Date of Graduation

5-2025

Document Type

Thesis

Degree Name

Bachelor of Science in Biological Engineering

Degree Level

Undergraduate

Department

Biological and Agricultural Engineering

Advisor/Mentor

Runkle, Benjamin

Committee Member

Moreno-Garcia, Beatriz

Second Committee Member

Wang, Dongyi

Third Committee Member

Reba, Michele

Abstract

Rice production contributes approximately 8% of global anthropogenic methane emissions. Methane (CH4) is a powerful greenhouse gas with a global warming potential 28 times greater than carbon dioxide (CO2) which makes it important to monitor for understanding and mitigating its contribution to climate change. The Eddy Covariance (EC) method is used to monitor greenhouse gas emissions such as CO2, CH4, etc. As part of the EC method, the instruments on the EC towers are used to record the gas concentrations in the atmosphere; these concentrations are then run in the EddyPro software to be able to analyze the collected data. This study examines the methane emissions from a commercial rice field in Lonoke, Arkansas on July 06, 2022, and evaluates the effect of sensor separation values on corrected fluxes from EddyPro. A spectral analysis was done with the spectral correction factor in EddyPro using the method described by Moncrieff et al (1997) method. It was found that the methane fluxes were higher in the morning, dipped around 6:00 am, remained low during the day, and went up again later in the afternoon. The methane emission patterns were also influenced by atmospheric conditions. The sensor separation correction, applied during data processing, significantly affected CH₄ flux estimates, with greater horizontal separation leading to larger correction factors. In contrast, vertical sensor separation had minimal impact on final flux values compared to horizontal separation. Canopy height also had minimal impact on corrected methane fluxes. The findings emphasize the importance of careful sensor separation and tower setup measurements in EC studies. Small errors in measurement configuration can introduce notable biases, affecting data accuracy and interpretation.

Keywords

Eddy Covariance; Methane; Rice; EddyPro; Spectral Correction Factor; Sensor Separation

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