Abstract
The multivariate statistical technique of principal components analysis was used to evaluate the relationship of climatic indices and variables to corn ( Zea mays L.) yields in the Great Plains and Midwest of the United States. Results demonstrated that during years in which precipitation was consistently above or below normal throughout the growing season, the Palmer Drought Severity Index (PDSI) and the Crop Moisture Index (CMI) related well to actual yield. However, during years in which there was an occurrence of dry or wet spells lasting for several weeks (particularly during pollination), the magnitude of the PDSI and the CMI did not adequately reflect the impact of that dry or wet period on the crop. The results of this analysis demonstrate that principal components analysis is a powerful statistical tool for evaluating the relationship between crop yield and climatic variables and the associated regional patterns for these relationships.
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