Quantification of soil N2O and CH4 fluxes using the flux gradient method on a drainage water managed farm on the eastern shore of Maryland

Drainage water management (DWM) is an effective best management practice (BMP) to reduce hydrological nitrate export from croplands to surface and ground water by controlling the timing and the amount of ditch discharge and retaining water within ditches and adjacent fields using drainage control structures (DCS). While the intended consequences of maintaining higher water table levels are to increase denitrification and thereby decrease nitrate leaching, an unintended consequence is possible increased production of nitrous oxide (N2O) from denitrification and methane (CH4) from methanogenesis, both potent greenhouse gasses (GHGs). Hence, application of DWM may lead to a "pollution swapping" concern - i.e., does DWM trade reduction of nitrate concentrations in ditch water for increases in soil emissions of N2O and CH4 to the atmosphere? Here we employed the micrometeorological flux gradient (FG) technique to a corn-soybean rotation on an operational farm with DCS in eastern Maryland on the Delmarva Peninsula to answer this question. Soil N2O and CH4 fluxes were quantified under DWM and non-DWM management of a soybean crop without N fertilization (2018) and a corn crop with synthetic N fertilization (2019). To our knowledge, this is the first study employing a micrometeorological method to evaluate the effects of agricultural DWM on GHG emissions. No consistent or statistically significant differences in N2O and CH4 fluxes were observed between DWM and non-DWM fields, suggesting that DWM did not trade reduced nitrate leaching for increased soil GHG emissions. The FG measurements were also compared with chamber measurements, which revealed similar patterns, but chamber measurements had large spatial variability and overall higher daily mean flux estimates, while the tower measurements revealed more frequent, short-lived N2O pulses following fertilization and precipitation events. This research adds to the existing understanding of benefits and risks of DWM as a BMP.