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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #310508

Title: In-season nitrogen requirement for maize using model and sensor-based recommendation approaches

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

Submitted to: International Conference on Precision Agriculture Abstracts & Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 3/1/2014
Publication Date: N/A
Citation: N/A

Interpretive Summary: Nitrogen (N) management for corn grain production can be improved by applying a portion of the total N during the growing season, allowing for adjustments which are responsive to actual field conditions. This study was conducted to evaluate two different approaches for determining in-season N rates: a crop growth model approach and active crop canopy sensing approach. In a 2-year study, a total of 12 sites were evaluated over a 3-state region, including sites in Missouri, Nebraska, and North Dakota. In-season N recommendations were generally lower when using the sensor-based approach than the model-based approach. This resulted in observed trends of higher agronomic N use efficiency for the sensor-based approach than the model-based approach. At a few sites, conditions leading to high levels of mineralized N becoming available to the crop during the growing season and this improved environmental and economic benefits with the sensor-based approach. However over most of the sites, the model-based approach estimated an N rate that was closer to the determined optimal N rate than the sensor-based approach. Three different ways for calculating N rate using canopy sensing were also compared. The Missouri/USDA-ARS method had the closest approximation of optimum N rate, but in some cases still over-recommended N. Farmers will benefit from this research because they can reduce excess N applications, which with increasing N fertilizer cost, should save them money. If fertilizer can be better matched with crop need, N fertilizer loss to the environment will be reduced, thus helping to protect soil, water, and air resources.

Technical Abstract: Nitrogen (N) management for corn (Zea mays L.) can be improved by applying a portion of the total required N in-season, allowing for adjustments which are responsive to actual field conditions. This study was conducted to evaluate two approaches for determining in-season N rates: Maize-N model and active crop canopy sensor. The effects of corn hybrid and planting population on recommendations with these two approaches were considered. In a 2-yr study, a total of 12 sites were evaluated over a 3-state region, including sites in Missouri, Nebraska, and North Dakota. Over all site-years combined, in-season N recommendations were generally lower when using the sensor-based approach than the model-based approach. This resulted in observed trends of higher partial factor productivity of N and agronomic efficiency for the sensor-based treatments than the model-based treatments. Overall, yield was better protected by using the model-based approach than the sensor-based approach. For two Nebraska sites in 2012 where high levels of N mineralization were present, the sensor approach appropriately reduced N application, resulting in no decrease in yield and increased profitability when compared with the non-N-limiting reference. This indicates that specific conditions will increase the environmental and economic benefit of the sensor-based approach. The optimal N rate (ONR) was also determined using a linear-plateau model, considering hybrid and population differences (P=0.05) for both the linear and plateau parts of the model. Compared to the ONR, the model-based approach more closely estimated ONR than the sensor-based approach when considering all sites collectively. Overall, the model-based approach erred by over-recommending N, while the sensor-based approach erred by under-recommending N.