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Towards a multiscale crop modelling framework for climate change adaptation assessment.

Bin Peng1,2, Kaiyu Guan3,4,5,6, Jinyun Tang7

  • 1Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA. binpeng@illinois.edu.

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Summary

Developing advanced crop models is crucial for predicting genotype and management impacts on agriculture under climate change. These models will guide sustainable farming practices from gene to global scales.

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Area of Science:

  • Agricultural Science
  • Ecosystem Modelling
  • Climate Change Adaptation

Background:

  • Predicting genotype (G) and agronomic management (M) effects on agricultural ecosystems under future environmental (E) conditions is challenging.
  • Crop modelling offers a pathway to assess G × M technologies for climate change adaptation in crop production.
  • Current modelling capabilities require enhancement for gene-to-global scale predictions.

Purpose of the Study:

  • To highlight the need for improved multiscale crop modelling frameworks.
  • To guide the design of G × M adaptation strategies for crop production.
  • To ensure crop productivity and ecosystem sustainability under changing climates.

Main Methods:

  • Advancing multiscale crop modelling by incorporating genetic traits.
  • Interfacing crop models with large-scale environmental models.
  • Improving physiological response representations and data integration for enhanced predictability.

Main Results:

  • Identified opportunities to advance multiscale crop modelling frameworks.
  • Highlighted the challenge of balancing process detail with system predictability.
  • Proposed an advanced framework for gene-to-farm design of resilient crop systems.

Conclusions:

  • Enhanced multiscale crop modelling is essential for climate change adaptation in agriculture.
  • Improved models will facilitate the design of sustainable and resilient crop production systems.
  • Addressing data gaps and harnessing multisource data are key to model improvement.