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Gaps and strategies for accurate simulation of waterlogging impacts on crop productivity

Nature Food volume 6, pages 553–562 (2025)Cite this article

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Abstract

With the changing climate, soil waterlogging is a growing threat to food security. Yet, contemporary approaches employed in crop models to simulate waterlogging are in their infancy. By analysing 21 crop models, we show that critical deficiencies persist in accurately simulating capillary rise, crop resistance to transient periods of waterlogging, crop recovery mechanisms, and the effects on soil nitrogen processes, phenology and yield components. This hinders the ability of such models to reliably simulate the impacts of excessive soil moisture. Advanced crop modelling analytics will enable scenario analysis and, with time, farming systems adaptation to climate change and increasing frequency of crop failure due to waterlogging.

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Fig. 1: Overview of the processes and drivers leading to waterlogging and associated effects on soil and crop performance.
Fig. 2: Processes in CMs involved in simulating waterlogging conditions.
Fig. 3: Mechanisms required to initiate waterlogging stress in CMs.
Fig. 4: Effects of waterlogging on soil properties and solute transport processes in CMs.
Fig. 5: Processes relating to waterlogging that are captured in CMs.

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Acknowledgements

M.G.-V. acknowledges funding from Consejería de Universidad, Investigación e Innovación—Junta de Andalucia through the Qualifica Project (QUAL21_023 IAS), and from WheatNet (‘Conexión TRIGO’) of the Spanish National Research Council (CSIC). The contribution of T.K.D.W. was made possible by the joint project of Digitalization in Organic Agriculture (DigiPlus, grant number 28 DE 207A 21), funded by the German Federal Office of Agriculture and Food. M.T.H. and K.L. were in part supported by funding from the Australian Grains Research & Development Corporation (GRDC contract code UOT1906-002RTX). T.G. acknowledges partial funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC 2070 – 390732324 and under the Collaborative Research Centre DETECT (grant number SFB1502/1–2022 -450058266).

Author information

Authors and Affiliations

  1. Instituto de Agricultura Sostenible, CSIC, Córdoba, Spain

    Margarita Garcia-Vila

  2. Institute of Bio- and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany

    Murilo dos Santos Vianna

  3. University of Bonn, Bonn, Germany

    Murilo dos Santos Vianna, Thomas Gaiser & Aram Gorooei

  4. Tasmanian Institute of Agriculture, University of Tasmania, Launceston, Tasmania, Australia

    Matthew Tom Harrison & Ke Liu

  5. Yangtze University, Jingzhou, China

    Ke Liu

  6. LEPSE, Université de Montpellier, INRAE, Institut Agro Montpellier, Montpellier, France

    Rogério de S. Nóia-Júnior

  7. University of Kassel, Kassel, Germany

    Tobias K. D. Weber

  8. China Agricultural University, Beijing, China

    Jin Zhao

  9. University of Milan, Milan, Italy

    Marco Acutis, Mara Gabbrielli & Alessia Perego

  10. Iowa State University, Ames, IA, USA

    Sotirios Archontoulis

  11. Department of Life Science Engineering, Digital Agriculture, HEF World Agricultural Systems Center, Technical University of Munich, Munich, Germany

    Senthold Asseng & Valentina Stocca

  12. Gembloux Agro-Bio Tech, Liege University, Gembloux, Belgium

    Pierre Aubry, Mathieu Delandmeter & Benjamin Dumont

  13. International Institute for Applied Systems Analysis, Laxenburg, Austria

    Juraj Balkovic & Christian Folberth

  14. Michigan State University, East Lansing, MI, USA

    Bruno Basso

  15. Fujian Agriculture and Forestry University, Fuzhou, China

    Xianguan Chen

  16. Chinese Academy of Sciences, Beijing, China

    Yi Chen & Fulu Tao

  17. University of São Paulo, Piracicaba, Brazil

    Quirijn de Jong van Lier

  18. Wageningen University and Research, Wageningen, the Netherlands

    Allard de Wit & Klaas Metselaar

  19. University of Florence, Florence, Italy

    Roberto Ferrise & Gloria Padovan

  20. University of Florida, Gainesville, FL, USA

    Gerrit Hoogenboom & Vakhtang Shelia

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

    Kurt Christian Kersebaum, Yean-Uk Kim, Claas Nendel, Diana Maria Seserman & Heidi Webber

  22. Global Change Research Institute of the Czech Academy of Sciences, Brno, Czech Republic

    Kurt Christian Kersebaum & Claas Nendel

  23. KIT/IMK-IFU, Garmisch-Partenkirchen, Germany

    David Kraus, Lioba Martin & Clemens Scheer

  24. National Engineering and Technology Center for Information Agriculture, Engineering Research Center of Smart Agriculture, Ministry of Education, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Nanjing Agricultural University, Nanjing, China

    Bing Liu & Yan Zhu

  25. Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany

    Claas Nendel

  26. CSIRO Agriculture and Food, Canberra, Australian Capital Territory, Australia

    Enli Wang & Zhigan Zhao

  27. Natural Resources Institute Finland (Luke), Helsinki, Finland

    Taru Palosuo

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  1. Margarita Garcia-Vila
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  2. Murilo dos Santos Vianna
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Contributions

M.G.-V., M.d.S.V., T.P., M.T.H., K.L., R.d.S.N.-J., T.K.D.W. and J.Z. conceived of the study. M.G.-V., M.d.S.V. and T.P. designed and coordinated the study. M.G.-V., M.d.S.V., M.T.H., K.L., R.d.S.N.-J., T.K.D.W., J.Z., M.A., S. Archontoulis, S. Asseng, P.A., J.B., B.B., X.C., Y.C., Q.d.J.v.L., M.D., A.d.W., B.D., R.F., C.F., M.G., T.G., A.G., G.H., K.C.K., Y.-U.K., D.K., B.L., L.M., K.M., C.N., G.P., A.P., D.M.S., C.S., V. Shelia, V. Stocca, F.T., E.W., H.W., Z.Z., Y.Z. and T.P. provided crop model information and discussed the results. M.G.-V. performed the formal analysis, produced the figures and wrote the initial paper. M.d.S.V., T.P., M.T.H., K.L., R.d.S.N.-J., T.K.D.W. and J.Z. contributed to the discussion, reviewed the paper and provided critical feedback. All authors contributed to editing the final paper.

Corresponding author

Correspondence to Margarita Garcia-Vila.

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Nature Food thanks Bin Wang, Qiang Yu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Garcia-Vila, M., dos Santos Vianna, M., Harrison, M.T. et al. Gaps and strategies for accurate simulation of waterlogging impacts on crop productivity. Nat Food 6, 553–562 (2025). https://doi.org/10.1038/s43016-025-01179-y

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