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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #344702

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

Location: Cropping Systems and Water Quality Research

Title: Soil and environmental factors affecting internal N efficiency of maize

Author
item CAMBERATO, J. - Purdue University
item SHAFER, M. - 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 Kitchen, Newell
item LABOSKI, C. - University Of Wisconsin
item NAFZIGER, E. - University Of Illinois
item NIELSON, R. - Purdue University
item SHANAHAN, J. - Fortigen
item SAWYER, J. - Iowa State University

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 9/6/2017
Publication Date: 10/22/2017
Citation: Camberato, J.J., Shafer, M., Carter, P.R., Ferguson, R.B., Fernandez, F.G., Franzen, D.W., Kitchen, N.R., Laboski, C.A., Nafziger, E.D., Nielson, R.L., Shanahan, J., Sawyer, J.E. 2017. Soil and environmental factors affecting internal N efficiency of maize. ASA-CSSA-SSSA Annual Meeting, October 22-25, 2017. Tampa, Florida. Paper #108990.

Interpretive Summary:

Technical Abstract: The inverse of internal N efficiency (IE), N needed per quantity of grain produced, is used in yield-goal based N recommendations to determine the target quantity of N needed to attain a chosen maize yield. Often the value of IE is considered static, irrespective of environment. Evaluation of 47 site-years of data across the U.S. Corn Belt demonstrated variation in IE at the economically optimum N rate (IEE). IEE ranged from 38 to 73 kg dry grain/kg plant N with a mean and standard deviation of 54 and 7 kg grain kg/kg N. A linear model based on soil properties determinable at planting (texture, organic matter, and pH) explained only 16% of the variation in IEE. Just prior to sidedress at growth stage V9, 38% of the variation in IEE was explained by soil texture, soil nitrate-N, and NDVI. Considering all variables at the end of the season, including soil, weather, and crop parameters, only about 60% of the variation in IEE was explained by linear models. Unpredictable variation in IEE introduces meaningful variation in yield-goal based N recommendations.