Regenerative agriculture and soil carbon storage in the Upper Corn Belt

Abstract

Land use conversion, agricultural mismanagement, and topsoil erosion have depleted global soil organic carbon (SOC) stocks in the top two meters of soil by an estimated 133 petagrams (Pg), resulting in a significant SOC debt. Regenerative cropping practices, such as no-till and cover cropping, are recognized for their potential to enhance soil organic carbon (SOC) stocks and bolster soil health, all while allowing producers to maintain commodity crop production systems. Evaluations of these practices are typically conducted through agricultural experiments with randomized and replicated statistical designs. While these experiments are essential for understanding the mechanisms behind changes in soil properties as a function of management, they often fail to capture the complexities of diverse agricultural settings and management choices. Through an interdisciplinary, system-level study of commercial farms in the Upper Corn Belt region, I evaluated how regenerative management affects SOC storage, erosion processes, and microbial community structure. Factors such as topography, time since adoption of regenerative practices, climate, and soil texture significantly influenced SOC stocks and microbial community structure. Slope and historical erosion emerged as a key control on SOC stocks, which is largely overlooked in current process-based models. I present a method for coupling estimated soil erosion with the DayCent model to improve simulations of SOC stocks on farmland with slight slopes. I also discuss the unique challenges of simulating commercial farm scenarios using data collected from real-world farmers. The dissertation concludes with a collaborative, social science chapter on the impact of social networks on the adoption of regenerative practices in Iowa agricultural communities. In summary, this dissertation contributes to our knowledge of regenerative agriculture and its impacts on SOC storage, soil microbial diversity, and social connections in agricultural communities, including unique methods to measure, evaluate, and model these impacts.