Bayesian spatially-varying functional regression for crop yield modeling

Oregon Convention Center 

Accurate crop modeling is essential to maintain food security in the United States, especially in the face of ongoing climate change. As climate conditions change regionally, it will be essential to understand both marginal and interaction effects of climate variables on crop yield.

We present a Bayesian Spatially-Varying Functional Regression model for crop yield. This model looks at the combined effect of two climate variables on crop yield. Previous models have either modeled the combined effect as a scalar term, or have used functional regression but neglected the interaction. The estimated weight functions from the functional regression are used to identify periods of vulnerability during different crop growth stages. We also account for spatial variability in modeling climate variable effects on yield.

We apply our model to recent crop yield data in the midwestern United States using the climate variables of vapor pressure deficit and soil moisture. We demonstrate the performance and interpretability of the model and compare to other crop yield models.

spatial statistics

functional data analysis

Bayesian statistics

crop yield modeling 

Section on Statistics and the Environment