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ARS Home » Plains Area » Bushland, Texas » Conservation and Production Research Laboratory » Soil and Water Management Research » Research » Publications at this Location » Publication #293145


Location: Soil and Water Management Research

Title: Comparison of the performances of DRAINMOD-NII and ADAPT models in simulating nitrate losses from subsurface drainage systems

item Ale, Srinivasulu - Texas Agrilife Research
item Gowda, Prasanna
item Mulla, David - University Of Minnesota
item Moriasi, Daniel
item Youssef, Mohamed - North Carolina State University

Submitted to: Agricultural Water Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/9/2013
Publication Date: 8/9/2013
Citation: Ale, S., Gowda, P., Mulla, D., Moriasi, D.N., Youssef, M. 2013. Comparison of the performances of DRAINMOD-NII and ADAPT models in simulating nitrate losses from subsurface drainage systems. Agricultural Water Management. 129:21-30.

Interpretive Summary: Subsurface tile drainage is a commonly used agricultural practice to enhance crop yield in poorly drained, but highly productive soils. More than 30% of the crop lands in the Midwest U.S. are equipped with subsurface tile drainage systems. It improves soil aeration and microbial activity, increases the availability of plant nutrients, and enhances crop productivity by facilitating timely farm operations. However, research has shown that the subsurface drainage systems expedite the transport of plant nutrients that often contain significant amounts of nitrate to streams and lakes through subsurface drainage Therefore, monitoring and modeling of quality and quantity of subsurface drainage is useful in assessing the impact of agricultural management practices on surface and groundwater quality. In this study, performance two widely used water quality models such as DRAINMOD and ADAPT were compared for predicting nitrate losses in subsurface tile drainage using a long term experimental dataset from southern Minnesota. Results indicated that performance of DRAINMOD was slightly better than the ADAPT model in predicting nitrogen losses from the tile-drained agricultural system in southern Minnesota.

Technical Abstract: Adequate knowledge on the movement of nitrate under different subsurface (tile) drain configurations and management practices in the U.S. Midwest, is essential for developing remedial measures for reducing hypoxic conditions in the Gulf of Mexico. In this study, DRAINMOD-NII, a daily time-step soil carbon (C) and N model, was calibrated, and validated for subsurface drainage, and associated nitrate losses, and crop yield. Long term (1983-1996) monitoring data measured on three experimental plots under continuous corn (Zea mays L.) with conventional tillage practice at the University of Minnesota's Southern Research, and Outreach Center near Waseca, southern Minnesota, was used for this purpose. Nash-Sutcliffe efficiency (NSE), Percent Error (PE) and Index of agreement (d) were used for assessing the model performance. DRAINMOD-NII predicted monthly subsurface drainage matched well with measured data during calibration (NSE=0.81, PE= -7.8%, and d=0.94), and validation (NSE=0.67, PE= -0.7%, and d=0.88) periods. Performance of DRAINMOD-NII for predicting monthly NO3-N losses in subsurface drainage was also good for both calibration (NSE=0.64, PE=0.8%, and d=0.85) and validation (NSE=0.62, PE=-5.3%, and d=0.83) periods. DRAINMOD-NII predicted average (1983-1992) annual corn relative yield (93%), a ratio of crop yield in a year to the long term average crop yield, was close to the observed relative yield (92.5%). DRAINMOD-NII simulation results were also compared and contrasted with those obtained by the Agricultural Drainage and Pesticide Transport (ADAPT) model with the same dataset. Both models performed equally well in predicting monthly subsurface drainage. However, DRAINMOD-NII performed slightly better in predicting monthly nitrate losses and annual N budget, in addition to showing potential to simulate the effects of excess and deficit water stresses on crop yield.