Predicting phenological events using event-history analysis

This paper presents an approach to phenology, one based on the use of a method developed by the authors for event history data. Of specific interest is the prediction of the so-called "bloom--date" of fruit trees in the agriculture industry and it is this application which we consider, although the method is much more broadly applicable. Our approach provides sensible estimate for a parameter that interests phenologists -- Tbase, the thresholding parameter in the definition of the growing degree days (GDD). Our analysis supports scientists' empirical finding: the timing of a phenological event of a prenniel crop is related the cumulative sum of GDDs. Our prediction of future bloom--dates are quite accurate, but the predictive uncertainty is high, possibly due to our crude climate model for predicting future temperature, the time-dependent covariate in our regression model for phenological events. We found that if we can manage to get accurate prediction of future temperature, our prediction of bloom--date is more accurate and the predictive uncertainty is much lower.