Interpretable machine learning approach to understand U.S. prevented planting events and project climate change impacts (Papers Track)
Haynes Stephens (University of Chicago)
Abstract
Extreme weather events in 2019 prevented U.S. farmers from planting crop on a record 19.4 million acres, more than double the previous record. Insurance reports show the majority of these were intended to be corn acres and prevented due to excess soil moisture and precipitation. However, we still lack a detailed understanding of how weather and soil conditions lead to prevented planting, as well as how climate change may impact future outcomes. Machine learning provides a promising approach to this challenge. Here, we model the drivers of prevented corn planting using soil characteristics, monthly weather conditions, and geospatial information. Due to the extreme nature of events causing prevented planting, we use a novel-design zero-inflated regression (ZIR) model that predicts the occurrence of prevented planting as well as the potential severity. We identify key environmental drivers of prevented planting, including May rainfall and soil drainage class. Under climate change scenarios, the model interestingly projects future instances of prevented planting to be less frequent but more severe relative to the historical period.