Daycent Application to Model Greenhouse Gas Fluxes from Switchgrass Land Managed with Nitrogen Fertilizer Levels Under Different Landscape Positions.

Switchgrass (Panicum virgatum L.) is a warm-season perennial grass and has been grown as bioenergy crop in the North Central America because of its benefits on soil quality and biomass yield. The present study was conducted at Bristol, South Dakota to assess the impacts of nitrogen fertilization and landscape position on soil surface greenhouse gas (GHG) fluxes from switchgrass land. A total of six DAYCENT models were built to represent two landscape positions (shoulder and footslope) and three nitrogen rates (0, 56, 112 kg N ha^-1). These models were calibrated using measured soil properties (inorganic and organic carbon, pH, nitrogen, bulk density and moisture content, and CO₂, CH₄, and N₂O fluxes). Soil surface GHG fluxes were collected bi-weekly using static chambers from 2010 through 2014. Inverse modeling approach of Parameter Estimation (PEST) was used to calibrate the DAYCENT model, and to improve the predictions. Modeling results show that DAYCENT-PEST predicted CO₂ fluxes were reasonably well with R² >0.55. Similar results were observed for the N₂O fluxes, however, soil CH₄ fluxes were not well predicted using the DAYCENT model. Long-term simulations indicated that nitrogen did not impact CO₂ and CH₄. The landscape position, however, impacted long-term GHG emissions with higher CO₂ and N₂O fluxes at toe compared to those at shoulder position. The long-term carbon turnover at the toe also led to low bulk density at the toe position. This study demonstrated that DAYCENT model is important to assess the long-term management impacts on GHG emissions, which otherwise unable to assess using short-term measurements.