Updates to the Annual P Loss Estimator (APLE) model.

J Environ Qual

USDA-ARS, Office of National Programs, 5601 Sunnyside Ave., Beltsville, MD, 20705, USA.

Published: September 2022

AI Article Synopsis

  • The Annual P Loss Estimator (APLE) is a user-friendly spreadsheet model that forecasts annual phosphorus (P) loss and changes in soil test P, but it originally lacked the ability to calculate runoff and didn't account for uncertainties.
  • Modifications to APLE now enable users to estimate runoff using the Curve Number method and incorporate uncertainty through Monte Carlo simulations, leading to more accurate predictions.
  • Examples from Mississippi and Maryland illustrate how these updates enhance the understanding of P loss and soil concentration changes over time, reflecting a more realistic model performance.

Article Abstract

The Annual P Loss Estimator (APLE) is a spreadsheet-based model developed for predicting annual field-scale P loss in surface runoff and changes in soil test P. This empirically based model was designed for use by those without significant modeling experience. However, a significant limitation with the model is that it does not calculate runoff. Moreover, APLE is deterministic and thus predicts a single value for a given set of inputs, thereby ignoring any uncertainties associated with model inputs. Here, we describe modifications to APLE that allow users to estimate runoff using the Curve Number method. Using Monte Carlo simulations, the updated version of APLE also provides users the ability to account for model input uncertainties in estimating model prediction errors. We provide examples of using the revised version of APLE (ver. 3.0) for calculating P loss from two fields in Mississippi over a 4-yr period and calculating the change in Mehlich-3 P concentrations over a 9-yr period at three locations in Maryland following cessation of P application. Both examples demonstrate that incorporating estimates of uncertainties in both measured data and model predictions provides modelers with a more realistic understanding of the model's performance.

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Source
http://dx.doi.org/10.1002/jeq2.20378DOI Listing

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