Use of Annual Phosphorus Loss Estimator (APLE) Model to Evaluate a Phosphorus Index

The Phosphorus (P) Index was developed to provide a relative ranking of agricultural fields according to their potential for P loss to surface water. Recent efforts have focused on updating and evaluating P Indices against measured or modeled P loss data to ensure agreement in magnitude and directio...

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Bibliographic Details
Published in:Journal of environmental quality 2017-11, Vol.46 (6), p.1380-1387
Main Authors: Fiorellino, Nicole M., McGrath, Joshua M., Vadas, Peter A., Bolster, Carl H., Coale, Frank J.
Format: Article
Language:English
Subjects:
Environmental Monitoring
Maryland
Models, Theoretical
Phosphorus - analysis
Water Pollutants - analysis
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Summary:The Phosphorus (P) Index was developed to provide a relative ranking of agricultural fields according to their potential for P loss to surface water. Recent efforts have focused on updating and evaluating P Indices against measured or modeled P loss data to ensure agreement in magnitude and direction. Following a recently published method, we modified the Maryland P Site Index (MD‐PSI) from a multiplicative to a component index structure and evaluated the MD‐PSI outputs against P loss data estimated by the Annual P Loss Estimator (APLE) model, a validated, field‐scale, annual P loss model. We created a theoretical dataset of fields to represent Maryland conditions and scenarios and created an empirical dataset of soil samples and management characteristics from across the state. Through the evaluation process, we modified a number of variables within the MD‐PSI and calculated weighting coefficients for each P loss component. We have demonstrated that our methods can be used to modify a P Index and increase correlation between P Index output and modeled P loss data. The methods presented here can be easily applied in other states where there is motivation to update an existing P Index. Core Ideas Our methods expanded upon methods developed by Bolster to modify and evaluate PIs. Our methods provide practical guidance to other states for modification of PIs. A theoretical dataset was simulated to represent geographical conditions in Maryland. Removal of categorical variables and weights increased PI and P loss correlation. Fertilizer and subsurface components should be evaluated like surface components.
ISSN:0047-2425
1537-2537
DOI:10.2134/jeq2016.05.0203