Abstract:
Agricultural lands contribute significant nitrogen loads to surface waters. Excessive nitrogen input leads to eutrophication, the process by which aquatic ecosystems become nutrient rich. Eutrophication is associated with a wide range of undesirable changes, including shifts in physical and chemical states, changes in species composition, and the loss of ecosystem services. In agricultural areas, excessive nutrient loading is addressed through the implementation of Best Management Practices (BMPs). However, field-scale nutrient export is controlled by a complex array of interacting factors that operate at different spatial scales. Multilevel regression is a statistical technique that allows for the exploration of group-level factors that may explain variation in the overall model coefficients. In this study, multilevel regression models for dissolved and particulate nitrogen loading are fit to USDA agricultural data. The results indicate that the impact of management practice depends on the form of nitrogen as well as predictors such as soil texture that operate on large spatial scales. Specific management recommendations include soil nitrogen testing and the use of conservation measures that address water runoff. Management applications of the fitted models include load estimation as part of watershed leveling modeling efforts as well as the evaluation of proposed policy guidelines for nutrient control.
