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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #332587

Research Project: Sustainable Intensification of Grain and Biomass Cropping Systems using a Landscape-Based GxExM Approach

Location: Cropping Systems and Water Quality Research

Title: fusing regional and weather variability with site-specific canopy reflectance for improved in-season N fertilizer recommendation

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

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 8/30/2016
Publication Date: N/A
Citation: N/A

Interpretive Summary:

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 of EONR when target corn received 45 kg N/ha 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), for corn receiving 45 kg N/ha at-planting the RMSE was 74 kg N/ha without soil and weather and 52 kg N/ha with the soil and weather adjustment. This suggests the incorporation of soil and weather information into other canopy sensor algorithms may enhance their accuracy at predicting site-specific EONR.