Predicting the consequences of manipulating genotype (G) and agronomic management (M) on agricultural ecosystem performances under future environmental (E) conditions remains a challenge. Crop modelling 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 modelling capabilities from gene to global 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 modelling 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 the scales feasible to measure the impact. An advanced multiscale crop modelling framework will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate at regional-to-global scales.
Bibliographical noteFunding Information:
B.P., K.G., H.K. and W.Z. are supported by the United States National Science Foundation (NSF) Career Award (grant no. 1847334), National Aeronautics and Space Administration (NASA) Carbon Monitoring System managed by NASA Terrestrial Ecology Program (grant no. 80NSSC18K0170), United States Department of Agriculture (USDA) National Institute of Food and Agriculture (NIFA) Program (grant no. 2017-67013-26253) to K.G. B.P., K.G., S.A. and D.I.G acknowledge support by USDA NIFA (grant no. 2017-68002-26789). B.P., K.G., W.Z., A.M.C. and J.C.S. acknowledge support by the Foundation for Food and Agriculture Research (FFAR) (grant no. 602757). B.P., K.G., D.M.L. and D.L.L acknowledge support by the National Center for Atmospheric Research, which is a major facility sponsored by the NSF under cooperative agreement no. 1852977. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the FFAR. Any opinions, findings and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the USDA. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. Due to space constrictions, we could not cite all relevant literature. We apologize to the authors whose important work was not cited in this Perspective.
PubMed: MeSH publication types
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