STTR Phase I: A Novel Approach to Manage Nitrogen Fertilizer for Potato Production using Remote Sensing

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The broader impact/commercial impact of this Small Business Technology Transfer Research (STTR) project is to reduce the environmental impact of agricultural production while optimizing net income to producers. Over-application of nitrogen fertilizer contributes to groundwater contamination via nitrate-nitrogen leaching, and puts substantial financial burden on rural municipalities and private well owners who are required to install and pay for treatment of their drinking water. An estimated 1% of global energy consumption is attributed to the production of synthetic nitrogen fertilizer. Producers operate under tight margins, and face pressure to maximize crop yields to remain profitable and sustain their business. The proposed technology aims to optimize nitrogen application and minimize the susceptibility of loss to the environment, while accounting for the year to year weather variability that poses the largest production challenge. The technology not only determines the optimum nitrogen rate for achieving maximum profit, but it also provides transparency in nitrogen management and can serve as a means for demonstrating compliance with incentive or regulatory programs.This STTR Phase I project proposes to refine and test a novel algorithm for making real-time nitrogen fertilizer recommendations during the growing season using remote sensing. The need for this technology is rooted in the issue that producers are not satisfied with current methods for in-season nitrogen management because of lack of accuracy and poor temporal and spatial resolution. The research objectives of this project are to: (i) predict crop nitrogen concentration using multispectral information, (ii) compare the algorithm to conventional methods, (iii) calibrate and validate a crop growth model for prediction of above-ground biomass, (iv) develop predictive algorithms for radiation use efficiency, and (v) optimize the algorithm and develop a software application suitable for use by producers. The algorithm needs to account for yearly variability in crop growth dynamics caused by climatic conditions, crop variety, and nitrogen management practices. A field experiment will be conducted to collect data necessary to evaluate these objectives. It is anticipated that this technology will perform with reasonable accuracy and have similar or superior performance to existing N management methods, making it a vast improvement over current practices because of its ability to consider spatial and temporal variability in a scalable manner.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.