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DRAINMOD-based tools for quantifying reductions in annual drainage flow and nitrate losses resulting from drainage water management on croplands in eastern North Carolina

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  • Negm, L.M.
  • Youssef, M.A.
  • Chescheir, G.M.
  • Skaggs, R.W.

Abstract

Nitrogen (N) leachate of drained agriculture has continued to be pervasive in the U.S. water resources. Nitrogen credit exchange program is a trading market to facilitate pollutant reductions and protect the environment. A simple tool suitable for eastern North Carolina (NC) was developed to quantify drainage flow and N mass reductions resulting from drainage water management (DWM); an efficient and common conservation practice for drained agricultural lands. The tool comprises a set of regression equations estimating the performance of DWM as a function of local site conditions. DRAINMOD and DRAINMOD-NII models simulations were conducted for a wide range of soil types, weather conditions, and management practices for different locations in eastern NC. Simulation results were used with SAS 9.3 software to develop a set of multi-linear regression equations to estimate DWM-caused reductions in annual drainage flow and corresponding nitrate-N (NO3-N) losses for continuous corn (CC) and corn–wheat–soybean (CWS) cropping systems. The regression model estimations of annual drainage flow were highly correlated with DRAINMOD simulated values with an adjusted coefficient of multiple determination (R2adj) equal to 0.91 or higher for different management scenarios. Similarly, the regression model estimations of annual nitrate losses achieved an R2adj of 0.88 or higher for all management scenarios. The developed regression models were further compared on a year-by-year basis to the calibrated DRAINMOD and DRAINMOD-NII models for local conditions of an experimental site in eastern NC over 25 years. Estimated annual drainage flow and NO3-N losses were in good agreement with corresponding values simulated by DRAINMOD-based models for CC and CWS under free and controlled drainage modes. In terms of DWM-induced annual reductions in drainage flow and N losses, noticeable differences occurred in several years between predictions of DRAINMOD-NII and the regression models. A comparison based on the 5-year moving average of DWM-induced reductions smoothed out the extreme year-to-year variations and indicated very similar reduction trends provided by both methods. The results presented in this case study indicated that the simple regression method provides an adequate alternative to the processes based DRAINMOD suite of models for estimating annual reductions in drainage rates and N mass losses resulting from implementation of DWM. Similar tools can be developed for other regions in the US and abroad that initiate nitrogen trading markets involving DWM.

Suggested Citation

  • Negm, L.M. & Youssef, M.A. & Chescheir, G.M. & Skaggs, R.W., 2016. "DRAINMOD-based tools for quantifying reductions in annual drainage flow and nitrate losses resulting from drainage water management on croplands in eastern North Carolina," Agricultural Water Management, Elsevier, vol. 166(C), pages 86-100.
    • Handle: RePEc:eee:agiwat:v:166:y:2016:i:c:p:86-100
    DOI: 10.1016/j.agwat.2015.12.014
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    References listed on IDEAS

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    2. Singh, Shailendra & Bhattarai, Rabin & Negm, Lamyaa M. & Youssef, Mohamed A. & Pittelkow, Cameron M., 2020. "Evaluation of nitrogen loss reduction strategies using DRAINMOD-DSSAT in east-central Illinois," Agricultural Water Management, Elsevier, vol. 240(C).
    3. Askar, Manal H & Youssef, Mohamed A & Chescheir, George M & Negm, Lamyaa M & King, Kevin W & Hesterberg, Dean L & Amoozegar, Aziz & Skaggs, R. Wayne, 2020. "DRAINMOD Simulation of macropore flow at subsurface drained agricultural fields: Model modification and field testing," Agricultural Water Management, Elsevier, vol. 242(C).
    4. Revuelta-Acosta, J.D. & Flanagan, D.C. & Engel, B.A. & King, K.W., 2021. "Improvement of the Water Erosion Prediction Project (WEPP) model for quantifying field scale subsurface drainage discharge," Agricultural Water Management, Elsevier, vol. 244(C).

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