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.