Theses and Dissertations
ORCID
https://orcid.org/0009-0003-9616-3102
Advisor
Jha, Prakash Kumar
Committee Member
Dash, Padmanava
Committee Member
Bheemanahalli, Raju
Committee Member
Ambinakudige, Shrinidhi
Date of Degree
12-12-2025
Original embargo terms
Embargo 2 years
Document Type
Graduate Thesis - Open Access
Major
Plant and Soil Sciences (Weed Science)
Degree Name
Master of Science (M.S.)
College
College of Agriculture and Life Sciences
Department
Department of Plant and Soil Sciences
Abstract
This study integrates crop modeling, remote sensing, and machine-learning approaches to quantify nitrogen (N) leaching from southern Mississippi croplands and its ecological impact on the Mississippi Sound. Land use and land cover (LULC) changes from 2008 to 2024 were mapped using USDA CDL data to identify agricultural N use hotspots. The DSSAT crop model simulated N leaching, mineralization, and gaseous losses for major crops under varying fertilizer and irrigation regimes and climate scenarios. Results revealed high N losses in sandy loam soils and strong sensitivity to rainfall intensity and management practices. Leached nitrate fluxes were correlated with remotely sensed chlorophyll-a (Chl-a) and net primary productivity (NPP) to assess coastal responses, assuming 1-year lagged relationships between agricultural N export and estuarine productivity. Machine learning models using DSSAT-derived N variables, rainfall, groundwater, and erodibility achieved robust prediction of Chl-a (R² ≤ 0.48) and NPP (R² ≤ 0.31). The integrated framework provides a replicable tool for managing nutrient loading and sustaining coastal ecosystem health.
Recommended Citation
Bayalusime, Prasanna Ramesh, "Simulating nitrogen leaching from southern Mississippi croplands and its impact on Mississippi sound using crop model and remote sensing" (2025). Theses and Dissertations. 6819.
https://scholarsjunction.msstate.edu/td/6819
