Crop models for future food systems

de Souza Noia Junior, Rogerio; Ruane, Alex C.; Athanasiadis, Ioannis N.; Ewert, Frank; Harrison, Matthew Tom; Jägermeyr, Jonas; Martre, Pierre; Müller, Christoph; Palosuo, Taru; Salmerón, Montserrat; Webber, Heidi; Sefakor Maccarthy, Dilys; Asseng, Senthold

doi:10.1016/j.oneear.2025.101487

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Crop models for future food systems

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de Souza Noia Junior, Rogerio
External Organizations;

Ruane, Alex C.
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Athanasiadis, Ioannis N.
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Ewert, Frank
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Harrison, Matthew Tom
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Jägermeyr, Jonas
Potsdam Institute for Climate Impact Research;

Martre, Pierre
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Müller, Christoph
Potsdam Institute for Climate Impact Research;

Palosuo, Taru
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Salmerón, Montserrat
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Webber, Heidi
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Sefakor Maccarthy, Dilys
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Asseng, Senthold
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Citation

de Souza Noia Junior, R., Ruane, A. C., Athanasiadis, I. N., Ewert, F., Harrison, M. T., Jägermeyr, J., Martre, P., Müller, C., Palosuo, T., Salmerón, M., Webber, H., Sefakor Maccarthy, D., Asseng, S. (2025): Crop models for future food systems. - One Earth, 8, 10, 101487.
https://doi.org/10.1016/j.oneear.2025.101487

Cite as: https://publications.pik-potsdam.de/pubman/item/item_32791

Abstract

Global food systems face intensifying pressure from climate change, resource scarcity, and rising demand, making their transformation toward resilience and sustainability urgent. Process-based crop growth models (CMs) are critical for understanding cropping system dynamics and supporting decisions from crop breeding to adaptive management across diverse environments. Yet, current CMs struggle to capture extreme events, novel production systems, and rapidly evolving data streams, limiting their ability to inform robust and timely decisions. Here, we outline CM structure, identify key knowledge gaps, and propose six priorities for next-generation CMs: (1) expand applications to extremes and to diverse systems; (2) support climate-resilient breeding; (3) integrate with machine learning for better inputs and forecasts; (4) link with standardized sensor and database networks; (5) promote modular, open-source architectures; and (6) build capacity in under-resourced regions. These priorities will substantially enhance CM robustness, comparability, and usability, reinforcing their role in guiding sustainable food system transformation.