{
  "id": "R44331",
  "type": "CRS Report",
  "typeId": "REPORTS",
  "number": "R44331",
  "active": true,
  "source": "EveryCRSReport.com",
  "versions": [
    {
      "source": "EveryCRSReport.com",
      "id": 448537,
      "date": "2016-01-06",
      "retrieved": "2016-04-06T17:34:59.163756",
      "title": "Big Data in U.S. Agriculture",
      "summary": "Recent media and industry reports have employed the term big data as a key to the future of increased food production and sustainable agriculture. A recent hearing on the private elements of big data in agriculture suggests that Congress too is interested in potential opportunities and challenges big data may hold. While there appears to be great interest, the subject of big data is complex and often misunderstood, especially within the context of agriculture.\nThere is no commonly accepted definition of the term big data. It is often used to describe a modern trend in which the combination of technology and advanced analytics creates a new way of processing information that is more useful and timely. In other words, big data is just as much about new methods for processing data as about the data themselves. It is dynamic, and when analyzed can provide a useful tool in a decisionmaking process. Most see big data in agriculture at the end use point, where farmers use precision tools to potentially create positive results like increased yields, reduced inputs, or greater sustainability. While this is certainly the more intriguing part of the discussion, it is but one aspect and does not necessarily represent a complete picture.\nBoth private and public big data play a key role in the use of technology and analytics that drive a producer\u2019s evidence-based decisions. Public-level big data represent records collected, maintained, and analyzed through publicly funded sources, specifically by federal agencies (e.g., farm program participant records and weather data). Private big data represent records generated at the production level and originate with the farmer or rancher (e.g., yield, soil analysis, irrigation levels, livestock movement, and grazing rates). While discussed separately in this report, public and private big data are typically combined to create a more complete picture of an agricultural operation and therefore better decisionmaking tools. \nBig data may significantly affect many aspects of the agricultural industry, although the full extent and nature of its eventual impacts remain uncertain. Many observers predict that the growth of big data will bring positive benefits through enhanced production, resource efficiency, and improved adaptation to climate change. While lauded for its potentially revolutionary applications, big data is not without issues. From a policy perspective, issues related to big data involve nearly every stage of its existence, including its collection (how it is captured), management (how it is stored and managed), and use (how it is analyzed and used). It is still unclear how big data will progress within agriculture due to technical and policy challenges, such as privacy and security, for producers and policymakers. As Congress follows the issue a number of questions may arise, including a principal one\u2014what is the federal role?",
      "type": "CRS Report",
      "typeId": "REPORTS",
      "active": true,
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          "url": "http://www.crs.gov/Reports/R44331",
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      "topics": []
    }
  ],
  "topics": [
    "Agricultural Policy",
    "Economic Policy",
    "Energy Policy",
    "Foreign Affairs"
  ]
}