|STEVENS, L - University Of Nebraska|
|FERGUSON, R - University Of Nebraska|
|MAMO, M - University Of Nebraska|
|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
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.