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A comparison of sweet cherry chilling requirements estimated using statistical and biological approaches

Author:
Wenden, B., Quero-Garcia, J., Dirlewanger, E., Darbyshire, R.
Source:
Acta horticulturae 2018 no.1229 pp. 177-182
ISSN:
0567-7572
Subject:
Prunus avium, chilling requirement, climate change, climatic factors, cross pollination, dormancy, flowering, fruit set, fruit trees, fruits, models, phenology, risk, spring, winter, Europe
Abstract:
Many key phenological stages of temperate fruit trees are highly dependent on environmental conditions. This includes the timing of dormancy release and flowering which are essential to ensure good fruit production and quality. Global changes in environmental conditions including warmer winters and higher risks of frosts in the early spring, may lead to a wide range of problems, including poor flowering, fruit set and cross-pollination, and novel host-pest interaction. In the context of climate change, one challenge for researchers is to better understand possible impacts on flowering and subsequently to breed fruit trees adapted to future climatic conditions. Predictive models for flowering phenology provide a valuable tool to assist in this process. Here we assessed two methods to determine the chilling requirement, a key parameter to develop a predictive phenology model. Following the collection of sweet cherry flowering data recorded across Europe, we present an exploration of the chill overlap model for the reference 'Burlat'. The model was tested and optimised for a wide range of potential chilling requirement values. The best fit was obtained for a critical chilling requirement of 49 chill portions. Using forcing experiments, the chilling requirement was found to vary between the two experimental years (40 and 76 chill portions) and did not correspond to the statistically determined chilling requirement. These results highlight that further investigation is needed for both phenological models and experimental analyses of dormancy and flowering in order to develop more robust models based on biologically-sound parameters.
Agid:
6376943