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1. Introduction
Advancements in computer technology have greatly expanded the amount of information that is accessible for use in agricultural economics research, as well as the machine-based tools that can be used to synthesize such large amounts of information relatively quickly. The datasets that are comprised of more diverse and numerous points of information are commonly defined by the term “Big Data.” Several studies such as Coble et al. (2018) have helped define Big Data in more general terms. Specifically, Coble et al. (2018) define Big Data based on several criteria pertaining to the volume (amount), velocity (flow), variety (i.e. lack of structure) and veracity (i.e. accuracy or representativeness) of the information points being measured and categorized to form a dataset. The important part of this definition is that it captures a defining characteristic of Big Data such that they are “integrated” or a combination of multiple types of data from multiple sources. Coble et al. (2018) also suggest that Big Data is relevant at every stage of the agricultural value chain. For example, the rise of the E-commerce business channel and its associated large number of digital transactions lead to revolutionary changes in supply chain research, especially during the COVID-19 pandemic (Du et al., 2016; Lu and Reardon, 2018; Zilberman et al., 2019; Guo et al., 2020; Min et al., 2020; Reardon et al., 2021a, b). Big data generated from these transactions are not only large in number of observations but also diverse in type and data processing methods. New data types such as text data and graphic data in online shopping platforms allow agricultural economists to study consumers’ food preferences in much more fine detail than they could prior to such data being available.
The perennial challenge that exists as a change in the information landscape occurs is that it takes the design of methods and strategies to process such information into formats that are useful for inclusion in economic and policy models. The goal of this article is to expand on previous review articles such as that by Coble et al. (2018), which focused on providing an assessment of the needs and opportunities that have emerged in the new Big Data environment, by describing in detail...