Agricultural Economics Department
First Advisor
Bradley D. Lubben
Second Advisor
Joe Luck
Third Advisor
Taro Mieno
Date of this Version
12-2016
Document Type
Thesis
Citation
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 Bradley D. Lubben
Lincoln, Nebraska, December 2016
Abstract
An ever-increasing global demand for food, coupled with increasingly volatile commodity prices have charged producers with the task of becoming more efficient. As such, technologies aimed at producing more with less are continually being developed and marketed to producers. However, whether or not these expensive new technologies have resulted in improved profitability is still unknown, as the vast majority of studies showing their impact on profitability have been performed using hypothetical farms and simulations. These studies have shown the potential for increases in profitability from use, but their impact in the real world is still uncertain.
This project uses various fixed-effect panel data models to examine the realized economic impact of using precision agriculture technologies amongst a sample of producers across Nebraska using financial data from 1995-2014. Results of the study show the existence of a strong, positive relationship between number of technologies used and net farm income, indicating that precision agriculture use is associated with higher profitability. However, whether use is driving profitability or profitability is driving use remains somewhat unclear. Pre-and-post analysis among users of the technologies suggest profitability has in fact increased from use, but the result is not statistically significant. This may be a consequence of mixed results among users, with many factors influencing the level of benefit achievable from use. Nonetheless, an obvious learning effect exists for users, with profitability increasing more as experience with the technologies increases. This would be expected due to the need to produce data regarding within-field variability on which to capitalize, along with the investment in learning the ideal use of these relatively complicated technologies. Overall, it is obvious that further research regarding the impact of these technologies is of great relevance.
Advisor: Bradley D. Lubben
Comments
Copyright 2016, Michael H. Castle