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United States Department of Agriculture

Agricultural Research Service

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

Location: Food Animal Environmental Systems Research Unit

Title: Uncertainty analysis for a field-scale P loss model

Authors
item Bolster, Carl
item Vadas, Peter

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: August 27, 2013
Publication Date: November 8, 2013
Citation: Bolster, C.H., Vadas, P.A. 2013. Uncertainty analysis for a field-scale P loss model. ASA-CSSA-SSSA Annual Meeting Abstracts. Abstract.

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 assessed the effect of model input error on predictions of annual P loss by the Annual P Loss Estimator (APLE) model. Specifically, our objectives were to: (1) conduct a sensitivity analyses for all APLE input variables to determine which variables the model is most sensitive to; (2) determine whether the relatively easy to implement first-order approximation (FOA) method provides accurate estimates of model prediction uncertainties by comparing results with the more accurate Monte Carlo simulation (MCS) method; and (3) to evaluate the performance of the APLE model against measured P loss data when uncertainties in both model predictions and measured data are included. Results showed that for low to moderate uncertainties in APLE input variables, the FOA method yields reasonable estimates of model prediction uncertainties, though for cases where manure solid content is between 14 – 17%, the FOA method may not be as accurate as the MCS method due to a discontinuity in the manure P loss component of APLE at a manure solid content of 15%. The estimated uncertainties in APLE predictions based on assumed errors in the input variables ranged from ± 2 to 64% of the predicted value. Results from this study highlight the importance of including reasonable estimates of model uncertainty when using models to predict P loss.

Last Modified: 10/22/2014
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