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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #314720
Title: Model and sensor-based recommendation approaches for in-season nitrogen management in corn

Author
item THOMPSON, L - University Of Nebraska
item FERGUSON, R - University Of Nebraska
item Kitchen, Newell
item FRANZEN, D - North Dakota State University
item MAMO, M - University Of Nebraska
item YANG, H - University Of Nebraska
item SCHEPERS, JAMES - Retired ARS Employee

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/22/2015
Publication Date: 8/15/2015
Citation: Thompson, L.J., Ferguson, R.B., Kitchen, N.R., Franzen, D.W., Mamo, M., Yang, H., Schepers, J.S. 2015. Model and sensor-based recommendation approaches for in-season nitrogen management in corn. Agronomy Journal. 107:2020–2030. doi: 10.2134/agronj15.0116.

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 higher agronomic N use efficiency for the sensor-based approach than the model-based approach. At a few sites weather conditions leading to high levels of mineralized N becoming available to the crop resulted in the sensor-based approach performing better than the model at recommending an N rate most similar to optimal N rate. However over most of the sites in this study the model-based approach estimated N rate closer to the optimal N rate. Newer recently released versions of sensor algorithms, maintaining a base N fertilizer recommendation regardless of the sensor reading, potentially will make these two approaches more alike. Additional research is needed to verify this. With canopy sensing we also found using a reference strip with plant population different from the target crop resulted in recommendations different from those obtained using a reference strip of the same population. 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 N in-season. This investigation was conducted to evaluate crop modeling (Maize-N) and active crop canopy sensing approaches for recommending in-season N fertilizer rates. These approaches were evaluated during 2012-13 on 12 field sites, two each year in Missouri, Nebraska, and North Dakota. Nitrogen management also included a no N treatment (check) and a non-limiting N reference (all at planting). Nitrogen management treatments were assessed for two hybrids and at low and high seeding rates, arranged in a randomized complete block design. In 10 of 12 site-years, the sensor-based approach recommended lower in-season N rates than the model (collectively 58% less), resulting in trends of higher partial factor productivity of N and higher agronomic efficiency than the model. However, yield was better protected by the model-based approach. In some situations canopy sensing more closely recommended N compared to the optimal N rate than did the model approach. With abnormally warm and moist soil conditions for the 2012 Nebraska sites and presumed high levels of inorganic N from mineralization, N application was appropriately reduced, resulting in no yield decrease and an average N savings of 190 kg/ha compared to the non-N-limiting reference. With canopy sensing we found using a reference strip with plant population different from the target crop resulted in recommendations different from those obtained using a reference strip of the same population. Depending on the site, both recommendation approaches were successful. A combination of model and sensor information may provide the most informed recommendation strategy for farmers striving to improving N fertilizer use efficiency and reducing N loss of fields into waterways.