Modelling grapevine phenology against weather: Considerations based on a large data set

Abstract

Phenological data for 11 districts, 12 varieties and four seasons were modelled against averages of daily records of maximum and minimum temperatures and wet bulb depression. Weather data were taken from the nearest government weather station, which was commonly up to 10 km from the vineyard. The use of so-called ‘heat degree day’ methods (e.g. Winkler et al., 1974) were abandoned because the inherent correlation between elapsed time and the value of an accumulation confounds regression procedures and interpretation; averages, rather than accumulations, were used as variates in all regressions. The model of budburst involved small district and year effects, and accounted for 94.5% of the variation in the data. This demonstrated that off-site weather data can provide effective models. Models of flowering and harvest accounted for 89.2% and 92.5% of the variation in the data respectively, but involved large district and year effects. Averages of weather taken from 3 weeks or more prior to all events were significantly associated with the dates of those same events. The wet bulb depression provided significant and useful variates in the model of budburst. In addition to other associations, the date of flowering was significantly associated with the weather conditions prior to budburst. Likewise, the date of harvest was significantly associated with weather conditions prior to both budburst and flowering. Budburst and harvest dates were also associated with averages of weather taken over the 10 days just before the event. The variety of these associations encourages the use of a wider range of variates than is common in phenological studies. The district and year effects in the case of the flowering and harvest models could result from the complicated effect of the canopy on vine microclimate, or the influence of short term weather events such as storms, or, in the case of harvest, they could result from imprecise definitions of maturity. Time series methods are advocated as a means to cope better with short term weather events and also to facilitate physiological interpretation of phenological models.