Kansas Geological Survey, Current Research in Earth Sciences, Bulletin 258, part 1
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Conclusions and Recommendations

The analysis carried out in this report leads to the following conclusions:

1. Soil coring down to 15.2 m and ground-water sampling in the treated wastewater-irrigation area south of Dodge City, Kansas, indicated that nitrate-N is accumulating in the vadose zone and has reached the underlying ground water.
2. Soil coring and dye-tracing experiments demonstrated the existence of preferential pathways through macropores from decaying root channels throughout the sampled depths.
3. The near-surface residual soil nitrate-N peak in the soil profile is gradually propagating downwards in the soil, albeit attenuated, thus increasing the residual soil nitrate-N at depth. In addition, the nitrate-N in the underlying ground water is also progressively increasing with time.
4. The source of the nitrate-N in both the soil water and ground water in the treated wastewater-irrigated sites is shown to be from the treated wastewater applications.
5. Simulations using RZWQM indicated that high intensity rainfall events promote macropore preferential flows, which can transport nitrate deeper in the soil profile as indicated in items 1 through 3 above. However, we identified macropore conceptual limitations in the RZWQM in that the model does not allow macropore flow from water ponded over the subsurface layers of low-hydraulic conductivity.
6. Based on sensitivity analysis, the bulk density, saturated-water content, and Brooks and Corey parameters (λ and ψ_a) are the most sensitive parameters affecting soil-water flow, whereas the CERES-Maize parameters P1, P5, G2, and G3 were the most sensitive plant parameters. Experimentally measuring the aforementioned hydraulic parameters will enhance soil-water simulations and consequently simulations of soil-nitrate transport, which will be further improved by carefully field-measuring the above-identified CERES-Maize parameters.
7. The RZWQM was calibrated for the 2005 planting season based on limited data and was tested/verified for the 2006 cropping season. The model acceptably approximated the overall patterns of the observed soil-water and nitrate profiles but not their detailed patterns, and generally overestimated the profile soil nitrate. In our judgment, better procedures for estimating the humus and microbial pools and plant-growth parameters as well as enhancement of the plant-growth module in the RZWQM will further improve the present state of N simulation. The incorporation of the Decision Support System for Agrotechnology Transfer, DSSAT4.0 suite of crop-growth models in the latest released RZWQM (RZWQM2, version 1.5), is a step in the right direction. Model results may also be improved by increasing the number of soil horizons that the model can handle and by obtaining additional soil hydraulic data (as also mentioned in item 6 above).
8. The calibrated RZWQM model was used to evaluate the impact of management practices using alternative reduced-N amounts on NUE and soil N. Thus, the model showed that reducing the wastewater N-application rates to around 170 kg/ha increases the NUE significantly.

Adopting such reduced-N application measures would definitely reduce the size of residual nitrate stored in the thick vadose zone in the area and slow down its downward migration. Combining such measures with a crop rotation that includes alfalfa should further reduce the amounts of residual nitrate in the soil.

Acknowledgments

This study was funded by the U.S. Geological Survey through the Kansas Water Resources Institute. Numerous people and agencies assisted us during the conduct of this study: Dr. Jay Jabro, research soil scientist with USDA-ARS Sidney, Montana, and Dr. Saseendran Anapalli, crop scientist with USDA-ARS, Fort Collins, Colorado, provided detailed and constructive comments that helped to improve this manuscript. Dr. L. R. Ahuja, Research Leader, Agricultural Systems Research Unit, USDA-ARS, Fort Collins, Colorado, provided useful comments on an earlier version of this manuscript and shared his insights on macropore flow. Kansas NRCS personnel J. Warner, S. Graber, R. Still, T. Cochran, and C. Watts assisted us with field characterization and soil-property lab analyses through the National Soils Laboratory in Lincoln, Nebraska. NRCS Soil Mechanics Laboratory in Lincoln, Nebraska, performed saturated hydraulic conductivity and related analyses. Dr. T. Willson and other personnel from the Garden City Experiment Station, Kansas State University, assisted us with neutron-probe readings and related analyses. D. Schuette of Servi-Tech assisted us with coring, sampling, and dye-tracer experiments. W. McCall of Geoprobe Systems assisted us with the Geoprobe equipment during field-site installations. The farmer-operator of the field sites C. Nicholson and his office personnel assisted us with land-use information and access to the sites. K. Rojas of NRCS at Fort Collins, Colorado, assisted us with the RZWQM. Numerous other personnel from OMI and KGS assisted us at different stages of this project. Finally, our KGS colleagues Jim Butler, Marla Adkins-Heljeson, and Mark Schoneweis provided useful review comments, edited the manuscript, and perfected the figures, respectively.

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Appendix A

RZWQM2 sensitivity graphs for soil hydraulic parameters (fig. A1), macropore properties (fig. A2), organic pools (fig. A3), and corn (CERES-Maize) parameters (fig. A4).

Figure A1--Sensitivity of simulated soil-water content to various soil hydraulic parameters compared to a calibrated base case for a randomly selected depth (36 cm) as a function of time. The indicated parameters were perturbed by ±20% from those of the base case.

Figure A2--Sensitivity of simulated nitrate-N (NO₃-N) concentrations to various macropore parameters for a randomly selected depth (50 cm) as a function of time.

Figure A3--Sensitivity of simulated nitrate-N (NO₃-N) concentrations to various organic pools in model layers 1 through 5 for a randomly selected depth (50 cm) as a function of time.

Figure A4--Sensitivity of simulated nitrate-N (NO₃-N) concentrations to various CERES-Maize parameters (see table 4) for a randomly selected depth (50 cm) as a function of time.

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