1. A FRAMEWORK FOR ASSESSING THE PERFORMANCE OF DWM AT LARGE SCALE MOHAMED A. YOUSSEF and R. WAYNE SKAGGS 1 By

2. 2  Most field experiments, conducting a side-by-side comparison between managed and unmanaged drainage systems, document reductions in annual drain flow and N drainage loss by a range of 20% to 60%.  This wide range is expected as the performance of DWM is affected by many factors including:  weather (precipitation, temperature, and other factors affecting ET),  soil (hydraulic conductivity, texture, presence of shallow impermeable layer, SOM,…),  farming practices (cropping system, nutrient management, tillage and residue management, cover crops,…), and  drainage system design (drain depth and spacing, degree of surface drainage)

3. 3  The performance of DWM measured in a field experiment represents the performance of the practice for a specific set of site conditions. {S 1, S 2,..., S i,… S n } = {P 1, P 2, …, P i, …P n } where P i is the performance of DWM for the site conditions S i.  Thus, each experiment assessing the performance of DWM only represents one realization (S i, P i ) of the population of the performance of DWM for all possible site conditions, n.

4. 4  It is practically impossible to conduct a large enough number of experiments to define the relationship P(S), or the performance as a function of the site conditions.  Experiments are usually conducted for a relatively short period ( 3 to 5 years) and thus cannot assess the performance of the practice over the long term.  Thus, there is a need for a tool that extrapolates the results of field experiments and assesses the performance of DWM for different site conditions.

5. 5  A conceptual representation of a tool for assessing the performance of DWM. DWM Assessment Tool Site Conditions  Weather  Soil  Farming practices  Drainage system  Reduction in drain flow  Reduction in N drainage losses

6. 6  It is computer modeling based tool. DRAINMOD DRAINMOD-NII Site Conditions  Weather  Soil  Farming practices  Drainage system  Reduction in drain flow  Reduction in N drainage losses  Both DRAINMOD and DRAINMOD-NII models have been successfully tested using data sets from several Midwestern States (Illinois, Indiana, Minnesota, Iowa)

7. 7 Development:  Select a set of benchmark soils, common farming practices, long-term weather records covering the geographic region of interest.  Use DRAINMOD to simulate the hydrology and DRAINMOD-NII to simulate N dynamics for each unique combination of soil type-farming practices-weather record.  Conduct the simulations for common drain depths and spacings and for both managed and unmanaged scenarios.  Determine the reduction in drain flow and N drainage losses caused by DWM for all simulated site conditions.  For each site conditions, reductions can be estimated for dry, normal, and wet years.

8. 8  Application:  Identify the conditions of the site of interest.  Select the reductions in drain flow and N drainage losses predicted by the models for the conditions closest to the site of interest.  Advantages  Simplest (the user of the tool does not need to run the models),  least input requirements.  Suitable for large scale assessment of DWM (how much reduction in N losses is expected if DWM is implemented on drained lands within a specific watershed, river basin, a state or the entire Midwest)  Disadvantages  least accurate predictions (compared with Approaches 2 and 3).  Less reliable for making site specific predictions that are accurate enough to be used with an incentive program.

9. 9 Development:  Select a set of benchmark soils, common farming practices, long- term weather records covering the geographic region of interest.  Use DRAINMOD to simulate the hydrology and DRAINMOD-NII to simulate N dynamics for each unique combination of soil type- farming practices-weather record.  Determine the annual flow-weighted nitrate-N concentration for all simulated site conditions.  For each site conditions, determine nitrate-N concentrations for dry, normal, and wet years.

10. 10 Application:  Identify the conditions of the site of interest and prepare site specific inputs for DRAINMOD.  Run DRAINMOD for the site of interest under the managed and unmanaged scenarios and calculate reductions in drain flow  Select the flow-weighted nitrate concentration predicted by DRAINMOD-NII for the conditions closest to the site of interest.  Calculate reduction in N mass loss

11. 11  Advantages  Medium level of complexity (the user of the tool must know how to run the hydrologic model DRAINMOD),  Medium level of input requirements (hydrologic inputs for DRAINMOD).  Compared to approach 1, this approach can make predictions that better represent the local site conditions, including the year to year variability caused by precipitation.  Model predictions should be accurate enough to be used with an incentive program. (A hypothesis that needs to be tested)  Disadvantages  More difficult than approach 1.

