Principal Component Analysis of Crop Yield Response to Climate Change
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The objective of this study is to compare the effects of climate change on crop yields across different regions. A Principal Component Regression (PCR) model is developed to estimate the historical relationships between weather and crop yields for corn, soybeans, cotton, and peanuts for several northern and southern U.S. states. Climate change projection data from three climate models are applied to the estimated PCR model to forecast crop yield response. Instead of directly using weather variables as predictor variables, the PCR model uses weather indices transformed from original weather variables by the Principal Component Analysis (PCA) approach. A climate change impact index (CCII) is developed to compare climate change effects across different regions. The key contribution of our study is in identifying a different climate change effects in crop yields in different U.S. states. Specifically, our results indicate that future warmer weather will have a negative impact for southern U.S. counties, while it has insignificant impact for northern U.S. counties in the next four decades.