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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. 229-259
    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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Spatial Relationships of Soil Properties, Crop Indices, and Nitrogen Application Pattern with Wheat Growth and Yield in a Field

  1. Ole Wendroth,
  2. K. Christian Kersebaum,
  3. G. Schwab and
  4. L. Murdock
  1. O. Wendroth (owendroth@uky.edu) and G. Schwab, Univ. of Kentucky, Dep. of Plant and Soil Sciences, Lexington, KY 40546; K.C. Kersebaum, Leibniz Centre for Agricultural Landscape Research (ZALF), Institute for Landscape System Analysis, Müncheberg, Germany; L. Murdock, Univ. of Kentucky, Dep. of Plant and Soil Sciences, Research and Education Center at Princeton, Princeton, KY 42445


Inherent spatial variability of agricultural fields causes spatial differences in crop growth and demand of resources. Efficient use of input and minimizing the risk of environmental hazard and economical losses require appropriate concepts and tools on which to base site-specific management decisions. The objective of this study was to identify how helpful site-specific soil and crop field sampling is for explaining crop growth and yield variability, how sensitive remotely sensed crop reflectance indices are to quantify site-specific crop N demand, and how sensitive an uncalibrated crop growth simulation model (DSSAT) is to site-specific soil input. A 27-m-wide and 645-m-long strip in a farmer's field in Kentucky was planted with winter wheat. Nitrogen fertilizer was applied in 43 cells each 27 m wide and 15 m long in a sinusoidal spatial pattern. Mineral soil N was sampled site-specifically as well as aboveground plant biomass. Optical reflectance indices were monitored with two active sensors, the GreenSeeker and the Yara-ALS. Crop indices derived from sensor measurements taken in the spring reflected the grain yield patterns very well. Thus, these integrated state variables are good choices to predict site-specific yield variability and as management decision aids. Cell-specific input was given to the crop growth simulation model DSSAT 4.0. Measured state variables were compared with simulated ones. On an absolute basis, model results deviated substantially from measurements. On a relative basis, among all simulation scenarios applied here, simulated grain yield based on average soil input resulted in the highest correlation with measured grain yield. Further research with an improved calibrated, validated crop model is needed to explore the advantages of site-specific inputs for modeling.

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