A dataset of cherry full bloom dates across the full diversity of Australian growing regions was compiled and utilised for the first time. The primary data source was from growers located across Australia's major cherry growing districts. Records were of varying length and were used to investigate the potential of using data that substitute 'space-for-time' in phenology assessments. Full bloom timing for three cultivars, Bing, Lapins and Van, were collated. The data showed variation in full bloom time across the sites as well as inter-annual variability within sites. This highlighted the potential benefit of a predictive model for growers to better manage the flowering period. The performance of the sequential and the chill overlap models were evaluated using this data. Both models resulted in good statistical representation of the data with RMSE of 3.2 to 5.3 days and 3.3 to 5.4 days for the training and validating data, respectively. The parameterisations of the models differed with the sequential model estimating higher chilling requirement (65-68 CP) than the chill overlap model (46-48 CP). The sequential model estimated similar heat requirements between the cultivars, with Van marginally higher (7250-8000 GDH). The chill overlap model estimated a larger difference in the minimum heat requirement with Van higher (8223 GDH) than Bing and Lapins (6262 and 6486 GDH, respectively). Collation of grower records proved to be a valuable data source which, in the absence of funded programs, could be better utilised for further research that aligns outcomes with industry needs, such as decision support around polliniser varieties, design of growing systems and use of dormancy breakers to synchronise flowering for optimal fruit set and quality.

中文翻译：

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在气候多样化的生长环境中使用“空间换时间”模拟樱桃盛开

首次编译和利用了澳大利亚各种种植区的樱桃盛开日期数据集。主要数据来源来自澳大利亚主要樱桃种植区的种植者。记录的长度各不相同，用于研究在物候评估中使用数据替代“空间换时间”的潜力。整理了三个品种 Bing、Lapins 和 Van 的盛开时间。数据显示了不同地点盛开时间的变化以及地点内的年际变化。这突出了预测模型对种植者更好地管理开花期的潜在好处。使用该数据评估了顺序和冷却重叠模型的性能。两种模型都产生了良好的数据统计表示，训练和验证数据的 RMSE 分别为 3.2 至 5.3 天和 3.3 至 5.4 天。模型的参数化与顺序模型不同，估计比冷却重叠模型（46-48 CP）更高的冷却要求（65-68 CP）。序列模型估计了不同品种之间相似的热量需求，Van 略高（7250-8000 GDH）。寒冷重叠模型估计，Van 的最低热量需求（8223 GDH）比 Bing 和 Lapins（分别为 6262 和 6486 GDH）的差异更大。种植者记录的整理被证明是一个有价值的数据源，在没有资助计划的情况下，可以更好地利用它进行进一步研究，使结果与行业需求保持一致，