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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #329619
Title: Modifying the University of Missouri corn canopy sensor algorithm using soil and weather information

Author
item BEAN, GREGORY - 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 MILES, R. - University Of Missouri
item NAFZIGER, E - University Of Illinois

Submitted to: International Conference on Precision Agriculture Abstracts & Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 6/15/2016
Publication Date: 7/31/2016
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., Miles, R., Nafziger, E.D. 2016. Modifying the University of Missouri corn canopy sensor algorithm using soil and weather information. In: International Conference on Precision Agriculture, July 31-August 4, 2016, St. Louis, Missouri. Available: https://ispag.org/proceedings/?action=abstract&id=1878&search=types.

Interpretive Summary: Corn production across the U.S. Corn belt can often be limited by the loss of nitrogen fertilizer. The use of canopy sensors for making mid-season nitrogen fertilizer applications has been proven effective in matching corn plant needs just prior to active nitrogen uptake, while reducing the amount of fertilizer lost to the environment. However, nitrogen recommendation equations used with canopy sensor measurements have not proven accurate in meeting corn fertility needs. This study was conducted to determine if soil and weather information could be used to make the University of Missouri canopy reflectance sensing equation more accurate. Nitrogen response trials were conducted across eight states over two growing seasons, totaling 32 sites (four per state) with soils ranging in productivity. Reflectance measurements taken midseason were used with the University of Missouri canopy sensor equation to calculate a mid-season nitrogen fertilizer recommendation. The University of Missouri equation was found to be mediocre when comparing its recommendation to the economically optimum nitrogen rate for each site. However, when this equation was adjusted using weather and either measured or USDA SSURGO soil properties the suggested nitrogen fertilizer recommendation improved. This suggests the incorporation of soil and weather information into other canopy sensor equations may enhance their accuracy at predicting site-specific economically optimum nitrogen rates. This research will aid in the development of improved predictions of corn nitrogen fertilizer needs, which will help farmers, farm advisors, and industry reduce environmental pollution and increase grower profits.

Technical Abstract: Corn production across the U.S. Corn belt can be often limited by the loss of nitrogen (N) due to leaching, volatilization and denitrification. The use of canopy sensors for making in-season N fertilizer applications has been proven effective in matching plant N requirements with periods of rapid N uptake (V7-V11), reducing the amount of N lost to these processes. However, N recommendation algorithms used in conjunction with canopy sensor measurements have not proven accurate in making N recommendations for many fields of the U.S. Corn Belt. The objective of this research was to determine if soil and weather information could be used to make the University of Missouri canopy reflectance sensing algorithm more accurate. Nitrogen response trials were conducted across eight states over two growing seasons, totaling 32 sites (four per state) with soils ranging in productivity. Reflectance measurements at ±V9 were used with the University of Missouri canopy sensor algorithm to calculate an in-season N fertilizer recommendation. This recommendation was related to the economic optimal N rate (EONR). The University of Missouri algorithm was only mediocre in predicting EONR, averaging within 74 kg N ha-1 of EONR when target corn received 45 kg N ha-1 at-planting. However, when this algorithm was adjusted using weather and either measured or USDA SSURGO soil properties the suggested N fertilizer recommendation improved. The error, as determined by the root mean square error (RMSE), decreased from 74 kg N ha-1 down to 52 kg N ha-1 with the soil and weather adjustments. This suggests the incorporation of soil and weather information into other canopy sensor algorithms may enhance their accuracy at predicting site-specific EONR.