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Nitrogen (N) management in cereal crops has been the subject of considerable research and debate for several decades. Historic N management practices have contributed to low nitrogen use efficiency (NUE). Low NUE can be caused by such things as poor synchronization between soil N supply and crop demand, uniform application rates of fertilizer N to spatially variable landscapes, and failure to account for temporally variable influences on soil N supply and crop N need. Active canopy reflectance sensors and management zones (MZ) have been studied separately as possible plant- and soil-based N management tools to increase NUE. Recently, some have suggested that the integration of these two approaches would provide a more robust N management strategy that could more effectively account for soil and plant effects on crop N need. For this reason, the goal of this research was to develop an N application strategy that would account for spatial variability in soil properties and use active canopy reflectance sensors to determine in-season, on-the-go N fertilizer rates, thereby increasing NUE and economic return for producers over current N management practices. To address this overall goal, a series of studies were conducted to better understand active canopy sensor use and explore the possibility of integrating spatial soil data with active canopy sensors. Sensor placement to assess crop N status was first examined. It was found that the greatest reduction in error over sensing each individual row for a hypothetical 24-row applicator was obtained with 2-3 sensors estimating an average chlorophyll index for the entire boom width. Next, use of active sensor-based soil organic matter (OM) estimation was compared to more conventional aerial image-based soil OM estimation. By adjusting regression intercept values for each field, OM could be predicted using either a single sensor or image data layer. The final study consisted of validation of the active sensor algorithm developed by Solari (2006), identification of soil variables for MZ delineation, and the possible integration of MZ and active sensors for N application. Crop response (sensor measured sufficiency index and yield) had the highest correlation with soil optical reflectance readings in sandy fields and with apparent soil electrical conductivity in silt loam fields with eroded slopes. Therefore, using these soil variables to delineate MZ allowed characterization of spatial patterns in both in-season crop response (sufficiency index) and yield. Compared to uniform N application, integrating MZ and sensor-based N application resulted in substantial N savings (~40-120 kg ha-1) and increases in partial factor productivity (~13-75 kg grain (kg N applied)-1) for fine-textured soils with eroded slopes. However, for coarser texture soils the current sensor-based N application algorithm may require further calibration, and for fields with no spatial variability there appears to be no benefit to using the algorithm. Collectively, results from these studies show promise for integrating active sensor-based N application and static soil-based MZ to increase NUE and economic return for producers over current N management strategies, but further research is needed to explore how best to integrate these two N management strategies.