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. By harnessing a discrete global grid system data cube and cutting-edge machine learning techniques, the project offers spatially explicit solutions.
