Consequences of Changing Rainfall Patterns on Nitrous Oxide Fluxes in Continuous Corn Vs. Switchgrass Cropping Systems.

Climate change impacts agriculture through increasing temperatures and changing rainfall patterns. In the US Midwest, under current greenhouse gas emissions, both the increasing length of dry intervals between precipitation events and the amount of precipitation falling in single events is documented and predicted to increase in global circulation models.

Nitrous oxide (N₂O) is the dominant natural ozone-consuming substance in the stratosphere and a strong greenhouse gas with 265 times the radiative forcing of CO₂. N₂O fluxes are closely linked with soil moisture. Consequently, changing rainfall patterns will likely influence N₂O fluxes. Since the majority of anthropogenic N₂O production is from agricultural soils, in which they are controlled by numerous factors including oxygen, nitrate, and carbon availability (all of which are strongly tied to soil moisture status), it is important to understand the effects of changing rainfall patterns. Our objective is to test hypotheses that changing rainfall patterns strongly alter N₂O fluxes in agricultural soils as modulated by cropping system and landscape position.

Rainfall manipulation shelters were used to create soils exposed to same amount of rainfall delivered at different intervals (3-day, 2-week, and 4-week). The initial phase of this experiment was conducted for 10-weeks in a no-till continuous-corn system in place from 2008 at the Kellogg Biological Station Long-Term Ecological Research site.

Results from the first field season show cumulative N₂O fluxes were 4 times higher when rainfall occurred in 4-week rather than sub weekly or 2-week intervals. Results will also be reported from the 2016 field season and related to changes in denitrifier enzyme activity. Understanding patterns and mechanisms for N₂O fluxes from the soil is important for achieving sustainable agriculture, developing mitigation practices, and parameterizing biogeochemical models.