Graduation Term

2020

Degree Name

Master of Science (MS)

Department

Department of Agriculture

Committee Chair

Aslihan D. Spaulding

Abstract

Farming is undergoing a digital revolution (Bronson & Knezevic, 2016). The advent of plant genetics, chemical inputs, and more recently guidance systems have transformed the industry into one that is increasingly technology-intense and data-rich (Stubbs, 2016). Concerns are being raised including big agricultural companies’ control of a data trove that presents privacy and business risks to farmers who do not want to share their operational data with competitors or the government (Singh & Kaskey, 2014). An overwhelming majority of agricultural producers believe farm data belongs to them (Banham, 2014). This belief of ownership has resulted in much discussion of developing a farm data exchange – an arrangement, in which producers could be compensated for sharing of their data (Shickler, 2015; Banham, 2014; Singh & Kaskey, 2014). The purpose of this study is to identify factors that influence U.S. agricultural producers’ adoption of Big Data technologies focusing on the Midwestern region and some of the challenges these farmers encounter in the acquisition, use and control of the gathered data for production management and agricultural decision-making purposes. A survey was conducted to collect data from farmers in Illinois, Indiana and Iowa. The survey was distributed to 12,176 farmers and had a 2.4 percent response rate with 241 complete responses. About 90 percent of farmers belief their farm data belonged to them. About 79 percent of farmers were concerned about how their farm data was shared, 79 percent were concerned about third parties who used their farm data while 78 percent were concerned about third party access to their farm data. A Poisson regression model was used to identify factors influencing the number of technologies adopted by farmers. Results showed a statistically significant relationship between acres farmed and adoption group of farmers. Results of the binary logistic regression showed that, the age of farm operator, educational level of farm operator, number of technologies used on the farm, increase in yield as a reason for using technology and difficulty with understanding data as a reason for not using technology were statistically significant.

Access Type

Thesis-Open Access

DOI

https://doi.org/10.30707/ETD2020.20210309065832396170.100

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Agriculture Commons

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