Active-Optical Reflectance Sensing Corn Algorithms Evaluated over the United States Midwest Corn Belt

Authors

• G. M. Bean, University of MissouriFollow
• N. R. Kitchen, USDA, Agricultural Research ServiceFollow
• J. J. Camberato, Purdue University
• R. B. Ferguson, University of Nebraska-LincolnFollow
• F. G. Fernandez, University of Minnesota
• D. W. Franzen, North Dakota State University--Fargo
• C. A. M. Laboski, University of Wisconsin-Madison
• E. D. Nafziger, University of Illinois
• J. E. Sawyer, Iowa State University
• P. C. Scharf, University of Missouri
• J. Schepers, University of Nebraska-LincolnFollow
• J. S. Shanahan, Fortigen

Document Type

Article

Date of this Version

2018

Citation

Agron. J. 110:2552–8 (2018)

Comments

Copyright © 2018 by the American Society of Agronomy

Open access

Abstract

Uncertainty exists with corn (Zea mays L.) N management due to year-to-year variation in crop N need, soil N supply, and N loss from leaching, volatilization, and denitrification. Activeoptical reflectance sensing (AORS) has proven effective in some fields for generating N fertilizer recommendations that improve N use efficiency, but locally derived (e.g., within a US state) AORS algorithms have not been tested simultaneously across a broad region. The objective of this research was to evaluate locally developed AORS algorithms across the US Midwest Corn Belt region for making in-season corn N recommendations. Forty-nine N response trials were conducted across eight states and three growing seasons. Reflectance measurements were collected and sidedress N rates (45–270 kg N ha^–1 on 45 kg ha–1 increments) applied at approximately V9 corn development stage. Nitrogen recommendation rates from AORS algorithms were compared with the end-of-season calculated economic optimal N rate (EONR). No algorithm was within 34 kg N ha^–1 of EONR > 50% of the time. Average recommendations differed from EONR 81 to 147 kg N ha^–1 with no N applied at planting and 74 to 118 kg N ha^–1 with 45 kg of N ha^–1 at planting, indicating algorithms performed worse with no N applied at planting. With some algorithms, utilizing red edge instead of the red reflectance improved N recommendations. Results demonstrate AORS algorithms developed under a particular set of conditions may not, at least without modification, perform very well in regions outside those within which they were developed.