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Abstract

 

This article in AJ

  1. Vol. 102 No. 4, p. 1090-1098
     
    Received: Jan 8, 2010


    * Corresponding author(s): John.Shanahan@ars.usda.gov
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doi:10.2134/agronj2010.0009

An Active Sensor Algorithm for Corn Nitrogen Recommendations Based on a Chlorophyll Meter Algorithm

  1. Fernando Solaria,
  2. John F. Shanahan *b,
  3. Richard B. Fergusonc and
  4. Viacheslav I. Adamchukd
  1. a Monsanto, Pergamino, Buenos Aires, Argentina
    b USDA-ARS, Lincoln, NE 68583
    c Dep. of Agronomy and Horticulture, Univ. of Nebraska, Lincoln, NE 68583
    d Dep. of Biological Systems Engineering, Univ. of Nebraska, Lincoln, NE 68583. Mention of commercial products and organizations in this article is solely to provide specific information. It does not constitute endorsement by the authors, USDA-ARS or the University of Nebraska-Lincoln over other products and organizations not mentioned. The USDA-ARS is an equal opportunity/affirmative action employer and all agency services are available w/o discrimination

Abstract

In previous research we found active canopy sensor reflectance assessments of corn (Zea mays L.) N status have potential for directing in-season N applications, but emphasized an algorithm was needed to translate sensor readings into appropriate N application (Napp) rates. The objectives of this work were to: (i) develop an active canopy sensor algorithm based on a SPAD chlorophyll meter algorithm and (ii) validate the active canopy sensor algorithm using data collected from a companion study. We derived the sensor algorithm using a linear relationship between sensor sufficiency index (SIsensor) and SISPAD values established in the previous research and a published SPAD algorithm employing a quadratic equation to calculate Napp as a function of SISPAD The resulting equation: N app =317 0.97− SI sensor represents the function for translating SIsensor to Napp To validate the algorithm, SIsensor values collected from small plots receiving varying N amounts were converted into Napp using the algorithm. Then Napp was converted into crop N balance (Nbalance) estimates, where Nbalance = applied N-Napp Negative Nbalance values indicate N deficiency while positive values indicate excess N. The Nbalance values were compared with relative yields and a quadratic-plateau model fit to the data set for both growth stages (V11 and V15), producing an R 2 of 0.66. Relative yields plateaued at an Nbalance near zero (−11 kg N ha−1), indicating the algorithm provided reasonable estimates of Napp for maximizing yields. The equation provides a basis for the use of active crop canopy sensors for in-season N management of irrigated corn

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Copyright © 2010. American Society of AgronomyCopyright © 2010 by the American Society of Agronomy