@article{b966d5e5894544dfb4b8f5fe3c767dd2,
title = "An Expected Value of Sample Information (EVSI) Approach for Estimating the Payoff from a Variable Rate Technology",
abstract = "This paper examines the expected payoff to variable rate technology for fertilizer application in terms of a Bayesian expectation of the value of sample information (EVSI). The optimal variable rate for each cell in a field is conditioned on a signal in the form of the electrical conductivity of soil at that cell. Using corn response to nitrogen data from ten on-farm field-level experiments, we calculate the expected payoff from variable rate technology versus a uniform rate applied to all cells to be about $1.81/acre.",
keywords = "Bayesian decision making, EVSI, precision agriculture, VRT",
author = "Queiroz, {Pedro W.V.} and Perrin, {Richard K} and Fulginiti, {Lilyan E.} and Bullock, {David S.}",
note = "Funding Information: Pedro W.V. Queiroz is a lecturer in the Agricultural Sciences Department at Clemson University. Richard K. Perrin is Roberts Professor and Lilyan E. Fulginiti is Frederick Professor in the Department of Agricultural Economics at the University of Nebraska, Lincoln. David S. Bullock is a professor in the Department of Agricultural and Consumer Economics at the University of Illinois. This research is partially supported by the Agriculture and Food Research Initiative (AFRI) Food Security Program Coordinated Agricultural Project, titled “Using Precision Technology in On-Farm Field Trials to Enable Data-Intensive Fertilizer Management,” (Accession Number 2016-68004-24769) from the USDA National Institute of Food and Agriculture, by the Nebraska Agricultural Experiment Station with funding from the Hatch Multistate Research capacity funding program (accession numbers NEB 1011054, NEB 227784), and by USDA-NIFA Hatch Project 470-362. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Review coordinated by Dayton M. Lambert. Publisher Copyright: Copyright {\textcopyright} 2023 the authors.",
year = "2023",
month = jan,
doi = "10.22004/ag.econ.320680",
language = "English (US)",
volume = "48",
pages = "1--13",
journal = "Journal of Agricultural and Resource Economics",
issn = "1068-5502",
publisher = "Colorado State University",
number = "1",
}