Gro’s Yield Forecast Models use a suite of machine-learning models to estimate in-season yields at the district, province, and/or national levels on a daily basis. The model contains province-level ground truth data going back to 2010.
Customers Use the Model to
Why It Matters
China is a major player in crop production and consumption patterns driving global trends and dynamics. Domestic Chinese corn consumption has more than doubled in the past decade, largely due to demand for livestock feed. Currently the country consumes over 280 million tonnes of corn annually, meaning that Chinese corn inventories have significant influence on the global corn balance sheet. However, it can be challenging to obtain public sources for in-season yield estimates for China. This model fills the gap, enabling users to keep track of yield figures in the main corn-producing provinces.
These machine-learning models are driven by inputs reflecting long-term trends as well as in-season changes. The models run daily throughout the growing season, and rely on province-by-province signals derived using our domain expertise. They rely on proprietary land cover data, built using high resolution satellite imagery to match estimates of area planted from each source. Major model inputs and features include: