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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #344713
Title: Fusing corn nitrogen recommendation tools for an improved canopy reflectance sensor performance

Author
item RANSOM, C. - University Of Missouri
item Kitchen, Newell
item BEAN, G. - University Of Missouri
item CAMBERATO, J. - Purdue University
item CARTER, P. - Dupont Pioneer Hi-Bred
item FERGUSON, R. - University Of Nebraska
item FERNANDEZ, F. - University Of Minnesota
item FRANZEN, D. - North Dakota State University
item LABOSKI, C. - University Of Wisconsin
item NAFZIGER, E. - University Of Illinois
item SAWYER, J. - Iowa State University
item SHANAHAN, J. - Fortigen

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 9/6/2017
Publication Date: 10/22/2017
Citation: Ransom, C., Kitchen, N.R., Bean, G.M., Camberato, J.J., Carter, P.R., Ferguson, R.B., Fernandez, F.G., Franzen, D.W., Laboski, C.A., Nafziger, E.D., Sawyer, J.E., Shanahan, J. 2017. Fusing corn nitrogen recommendation tools for an improved canopy reflectance sensor performance [abstract]. ASA-CSSA-SSSA Annual Meeting, October 22-25, 2017, Tampa, Florida. Poster number 1249.

Interpretive Summary:

Technical Abstract: Nitrogen (N) rate recommendation tools are utilized to help producers maximize corn grain yield production. Many of these tools provide recommendations at field scales but often fail when corn N requirements are variable across the field. Canopy reflectance sensors are capable of capturing within-field variability, although the sensor algorithm recommendations may not always be as accurate at predicting corn N needs compared to other tools. Therefore, the fusion of within-field canopy reflectance sensor with field-scale N recommendation tools may help account for yield variability from N applications, and improve N rate recommendations by utilizing the strengths of multiple tools. Research was conducted on 49 N response trials over eight Midwest states to determine which N rate recommendation tool was most effective at recommending economical optimal N rates (EONR) under varying soil and weather conditions. Field-scale tools that were evaluated included pre-plant soil nitrate test, pre-sidedress soil nitrate test, maximum return to N (MRTN), yield goal based calculations, and the Maize-N crop growth model. A second objective was to determine if the Holland and Schepers canopy reflectance sensor algorithm could be improved by integrating the best performing N recommendation tools that were previously evaluated. Tools were integrated by replacing the base N rate in the algorithm, the farmer’s N rate, with the N recommendation from the best performing tools. Results showed the canopy reflectance sensor underestimated EONR but was improved by using better performing tools as the base N rate and adjusting the recommendation using a management zone scaling factor. The management zone scaling factor could be estimated using soil or weather information.