The Mississippi River has the third-largest drainage basin and represents one of the most productive agricultural regions in the world, yielding >80% of US total corn and soybean production and 92% of the nation’s agricultural exports. Large-scale industrial agriculture has led to significant socio-economic gains, but at environmental costs (soil erosion, nutrient pollution, and aquatic acidification) in this region. Climate-smart agriculture (CSA) management practices have been proposed as solutions to these costs, as they not only increase crop yield, but also reduce greenhouse gas emissions, and sustain soil and water quality. However, the effectiveness of CSA practices varies under diverse climate and land use conditions and involves tightly coupled carbon, water, and nutrient cycles. These interactions have not been well studied, and this knowledge gap has hindered understanding and efficient application of CSA practices to achieve the benefits of enhancing food production, climate mitigation, and environmental sustainability. The overall goal of this project is to develop an integrated ecosystem monitoring, modeling, and machine learning framework (EcoM3) that incorporates field observations, satellite remote sensing data, process-based modeling, and a deep-learning approach to systematically investigate specific effects of CSA practice (no-tillage and cover crops) on key agroecosystem indicators (crop yield, soil carbon storage, greenhouse gases, and carbon/nitrogen leaching) at multiple scales. This project will use a long-term field site in Kentucky (continuous observations over 50 years) as one testing site to investigate CSA practice effects from daily to seasonal, annual, decadal scales; examine varied CSA effects at multiple sites with diverse climate and soil conditions across the Mississippi River basin; and predict the potential impacts of CSA practices at the entire river basin scale. Multi-scale data and model results will be integrated into the learning platform of the EcoM3 framework to communicate temporal and spatial CSA effectiveness with diverse stakeholders and policy-makers.
This study addresses a challenging question: Will an enhanced systems approach advance our understanding of the interconnected relationships among agroecosystems, climate, and environment systems sufficiently to allow us to simultaneously manage multiple goals (food security, carbon sequestration, and environmental sustainability)? This study represents a systematic method to investigate the comprehensive effects of CSA practices in agricultural systems at both site and regional scales under heterogeneous climate and soil conditions. The proposed EcoM3 framework incorporates CSA management that is targeted to advance conceptual and operational understanding of interactions and feedback loops among climate, land use/management, and ecosystems. Products derived from this study will improve the mechanistic representation of the agroecosystem in Environmental System Models toward a more accurate prediction of biogeochemical cycles and future climate change and will provide viable recommendations for farmers and a scientific basis for making evidence-informed policy about building sustainable and climate-resilient agriculture. Research findings will be communicated with farmers through local extension meetings and the Multi-state Farmer Summit (representatives across regions in Mississippi River basin). Project products will enhance awareness about the importance of CSA management in building climate-resilient agroecosystems and preserving soil and water health. Multi-scale datasets will be made publicly available for research and education.
This project is jointly funded by the CBET Environmental Sustainability program and the Established Program to Stimulate Competitive Research (EPSCoR).
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.
