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 uses spatially explicit weather, vegetation health, and soil data, to monitor environmental and crop conditions during the growing season and continuously forecast final yield.
Customers Use the Model to
Why It Matters
Soybeans are significant as an input in the livestock industry, as a raw material for biofuel, and for human consumption. The US is the second-largest producer and exporter of soybeans, and changes to its crop have ramifications on a global scale, both for other key soy producers and for livestock producers. As such, following estimates of US production throughout the season can guide decision-makers across the value chain of the crop.
We modeled county-level yields and then aggregated them to the national level, weighting by reported harvested area. The input variables of the model included:
Next, we aggregated the gridded spatial variables to the county level. We applied our soybean land cover to soil texture data, elevation, and the percentage of irrigated fields. We evaluated our model performance through ”leave-one-year-out” cross-validation using 2009-2016 data.