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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #319858
Title: Integrating soil information into canopy sensor algorithms for improved corn nitrogen rate recommendation

Author
item BEAN, G - University Of Missouri
item Kitchen, Newell
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.A. - University Of Wisconsin
item NAFZIGER, E - University Of Illinois
item RANSOM, C - University Of Missouri
item SAWYER, J - Iowa State University
item SHANAHAN, J - Dupont Pioneer Hi-Bred

Submitted to: Meeting Abstract
Publication Type: Other
Publication Acceptance Date: 7/1/2015
Publication Date: 8/3/2015
Citation: Bean, G.M., Kitchen, N.R., Camberato, J.J., Carter, P.R., Ferguson, R.B., Fernandez, F.G., Franzen, D.W., Laboski, C.M., Nafziger, E.D., Ransom, C.J., Sawyer, J.E., Shanahan, J. 2015. Integrating soil information into canopy sensor algorithms for improved corn nitrogen rate recommendation. Meeting Abstract. Poster.

Interpretive Summary:

Technical Abstract: Crop canopy sensors have proven effective at determining site-specific nitrogen (N) needs, but several Midwest states use different algorithms to predict site-specific N need. The objective of this research was to determine if soil information can be used to improve the Missouri canopy sensor algorithm for in-season corn N rate recommendations. During the 2014 growing season N rate experiments were conducted using standardized protocol for 16 sites in the US Midwest (2 sites for each state of Iowa, Illinois, Indiana, Nebraska, North Dakota, Minnesota, Missouri, and Wisconsin). Canopy sensor measurements were taken when the corn was at growth stage V9. The Missouri Algorithm alone was not an accurate predictor of the economic optimal N rate (EONR) for the 2014 growing season. This was likely due to a lack of N loss resulting from ideal weather conditions that most of the Midwest received. When the Missouri algorithm was adjusted using either measured percent soil organic matter or USDA SSURGO plant available water content (top 90 cm of the soil profile) the N recommendation averaged within 25 kg/ha of EONR. This suggests the incorporation of soil information into the Missouri algorithm can greatly enhance its accuracy at predicting site-specific EONR.