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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #298088

Title: Determining in-season nitrogen requirements for maize using model and sensor based approaches

item STEVENS, L - University Of Nebraska
item FERGUSON, R - University Of Nebraska
item FRANZEN, D - North Dakota State University
item Kitchen, Newell

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 8/30/2013
Publication Date: 11/5/2013
Citation: Stevens, L., Ferguson, R.B., Franzen, D.W., Kitchen, N.R. 2013. Determining in-season nitrogen requirements for maize using model and sensor based approaches [abstract]. ASA-CSSA-SSSA Annual Meeting. 281-14.

Interpretive Summary:

Technical Abstract: Nitrogen (N), an essential element, is often limiting to plant growth. There is great value in determining the optimum quantity and timing of N application to meet crop needs while minimizing losses. Applying a portion of the total N during the growing season allows for adjustments which can be responsive to actual field conditions which result in varying N needs. Two methods of determining in-season N needs were evaluated, a model and handheld crop canopy sensor. The Maize-N model was developed to estimate the economically optimum N fertilizer rates for maize by taking into account soil properties, indigenous soil N supply, local climatic conditions and yield potential, crop rotation, tillage and fertilizer formulation, application method and timing. The active crop canopy sensor is responsive to canopy N status during the growing season and when used with high N reference plots, can be used to determine in-season N application rates. Four replications of randomized complete blocks were conducted at each of 6 sites over a 3-state region including Missouri, Nebraska and North Dakota. The model and sensor based approaches were evaluated for yield, N partial factor productivity, and agronomic efficiency. For all sites, in-season N application rates for model-based treatments exceeded that of sensor-based treatments. Additionally, sensor-based treatments had higher N use efficiency as seen by partial factor productivity. In a year with high mineralization for Nebraska sites, sensor based application produced higher partial factor productivity of N since the sensor application method required less N and yields were similar between model and sensor based treatments, indicating that in 2012, the sensor-based approach was more responsive to in-season growing conditions.