GC119-0011
Towards a multiscale crop modelling framework for climate change adaptation assessment
Wednesday, 16 December 2020
Poster
Bin Peng1, Kaiyu Guan2, Jinyun Tang3, Elizabeth A. Ainsworth4, Senthold Asseng5, Carl Bernacchi6, Mark Cooper7, Evan H DeLucia8, Joshua Wright Elliott9, Frank Ewert10, Robert F Grant11, David I Gustafson12, Graeme L Hammer13, Zhenong Jin14, James W Jones15, Hyungsuk Kimm16, David M Lawrence17, Yan Li18, Danica L. Lombardozzi17, Amy Marshall-Colon19, Carlos D. Messina20, Donald R Ort21, James Schnable22, C. Eduardo Vallejos23, Alex Wu24, Xinyou Yin25 and Wang Zhou26, (1)University of Illinois at Urbana Champaign, National Center for Supercomputing Applications, Urbana, IL, United States, (2)University of Illinois at Urbana Champaign, College of Agricultural, Consumer and Environmental Sciences, Urbana, IL, United States, (3)Lawrence Berkeley Natl Lab, Berkeley, CA, United States, (4)University of Illinois at Urbana-Champaign, Department of Plant Biology and Carl. R. Woese Institute for Genomic Biology, Urbana, IL, United States, (5)University of Florida, Ft Walton Beach, FL, United States, (6)University of Illinois at Urbana-Champaign, Department of Plant Biology, Urbana, IL, United States, (7)The University of Queensland, Centre for Crop Science, Brisbane, QLD, Australia, (8)University of Illinois at Urbana-Champaign, Center for Bioenergy and Bioproducts Innovation; Institute for Sustainability, Energy, and Environment; Department of Plant Biology; Carl R. Woese Institute for Genomic Biology, Urbana, IL, United States, (9)University of Chicago, Department of Computer Science, Chicago, IL, United States, (10)University of Bonn, Institute of Crop Science and Resource Conservation (INRES), Bonn, Germany, (11)University of Alberta, Department of Renewable Resources, Edmonton, AB, Canada, (12)Independent scientist, St. Louis, MO, United States, (13)the University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Brisbane, Australia, (14)University of Minnesota-Twin Cities, Department of Bioproducts and Biosystems Engineering, Saint Paul, MN, United States, (15)Professor, Agricultural and Biological Engineering, Gainesville, FL, United States, (16)University of Illinois at Urbana Champaign, College of Agricultural, Consumer and Environmental Sciences, Urbana, United States, (17)National Center for Atmospheric Research, Boulder, CO, United States, (18)Beijing Normal University, Faculty of Geographical Sciences, Beijing, China, (19)University of Illinois at Urbana Champaign, Urbana, United States, (20)Corteva AgriScience, Johnston, IA, United States, (21)University of Illinois at Urbana Champaign, Urbana, IL, United States, (22)University of Nebraska Lincoln, Agronomy & Horticulture, Lincoln, NE, United States, (23)University of Florida, Horticultural Sciences Department, Gainesville, FL, United States, (24)The University of Queensland, Australian Research Council Centre of Excellence for Translational Photosynthesis, Brisbane, QLD, Australia, (25)Wageningen University & Research, Centre for Crop Systems Analysis, Wageningen, Netherlands, (26)University of Illinois at Urbana Champaign, Department of Natural Resources and Environmental Sciences, Urbana, IL, United States
Abstract:
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.
