Abstract

Agriculture in the United States is critical for food security across the world. The United States produces over 31% of the world's corn, and 34% of the world's soybeans, according to crop reports from the 2021-2022 agricultural year. Additionally, the agricultural industry contributes substantially to the United States' GDP. Any deterioration in the U.S. agricultural yield will have considerable consequences for worldwide economy and food security. To evaluate and maintain food security in the United States, it is essential to have models that can accurately predict crop yield based on climate factors.
To better understand climate impact on crop yield and guarantee food security even in the face of climate change, it is fundamental to know at what stages of plant growth the plants are most vulnerable to harmful environmental factors.
In this talk, we present and discuss modeling strategies that we have developed to determine how two important climatic conditions, heat stress and water stress, individually and jointly affect crop's health during the course of a growing season, ultimately affecting crop yield. Our models are specified within a statistical functional framework, estimated under the Bayesian paradigm, and they postulate spatially and temporally-varying effects as well as a potential non-linear interaction between heat and water stress. They allow to estimate regional and seasonal vulnerabilities to climatic conditions, providing useful information that can be extremely helpful in devising strategies aimed at mitigating the negative impact of climate change on agricultural productivity and food security.