Richard B. Ferguson
Joe D. Luck
Date of this Version
Crowther, J.D. 2018. Integrating management zones and canopy sensing to improve nitrogen recommendation algorithms. Master's thesis, University of Nebraska-Lincoln, Lincoln, NE.
Fertilizer nitrogen use efficiency (NUE) in maize (Zea mays L.) production is historically inefficient, presenting significant environmental and economic challenges. Low NUE can be attributed to poor synchrony between soil N supply and crop demand, applying uniform rates of N fertilizer to spatially variable landscapes, and failure to account for temporal variability in crop response to N. Innovative N management strategies, including crop canopy sensing and management zones (MZ), are tools that have proven useful in increasing NUE. Several researchers have proposed that the integration of these two approaches may result in further improvements in NUE and in profitability by synthesizing both crop- and soil-based information for more robust N management. The objectives of this research were to identify soil and topographic variables that could be used to delineate MZ that appropriately characterize areas with differential crop response to N fertilizer and then to test a sensor-based N application algorithm and evaluate the potential of an integrated MZ- and sensor-based approach compared to uniform N management and to sensor-based N management alone. Management zones delineated with a field-specific approach were able to appropriately characterize the spatial variability in in-season crop response to N in all eight fields and in yield response to N in three of six fields. Sensor-based application resulted in significantly increased NUE compared to uniform N management in six of eight fields, and marginal net return was significantly increased in four of eight fields. Delineated MZ appropriately classified areas of differing NUE in six of eight fields. Results from these studies indicate that integrating field-specific MZ and sensor-based N application has potential to increase NUE and profitability compared to sensor-based or MZ-based N management approaches alone. Additional research is needed to explore how to best incorporate static soil information into a sensor-based algorithm that can be generalized for a variety of soil, climatic, and managerial factors.
Advisors: Richard B. Ferguson and Joe D. Luck