Understanding the farm data lifecycle: collection, use, and impact of farm data on U.S. commercial corn and soybean farms

Abstract

Enthusiasm regarding the “digital agriculture” revolution is widespread, yet objective research on how commercial farms actually use data and data services remains limited. The purpose of this research is to better understand the current positioning of U.S. commercial corn and soybean farms within the farm data lifecycle, including the collection, use, and impact of farm data. Using survey data from a sample of 800 commercial-scale U.S. corn and soybean farms, the factors associated with progression within the farm data lifecycle are examined. Results indicate that the majority of commercial U.S. corn and soybean farms collect data, indicate that the data they collect influences their decisions, and perceive positive yield benefits as a result of their data-informed decisions. However, farms vary in intensity of their data usage. Investments in data management and analysis resources are associated with progression within the farm data lifecycle. These investments comprise software products that manage and analyze data, including creating GPS maps, layering different data sources, and generating recommendations. Investments in human capital, either in on-farm employees with designated data responsibilities or in trusted off-farm service providers, are also associated with progression within the farm data lifecycle. Farms that have not yet invested in these types of data management and data analysis resources may be forfeiting the potential benefits associated with using their farm’s data to improve on-farm decision making.

Patterns of Inequalities in Digital Agriculture: A Systematic Literature Review

“…The exclusion of farmers who adopt a different approach contributes to increased inequality not only between farmers and agribusinesses but also among farmers themselves (e.g., small scale vs. large scale, conventional vs. organic) [11]. Adoption and use of digital farming technologies is already higher on large farms dedicated to the production of commodity crops, particularly in industrialized countries with a highly concentrated farm structure [12,51]. Here digitalization deepens the digital divide between larger, capital-intensive farms and those unable or unwilling to purchase digital technologies [11,24,44].…”

The economics of property rights in digital farming data: Implications for farmland markets

“…However, positive effects of data on farm productivity and profitability are not guaranteed and are often shown to be small in magnitude (Schimmelpfennig, 2016). Data must be processed, often with the help of outside service providers, to be made actionable (Thompson et al, 2021). The profitability of site‐specific farming depends on the operator's ability to collect and use field information effectively, meaning the impact of data on farmland values will vary by data collection technology and operator (Bullock et al, 2009; Bullock & Bullock, 2000).…”

Precision livestock agriculture and productive efficiency: The case of milk recording in Ireland

“…3 Of course, many more factors than information may exist such as the initial farmers' capability to solve problems, the human capital (e.g., experience, education), investment levels, subsidies, and so forth and in turn technical efficiency (e.g., Hadley, 2006;Davidova & Latruffe, 2007;Zhu & Oude Lansink, 2010;Alvarez & del Corral, 2010;Sauer & Latacz-Lohmann, 2015;Skevas et al, 2018b;Martinez-Cillero et al, 2018). 4 There is a difference between data and information (Poe et al, 1991;Schimmelpfennig & Ebel 2016;Thompson et al, 2021). Data can be seen as any set of nonrandom symbols (e.g., qualities, goals, etc.…”

“…), that result from experimentation or sampling (Davis, 1963;Harsh, 1978;Eisgruber, 1973;Chavas & Pope, 1984). Information is data that has been processed or organized into a form that is useful to the decision maker (Poe et al, 1991;Schimmelpfennig & Ebel, 2016;Thompson et al, 2021). Information technologies broadly include all those developments designed to measure, store, retrieve, process, and communicate data or information Schimmelpfennig & Ebel, 2016;Thompson et al, 2021).…”

“…Information is data that has been processed or organized into a form that is useful to the decision maker (Poe et al, 1991;Schimmelpfennig & Ebel, 2016;Thompson et al, 2021). Information technologies broadly include all those developments designed to measure, store, retrieve, process, and communicate data or information Schimmelpfennig & Ebel, 2016;Thompson et al, 2021). DeLay et al (2021) suggested other complementary technologies to yield and soil mapping should be taken into account in their analysis, as these technologies in total can be part of a broader precision agriculture strategy (bundle of technologies).…”