Integrating Water and Nitrogen Management for Sustainable Agriculture: Optimizing Resource Use Efficiency and Maximizing Crop Productivity

Advisors: Derek Heeren and Daran Rudnick Maize, accounting for over 95% of national grain production in the United States, is highly sensitive to water and nitrogen (N) inputs. Conventional agricultural practices often lead to excessive application, causing groundwater contamination through nitrate leaching. Therefore, there is a demand for integrating water and nitrogen management with innovative scheduling methods for sustainable agricultural development. This dissertation first reviewed two decades of U.S.-based research, highlighting the optimal management of water and N to enhance yield, water use efficiency (WUE), and nitrogen use efficiency (NUE). Findings indicate that maintaining optimal levels of N and water is crucial, as excessive inputs beyond the optimal threshold reduce their effectiveness. Factors such as field capacity, temperature, and precipitation must be considered in management decisions. The results also suggested potential savings of 26% in irrigation and 13% in nitrogen application without compromising yield. Advanced remote sensing techniques, particularly UAV-mounted multispectral sensors, have proven reliable for in-season N stress detection and guiding spatial fertigation decisions. The third chapter introduced a novel sensor-based fertigation strategy, utilizing the normalized difference red-edge (NDRE) vegetation index. Field experiments in North Platte, Nebraska, compared UAV-based multispectral sensor applications with conventional soil sample-based methods under different irrigation levels, demonstrating that sensor-based treatments, despite a 12% yield loss, significantly improved NUE parameters and reduced N leaching. The combination of sensor-based N management and full irrigation offered the best ecological return. Future research should focus on economic returns and adaptability across different environments and corn hybrids. In the fourth chapter, the DNDC model, calibrated with in-situ data, effectively predicted yield and N uptake but had limitations in water dynamics prediction. Sensor-based treatments reduced nitrate leaching by 44%-70%, highlighting the importance of dynamic temporal N management. Model-optimized strategies suggested a 75% reduction in water input could enhance ET and reduce deep percolation while maintaining yield. It underscored the importance of frequent water and nitrogen management for maximizing resource use efficiency. However, future research is needed to integrate spatial variability, thereby improving the assessment and development of resilient cropping systems.