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Using the Functional Data Explorer in JMP to Model and Predict Crop Yield Based on Weather History (2019-US-EPO-220)

Level: Intermediate


Jerry Fish, JMP Systems Engineer, SAS


Crop yield is affected by many variables that Mother Nature controls (temperature, rainfall, etc.). Each of these inputs has an annual profile, so some years we have abundant rain in the spring, others we have dry summers, etc. The JMP Functional Data Explorer is an ideal tool for studying these weather patterns and correlating them to annual crop yield. We develop a regression model for predicting corn and soy bean yield (bushels per acre) in Ohio with inputs of several annual weather histories (rainfall, snowfall, temperature, humidity, moon phases, etc.). We will then attempt to predict 2019’s crop yield based on to-date weather patterns.