Precision Nitrogen Management for Corn: Uncertainty in the Optimal Rate and Accuracy of Drone Hyperspectral Imaging in Predicting Uptake

Tyler Nigon's PhD defense seminar
Thursday, May 27, 2021 | 9 AM


Precision Nitrogen Management for Corn: Uncertainty in the Optimal Rate and Accuracy of Drone Hyperspectral Imaging in Predicting Uptake

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, corn 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 corn 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 corn. 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 corn nitrogen uptake (relative RMSE < 17%). 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.
In partial fulfillment of the requirements for the doctoral degree in the Graduate Program in Land and Atmospheric Science.

Event Speaker

Tyler Nigon
LAAS PhD candidate advised by Profs. Ce Yang and David Mulla