Skip to main content
ARS Home » Midwest Area » Bowling Green, Kentucky » Food Animal Environmental Systems Research » Research » Publications at this Location » Publication #306598

Research Project: Efficient Management and Use of Animal Manure to Protect Human Health and Environmental Quality

Location: Food Animal Environmental Systems Research

Title: Estimating the magnitude of prediction uncertainties for the APLE model

Author
item Bolster, Carl
item Vadas, Peter
item Boykin, Deborah - Debbie

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.