Overall event summary from the Data Science in Agriculture Summit.
The final agenda and biographies for the Data Science in Agriculture Summit.
Stakeholder Ideas Engine Input from the Data Science in Agriculture Summit.
Program for the Data in Agriculture Summit, including agenda and biographies.
On Oct. 10, the U.S. Department of Agriculture’s National Institute of Food and Agriculture (NIFA) convened a summit to identify the frontiers and future of data in agriculture and build on existing U.S. government-wide efforts and investments in big data.
On Oct. 10, 2016, NIFA convened a summit to identify the frontiers and future of data in agriculture and build on existing U.S. government-wide efforts and investments in big data. The summit featured distinguished leaders in the fields of data science and agriculture and engaged a diverse array of stakeholders to identify new opportunities for data science in agriculture. At this summit, NIFA Director Sonny Ramaswamy announced a new initiative, Food and Agriculture Cyberinformatics and Tools (FACT), designed to develop data-driven solutions for addressing complex problems facing agriculture today. “Data, technology, and approaches that integrate individual and societal considerations are essential to meeting this challenge,” said Dr. Ramaswamy in his welcome address. “To achieve this, NIFA envisions a future for agriculture that is connected, data-driven, personalized, and sustainable.”
Joint Economic Research Service (ERS) and NIFA Program. This program invites proposals that address issues surrounding how big data influences markets, industry structure, and agricultural and food value chains. Invited projects include research only on development and applications of big data and integrated projects designed to train agricultural economists in the use of data science.
The AFRI Food and Agriculture Cyberinformatics and Tools (FACT) initiative seeks to catalyze activities that harness big data for synthesizing new knowledge, to make tailored and accurate data driven predictions, and foster data-supported innovation in agriculture.