Submitted to: Book Chapter
Publication Type: Book / chapter
Publication Acceptance Date: 9/7/2007
Publication Date: 7/1/2008
Citation: Delgado, J.A., Shaffer, M.J. Nitrogen Management Modeling Techniques: Assessing Cropping Systems/Landscape Combinations. Book Chapter In R.F. Follett and J.L. Hatfield (eds) Nitrogen in the Environment: Sousrces, Problems and Management. pp. 539-570. Elsevier Science, New York, USA. Interpretive Summary: Field applications of models for nitrogen management are challenging from the standpoints of selecting an appropriate model from the long list of available tools and then applying the model to field situations that often are removed from conditions and locations where the model was developed and tested. The potential user must be prepared to collect a reasonable amount of field data to calibrate the model for the soils, management, and climate conditions in the study area. This will involve one or more seasons or years of field work to collect the required crop yield, soil nitrogen, and soil water data that are needed. Once this is accomplished, calibration of the model should be done using a systematic approach based on a prior sensitivity analyses run on the tool. Once the calibration procedure has been completed, the model can be applied to test alternative scenarios involving nitrogen management. Potential scenarios should be developed in cooperation with local producer, commodity, and action agency groups. Comparisons among simulations of various management scenarios should be done taking into account the uncertainty in the results obtained from the calibration and validation studies. For most nitrogen studies in the field, this means that small differences for simulated residual soil nitrates and nitrate leached will not be statistically meaningful for comparisons of some management scenarios. Larger potential differences should be targeted when selecting management scenarios to be tested, especially if producers are expected to demonstrate positive benefits from the adoption of BMPs. Indeed, helping to identify scenarios with substantial potential benefits is one of the better uses for modeling in the N management area. These studies have demonstrated how the application of a C/N model such as NLEAP can make a difference in recommended N management scenarios.
Technical Abstract: Nitrogen use efficiency (NUE) in production agriculture often is too low resulting in losses of excess N to ground water as NO3-N, to gaseous emissions of NH3 and N2O, and to N losses in surface runoff and erosion. Best management practices (BMPs) are needed to improve efficiency levels while maintaining proper nutrition for crops. Field studies designed to investigate potential BMPs are both time consuming and costly, and cannot cover all scenarios. Application of simulation models with N cycling components in conjunction with associated field investigations offers methodology that can help identify BMPs that show promise in increasing NUE, but at reduced cost and time expended. Credible BMP studies employing simulation tools need to proceed along a well-defined path involving model selection, model adaptation and calibration, sensitivity analyses, data requirements and availability, model application, and model results interpretation and limitations. We can use models with geographic information systems (GIS), global positioning systems, and remote sensing, to evaluate various BMPs, enabling them to assess which ones use nitrogen most efficiently. Early and continuing interaction with local producers, consultants, conservationists, and field research programs are essential parts of these BMP modeling studies. These model evaluations can be conducted with GIS to assess the BMPs for high risky landscape scenarios with the potential to identify use of Precision Conservation Practices to increase NUE and reduce N losses. Examples from irrigated agriculture and rainfed agriculture, remote sensing, GIS, site specific agriculture and precision conservation illustrate cases where models have been successfully used to identify potential BMPs to improve NUE and reduce leaching of NO3-N.