Abstract

Controlled drainage (CD) has been identified as a sustainable management practice whereby more soil water can be conserved and less nutrients are leached; alongside its potential benefit of alleviating drought stress and increasing yield. More than 12 million hectare of cropland in the US Midwest are suitable for implementing CD; however, the effectiveness of the practice can vary across the region with the variation in environmental conditions and management practices. The main objective of this study is to evaluate the performance of the integrated agro-ecosystem model; DRAINMOD-DSSAT, for simulating the effects of CD on drainage flow, nitrogen losses via drainage water and crop yield.Herein, we utilized a 4-yr dataset (2006–2009) that was collected from a corn–soybean cropping system near Story City, Iowa. This site was artificially drained under free drainage (FD) and CD treatments. The model was calibrated using the data collected from the FD plots, and validated for the CD plots. DRAINMOD-DSSAT predictions of drainage flow and nitrate-nitrogen (NO3-N) losses were in good agreement with measured values under FD and CD, with the former treatment showed slightly better performance. The modeling efficiencies (NSE’s) for simulating monthly drainage flows were 0.81 and 0.60 for FD and CD, respectively. Monthly NO3–N mass losses were simulated with NSE’s of 0.76 and 0.66 for FD and CD, respectively. DRAINMOD-DSSAT well simulated CD-induced percent reductions in annual drainage flow (measured=24.6%, simulated=27.1%), and NO3-N losses (measured=34.8%, simulated=33.5%). Low percent error (PE) values were associated with the model predictions of corn yields (−1.3≤PE≤1.3) and soybean yields (−6.0≤PE≤12.6). Overall, results obtained from this relatively short-term modeling study demonstrated the potential use of DRAINMOD-DSSAT as a management design tool. Yet, further model testing CD effectiveness under different conditions is critically needed to establish a higher credibility in model predictions and to allow for further model improvement and expansion.

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