Towards a multiscale crop modelling framework for climate change adaptation assessment

Authors: PENG, BIN - University Of Illinois; GUAN, KAIYU - University Of Illinois; TANG, JINYUN - Lawrence Berkeley National Laboratory; Ainsworth, Elizabeth - Lisa; ASSENG, SENTHOLD - University Of Florida; Bernacchi, Carl; COOPER, MARK - University Of Queensland; DELUCIA, EVAN - University Of Illinois; ELLIOTT, JOSHUA - University Of Chicago; EWERT, FRANK - University Of Bonn

Submitted to: Nature Plants
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/24/2020
Publication Date: 4/15/2020
Citation: Peng, B., Guan, K., Tang, J., Ainsworth, E.A., Asseng, S., Bernacchi, C.J., Cooper, M., DeLucia, E.H., Elliott, J., Ewert, F., et al. 2020. Towards a multiscale crop modelling framework for climate change adaptation assessment. Nature Plants. 6:338-348. https://doi.org/10.1038/s41477-020-0625-3.
DOI: https://doi.org/10.1038/s41477-020-0625-3

Interpretive Summary: Understanding how different crop genotypes perform under different management conditions and environments is one of the biggest limitations to crop improvement. Computational modeling can help address this limitation, but requires additional research to develop and improve modeling capabilities at scales that range from the gene to the globe. There are a number of emerging opportunities to improve modeling efforts including better representation of crop genetic traits, interfacing crop models with large-scale models, improving the representation of physiological responses to climate change and management practices, greater data inclusion, and harnessing multisource data to improve model predictability and enable identification of emergent relationships. Improved multiscale crop modeling will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate.

Technical Abstract: Predicting the consequence of manipulating genotype (G) and agronomic management (M) on agricultural ecosystem performances under future environmental conditions (E) remains a challenge. Crop modeling has the potential to enable society to assess the efficacy of G×M technologies to mitigate and adapt crop production systems to climate change. Despite recent achievements, dedicated research to develop and improve modeling capabilities from gene to globe scales is needed to provide guidance on designing G×M adaptation strategies with full consideration of their impacts on both crop productivity and ecosystem sustainability under varying climatic conditions. Opportunities to advance the multiscale crop modeling framework include representing crop genetic traits, interfacing crop models with large-scale models, improving the representation of physiological responses to climate change and management practices, closing data gaps, and harnessing multisource data to improve model predictability and enable identification of emergent relationships. A fundamental challenge in multiscale prediction is the balance between process details required to assess the intervention and predictability of the system at scales feasible to measure the impact. An advanced multiscale crop modeling framework will enable a gene-to-farm design of resilient and sustainable crop production systems under changing climate at regional-to-global scales.