Modeling nitrogen fluxes in a tile-drained cropping system in the Midwest using an enhanced SWAT model

Artificial (tile) drainage systems are extensively implemented across the U.S. Midwest to enhance crop production in poorly drained soils; however, they also pose environmental challenges by significantly altering nitrogen fluxes within agricultural landscapes. In response, sustainable intensification strategies seek to increase agricultural productivity while reducing environmental impacts, often through improved management practices such as cover cropping and conservation tillage. Effectively evaluating the trade-offs and synergies of agricultural management practices demands advanced modeling tools capable of representing coupled biogeochemical and hydrological processes across diverse spatial and temporal scales. This study presents the first application of an enhanced version of the Soil and Water Assessment Tool (SWAT), integrated with Century/DayCent-based biogeochemical modules, to simulate both nitrate (NO 3 ⁻) loss and nitrous oxide (N 2 O) fluxes in a tile-drained corn-soybean system. The model was applied to long-term field data (2004–2010) from an Iowa site with two treatments: with and without winter rye cover crops. With careful calibration, the model reproduced tile discharge and crop yields well and captured the direction and magnitude of cover-crop reductions in NO 3 ⁻ losses. However, interannual variability in NO 3 ⁻ export and event-scale N 2 O peaks remained difficult to reproduce, likely due to limited sampling frequency and structural constraints in soil hydrology, solute transport, and vertical resolution. The model simulated a ∼41 % reduction in NO 3 ⁻ leaching with cover crops, close to the observed ∼50 %. In contrast, effects on average daily N 2 O flux varied by year and conditions, ranging from −30–67 % (observed: −24–28 %). These results support the model’s use for assessing long-term nitrogen-loss responses to cover crops in tile-drained systems, while highlighting priorities for improving event-scale biogeochemical simulations.