Model and Sensor-Based Recommendation Approaches for In-Season Nitrogen Management in Corn

Nitrogen management for corn (Zea mays L.) may 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 2012–2013 on 11 field sites, 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 9 of 11 site-years, the sensor-based approach recommended lower in-season N rates than the model (collectively 59% less N), resulting in trends of higher partial factor productivity of nitrogen (PFPN) and higher agronomic efficiency (AE) than the model. However, yield was better protected by the model-based approach. In some situations, canopy sensing excelled at optimizing the N rate for localized conditions. 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 N savings compared to the non-limiting N reference. Depending on the site, both recommendation approaches were successful; a combination of model and sensor information may optimize in-season decision support for N recommendation