Nigon, Tyler2024-01-052024-01-052021-06https://hdl.handle.net/11299/259623University of Minnesota Ph.D. dissertation.June 2021. Major: Land and Atmospheric Science. Advisors: Ce Yang, David Mulla. 1 computer file (PDF); vi, 137 pages.Over the past century, the global nitrogen cycle has been substantially altered by nitrogen fixation via the Haber-Bosch process. This fixed nitrogen is primarily used as fertilizer, ultimately supporting food, fuel, and fiber production for the ever-growing global human population. In the United States, maize production uses far more Haber-Bosch nitrogen than any other activity. Nitrogen fertilizer is necessary to achieve optimal profits, but also contributes to unintended environmental pollution, especially when applied in excess. A great deal of research has been conducted over the past several decades to improve maize nitrogen fertilizer recommendations. However, recommendations are still less accurate than necessary at the field level to successfully balance the resulting economic and environmental tradeoffs. The overarching goal of this research was to improve the understanding and extensibility of precision nitrogen fertilizer recommendations for maize. This goal was addressed by focusing on two areas that currently leads to much of the uncertainty around recommendations: i) uncertainty around the modeled economic optimal nitrogen rate derived from yield response data and ii) quality control standards for developing and implementing remote sensing-based models for predicting in-season crop nitrogen status. The focal point of each of these research areas is the spatial and temporal variation that exists in nitrogen requirements across space and from season to season. The results from this research show there was substantial variability in the modeled economic optimal nitrogen rates for several sites across Minnesota (90% confidence intervals ranged from 42 to 485 kg ha-1). Any regional economic or social analyses are only as reliable as this range of uncertainty around the modeled optimal rate, so caution must be taken to avoid misguided policy recommendations. Hyperspectral imaging was used to accurately predict early-season maize nitrogen uptake (relative RMSE < 24%). Optimizing the image processing protocol improved accuracy further, but it remains a challenge to predict the optimal nitrogen rate from early-season nitrogen status metrics such as nitrogen uptake. Doing so is a necessary step towards estimating nitrogen need and applying nitrogen at the most suitable rates and times so nitrogen recovery is maximized and nutrient loss is minimized.enCrop nitrogen statusCross-validationHyperspectralImage processingMachine learningSupervised regressionThesis or Dissertation