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, truth-grounded statistics on keyChinese agricultural commodities, coupled with suitable ML models, to better quantify andforecast Chinese agricultural production and imports with a focus on the impacts on United States (US) agricultural trade. We propose to address the three following objectives to be discussed in detailin the following sections:Objective 1:Develop reliable and granular statistics on production, consumption, and stocks ofmajor agricultural commodities in China by leveraging planting intentions surveys, satellite data,and ML methods (Lead: Zhang; Co-lead: He, Hayes, Xiong, Hu).Objective 2:Develop ML models, including supervised and unsupervised models, to fit andforecast China's overall agricultural production, consumption, and imports of major commoditiesover time (Lead: Hu; Co-lead: Hayes, He, Zhang).Objective 3:Disseminate better data on China's key agricultural commodities and validated MLmodels via an open-source platform that readily allows collaboration, contribution, andutilization by other researchers (Lead: He; Co-lead: Hayes, Hu, Zhang, Xiong).