Estimating Non-linear Weather Impacts on Corn Yield--A Bayesian Approach

Tian Yu, Bruce A. Babcock
April 2011  [11-WP 522]

We estimate impacts of rainfall and temperature on corn yields by fitting a linear spline model with endogenous thresholds. Using Gibbs sampling and the Metropolis - Hastings algorithm, we simultaneously estimate the thresholds and other model parameters. A hierarchical structure is applied to capture county-specific factors determining corn yields. Results indicate that impacts of both rainfall and temperature are nonlinear and asymmetric in most states. Yield is concave in both weather variables. Corn yield decreases significantly when temperature increases beyond a certain threshold, and when the amount of rainfall decreases below a certain threshold. Flooding is another source of yield loss in some states. A moderate amount of heat is beneficial to corn yield in northern states, but not in other states. Both the levels of the thresholds and the magnitudes of the weather effects are estimated to be different across states in the Corn Belt.

Keywords: Bayesian estimation, Gibbs sampler, hierarchical structure, Metropolis-Hastings algorithm, non-linear

JEL codes: C11, C13, Q10, Q54.

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