| Title: |
Integration of machine learning into process-based modelling to improve simulation of complex crop responses |
| Authors: |
Droutsas, Ioannis; Challinor, Andrew J; Deva, Chetan R; Wang, Enli |
| Contributors: |
Hammer, Graeme; CONFER; AfriCultuReS; Bean Breeding for Adaptation to a Changing Climate and Post-Conflict Colombia |
| Source: |
in silico Plants ; volume 4, issue 2 ; ISSN 2517-5025 |
| Publisher Information: |
Oxford University Press (OUP) |
| Publication Year: |
2022 |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1093/insilicoplants/diac017 |
| DOI: |
10.1093/insilicoplants/diac017/45348945/diac017.pdf |
| Availability: |
https://doi.org/10.1093/insilicoplants/diac017; https://academic.oup.com/insilicoplants/advance-article-pdf/doi/10.1093/insilicoplants/diac017/45348945/diac017.pdf; https://academic.oup.com/insilicoplants/article-pdf/4/2/diac017/45946568/diac017.pdf |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.2D0C16A |
| Database: |
BASE |