Integrating Management Zones and Canopy Sensors to Improve Nitrogen Recommendation Algorithms.

Active crop canopy sensors have been studied as a tool to direct spatially variable nitrogen (N) applications, with the goal of increasing the synchrony between soil N supply and crop demand and thus improving N use efficiency (NUE). However, N recommendation algorithms have often proven inaccurate in certain subfield regions due to local spatial variability. Modifying these algorithms by integrating soil-based management zones (MZ) may improve their accuracy by allowing the sensors to accommodate the entire spectrum of field conditions. The objective of this study was to use soil properties and corn yield response to N to delineate field-specific MZ, which will be used to refine current sensor-based N recommendation algorithms. Experiments were conducted in 2016 on 4 producers’ irrigated fields in south central Nebraska, USA, each differing greatly in local topography and soil type. Soil electrical conductivity, reflectance, and landscape position data were collected with a Veris® MSP3 on-the-go soil sensing platform. Field-length strips were then chosen in regions of greatest spatial variability. 10 to 16 N response blocks (45m x 12m) were placed end to end in the strip. Blocks consisted of six smaller plots arranged in a 2x3 randomized complete block design. 6 N rates ranged from 0 to 280 kg ha^-1, with increments of 56 kg ha^-1. While crop reflectance and yield data are not yet available, given preliminary soil analyses, we anticipate high variability in crop N response within each block. Thus, there may be an opportunity to improve NUE through algorithm refinement using a combined MZ crop canopy sensor-based approach.