Evaluation of the performance of agricultural Beneficial Management Practices (BMPs) intended to protect groundwater resources that may be impacted by the leaching of excess agricultural nutrients is both essential and problematic. Many field-monitoring techniques are hampered by the substantial lag time that often exists between when the BMP is implemented and when a related impact on the groundwater quality might be observed. As a result, agricultural nitrogen models that are adapted to site-specific field conditions are often utilized in concert with field observations to provide estimates of BMP performance. In the current work, the Root Zone Water Quality Model was used to evaluate the long-term reduction of nitrate loading as a result of regional nutrient reduction BMP implementation across agricultural fields located within a municipal well field capture zone. Soil nitrate concentration and soil moisture content profiles were collected from a series of monitoring locations. These data, in conjunction with a heuristic optimization algorithm, were used to calibrate and validate the model. Validation results showed that the simulated moisture content profiles matched very well with the observed profiles, and that the simulated soil nitrate concentration was in general agreement with field observations except for the highly reactive and transient rooting zone. The calibrated model was used to investigate a series of potential nutrient reduction BMP scenarios. Results indicated that the annual nitrate loading varied both spatially and temporally relative to the subsurface conditions and the agricultural land management. The overall results indicate that BMP effectiveness needs to be investigated over relatively long time periods and that short-term, point-scale field measurements may not provide sufficient information to evaluate BMP performance. The results obtained through the integration of field data and an agriculture nitrogen model indicates that this approach can be highly beneficial to assess or evaluate the potential long-term performance of agricultural BMPs.