Closing the phenotyping gap with non-invasive belowground field phenotyping
<p>Breeding climate-robust crops is one of the needed pathways for adaptation to the changing climate. To speed up the breeding process, it is important to understand how plants react to extreme weather events such as drought or waterlogging in their production environment, i.e. under field co...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-01-01
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| Series: | SOIL |
| Online Access: | https://soil.copernicus.org/articles/11/67/2025/soil-11-67-2025.pdf |
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| Summary: | <p>Breeding climate-robust crops is one of the needed pathways for adaptation to the changing climate. To speed up the breeding process, it is important to understand how plants react to extreme weather events such as drought or waterlogging in their production environment, i.e. under field conditions in real soils. Whereas a number of techniques exist for aboveground field phenotyping, simultaneous non-invasive belowground phenotyping remains difficult. In this paper, we present the first data set of the new HYDRAS (HYdrology, Drones and RAinout Shelters) open-access field-phenotyping infrastructure, bringing electrical resistivity tomography, alongside drone imagery and environmental monitoring, to a technological readiness level closer to what breeders and researchers need. This paper investigates whether electrical resistivity tomography (ERT) provides sufficient precision and accuracy to distinguish between belowground plant traits of different genotypes of the same crop species. The proof-of-concept experiment was conducted in 2023, with three distinct soybean genotypes known for their contrasting reactions to drought stress. We illustrate how this new infrastructure addresses the issues of depth resolution, automated data processing, and phenotyping indicator extraction. The work shows that electrical resistivity tomography is ready to complement drone-based field-phenotyping techniques to accomplish whole-plant high-throughput field phenotyping.</p> |
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| ISSN: | 2199-3971 2199-398X |