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Title:   Author  item
                                            Bolster, Carlitem
                                            Vadas, Peteritem
                                            Boykin, Deborah
  Submitted to: Meeting Abstract
                                    Publication Type: Abstract Only
                                    Publication Acceptance Date: 3/28/2015
                                    Publication  Date: 9/22/2015
                                    Publication URL: https://handle.nal.usda.gov/10113/62944Citation: Bolster, C.H., Vadas, P.A., Boykin, D.L. 2015. Model parameter uncertainty analysis for an annual field-scale phosphorus loss model. Meeting Abstract #94.Interpretive Summary:
                                Technical Abstract: Phosphorous (P) loss models are important tools for developing and evaluating conservation practices aimed at reducing P losses from agricultural fields. All P loss models, however, have an inherent amount of uncertainty associated with them. In this study, we conducted an uncertainty analysis with the Annual P Loss Estimator (APLE) model, an empirically-based spreadsheet model developed to describe annual, field-scale P loss when surface runoff is the dominant P loss pathway. We first estimated and evaluated model parameter uncertainties associated with five internal regression equations used by APLE to calculate total soil P from measurements of soil clay content, organic matter, and labile P; the P enrichment ratio determined from erosion rates; concentration of P in runoff calculated from labile soil P; and partitioning of P between runoff and infiltration for applied manures and fertilizers based on runoff ratio. Our analysis included calculating parameter uncertainties and 95% confidence and prediction intervals for five internal regression equations in APLE. We then predicted P loss while including uncertainties in both model parameters and model inputs and compared the relative magnitude of these sources of uncertainty to the overall uncertainty associated with predictions of P loss. Good correlations between predicted and observed data were found, though a significant amount of observed variability was not captured by the equations. This resulted in uncertainties in predicted P loss ranging from 5 to 50 %. Results from this study highlight the importance of including reasonable estimates of model parameter uncertainties when using models to predict P loss. Our results also demonstrate how the estimation of model parameter uncertainty can identify model limitations.


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