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Towards a multiscale crop modelling framework for climate change adaptation assessment

Nature Plants volume 6, pages 338–348 (2020)Cite this article

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Abstract

Predicting the consequences of manipulating genotype (G) and agronomic management (M) on agricultural ecosystem performances under future environmental (E) conditions remains a challenge. Crop modelling has the potential to enable society to assess the efficacy of G × M technologies to mitigate and adapt crop production systems to climate change. Despite recent achievements, dedicated research to develop and improve modelling capabilities from gene to global scales is needed to provide guidance on designing G × M adaptation strategies with full consideration of their impacts on both crop productivity and ecosystem sustainability under varying climatic conditions. Opportunities to advance the multiscale crop modelling framework include representing crop genetic traits, interfacing crop models with large-scale models, improving the representation of physiological responses to climate change and management practices, closing data gaps and harnessing multisource data to improve model predictability and enable identification of emergent relationships. A fundamental challenge in multiscale prediction is the balance between process details required to assess the intervention and predictability of the system at the scales feasible to measure the impact. An advanced multiscale crop modelling framework will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate at regional-to-global scales.

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Fig. 1: Crop modelling plays a central role in assessing agricultural CC adaptation for food security and environmental sustainability.
Fig. 2: A conceptual illustration of the multiscale crop modelling framework.
Fig. 3: Temperature free-air controlled enhancement experiment for soybean in Illinois, USA.

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Acknowledgements

B.P., K.G., H.K. and W.Z. are supported by the United States National Science Foundation (NSF) Career Award (grant no. 1847334), National Aeronautics and Space Administration (NASA) Carbon Monitoring System managed by NASA Terrestrial Ecology Program (grant no. 80NSSC18K0170), United States Department of Agriculture (USDA) National Institute of Food and Agriculture (NIFA) Program (grant no. 2017-67013-26253) to K.G. B.P., K.G., S.A. and D.I.G acknowledge support by USDA NIFA (grant no. 2017-68002-26789). B.P., K.G., W.Z., A.M.C. and J.C.S. acknowledge support by the Foundation for Food and Agriculture Research (FFAR) (grant no. 602757). B.P., K.G., D.M.L. and D.L.L acknowledge support by the National Center for Atmospheric Research, which is a major facility sponsored by the NSF under cooperative agreement no. 1852977. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the FFAR. Any opinions, findings and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the USDA. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. Due to space constrictions, we could not cite all relevant literature. We apologize to the authors whose important work was not cited in this Perspective.

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Authors and Affiliations

  1. Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Bin Peng, Kaiyu Guan, Evan H. Delucia, Hyungsuk Kimm & Wang Zhou

  2. National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Bin Peng, Kaiyu Guan, Evan H. Delucia & Amy Marshall-Colon

  3. Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Kaiyu Guan, Carl J. Bernacchi, Evan H. Delucia & Amy Marshall-Colon

  4. Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Kaiyu Guan, Elizabeth A. Ainsworth, Carl J. Bernacchi, Evan H. Delucia, Amy Marshall-Colon & Donald R. Ort

  5. Climate Sciences Department, Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Jinyun Tang

  6. Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Elizabeth A. Ainsworth, Carl J. Bernacchi, Evan H. Delucia, Amy Marshall-Colon & Donald R. Ort

  7. USDA ARS Global Change and Photosynthesis Research Unit, Urbana, IL, USA

    Elizabeth A. Ainsworth & Carl J. Bernacchi

  8. Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Elizabeth A. Ainsworth, Carl J. Bernacchi, Evan H. Delucia, Amy Marshall-Colon & Donald R. Ort

  9. Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, USA

    Senthold Asseng & James W. Jones

  10. Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland, Australia

    Mark Cooper, Graeme L. Hammer & Alex Wu

  11. Department of Computer Science, University of Chicago, Chicago, IL, USA

    Joshua W. Elliott

  12. Crop Science Group, INRES, University of Bonn, Bonn, Germany

    Frank Ewert

  13. Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany

    Frank Ewert

  14. Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada

    Robert F. Grant

  15. Independent scientist, St. Louis, MO, USA

    David I Gustafson

  16. Australian Research Council Centre of Excellence for Translational Photosynthesis, The University of Queensland, Brisbane, Queensland, Australia

    Graeme L. Hammer & Alex Wu

  17. Department of Bioproducts and Biosystems Engineering, University of Minnesota-Twin Cities, St. Paul, MN, USA

    Zhenong Jin

  18. National Center for Atmospheric Research, Boulder, CO, USA

    David M. Lawrence & Danica L. Lombardozzi

  19. State Key Laboratory of Earth Surface Processes and Resources Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China

    Yan Li

  20. Corteva AgriScience, Johnston, IA, USA

    Carlos D. Messina

  21. Department of Crop Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Donald R. Ort

  22. Department of Agronomy & Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA

    James C. Schnable

  23. Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, USA

    James C. Schnable

  24. Horticultural Sciences Department, University of Florida, Gainesville, FL, USA

    C. Eduardo Vallejos

  25. Centre for Crop Systems Analysis, Department of Plant Sciences, Wageningen University & Research, Wageningen, The Netherlands

    Xinyou Yin

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Contributions

B.P. and K.G. conceived the research. B.P., K.G. and J.T. wrote the paper; B.P. and K.G. designed the figures. B.P and H.K. produced the figures. B.P. designed and produced the table. E.A.A., S.A., C.J.B., M.C., E.H.D., J.W.E, F.E., R.F.G., D.I.G, G.L.H., J.W.J., Z.J., H.K., D.M.L., Y.L., D.L.L., A.M.C., C.D.M., D.R.O., J.C.S., C.E.V., A.W., X.Y. and W.Z. all contributed to the text.

Corresponding authors

Correspondence to Bin Peng or Kaiyu Guan.

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Peng, B., Guan, K., Tang, J. et al. Towards a multiscale crop modelling framework for climate change adaptation assessment. Nat. Plants 6, 338–348 (2020). https://doi.org/10.1038/s41477-020-0625-3

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