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ARS Home » Midwest Area » Bowling Green, Kentucky » Food Animal Environmental Systems Research » Research » Publications at this Location » Publication #270512

Title: Using a P loss model to evaluate and improve P indices

item Bolster, Carl
item Vadas, Peter
item SHARPLEY, ANDREW - University Of Arkansas
item LORY, JOHN - University Of Missouri

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 7/1/2011
Publication Date: 10/18/2011
Citation: Bolster, C.H., Vadas, P.A., Sharpley, A.N., Lory, J.A. 2011. Using a P loss model to evaluate and improve P indices. ASA-CSSA-SSSA Annual Meeting Abstracts. Abstract Only.

Interpretive Summary:

Technical Abstract: In most states, the phosphorus (P) index (PI) is the adopted strategy for assessing a field’s vulnerability to P loss when preparing comprehensive nutrient management plans. Most state PIs, however, have not been rigorously evaluated against measured P loss data to determine how well the PI assigns P loss risk – a major reason being the lack of field data available for such an analysis. Here we demonstrate an approach for evaluating and revising a PI using P loss data generated by a process-based model. Our first objective was to use regression analysis and model-generated P loss data to evaluate two different index structures, three different runoff factors for assessing P loss risk from surface applied manures and fertilizers, and two different approaches for relating sediment P loss to soil test P. Our second objective was to demonstrate how output from a process-based model can be used to evaluate and modify P index weights. The practical importance of our findings was assessed by comparing how well the different index formulations were able to predict outcomes of existing runoff studies. Our results show that a component formulation provides better correlation to simulated P loss data than a multiplicative formulation, though when evaluated against field-measured P loss, differences between the two formulations were minimal. Applying our approach to an archetypical PI based on the PA P Index significantly improved the correlation between the index and field-measured P loss data. The approach we use here can be used with any P loss model and PI and should serve as a guide to assist states in evaluating the accuracy and making modifications to their PI if necessary.