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Bayesian spatially-varying functional kernel regression for crop yield modeling

Abstract Number:

3570 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Lynsie Warr (1), Veronica Berrocal (2)

Institutions:

(1) N/A, N/A, (2) University of California, Irvine, N/A

Co-Author:

Veronica Berrocal  
University of California, Irvine

First Author:

Lynsie Warr  
N/A

Presenting Author:

Lynsie Warr  
N/A

Abstract Text:

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 Kernel Regression model for crop yield. This model has three primary features which are crucial to crop yield modeling, and which in combination contribute to the field. First, we model the interaction between two climate variables as a nonlinear surface (as opposed to a linear interaction, which oversimplifies the relationship). Second, we incorporate functional regression to estimate a weight function to identify periods of vulnerability during different crop growth stages. Lastly, we 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 predictive performance and interpretability of the model and compare to other crop yield models.

Keywords:

spatial statistics|functional data analysis|Bayesian statistics|crop yield modeling| |

Sponsors:

Section on Statistics and the Environment

Tracks:

Spatio-temporal statistics

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