MEASURING AND FORECASTING CHINA�S AGRICULTURAL PRODUCTION, STOCKS, AND IMPORTS WITH HIGH QUALITY DATA AND MACHINE LEARNING METHODS

The goal of this project is to gather and develop better quality, ground-truthed statistics on key Chinese agricultural commodities, coupled with suitable machine learning models, to better quantify and forecast Chinese agricultural production and imports with a focus on its impacts on U.S. agricultural trade.We propose to address the following three objectives to be discussed in detail in the following sections:Objective 1: Develop reliable and granular statistics on production, consumption, and stocks of major agricultural commodities in China leveraging planting intentions surveys, satellite data, and machine learning methods (Lead: Zhang; Co-lead: He, Hayes, Xiong, Hu).Objective 2: Develop machine learning models, including supervised and unsupervised models, to fit and forecast China's overall agricultural production, consumption, and imports of major commodities over time (Lead: Hu; Co-lead: Hayes, He, Zhang).Objective 3: Disseminate better data on China's key agricultural commodities and validated machine learning models via an open-source platform that readily allows collaboration, contribution, and utilization by other researchers (Lead: He; Co-lead: Hayes, Hu, Zhang, Xiong).