Economics of Experimental Design: Finding the Optimal Design of a Whole Field Randomized Experiment

Authors

Taro Mieno, University of Nebraska-LincolnFollow
David S. Bullock, University of Illinois at Urbana-ChampaignFollow
Aolin Gong, University of Illinois Urbana-ChampaignFollow

Date of this Version

8-22-2018

Document Type

Article

Citation

Cornhusker Economics, August 22, 2018, agecon.unl.edu/cornhuskereconomics

Comments

Copyright 2018 University of Nebraska

Abstract

An April 2017 Cornhusker Economics article Getting to Know Your Yield Response Better through Whole-field Randomized Experiments discussed a case study detailing a whole-field randomized agronomic experiment (nitrogen and seed rate). The study was conducted in Kentucky and funded through the USDA-NIFA Data Intensive Farm Management (DIFM) project. The project has so far resulted in more than 50 experiments across several states, including Illinois, Nebraska, Kansas, and Washington. The overarching goal of the project is to provide agricultural producers with the best input rate, variable or uniform, which maximizes their farm profits. This involves statistically estimating the crop response function and then the mathematical optimization to identify the best input rate based on the estimated response function.