Digital Agriculture: The Midwest Big Data Hub and Global Food Security
This presentation will describe the Midwest Big Data Hub and its focus on Digital Agriculture as a critical challenge for the 21st century. If the global population reaches over 9.5 billion by 2050, as expected, it is estimated that world food production must increase by 70%. Meeting these projected demands for food and feed will be a challenge while facing dwindling natural resources and climate variability. Significant advances in basic and applied interdisciplinary research, as well as in data and computational capabilities, must be achieved in order to better understand agro-ecosystems and leverage the services they provide. Underlying this economic and societal challenge are multiple data challenges, from data sharing to data privacy to data fusion. Brief overviews will be given of several high-impact data projects involving the Midwest and Great Plains regions, and ways to participate in the Big Data Hubs.
About Jennifer Clarke
Jennifer Clarke, Ph.D., is an Associate Professor in Food Science and Technology, and Statistics, and the Director of the Quantitative Life Sciences Initiative at the University of Nebraska Lincoln. Dr. Clarke received her undergraduate degrees in Mathematics and Psychology from Skidmore University, a M.S. in Statistics from Carnegie Mellon University and a doctorate in Statistics from the Pennsylvania State University under the mentorship of C.R. Rao. She conducted postdoctoral research at the National Institute of Statistical Sciences in Research Triangle Park and the Department of Statistical Sciences at Duke University before joining the faculty at Duke. Prior to coming to UNL in 2013, she was a faculty member at the University of Miami in the Division of Biostatistics and the Center for Computational Sciences. She serves on the steering committee of the Midwest Big Data Hub and is co-PI on an award from the NSF focused on data challenges in Digital Agriculture. Her current interests include statistical methodology for metagenomics and prediction, and training the next generation of data scientists.