Updates to the Annual P Loss Estimator (APLE) model

Authors: Bolster, Carl; Vadas, Peter

Submitted to: Journal of Environmental Quality
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/9/2022
Publication Date: 6/6/2022
Citation: Bolster, C.H., Vadas, P.A. 2022. Updates to the Annual P Loss Estimator (APLE) model. Journal of Environmental Quality. 1-7. https://doi.org/10.1002/jeq2.20378.
DOI: https://doi.org/10.1002/jeq2.20378

Interpretive Summary: Phosphorus (P) is an essential plant nutrient that is often applied to agricultural fields to maintain or increase crop yields. However, depending on climatic factors, soil properties, P application rate and timing, and other management and field properties, once applied to the field, there is a risk of added P being transported off site during precipitation events, potentially degrading water quality. Developing effective management practices to reduce agricultural P loss requires reliable models to predict P loss from agricultural fields. In this research, we update the existing Annual P Loss Estimator (APLE) model to include equations for predicting surface runoff and for incorporating model input uncertainties into model predictions of P loss and changes in soil P over time.

Technical Abstract: The Annual P Loss Estimator (APLE) is a simple spreadsheet model developed for predicting annual field-scale dissolved and particulate P loss in surface runoff as well as annual changes in soil test P (STP). The model accounts for field application of P through the addition of animal manure (solid and liquid), dung from grazing cattle, and inorganic fertilizer. This empirical annual time step model was designed to provide a relatively easy-to-use tool for those without significant modeling experience. However, a significant limitation with the model is that it does not calculate runoff or erosion and thus requires users to run more sophisticated models for estimating runoff and erosion prior to using APLE. 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 include calculations of runoff and the ability to estimate model prediction errors using Monte Carlo simulations. The revised version of APLE (APLE Runoff and UNcertainty; APLE RUN) still uses Excel so that it can be run by users without experience with more sophisticated software packages.