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Aslihan D. Spaulding

Mentor Department



Iuliia Tetteh

Co-Mentor Department



Farming is undergoing a digital revolution (Bronson and 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, Big Data in U.S. Agriculture, Congressional Research Service, 2016). In 2015, investors poured $661 million into 84 agricultural startups to help farmers transform agriculture into the next big data industry (Pham and Stack, 2018, Burwood-Taylor, Leclerc, & Tilney, 2016). Farm machines in today’s agriculture are equipped with sensors and cameras that capture fieldlevel data like soil moisture, leaf greenness, temperature, seeding, fertilizer and pesticide spraying rate, yield, fuel usage and machine performance (Pham & Stack, 2018). Approximately 70 percent of tractors in the U.S. have GPS with auto steering technologies and 40 percent of all corn farms can potentially use yield monitors (Schimmelpfennig, 2016). Though big data is seen as having a lot of prospects for the agricultural sector, certain issues including who has access to the data generated and to whom the data generated belongs to is of concern. Many producers are skeptical of data storage companies allowing their data to end up in the wrong hands which has prompted discussions by a number of articles (Castle et al. 2016). Singh and Kaskey (2014) state that “big agricultural companies could now control a data trove that presents privacy and business risks to farmers who don’t want to share the secrets of their trade with rivals or the government.” An overwhelming majority of producers believe farm data belongs to them and them alone (Banham, 2014). This belief of ownership has resulted in much discussion of developing a farm data exchange, 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 Midwestern U.S. agricultural producers’ adoption of big data technologies and some challenges these farmers encounter in the acquisition, use and control of these technologies for production management and agricultural decision-making purposes. Both online and paper survey were used in this study. Surveys were mailed and emailed to 620 and 11,556 farmers respectively within Illinois, Indiana and Iowa. Results of this study will add to the existing knowledge of literature and may assist stakeholders and policymakers to better understand rates of adoption of big data technologies and the concerns of farmers.

Big Data on the Midwest Farms: An Assessment of the Use, Concerns, and Challenges

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