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This article in JEQ

  1. Vol. 41 No. 6, p. 1758-1766
     
    Received: Dec 8, 2011
    Published: November 5, 2012


    * Corresponding author(s): carl.bolster@ars.usda.gov
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doi:10.2134/jeq2011.0457

Using a Phosphorus Loss Model to Evaluate and Improve Phosphorus Indices

  1. Carl H. Bolster *a,
  2. Peter A. Vadasb,
  3. Andrew N. Sharpleyc and
  4. John A. Loryd
  1. a USDA–ARS, 230 Bennett Ln., Bowling Green, KY 42104
    b USDA–ARS, 1925 Linden Dr. West, Madison, WI 53706
    c Dep. of Crop, Soil, and Environmental Sciences, Univ. of Arkansas, 115 Plant Sciences Building, Fayetteville, AR 72701
    d Division of Plant Sciences, Univ. of Missouri, 108 Waters Hall, Columbia, MO 65211. This research was part of USDA-ARS National Program 214: Agricultural and Industrial Byproducts. Assigned to Associate Editor Nathan Nelson

Abstract

In most states, the phosphorus (P) index (PI) is the adopted strategy for assessing a field’s vulnerability to P loss; however, many state PIs 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. Given the lack of P loss data available for PI evaluation, our goal was to demonstrate how a P loss model can be used to evaluate and revise a PI using the Pennsylvania (PA) PI as an example. Our first objective was to compare two different formulations—multiplicative and component—for calculating a PI. Our second objective was to evaluate whether output from a P loss model can be used to improve PI weighting by calculating weights for modified versions of the PA PI from model-generated P loss data. Our results indicate that several potential limitations exist with the original multiplicative index formulation and that a component formulation is more consistent with how P loss is calculated with P loss models and generally provides more accurate estimates of P loss. Moreover, using the PI weights calculated from the model-generated data noticeably improved the correlation between PI values and a large and diverse measured P loss data set. The approach we use here can be used with any P loss model and PI and thus can serve as a guide to assist states in evaluating and modifying their PI.

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Copyright © 2012. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.