“Data, Economics and Computational Agricultural Science” presented by John Angle, Oregon State University. A 2017 special issue of Agricultural Systems on “Next Generation Data, Models and Knowledge Products” presented a vision for accelerating the rate of agricultural innovation and meeting the growing global need for food and fiber. The authors envisage computational agricultural research and development that could complement, and increasingly substitute for, conventional experimental methods. They argue that significantly improved data and models could contribute to development of advanced farm-management systems that could accelerate the adoption and efficient use of more productive and more sustainable technologies. Various ongoing efforts by public sector organizations such as USDA and private firms such as Microsoft are working to develop and implement new technologies consistent with this vision, including new sensors, “big data,” and artificial intelligence. In this lecture I propose that economics has an important role to play in these developments. I draw parallels between advances in artificial intelligence and machine learning and recent developments in microeconometrics, and implications for the potential and challenges of “smart farming” systems and their role in generating data for computational science.

John Antle is a professor in the Department of Applied Economics at Oregon State University, Corvallis, Oregon, and a University Fellow at Resources for the Future, Washington, D.C. He received the Ph.D. in Economics from the University of Chicago in 1980. He has served on the President’s Council of Economic Advisers in Washington, D.C. (1989-90), and the National Research Council’s Board on Agriculture (1991-95). He is a Fellow and past President of the American Agricultural Economics Association. He is currently a co-PI of the Agricultural Model Inter-comparison and Improvement Project (AgMIP), and leading the Regional Economics component of AgMIP. He is the lead developer of an impact assessment model (TOA-MD) being used globally for agricultural sustainability and climate impact assessment. His current research focuses on quantitative modeling and assessment of the sustainability of agricultural systems in industrialized and developing countries.