Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 564608
Title Towards a multiscale crop modelling framework for climate change adaptation assessment
Author(s) Peng, Bin; Guan, Kaiyu; Tang, Jinyun; Ainsworth, Elizabeth A.; Asseng, Senthold; Bernacchi, Carl J.; Cooper, Mark; Delucia, Evan H.; Elliott, Joshua W.; Ewert, Frank; Grant, Robert F.; Gustafson, David I.; Hammer, Graeme L.; Jin, Zhenong; Jones, James W.; Kimm, Hyungsuk; Lawrence, David M.; Li, Yan; Lombardozzi, Danica L.; Marshall-Colon, Amy; Messina, Carlos D.; Ort, Donald R.; Schnable, James C.; Vallejos, C.E.; Wu, Alex; Yin, Xinyou; Zhou, Wang
Source Nature Plants 6 (2020)4. - ISSN 2055-026X - p. 338 - 348.
Department(s) Crop Physiology
Publication type Refereed Article in a scientific journal
Publication year 2020

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.

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