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Crop sciencie: a foundation for advancing predictive agriculture

Author: Messina, C.D.
Author: Cooper, M.
Author: Reynolds, M.P.
Author: Hammer, G. L.
Year: 2020
ISSN: 1435-0653 (Print)
URI: https://hdl.handle.net/10883/20844
Abstract: This special issue in Crop Science provides a diverse cross section of views from prior and current efforts to enable prediction in agriculture. The contributions discuss and demonstrate how current advances in phenomics, genomics, and artificial intelligence are being combined to explore new modeling paradigms and prediction frameworks to advance crop science and improve decision making in agriculture. The synthesis of these views can motivate a transdisciplinary dialogue to define predictive agriculture as a discipline and guide future research efforts for the integration of data‐driven and science‐based methodologies. Collectively, these methods can provide the needed foundation for design in agricultural and food systems (National Academies of Sciences, Engineering, and Medicine, 2019).
Format: PDF
Language: English
Publisher: Crop Science Society of America (CSSA)
Copyright: CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose.
Type: Article
Place of Publication: Madison (USA)
Pages: 544-546
Issue: 2
Volume: 60
DOI: 10.1002/csc2.20116
Agrovoc: PLANT SCIENCES
Agrovoc: CROP FORECASTING
Agrovoc: MODELS
Journal: Crop Science


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  • Wheat
    Wheat - breeding, phytopathology, physiology, quality, biotech

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