1. NSF SCC-IRG Track 1: Co-Producing Community – An integrated approach to building smart and connected nutrient management communities in the US Corn Belt
Enabling farmers to manage N and P with greater precision is needed to increase farmer profitability and decrease off-farm losses of nutrients, which can compromise water resources. The objective of this project is to develop science-driven recommendations on N and P management that can be tailored to different farmers’ needs, focusing on the heart of the US Corn Belt: Illinois. Through this project, we will: (i) identify major constraints on how Illinois farmers manage N and P; (ii) determine how much N and P are stocked in soils across a diversity of Illinois farms using soil sampling, soil sensors and satellite observations, and how this soil nutrient capital contributes to crop growth in order to model field-specific fertilizer needs; (iii) develop smart and connected technology solutions that enable constrained farmers to join a Nutrient Management Community.
2. NSF SitS: Spatial and Temporal Patterns of Soil N and P Cycles Quantified by a Sensor-Model Fusion Framework: Implications for Sustainable Nutrient Management
High crop productivity in the Midwestern US was achieved by artificially draining wetlands and applying millions of tons of nitrogen (N) and phosphorous (P) fertilizers. However, 40-80% of these N and P nutrient inputs are lost from soils and become pollutants in water bodies and the atmosphere. This project will integrate recent advances in nanotechnology, sensing technology, and machine learning to enable new methods for measuring and managing N and P in croplands to reduce losses to the environment. The outcomes of this project can be used directly by farmers to better manage field application of N and P fertilizers and by local/federal governments and other organizations to pinpoint pollution hotspots and develop strategies for nutrient reduction.
3. DOE ARPA-E SMARTFARM: The “System of Systems” Solutions for Commercial Field-Level Quantification of Soil Organic Carbon and Nitrous Oxide Emission for Scalable Applications (SYMFONI)
Accurate and rapid field-level quantification of carbon intensity at a regional scale is critical to facilitate adoption of new technologies to increase the bioeconomy’s feedstock productivity and reduce its carbon footprint. This project will develop a commercial solution, SYMFONI, to estimate soil organic carbon and the dynamics of nitrous oxide emissions at an individual field level. The solution can be scaled up to perform per-field estimates for an entire region. SYMFONI is a “system of systems” solution that integrates airborne-satellite remote sensing, process-based modeling, deep learning, atmospheric inversion, field-level sensing, and high-performance computing.
4. USAID SIIL Geospatial, Farming Systems, and Digital Tools Consortium: Building a New Era of Predictive Agricultural Innovation to Improve the Livelihood of Smallholder Farmers
This consortium will build upon the five domains — productivity, economics, environment, social and human condition — of the sustainable intensification framework, developing an interdisciplinary and solution-oriented geospatial framework, integrating remote sensing, farming systems modeling, and geospatial data layers to provide innovative data products to take actions toward more resilient farming systems, benefiting families and communities. Providing access to simple digital tools to researchers, extension personnel, policymakers and practitioners will allow them to make informed decisions to minimize risk and improve the resilience of people and farming systems.
5. USDA NIFA: High-resolution integrated assessments of tillage practice impacts on crop production and agroecosystem sustainability in the US Midwest – combining meta-analysis, airborne-satellite sensing, and process-based modeling
Tillage affects production costs, crop yields, and environmental sustainability. This project integrates meta-analysis, airborne-satellite sensing, and process-based modeling to assess tillage’s effects on crop productivity and environmental health in the Midwest, focusing on Illinois, Indiana, Iowa, and Minnesota. We’ll study tillage’s influence on corn and soybean yields and environmental metrics like greenhouse gas emissions and soil erosion. Objectives include: (1) Using historical data for meta-analyses on tillage impacts; (2) Mapping field-scale tillage trends from 2000-2023 using airborne imaging and satellite data; (3) Validating the Ecosys model on tillage impacts using experimental data and satellite measurements; and (4) Assessing tillage’s effects using the Ecosys model to guide policy and farming decisions.
6. NSF III: Medium: Advancing Deep Learning for Inverse Modeling
In earth and environmental sciences, models help predict and understand complex systems like streamflow prediction. These models rely on parameters like slope and soil type, which are often inaccurately known. Inverse modeling estimates these parameters by working backward from observed data. Current inverse modeling techniques are computationally intensive and struggle with large-scale data. This project aims to create advanced machine learning algorithms for inverse modeling that can efficiently handle vast datasets, offering enhanced predictions and reduced computational needs. These innovations can benefit sectors like health, agriculture, and engineering, addressing significant societal issues.
7. NSF CAREER: AI-enabled Integrated Nutrient, Streamflow, and Parcel sImulation for Resilient agroEcosystems (INSPIRE): a framework for climate-smart crop production and cleaner water
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.