress in Common Bean Genotypes (Phaseolus vulgaris L.) at Melkassa and Miesso, Ethiopia M. Tola et al. 10.1155/2023/8697497