Zhang Jingjing, Alavi Maryam, Guo Lindy, Richardson Annette C, Kramer-Walter Kris, French Victoria, Jesson Linley
The New Zealand Institute for Plant and Food Research Limited, Private Bag 92169, Auckland 1142, New Zealand.
Plants (Basel). 2024 Aug 12;13(16):2231. doi: 10.3390/plants13162231.
Accurate prediction of flowering times is essential for efficient orchard management for kiwifruit, facilitating timely pest and disease control and pollination interventions. In this study, we developed a predictive model for flowering time using weather data and observations of budbreak dynamics for the 'Hayward' and 'Zesy002' kiwifruit. We used historic data of untreated plants collected from 32 previous studies conducted between 2007 and 2022 and analyzed budbreak and flowering timing alongside cumulative heat sum (growing degree days, GDDs), chilling unit (CU) accumulation, and other environmental variables using weather data from the weather stations nearest to the study orchards. We trained/parameterized the model with data from 2007 to 2019, and then evaluated the model's efficacy using testing data from 2020 to 2022. Regression models identified a hierarchical structure with the accumulation of GDDs at the start of budbreak, one of the key predictors of flowering time. The findings suggest that integrating climatic data with phenological events such as budbreak can enhance the predictability of flowering in kiwifruit vines, offering a valuable tool for kiwifruit orchard management.
准确预测开花时间对于猕猴桃果园的高效管理至关重要，有助于及时进行病虫害防治和授粉干预。在本研究中，我们利用天气数据以及‘海沃德’和‘Zesy002’猕猴桃的萌芽动态观测数据，开发了一个开花时间预测模型。我们使用了从2007年至2022年期间进行的32项先前研究中收集的未处理植株的历史数据，并结合距离研究果园最近的气象站的天气数据，分析了萌芽和开花时间以及累积热量总和（生长度日，GDDs）、冷量单位（CU）积累和其他环境变量。我们用2007年至2019年的数据对模型进行训练/参数化，然后使用2020年至2022年的测试数据评估模型的有效性。回归模型确定了一个层次结构，其中萌芽开始时GDDs的积累是开花时间的关键预测因子之一。研究结果表明，将气候数据与萌芽等物候事件相结合，可以提高猕猴桃藤蔓开花的可预测性，为猕猴桃果园管理提供一个有价值的工具。
