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Book: Methods of Introducing System Models into Agricultural Research
Published by: American Society of Agronomy, Crop Science Society of America, Soil Science Society of America



  1.  p. 283-294
    Advances in Agricultural Systems Modeling 2.
    Methods of Introducing System Models into Agricultural Research

    Laj R. Ahuja and Liwang Ma (ed.)

    ISBN: 978-0-89118-196-5


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Quality Assurance of Weather Data for Agricultural System Model Input: A Case Study using the Walnut Creek Watershed in Central Iowa

  1. R.W. Malone,
  2. D.W. Meek,
  3. L. Ma,
  4. D.B. Jaynes,
  5. B.T. Nolan and
  6. D.L. Karlen
  1. R.W. Malone (rob.malone@ars.usda.gov), D.W. Meek (dave.meek@ars.usda.gov), D.B. Jaynes (dan.jaynes@ars.usda.gov), D.L. Karlen (doug.karlen@ars.usda.gov), USDA-ARS, National Laboratory for Agriculture and the Environment, 2110 University Boulevard, Ames, IA 50011-0001; L. Ma, USDA-ARS, Agricultural Systems Research Unit, 2150 Centre Ave. Bldg. D, Fort Collins, CO 80526 (liwang.ma@ars.usda.gov); B.T. Nolan, USGS, 413 National Center, Reston, VA 20192 (btnolan@usgs.gov).


Use of agricultural system models in field research requires quality weather data for input. This chapter provides methods to assess the integrity of rainfall, radiation, and humidity input for the Root Zone Water Quality Model (RZWQM). These methods were applied to a recent RZWQM application at Walnut Creek in central Iowa. Data bias of 10% in humidity, rainfall, and solar radiation results in long-term errors of more than 40% for nitrate loss in tile drains from these RZWQM simulations. Actual weather records show measurement errors in rain, solar radiation, and humidity of 10% or more. The quality assurance procedures described here will help model users detect erroneous weather measurements, which will help improve their model simulations through more accurate simulations and higher confidence in the weather input.

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