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Abstract The year 2022 marks the ten‐year anniversary of the White House's Big Data Research and Development Initiative. While this initiative, and the others it spawned,  helped to advance the many facets of data intensive research and discovery, obstacles and challenges still exist. If left unaddressed these obstacles will persist and at a minimum limit the potential of what can be achieved by harnessing the many new ways to collect, analyze, and share data and the insights that can be drawn from them. The opportunities and challenges related to Big Data in agriculture touch on all aspects of the general research data lifecycle; from instruments used to gather data, to advanced digital platforms used to store, analyze, and share data, and the innovative insights from using advanced computational methods. The eight papers included in this special issue were chosen in part because they highlight both the challenges and the opportunities that come from all stages of the data lifecycle common across agricultural research and development. These papers grew out of several workshops made possible by the support of the Midwest Regional Big Data Hub, which is sponsored by the National Science Foundation. 

Abstract One of the major challenges in ensuring global food security is the ever‐changing biotic risk affecting the productivity and efficiency of the global food supply system. Biotic risks that threaten food security include pests and diseases that affect pre‐ and postharvest terrestrial agriculture and aquaculture. Strategies to minimize this risk depend heavily on plant and animal disease research. As data collected at high spatial and temporal resolutions become increasingly available, epidemiological models used to assess and predict biotic risks have become more accurate and, thus, more useful. However, with the advent of Big Data opportunities, a number of challenges have arisen that limit researchers’ access to complex, multi‐sourced, multi‐scaled data collected on pathogens, and their associated environments and hosts. Among these challenges, one of the most limiting factors is data privacy concerns from data owners and collectors. While solutions, such as the use of de‐identifying and anonymizing tools that protect sensitive information are recognized as effective practices for use by plant and animal disease researchers, there are comparatively few platforms that include data privacy by design that are accessible to researchers. We describe how the general thinking and design used for data sharing and analysis platforms can intrinsically address a number of these data privacy‐related challenges that are a barrier to researchers wanting to access data. We also describe how some of the data privacy concerns confronting plant and animal disease researchers are addressed by way of the GEMS informatics platform. 

Precision agricultural technologies (PA) such as global positioning system tools have been commercially available since the early 1990s and they are widely thought to have environmental and economic benefit; however, adoption studies show uneven adoption among farmers in the U.S. and Europe. This study aims to tackle a lingering puzzle regarding why some farmers adopt precision agriculture as an approach to food production and why others do not. The specific objective of this study is to examine the social and biophysical determinants of farmers’ adoption of PA. This paper fills a research gap by including measurements of farmer identity—specifically their own conceptions of their role in the food system—as well as their perceptions of biophysical risks as these relate to the adoption of PA among a large sample of Midwestern U.S. farmers. The study has identified that farmer identity and perceptions of environmental risk do indeed influence PA adoption and that these considerations ought to be incorporated into further studies of PA adoption in other jurisdictions. The findings also appear to highlight the social force of policy and industry efforts to frame PA as not only good for productivity and efficiency but also as an ecologically beneficial technology.