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