Integration of machine learning into process-based modelling to improve simulation of complex crop responses

Droutsas, I, Challinor, AJ orcid.org/0000-0002-8551-6617, Deva, CR orcid.org/0000-0001-5434-5416 et al. (1 more author) (2022) Integration of machine learning into process-based modelling to improve simulation of complex crop responses. in silico Plants, 4 (2). diac017. ISSN 2517-5025

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Item Type: Article
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© The Author(s) 2022. Published by Oxford University Press on behalf of the Annals of Botany Company. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

Keywords: Crop model; GLAM-Parti; heat stress; machine learning; model development; SEMAC
Dates:
  • Published: 20 September 2022
  • Published (online): 11 August 2022
  • Accepted: 11 August 2022
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) > Inst for Climate & Atmos Science (ICAS) (Leeds)
Funding Information:
Funder
Grant number
EU - European Union
774652
Depositing User: Symplectic Publications
Date Deposited: 24 Aug 2022 14:47
Last Modified: 24 Jan 2023 16:29
Status: Published
Publisher: Oxford University Press
Identification Number: 10.1093/insilicoplants/diac017
Open Archives Initiative ID (OAI ID):

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