Summary
Future food production relies on efficient water use. This research project will focus on optimizing the utilisation of deep stored water in novel emerging agronomic systems in both semi-arid and temperate systems that incorporate three emerging agronomic interventions that lead to increased systems resilience; (i) altered crop phenology (earlier sowing of slower maturing crops); (ii) dual-purpose crops (used for grazing and grain); and (iii) summer cover crops. The project will utilise novel approaches for continuous deep water sensing to unprecedented depths combined with aerial canopy temperature imaging and autonomous root phenotyping using Convolutional Neural Network (CNN). Novel automated data capture on dynamics of water availability and deep root growth under various farming practices and contrasting soils and environments will also provide a validated dataset for model simulation. As a result, the project will contribute to formulation of a better decision-support system for farmers and breeders that can assist overcoming one of mankind’s greatest challenges. The overall project also aligns with the Sustainable Development Goals (SDGs), especially, Goal 2 (sustainable agriculture), Goal 6 (sustainable water management) and Goal 13 (urgent action for climate change) which EU has adopted and committed to implement. As such it is an excellent research and training opportunity for a Marie Skłodowska-Global Fellow.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/884364 |
| Start date: | 01-01-2021 |
| End date: | 01-09-2024 |
| Total budget - Public funding: | 293 129,28 Euro - 293 129,00 Euro |
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Original description
Future food production relies on efficient water use. This research project will focus on optimizing the utilisation of deep stored water in novel emerging agronomic systems in both semi-arid and temperate systems that incorporate three emerging agronomic interventions that lead to increased systems resilience; (i) altered crop phenology (earlier sowing of slower maturing crops); (ii) dual-purpose crops (used for grazing and grain); and (iii) summer cover crops. The project will utilise novel approaches for continuous deep water sensing to unprecedented depths combined with aerial canopy temperature imaging and autonomous root phenotyping using Convolutional Neural Network (CNN). Novel automated data capture on dynamics of water availability and deep root growth under various farming practices and contrasting soils and environments will also provide a validated dataset for model simulation. As a result, the project will contribute to formulation of a better decision-support system for farmers and breeders that can assist overcoming one of mankind’s greatest challenges. The overall project also aligns with the Sustainable Development Goals (SDGs), especially, Goal 2 (sustainable agriculture), Goal 6 (sustainable water management) and Goal 13 (urgent action for climate change) which EU has adopted and committed to implement. As such it is an excellent research and training opportunity for a Marie Skłodowska-Global Fellow.Status
SIGNEDCall topic
MSCA-IF-2019Update Date
28-04-2024
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