Agricultural Economics Department


First Advisor

Taro Mieno

Date of this Version

Summer 7-2021


A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska. In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Agricultural Economics. Under the Supervision of Professor Taro Mieno. Lincoln, Nebraska. August, 2021

Copyright 2021 Qianqian Du


Optimal N fertilizer rates for corn (Zea mays L.) vary substantially within and among fields, and by corn growth stages. Improving N side-dressing management can improve fertilizer use efficiency, farmers’ profitability, and the sustainability of crop production. The objective of this study is to introduce a framework along with a methodology that can find the site-specific economically optimal N rates (EONRs) within one field for a particular growing season. An on-farm experiment was conducted in the 2019 corn growing season. A base N rate was applied uniformly on the field. NDRE images from the Sentinel-2 satellite were observed during the V10 to V12 corn growth stages. Experimental side-dressing N rates ranging from 0 to 177 kg N/ha were applied. The marginal return of N fertilizer was calculated using estimated yield response functions assuming various NDRE levels. Consistent with agronomic expectations, results showed that the parts of the field with lower NDRE values had higher marginal returns from side-dressing N, and the areas with the higher NDRE values needed less N fertilizer to reach their yield plateaus. Simulations predicted that compared to the side-dressing strategy the farmer would have implemented if not participating in the OFPE, profits could have been increased by $54.85 per hectare by using the methodology presented, applying site-specific optimal N side-dressing can increase $ 4.53 per hectare compared to applying field-level optimal N. Results may vary under different base N and weather situations. Further study is needed to improve the featured methodology, for example, by considering adding covariates and finding better data resources for vegetation index maps.

Advisor: Taro Mieno