Tuesday, November 14, 2006 - 1:10 PM
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Can optical sensing identify the right N rate for corn?.

Robert Mullen, OARDC/Ohio State University, "1680 Madison Ave., School Nat. Res.", "1680 Madison Ave., School Nat. Res.", Wooster, OH 44691, United States of America, William Raun, 368 Agricultural Hall, Oklahoma State University, Oklahoma State University, Dept. of Plant & Soil Sciences, Stillwater, OK 74078-0507, James Schepers, 113 Keim Hall, Univ. of Nebraska, Univ. of Nebraska, Lincoln, NE 68583-0915, United States of America, Newell Kitchen, Univ. of Missouri/USDA-ARS, 243 Agricultural Engineer Bldg, 243 Agricultural Engineer Bldg, Columbia, MO 65211, United States of America, Gyles Randall, Univ. Minn.-So. Res. & Outreach Ctr, 35838 120th St., 35838 120th St., Waseca, MN 56093-4521, United States of America, Gregory Schwab, UK Dept. of Agronomy, N122T Ag Sci Bldg. North, N122T Ag Sci Bldg. North, Lexington, KY 40546, United States of America, Wade Thomason, Virginia Tech, 422 Smyth Hall, 422 Smyth Hall, Blacksburg, VA 24061, United States of America, Steven Phillips, Eastern Shore AREC, 33446 Research Dr., 33446 Research Dr., Painter, VA 23420, United States of America, John Shanahan, USDA-ARS, Lincoln, NE 68583-0934, United States of America, Dennis Francis, "USDA-ARS, Univ. Of Nebraska", PO Box 830934, Lincoln, NE 68583-0934, United States of America, and Jeffrey A. Vetsch, Univ. of Minnesota, Southern Research and Outreach Center, 35838 120th St., Waseca, MN 56093-4521.

Environmental and, more recently, economic pressure on nitrogen applications in corn production systems has facilitated research to find alternative methods of providing nitrogen recommendations.  In an effort to account for spatial and temporal variation in nitrogen demand, optical sensing technology is a promising tool that may capable of allowing decision makers to identify optimum nitrogen rates for a given field or smaller areas within a field for a given year.  Multiple algorithms have been developed by Land-Grant Universities to determine nitrogen recommendations based upon optical sensor measurements, but the information collected is confined to a relatively small geographic region.  Recently, an international group of scientists has been evaluating these optical sensors and the algorithms that have been developed to determine if a robust algorithm can function over multiple growing environments.  Researchers from several Land-Grant Universities and ARS scientists from the United States as well as researchers from Canada and Central and South America have followed a similar research protocol to determine the ability of optical sensors to identify optimum nitrogen rates.  Each experimental location followed a core protocol evaluating different nitrogen rates to determine the optimum, but researchers were allowed to include additional treatments as they saw fit to represent their region and cultural practices.  Findings from the research reveal that some algorithms can perform quite well in some environments. 

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