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In this paper, a Bayesian change-point model was used to examine the phenological changes in the predominant crop producing states of U.S over a 33-year period (1981–2013). Changes of phenological observation were categorized into a no-change model and  two change models. The change point and intensity of shifts were subsequently estimated under the selected change model. The experiments were conducted in the cropping regions using the state-level crop progress reports issued by the U.S. Department of Agriculture. The results demonstrated that the planted, silking and mature stages of corn were significantly advanced under the change models; the vegetative period was shortened, and the reproductive and growing seasons were lengthened. The soybean phenological metrics followed a similar trend as that of corn, even though more states tended to change under  a change model. The underlying drivers of such abrupt changes may be the confounding effects of crop breeding, agronomic management and climate change. Specific events, such as the adoption of genetically engineered crops in 1996–1997, can partly explain the changes in phenology. A comparison with the breakpoints function and Pettitt method demonstrated the feasibility and effectiveness of the Bayesian change-point model on crop phenological change detection

Phenological Changes of Corn and Soybeans over U.S. by Bayesian Change-Point Model