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Title:   Author  item
                                            Bolster, Carlitem
                                            Vadas, Peteritem
                                            Boykin, Deborah
  Submitted to: SERA-IEG 17 Bulletin
                                    Publication Type: Abstract Only
                                    Publication Acceptance Date: 6/15/2014
                                    Publication  Date: 7/24/2014
                                    Citation: Bolster, C.H., Vadas, P.A., Boykin, D.L. 2014. Estimating the magnitude of prediction uncertainties for the APLE model. SERA-IEG 17 Bulletin. Abstract.Interpretive Summary:
                                Technical Abstract: Models are often used to predict phosphorus (P) loss from agricultural fields. While it is commonly recognized that model predictions are inherently uncertain, few studies have addressed prediction uncertainties using P loss models. In this study, we conduct an uncertainty analysis for the Annual P Loss Estimator (APLE) model by estimating the uncertainty associated with model input variables and model parameters. Specifically, we estimate the parameter uncertainties associated with the regression equations used to estimate total soil P from measurements of soil clay content, organic matter, and labile P; the P enrichment ratio from erosion rates; concentration of P in runoff due to labile soil P; and partitioning of P between runoff and infiltration from applied manures and fertilizers. We also estimate the uncertainty associated with 10 model input variables based on error estimates published in the literature. Our analysis included calculating both confidence and prediction intervals. We then calculated predictions of P loss using the APLE model 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. We also demonstrate how the estimation of model parameter uncertainty can identify model limitations.


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