The optimization of model ensemble composition and size can enhance the robustness of crop yield projections
Abstract Linked climate and crop simulation models are widely used to assess the impact of climate change on agriculture. However, it is unclear how ensemble configurations (model composition and size) influence crop yield projections and uncertainty. Here, we investigate the influences of ensemble...
| Main Authors: | , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-10-01
|
| Series: | Communications Earth & Environment |
| Online Access: | https://doi.org/10.1038/s43247-023-01016-9 |
| _version_ | 1797556258394341376 |
|---|---|
| author | Linchao Li Bin Wang Puyu Feng Jonas Jägermeyr Senthold Asseng Christoph Müller Ian Macadam De Li Liu Cathy Waters Yajie Zhang Qinsi He Yu Shi Shang Chen Xiaowei Guo Yi Li Jianqiang He Hao Feng Guijun Yang Hanqin Tian Qiang Yu |
| author_facet | Linchao Li Bin Wang Puyu Feng Jonas Jägermeyr Senthold Asseng Christoph Müller Ian Macadam De Li Liu Cathy Waters Yajie Zhang Qinsi He Yu Shi Shang Chen Xiaowei Guo Yi Li Jianqiang He Hao Feng Guijun Yang Hanqin Tian Qiang Yu |
| author_sort | Linchao Li |
| collection | DOAJ |
| description | Abstract Linked climate and crop simulation models are widely used to assess the impact of climate change on agriculture. However, it is unclear how ensemble configurations (model composition and size) influence crop yield projections and uncertainty. Here, we investigate the influences of ensemble configurations on crop yield projections and modeling uncertainty from Global Gridded Crop Models and Global Climate Models under future climate change. We performed a cluster analysis to identify distinct groups of ensemble members based on their projected outcomes, revealing unique patterns in crop yield projections and corresponding uncertainty levels, particularly for wheat and soybean. Furthermore, our findings suggest that approximately six Global Gridded Crop Models and 10 Global Climate Models are sufficient to capture modeling uncertainty, while a cluster-based selection of 3-4 Global Gridded Crop Models effectively represents the full ensemble. The contribution of individual Global Gridded Crop Models to overall uncertainty varies depending on region and crop type, emphasizing the importance of considering the impact of specific models when selecting models for local-scale applications. Our results emphasize the importance of model composition and ensemble size in identifying the primary sources of uncertainty in crop yield projections, offering valuable guidance for optimizing ensemble configurations in climate-crop modeling studies tailored to specific applications. |
| first_indexed | 2024-03-10T16:59:19Z |
| format | Article |
| id | doaj.art-cd67f7af723d4053a595d8c8b8a3d0ec |
| institution | Directory Open Access Journal |
| issn | 2662-4435 |
| language | English |
| last_indexed | 2024-03-10T16:59:19Z |
| publishDate | 2023-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Earth & Environment |
| spelling | doaj.art-cd67f7af723d4053a595d8c8b8a3d0ec2023-11-20T11:01:56ZengNature PortfolioCommunications Earth & Environment2662-44352023-10-014111110.1038/s43247-023-01016-9The optimization of model ensemble composition and size can enhance the robustness of crop yield projectionsLinchao Li0Bin Wang1Puyu Feng2Jonas Jägermeyr3Senthold Asseng4Christoph Müller5Ian Macadam6De Li Liu7Cathy Waters8Yajie Zhang9Qinsi He10Yu Shi11Shang Chen12Xiaowei Guo13Yi Li14Jianqiang He15Hao Feng16Guijun Yang17Hanqin Tian18Qiang Yu19College of Soil and Water Conservation Science and Engineering, Northwest A&F UnviersityNSW Department of Primary Industries, Wagga Wagga Agricultural InstituteCollege of Land Science and Technology, China Agricultural UniversityNASA Goddard Institute for Space StudiesDepartment of Life Science Engineering, Technical University of MunichPotsdam Institute for Climate Impact Research (PIK), Leibniz AssociationCSIRO Climate InnovationNSW Department of Primary Industries, Wagga Wagga Agricultural InstituteNSW Department of Primary IndustriesState Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F UniversitySchool of Life Sciences, Faculty of Science, University of Technology SydneyCollege of Soil and Water Conservation Science and Engineering, Northwest A&F UnviersityKey Laboratory of Meteorological Disaster, Ministry of Education and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Jiangsu ProvinceKey Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of SciencesCollege of Water Resources and Architectural Engineering, Northwest A&F UniversityCollege of Water Resources and Architectural Engineering, Northwest A&F UniversityCollege of Soil and Water Conservation Science and Engineering, Northwest A&F UnviersityKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry SciencesSchiller Institute for Integrated Science and Society, Department of Earth and Environmental Sciences, Boston CollegeCollege of Soil and Water Conservation Science and Engineering, Northwest A&F UnviersityAbstract Linked climate and crop simulation models are widely used to assess the impact of climate change on agriculture. However, it is unclear how ensemble configurations (model composition and size) influence crop yield projections and uncertainty. Here, we investigate the influences of ensemble