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
Abstract
Machine learning (ML) is the most advanced field of predictive modelling and incorporating it into process-based crop modelling is a highly promising avenue for accurate predictions of plant growth, development and yield. Here, we embed ML algorithms into a process-based crop model. ML is used within GLAM-Parti for daily predictions of radiation use efficiency, the rate of change of harvest index and the days to anthesis and maturity. The GLAM-Parti-ML framework exhibited high skill for wheat growth and development in a wide range of temperature, solar radiation and atmospheric humidity conditions, including various levels of heat stress. The model exhibited less than 20% error in simulating the above-ground biomass, grain yield and the days to anthesis and maturity of three wheat cultivars in six countries (USA, Mexico, Egypt, India, the Sudan and Bangladesh). Moreover, GLAM-Parti reproduced around three quarters of the observed variance in wheat biomass and yield. Existing process-based crop models rely on empirical stress factors to limit growth potential in simulations of crop response to unfavourable environmental conditions. The incorporation of ML into GLAM-Parti eliminated all stress factors under high temperature environments and reduced the physiological model parameters down to four. We conclude that the combination of process-based crop modelling with the predictive capacity of ML makes GLAM-Parti a highly promising framework for the next generation of crop models.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 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: |
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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): | oai:eprints.whiterose.ac.uk:190323 |