The timing of the transition between endodormancy and ecodormancy remains uncertain. However, with advancements in phenology modelling, we can now fit models which allow for variable transitions between chilling and forcing models. Previous studies have primarily focused on single-cultivar parameterisation, and few have explored multi-cultivar comparative modelling. In this paper, we address this gap by evaluating three parameterisation approaches based on the recently developed PhenoFlex framework using a large flowering time dataset of twenty-six apple cultivars collected at the same location in England. The three parameterisation approaches were: cultivar-specific, group-specific with the groups derived using the K-means algorithm on mean bloom and variation of bloom dates, and a common model (for all twenty-six cultivars). The three PhenoFlex models fitted to each of three groups of cultivars based on their flowering time and the common model fitted to all cultivars achieved similar predictive performance, better than predictions using the average bloom date of each cultivar. The best approach to apply would depend on the amount of data present. The common model works best with large number of cultivars with small datasets (∼10 years), the mean flowering date grouped works best with medium numbers of datasets (∼20 years) and the cultivar-specific model should only be used when each cultivar has at least 30 years of data, however, it is more biased, so it is likely to predict bloom dates later than the observed bloom dates. Finally, the PhenoFlex model was shown to perform better than the StepChill model, where no overlapping is allowed between chilling and heat models. The result of this study indicates that the PhenoFlex model can be used to determine apple flowering time at the species level.

中文翻译：

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评估预测英格兰 26 个苹果品种开花时间的模型的性能


内休眠和生态休眠之间的过渡时间仍不确定。然而，随着物候建模的进步，我们现在可以拟合允许冷却模型和强迫模型之间可变转换的模型。以前的研究主要集中在单品种参数化，很少探索多品种比较模型。在本文中，我们通过评估基于最近开发的 PhenoFlex 框架的三种参数化方法来解决这一差距，使用在英格兰同一地点收集的 26 个苹果品种的大型开花时间数据集。三种参数化方法是：品种特定、群体特定（使用 K 均值算法得出平均开花和开花日期变化的群体）以及通用模型（适用于所有 26 个品种）。根据开花时间对三组品种进行拟合的三个 PhenoFlex 模型和对所有品种进行拟合的通用模型实现了类似的预测性能，优于使用每个品种的平均开花日期进行的预测。最佳应用方法取决于现有数据量。通用模型最适合用于小数据集（~10 年）的大量品种，分组的平均开花日期最适合中等数量的数据集（~20 年），并且仅当每个品种具有至少 30 年的数据，然而，它的偏差更大，因此它预测的开花日期很可能晚于观测到的开花日期。最后，PhenoFlex 模型的性能优于 StepChill 模型，其中冷却模型和加热模型之间不允许重叠。 这项研究的结果表明，PhenoFlex 模型可用于确定物种水平上的苹果开花时间。