configurations on crop yield projections and modeling uncertainty from Global Gridded Crop Models and Global Climate Models under future climate change. We performed a cluster analysis to identify distinct groups of ensemble members based on their projected outcomes, revealing unique patterns in crop yield projections and corresponding uncertainty levels, particularly for wheat and soybean. Furthermore, our findings suggest that approximately six Global Gridded Crop Models and 10 Global Climate Models are sufficient to capture modeling uncertainty, while a cluster-based selection of 3-4 Global Gridded Crop Models effectively represents the full ensemble. The contribution of individual Global Gridded Crop Models to overall uncertainty varies depending on region and crop type, emphasizing the importance of considering the impact of specific models when selecting models for local-scale applications. Our results emphasize the importance of model composition and ensemble size in identifying the primary sources of uncertainty in crop yield projections, offering valuable guidance for optimizing ensemble configurations in climate-crop modeling studies tailored to specific applications.https://doi.org/10.1038/s43247-023-01016-9 |
| spellingShingle | Linchao Li Bin Wang Puyu Feng Jonas Jägermeyr Senthold Asseng Christoph Müller Ian Macadam De Li Liu Cathy Waters Yajie Zhang Qinsi He Yu Shi Shang Chen Xiaowei Guo Yi Li Jianqiang He Hao Feng Guijun Yang Hanqin Tian Qiang Yu The optimization of model ensemble composition and size can enhance the robustness of crop yield projections Communications Earth & Environment |
| title | The optimization of model ensemble composition and size can enhance the robustness of crop yield projections |
| title_full | The optimization of model ensemble composition and size can enhance the robustness of crop yield projections |
| title_fullStr | The optimization of model ensemble composition and size can enhance the robustness of crop yield projections |
| title_full_unstemmed | The optimization of model ensemble composition and size can enhance the robustness of crop yield projections |
| title_short | The optimization of model ensemble composition and size can enhance the robustness of crop yield projections |
| title_sort | optimization of model ensemble composition and size can enhance the robustness of crop yield projections |
| url | https://doi.org/10.1038/s43247-023-01016-9 |
| work_keys_str_mv | AT linchaoli theoptimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT binwang theoptimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT puyufeng theoptimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT jonasjagermeyr theoptimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT sentholdasseng theoptimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT christophmuller theoptimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT ianmacadam theoptimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT deliliu theoptimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT cathywaters theoptimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT yajiezhang theoptimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT qinsihe theoptimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT yushi theoptimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT shangchen theoptimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT xiaoweiguo theoptimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT yili theoptimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT jianqianghe theoptimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT haofeng theoptimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT guijunyang theoptimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT hanqintian theoptimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT qiangyu theoptimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT linchaoli optimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT binwang optimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT puyufeng optimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT jonasjagermeyr optimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT sentholdasseng optimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT christophmuller optimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT ianmacadam optimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT deliliu optimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT cathywaters optimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT yajiezhang optimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT qinsihe optimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT yushi optimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT shangchen optimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT xiaoweiguo optimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT yili optimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT jianqianghe optimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT haofeng optimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT guijunyang optimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT hanqintian optimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections AT qiangyu optimizationofmodelensemblecompositionandsizecanenhancetherobustnessofcropyieldprojections |