As the global population grows, agricultural activities intensify, leading to increased fertiliser use and diffuse nutrient emissions. This escalating trend poses a significant threat to water bodies, as nutrient run-off from intensive farming practices degrades water quality. Traditional land and water management approaches often lack the precision needed to identify high-priority areas or offer spatially explicit solutions.
In this context, the ERC-funded WaterSmartLand project will pinpoint high-risk areas and propose targeted solutions. Using advanced analysis, modelling and machine learning, the project identifies optimal land management strategies, such as using wetlands and riparian buffer strips, to mitigate nutrient run-off.
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The project will result in global map layers that will allow the identification of critical nutrient run-off sites and carry out the related planning. All the analysis will take place on a global scale, and the results will be tested on pilot sites in Europe and elsewhere. The project will be based on open-source software, so that the resulting data cube solution and machine learning models will be accessible and available for further development by all.