Skip to main content
ARS Home » Research » Publications at this Location » Publication #243964

Title: Modeling denitrification in a tile-drained, corn and soybean agroecosystem of Illinois, USA

Author
item DAVID, M - University Of Illinois
item Del Grosso, Stephen - Steve
item HU, X - University Of Illinois
item MCISAAC, G - University Of Illinois
item PARTON, W - Colorado State University
item MARSHALL, E - World Resources Institute
item TONITTO, C - Cornell University
item YOUSSEF, M - North Carolina State University

Submitted to: Biogeochemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/16/2008
Publication Date: 3/1/2009
Citation: David, M.B., Del Grosso, S.J., Hu, X., Mcisaac, G.F., Parton, W.J., Marshall, E.P., Tonitto, C., Youssef, M.A. 2009. Modeling denitrification in a tile-drained, corn and soybean agroecosystem of Illinois, USA. Biogeochemistry. 93: 7-30.

Interpretive Summary: Denitrification is known as an important pathway for nitrate loss in agroecosystems. It is important to estimate denitrification fluxes to close field and watershed nitrogen (N) mass balances, determine greenhouse gas emissions (N2O), and help constrain estimates of other major N fluxes (e.g., nitrate leaching, mineralization, and nitrification). We compared predicted denitrification estimates for a typical corn and soybean agroecosystem on a tile drained soil from five models. We first calibrated each model using crop yield, water flow, and nitrate leaching observations. Model output for 1997–2006 was then compared for a range of annual, monthly and daily water and nitrate measurements. Each model was able to estimate corn and soybean yields accurately, and most did well in estimating water and nitrate flows. Monthly patterns in observed nitrate flux were generally predicted well by the models. Nitrogen fluxes that did not have corresponding measurements were quite variable across the models, with denitrification estimate, ranging from 3.8 to 21 kg N ha-1 year-1. There was also substantial variability in simulated soybean atmospheric N fixation, N harvest, and the change in soil organic N pools. Predicted daily fluxes during a high precipitation year (2002) varied considerably among models regardless of whether the models had comparable annual fluxes for the years. Some models predicted large denitrification fluxes for a few days, whereas others predicted large fluxes persisting for several weeks to months. Modeled denitrification fluxes were controlled mainly by soil moisture status and nitrate available to be denitrified, and the way denitrification in each model responded to moisture status greatly determined the flux. Because denitrification is dependent on the amount of nitrate available at any given time, modeled differences in other components of the N cycle no doubt led to differences in predicted denitrification. Model comparisons suggest our ability to accurately predict denitrification fluxes from the dominant agroecosystem in the midwestern Illinois is quite uncertain at this time.

Technical Abstract: Denitrification is known as an important pathway for nitrate loss in agroecosystems. It is important to estimate denitrification fluxes to close field and watershed N mass balances, determine greenhouse gas emissions (N2O), and help constrain estimates of other major N fluxes (e.g., nitrate leaching, mineralization, nitrification). We compared predicted denitrification estimates for a typical corn and soybean agroecosystem on a tile drained Mollisol from five models (DAYCENT, SWAT, EPIC, DRAINMOD-N II and two versions of DNDC, 82a and 82h), after first calibrating each model to crop yields, water flux, and nitrate leaching. Known annual crop yields and daily flux values (water, nitrate-N) for 1993–2006 were provided, along with daily environmental variables (air temperature, precipitation) and soil characteristics. Measured denitrification fluxes were not available. Model output for 1997–2006 was then compared for a range of annual, monthly and daily fluxes. Each model was able to estimate corn and soybean yields accurately, and most did well in estimating riverine water and nitrate-N fluxes (1997–2006 mean measured nitrate-N loss 28 kg N ha-1 year-1, model range 21–28 kg N ha-1 year-1). Monthly patterns in observed riverine nitrate-N flux were generally reflected in model output (r 2 values ranged from 0.51 to 0.76). Nitrogen fluxes that did not have corresponding measurements were quite variable across the models, including 10-year average denitrification estimates, ranging from 3.8 to 21 kg N ha-1 year-1 and substantial variability in simulated soybean N2 fixation, N harvest, and the change in soil organic N pools. DNDC82a and DAYCENT gave comparatively low estimates of total denitrification flux (3.8 and 5.6 kg N ha-1 year-1, respectively) with similar patterns controlled primarily by moisture. DNDC82h predicted similar fluxes until 2003, when estimates were abruptly much greater. SWAT and DRAINMOD predicted larger denitrification fluxes (about 17–18 kg N ha-1 year-1) with monthly values that were similar. EPIC denitrification was intermediate between all models (11 kg N ha-1 year-1). Predicted daily fluxes during a high precipitation year (2002) varied considerably among models regardless of whether the models had comparable annual fluxes for the years. Some models predicted large denitrification fluxes for a few days, whereas others predicted large fluxes persisting for several weeks to months. Modeled denitrification fluxes were controlled mainly by soil moisture status and nitrate available to be denitrified, and the way denitrification in each model responded to moisture status greatly determined the flux. Because denitrification is dependent on the amount of nitrate available at any given time, modeled differences in other components of the N cycle (e.g., N2 fixation, N harvest, change in soil N storage) no doubt led to differences in predicted denitrification. Model comparisons suggest our ability to accurately predict denitrification fluxes (without known values) from the dominant agroecosystem in the midwestern Illinois is quite uncertain at this time.