Author ORCID Identifier:
Date of Graduation
5-2026
Document Type
Thesis
Degree Name
Master of Science in Crop, Soil & Environmental Sciences (MS)
Degree Level
Graduate
Department
Crop, Soil & Environmental Sciences
Advisor/Mentor
Brye, Kristofor
Committee Member
Greub, Kelsey
Second Committee Member
Wood, Lisa
Third Committee Member
Daniels, Michael
Keywords
climate change; Arkansas cotton production, gas-flux-determination methods; GHG mitigation
Abstract
Increasing concerns of climate change have led to the evaluation of greenhouse gas (GHG) emissions [i.e., carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O)] from agroecosystems, particularly cotton (Gossypium hirsutum) production. Technology improvement and conservation agriculture methods [i.e., reduced-tillage (RT) instead of conventional tillage (CT) and the use of cover crops (CC)] require further evaluation regarding GHG emissions from Arkansas cotton production. The first objective of the study was to evaluate four gas-flux-determination methods [i.e., linear or exponential regression models, with negative fluxes (WNF) included in the dataset or replacing negative fluxes (RNF)] on CO2, CH4, and N2O fluxes and season-long emissions over the 2024 growing season in furrow-irrigated cotton in southeast Arkansas using a LI-COR field-portable chamber and gas analyzers. The second objective of the study was to evaluate the effects of CT with no CC and RT with CC on CO2, CH4, and N2O fluxes and season-long emissions during the 2025 furrow-irrigated cotton growing season in southeast Arkansas using a field-portable, GHG analyzer system. Exponential regression models for the 2024 growing season were influenced by abnormal CO2 and N2O gas concentration data points, indicating caution must be used with exponential models. Season-long 2024 CH4 emissions differed (P < 0.05) between the WNF (- 0.51 kg ha-1 season-1 and - 0.54 kg ha-1 season-1 for linear and exponential regressions respectively) and RNF (0.01 kg ha-1 season-1 for both linear and exponential regressions) GFD methods, revealing the RNF option may over-estimate CH4 emissions. During the 2025 growing season, CO2 fluxes differed between treatments over time, where the CO2 flux was 8.9 times greater from the conservation (166.6 mg m-2 hr-1) than conventional (18.7 mg m-2 hr-1) treatment on one measurement date, which correlated with the driest recorded soil water content. Methane fluxes differed over time (P < 0.01) for 2025, with a maximum recorded flux at 0.09 µg m-2 hr-1, which correlated with the largest recorded soil water content of 0.39 m3 m-3. Field treatments for 2025 did not affect (P > 0.05) CH4 and N2O fluxes nor season-long GHG emissions. Development of consistent GHG measurement methods and identifying agronomic field methods for potential GHG mitigation is critical to evaluate regional and/or global GHG emissions and accurate global warming potentials associated with production agriculture.
Citation
Seuferling, C. (2026). Impacts of Climate-smart Agriculture on Greenhouse Gas Emissions in Southeast Arkansas Cotton. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/6149
