HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Sharpley, A. N. Articles by Smith, S.J. Search for Related Content PubMed Articles by Sharpley, A. N. Articles by Smith, S.J. Agricola Articles by Sharpley, A. N. Articles by Smith, S.J.

Prediction of Bioavailable Phosphorus Loss in Agricultural Runoff

USDA-ARS, National Agricultural Water Quality Laboratory, P.O. Box 1430, Durant, OK 74702-1430.

^* Corresponding author.

ABSTRACT

The loss of bioavailable P (BAP) in both soluble (SP) and participate (BPP, bioavailable particulate P) forms in agricultural runoff, can accelerate the eutrophication of surface waters. Bioavailable P represents P that is potentially available for algal uptake. Although prediction of BAP loss would provide a more reliable assessment of the impact of agricultural management on surface water eutrophication than just SP or total P (TP), currently no predictive equations are available. Bioavailable P transport in soluble and particulate forms in runoff from 20 agricultural watersheds in the Southern Plains over a 5-yr period, was predicted using kinetic and enrichment ratio approaches, respectively. Measured and predicted SP and BPP concentrations of individual runoff events were similar for all watersheds and management practices over a range of 7 to 3522 and 3 to 4059 µg L^–1, respectively. Calculated as the sum of SP and BPP, BAP loss in runoff was predicted accurately, with only a 12% error in measured mean annual loss (662 g ha^–1 yr^–1). Although BAP loss below 100 g ha^–1 yr^–1 was underestimated, above this value measured and predicted values closely followed a 1:1 relationship. Inclusion of these equations in comprehensive water quality models will improve the prediction of agricultural management on the biological response of receiving water bodies.