Doctoral Dissertations

Orcid ID

https://orcid.org/0009-0004-9484-7719

Date of Award

12-2025

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Major

Environmental and Soil Sciences

Major Professor

Debasish Saha

Committee Members

Debasish Saha, Sindhu Jagadamma, Sean Schaeffer, Shawn Brown, Melissa Cregger

Abstract

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.

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