281-14
Determining In-Season Nitrogen Requirements for Maize Using Model and Sensor Based Approaches.

Poster Number 2117

Tuesday, November 5, 2013
Tampa Convention Center, East Hall, Third Floor

Laura Stevens, University of Nebraska - Lincoln, Lincoln, NE, Richard B. Ferguson, Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE, David W. Franzen, North Dakota State University, Fargo, ND and Newell R Kitchen, USDA-ARS Cropping Systems & Water Quality Research Unit, Columbia, MO
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 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, nitrogen 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 nitrogen 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.
See more from this Division: SSSA Division: Soil Fertility & Plant Nutrition
See more from this Session: Soil Fertility and Plant Nutrition Division and Nutrient Management and Soil and Plant Analysis Division Graduate Student Poster Competition (MS degree)

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