12. 12

13. 13  Thorp et al. (2007, 2009) evaluated and compared the performance of the RZWQM/DSSAT and DRAINMOD/DRAINMOD- N II models using ten years of measured hydrologic, water quality and crop yield data for a corn-soybean agricultural system on silty clay loam/clay loam soils in central Iowa, U.S. (42.2° N, 93.6° W).  We used DRAINMOD/DRAINMOD-NII models, calibrated for the Iowa corn-soybean production system, to simulate the performance of DWM as affected by differences in climatic conditions, crop planting and harvesting dates, and N fertilization rates across the U.S. Midwest.  We conducted the simulations using 25-years of historical climate data for 48 locations across the Midwest.

14. 14  Historical climate data were obtained from the National Solar Radiation Database.  The simulated drainage system consists of subsurface drains 145 cm deep, spaced 27.4 m apart.  The simulated cropping system includes corn, planted in even years, and soybean, planted in odd years.  Planting and harvesting dates were based on the state and county level National Agricultural Statistics Service (NASS) data(USDA, 2007).

15. 15  Nitrogen fertilization was applied seven days before planting at rates equal to 5-yr (2001-2006) avg., state level N fertilizer application rates.  DRAINMOD/DRAINMOD-N II simulated the hydrology, C and N dynamics, and crop yield for each of the 48 locations under both conventional drainage (CVD) and DWM.  The weir settings for the DWM scenario are 30 cm (off growing) season, 60 cm (in growing season), drain depth for 7 wks (planting) and 3 wks (harvesting).

16. 16 LocationPrecip. (cm) Drainage (cm)Runoff (cm) ET (cm) Seepage (cm) CVDDWMCVDDWMCVDDWMCVDDWM Memphis, TN 131.858.932.25.324.162.869.44.15.3 -26.7 (-45.3%)18.8 (70%)6.6 (25%)1.2 (5%) Evansville, IN 109.541.925.63.714.559.563.74.05.1 -16.3 (-38.9%)10.8 (66%)4.2 (26%)1.1 (7%) Sioux City, IA 65.712.610.31.71.947.649.13.54.1 -2.3 (-18.3%)0.2 (9%)1.5(65%)0.6 (26%) Avg.88.027.919.33.98.751.954.63.94.8 -8.6 (-30.%)4.8 (56%)2.7 (31%)0.9 (10%)

17. 17  DWM was most effective in reducing drain flow at the south and southeast locations and least effective at the North and Northwest locations.  The predicted fate of the water that did not pass through the drainage system because of implementing DWM varied across the region.  The large reductions in drain flow associated with implementing DWM in the south and southeast locations resulted in a substantial increase in surface runoff and only a modest increase in vertical seepage.  In the north and northwest locations where DWM is relatively less effective (drain flow reductions < 20%), the water that did not pass through the drainage system because of DWM was primarily lost through ET.

18. 18 LocationN fert. Kg N ha -1 N Drn loss Kg N ha -1 Den. loss Kg N ha -1 N runoff loss Kg N ha -1 N Seep. loss Kg N ha -1 CVDDWMCVDDWMCVDDWMCVDDWM Memphis, TN 17568.036.220.741.01.25.03.63.1 -26.7 (-46.8%)20.3 (64%)3.8 (12%)-0.5 (-2%) Evansville, IN 16555.733.915.028.61.34.24.13.7 -21.8 (-39.1%)13.6 (62%)2.9 (13%)-0.4 (2%) Sioux City, IA 14422.917.410.715.61.71.84.13.6 -5.5 (-24.0%)4.9 (89%)0.1 (2%)-0.5 (9%) Avg. 151.832.421.410.918.91.73.03.32.9 -11.0 (-32%)8.0 (73%)1.3 (12%)-0.4 (4%)

19. 19  Similar to the hydrology, DWM was most effective in reducing N drainage losses at the south and southeast locations and least effective at the north and northwest locations.  Model predictions support the hypothesis that DWM increases the anaerobic conditions in the soil profile, which promotes denitrification and reduces N leaching losses.  The small N concentrations in runoff water explain the modest increase in mass loss of N via surface runoff despite the large increase in surface runoff induced by DWM.  The increase in denitrification rates caused by DWM resulted in a decrease in the concentration of NO 3 -N in groundwater, and a corresponding reduction in NO 3 -N mass losses via vertical seepage despite the modest increase in vertical seepage flux.

20. 20  DWM did not affect N plant uptake and net N mineralization.  Model predictions also support the hypothesis that DWM does not significantly change nitrogen concentration in drainage water and thus the percent reduction in N drainage loss can be approximated by the percent reduction in drain flow.  The predicted N change in N cycling caused by DWM indicates that the modest changes in N concentration in drainage water do not necessarily mean that the practice have a little impact on N dynamics in the system

21. 21