SOIL HEALTH, MICROBIAL COMMUNITIES, AND NITROGEN DYNAMICS IN AGROECOSYSTEMS: INSIGHTS FROM LONG-TERM CROPPING SYSTEMS

This dissertation examines the legacy effects of long-term soil health practices on nitrogen cycling, soil aeration, microbial ecology, and predictive modeling of nitrous oxide (N₂O) emissions in continuous cotton systems, integrating five complementary studies, including a laboratory incubation and four field-based investigations, conducted within a 41-year experiment with contrasting tillage, cover cropping, and nitrogen (N) fertilization. Results showed that tillage and N inputs shaped nitrogen acquisition strategies in hairy vetch (HV) cover crops, with no-till (NT) enhancing soil organic matter and soil-derived N uptake, while conventional tillage increased reliance on biological nitrogen fixation (BNF), particularly under low fertilization. Long-term NT and HV cover cropping improved soil oxygen (O₂) availability and resilience after heavy rainfall, reducing the duration of O₂ stress relative to conventional tillage and no cover crops. Laboratory experiments revealed that cover crop residue decomposition, especially from high-quality vetch, accelerated O₂ depletion and promoted high N₂O emissions even under suboptimal water-filled pore space (WFPS) for denitrification, with aerobic-condition emissions posing greater environmental risks than water-induced anoxia. Microbial analyses identified depth-specific and seasonally dynamic associations between microbial taxa and N₂O fluxes, with N fertilization exerting the strongest influence through long-term acidification, favoring taxa linked to higher emissions. Finally, a novel “Class-Swap” machine learning approach, which classifies emissions into hot-moments and background before modeling, significantly improved prediction accuracy over traditional random forest models by capturing the distinct drivers of each emission type. Together, these findings reveal how long-term management shapes plant-soil-microbe interactions, controls biogeochemical processes underlying N₂O production, and advances predictive tools, providing actionable insights for optimizing nutrient management, enhancing soil function, and improving greenhouse gas forecasting in agroecosystems.