Washington University in St. Louis researchers have developed a simulation to guide farmers on soybean-planting strategies, using a 2008-2014 dataset.
The researchers inputted seed, weather, and soil/farm data from a vast stretch of the Midwest into the SimSoy model.
"The machine-learning-based simulation allows us to make predictions of how each of the 182 seed varieties would perform under various weather conditions in a target farm--the place where those seeds have not been grown before," says Washington University professor Lingxiu Dong.
Via descriptive analytics, predictive analytics, and prescriptive analytics, the researchers converted SimSoy into a 27-question form covering factors such as latitude, longitude, area, soybean varieties, irrigation, soil types and depths, acreage, and yields. The model then essentially generates a five-line answer for each seed variety that its analytics predict for each grower.
"We are using analytics telling us what should be the right proportion of these different varieties," notes Washington University's Durai Sundaramoorthi.
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