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Abstract:
Global food systems face intensifying pressures 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 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.
