2016
DOI: 10.1016/j.scienta.2015.11.032
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An evaluation of the chill overlap model to predict flowering time in apple tree

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Cited by 45 publications
(19 citation statements)
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“…This trade-off could be responsible for the interannual variability often reported in chill and heat requirement estimations. Assuming an overlap between chilling and warming periods has been shown to improve model fits (Darbyshire et al, 2016;Pope et al, 2014).…”
Section: Identification Of Chilling and Warming Periods By Pls Regresmentioning
confidence: 99%
“…This trade-off could be responsible for the interannual variability often reported in chill and heat requirement estimations. Assuming an overlap between chilling and warming periods has been shown to improve model fits (Darbyshire et al, 2016;Pope et al, 2014).…”
Section: Identification Of Chilling and Warming Periods By Pls Regresmentioning
confidence: 99%
“…The selected dynamic chill model was originally developed to study dormancy break of peach trees in Israel [6,23] and is, to date, widely accepted as the most robust phenological model for this Agronomy 2020, 10, 73 7 of 21 purpose [5,9,21]. As it does not predict phenological stages beyond dormancy break, it has been combined with a growing degree hour (GDH) accumulation estimation following [4], using the function 'Bloom_prediction3 in the R package 'chillR' [26].…”
Section: Dynamic + Gdh Modelmentioning
confidence: 99%
“…This continuous, undynamic approach has the advantage of fast and transparent computations that allow for easy adaptation to different fruits and local conditions. However, it results in less reliable estimations of chilling requirements [21]. Hence, the continuous model only leads to reasonable results within a narrow range of temperatures, where chilling is fulfilled before an accumulation of growing degree hours is possible.…”
Section: Introductionmentioning
confidence: 99%
“…'Burlat' confirmed a relation between flowering dates and latitudes, with flowering periods being significantly later in Northern growing sites (Figure 1). Although the dataset is not homogeneous regarding the record length, they represent a valuable asset for phenological studies, phenology models having been successfully tested and optimized using data sourced from different sites (Andreini et al, 2014;Darbyshire et al, 2016).…”
Section: Flowering Data Collectionmentioning
confidence: 99%