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Dr. Jing Yi is a Professor in the College of Economics and Management at China Agricultural University (CAU), a Visiting Professor at Cornell University, and an Honorary Fellow at the University of Wisconsin–Madison. Her research focuses on global agrifood systems, supply chain issues, and the application of AI & big data. Using economic methods, big data, and artificial intelligence, her work aims to make agrifood systems more efficient, sustainable, and resilient.

Her research treats the agrifood system as an input-output network. The network includes farm production, processing, transportation, wholesale and retail, and food service, as well as the upstream industries that supply inputs to these stages. On this basis, she has built a quantitative framework covering the entire agrifood chain. It measures three dimensions: economic value, natural resources, and jobs and compensations. Along these dimensions, it shows how agrifood value chains create value added, use natural resources, and generate employment and income. The framework moves beyond studying farms and consumers separately, toward an approach that links the entire chain. Her research also examines how policy, market power, and changing demand interact, and how they affect the efficiency, sustainability, and resilience of agrifood systems. This work has been published in Nature Food and other journals. The Global Food Dollar methodology developed in this framework has been adopted by FAOSTAT, the statistical database of the United Nations Food and Agriculture Organization (FAO).

This research approach and methodology are not limited to agrifood. They can be applied to other industries with complex, multi-stage supply chains. She is now extending them to the pharmaceutical industry, studying how artificial intelligence accelerates new drug development worldwide, viewed through firms’ investment in R&D and AI talent. A paper resulting from this research has been invited for presentation at the National Bureau of Economic Research (NBER) Conference on the Economics of Artificial Intelligence.

Research Areas

• Agrifood value chains and value-added accounting

• Agrifood employment, labor compensation, and farmer income growth

• Natural resource use and resource-environmental accounting in agrifood systems

• Agrifood supply chains and system resilience

• Big data, machine learning, and AI methods

• Rural finance, agricultural policy, and agricultural insurance

• Agrifood system transformation and sustainable development

• AI and global new drug development (a cross-industry application of the whole-chain approach)