Thursday, 31 May 2012: 3:00 PM
Evaluation of the Interrelationship between Pesticide and Turbulent Energy Fluxes and the Implications for Remotely-Sensed Estimates of Pesticide Volatilization
Alcott Room (Omni Parker House)
As the primary pathway for loss from agricultural systems, the volatilization of pesticides, such as Metolachlor and Atrazine, can adversely impact the quality of air and water, crop productivity, and public health. Nonetheless, effective methods for modeling volatilization and estimating the pesticide flux on both field and larger spatial scales remains elusive. Extending previous work that demonstrated clear relationships between the magnitude of the pesticide flux and environmental factors, such as soil moisture, this work sought to identify the relationship between pesticide flux and turbulent energy fluxes. The study used data collected over both dry and wet soil conditions at an experimental site near Beltsville, MD during a ten-day period at the beginning of the 2010 growing season. Constraining the analysis to unstable, daytime conditions, the results of this study showed that the volatilization of Metolachlor and Atrazine is strongly influenced by the available water and heat (energy). The results also showed that the magnitude of the pesticide fluxes and turbulent energy fluxes were well correlated. The pesticide and energy fluxes were found to have a sigmoidal relationship with the pesticide flux tending to plateau as the energy flux approached its maximum value. Even so, the turbulent energy fluxes can be used to predict the pesticide flux with reasonable accuracy; RMSE ranged from 0.03 to 0.70 ng m2 s-1. By combining the measurements of both the sensible and latent heat fluxes the predicted pesticide flux can be further enhanced; RMSE of the predicted fluxes of Metolachlor and Atrazine were consistently near 0.05 ng m2 s-1. Although these results represent only a single growing season, they strongly suggest that field and larger scale estimates of pesticide fluxes could be obtained by combining the relationships found here with a remote sensing-based energy balance model such as the two-source energy balance model. Research to further refine the results presented here by considering data collected during additional years is ongoing.