Can Crop Models Identify Critical Gaps in Genetics, Environment, and Management Interactions?

Area of Science:

• Agricultural Science
• Climate Change Adaptation
• Computational Biology

Background:

• Agricultural systems face increasing food demand under climate change.
• Crop models are vital tools for assessing genotype performance across diverse conditions.
• Understanding the genetic × environment × management interaction (GEMI) is key for plant breeding and agricultural sustainability.

Purpose of the Study:

• To evaluate the current readiness of crop models for supporting GEMI assessments.
• To identify limitations in modeling and data for crop simulation.
• To advocate for the improvement of crop modeling algorithms and convergence for enhanced agricultural applications.

Main Methods:

• Review of existing studies comparing crop model performance.
• Analysis of modeling and data limitations in representing GEMI.
• Discussion of the challenges in simulating crop responses under varying environmental stresses.

Main Results:

• Current crop models show variability in accurately representing GEMI, especially under stress conditions.
• Model ensembles can mitigate some uncertainties but may incur high costs.
• Accurate GEMI assessment requires improved crop models, skilled users, and realistic environmental data.

Conclusions:

• Enhancing crop models is essential for advancing agricultural research and breeding programs.
• Models need to better capture plant-environment interactions, including 3D nutrient and water transport.
• Continued development of crop models is necessary to effectively address future food security challenges.