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A simple method to predict dissolved phosphorus in runoff from surface-applied manures.

P A Vadas1, P J A Kleinman, A N Sharpley

  • 1USDA-Agricultural Research Service, Pasture Systems and Watershed Management Research Unit, Building 3702, Curtin Rd., University Park, PA 16802-3702, USA. Peter.Vadas@ars.usda.gov

Journal of Environmental Quality
|April 13, 2004
PubMed
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A new method accurately predicts dissolved phosphorus (P) release from surface-applied manures into runoff, improving water quality models. Field testing is needed for this phosphorus loss prediction approach.

Area of Science:

  • Environmental Science
  • Agricultural Science
  • Water Quality Management

Background:

  • Computer models are vital for identifying agricultural areas prone to phosphorus (P) loss.
  • Existing models struggle to accurately simulate dissolved P release from surface-applied manures into runoff.

Purpose of the Study:

  • To develop a simple, effective approach for predicting dissolved P release from manures.
  • To improve the accuracy of water quality models concerning P loss from agriculture.

Main Methods:

  • Developed a predictive approach based on laboratory P extraction from dairy, poultry, and swine manures.
  • Validated the approach using data from leaching experiments and soil box studies with simulated rainfall.
  • Refined the model by incorporating runoff-to-rainfall ratios and manure-specific correction factors.

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Main Results:

  • The approach accurately predicted dissolved inorganic and organic P release in laboratory settings (R2 = 0.70 and 0.73).
  • Initial predictions for dissolved inorganic P in runoff were poor (R2 = 0.18) but improved significantly after adjustments (R2 = 0.71).
  • Overprediction in runoff was attributed to P infiltration in freely draining slurries, necessitating manure-specific adjustments.

Conclusions:

  • The developed approach offers a promising, simple method for predicting dissolved P runoff from surface-applied manures.
  • This method has the potential to significantly enhance the accuracy of agricultural water quality models.
  • Further field validation is crucial before widespread implementation of the predictive approach.