New paper on agricultural data

Tractor on field.

Tractor on field.

Technological advances are driving rapid growth in the collection and use of farm and ranch production data. First introduced in 1863, U.S. Department of Agriculture (USDA) farm and ranch production surveys helped to reduce market information asymmetries that favored food-processing businesses. Today, the dramatic expansion of private agricultural data collection is leading to new information asymmetries and potential challenges for existing USDA agricultural data collection and use.
In 2016, the global market for “smart agriculture” technologies and services was estimated at $5.1 billion.The global “big data” agriculture market is expected to grow by 20.1% a year from 2018 to 2022.Rapid improvements in data collection, along with powerful analytical tools like artificial intelligence and machine learning, are helping to increase production efficiency and farm and ranch profitability, while reducing environmental impacts.
Innovations in agricultural data collection and analysis offer opportunities for USDA to create more effective programs and reduce costs. A broad range of stakeholders could benefit from improvements in the USDA’s data collection, analysis, and warehousing methods. These improvements could increase farmer and rancher survey response rates, reduce the cost and increase effectiveness of USDA programs, and benefit productivity, profitability, and environmental outcomes.
“Agricultural Data Innovation: Implications for the USDA,” explores a range of agricultural data collection trends and their implications for the USDA and the private sector. The paper describes:
  • USDA data collection, analysis, and warehousing related to agricultural production and conservation;
  • private-sector investment in agricultural conservation data collection, including satellite-based remote sensing; and
  • advances in agricultural conservation data and farm management information systems.
The authors also offer recommendations for data collection and analysis methods that USDA could use to generate data and information that are more timely, efficient, accessible, and robust.