CAREER: AI-enabled Integrated Nutrient, Streamflow, and Parcel sImulation for Resilient agroEcosystems (INSPIRE): a framework for climate-smart crop production and cleaner water

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Climate-smart agricultural practices hold the promise of reducing carbon (C) emissions from farming, yet their implementation often presents complex trade-offs, particularly affecting nitrogen (N) and phosphorus (P) management. Integrated management of C, N, and P to ensure climate-smart crop production while preserving clean waters is hindered by several knowledge and technology gaps. To approach a solution for this grand challenge, this project aims to significantly advance the holistic understanding and modeling of the interconnected C, N, P, and water cycles in the Upper Mississippi River Basin. This goal will be pursued by developing an AI-based framework of integrated nutrient, streamflow, and parcel simulation for resilient agroecosystems (INSPIRE) that can easily ingest multi-source observations and provide an accurate and speedy quantification from the field to basin scale. The outcomes from this project are expected to provide valuable insights for policymakers and farming communities, particularly in optimizing management practices for improved carbon sequestration, soil health, and water quality in the America's heartland. Additionally, this project intertwines its research objectives with an educational agenda, which is featured by developing a computational tool to foster broad participations in large-scale computing among undergraduates. The project will also introduce a cyber-physical watershed mesocosm as an innovative trial of using the digital twin technology to enhance STEM education related to agricultural and environmental sustainability.This project will develop under the overarching hypothesis that AI-assisted integrated simulation of C, N, P, and water fluxes, compared with existing process-based modeling approach, is better able to capture high resolution environmental variability and identify best practices for achieving climate-smart agriculture and water quality goals without sacrificing crop production. The scientific innovations will be achieved through four objectives. First, a Knowledge-Guided Machine Learning (KGML)-based INSPIRE-Field model will be developed to significantly improve the prediction accuracy of field-level C, N, P, and hydrological interactions. Second, INSPIRE-Field will be coupled with Graph Neural Network (GNN)-based hydrologic surrogate models that first aggregate field water and nutrient fluxes within small watersheds (i.e., INSPIRE-Watershed), and then routing watershed outputs throughout the Upper Mississippi River Basin (i.e., INSPIRE-Basin). To reduce the uncertainty of INSPIRE, a novel representation learning method to efficiently assimilate remote and in-situ sensing data via low-dimensional embeddings will be explored. Third, a user-friendly web interface will be developed that allows stakeholders to preview outcomes of different climate- smart management practices and identify field-specific preferred management strategies based on multiobjective optimizations for C, N, P, and hydrological goals. Finally, the education and practice of computing, sensing, and machine learning among the future workforce of agroecosystem engineers, educators, and decision-makers will be enhanced through project activities. The investigator aims to lead the frontier of data analytics for sustainable agriculture by integrating remote sensing, mechanistic modeling, and artificial intelligence, with the aspiration to enable monitoring and managing every cropland, track pollutants, forecast agricultural risks, provide farmers best solutions to minimize negative environmental impacts, and ultimately help the world to achieve a sustainable food future.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.