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山区苹果开花的物候模型。

Phenological models for blooming of apple in a mountainous region.

作者信息

Rea Roberto, Eccel Emanuele

机构信息

Department of Natural Resources, IASMA Research Centre, Via Mach 1, San Michele, 38010, Italy.

出版信息

Int J Biometeorol. 2006 Sep;51(1):1-16. doi: 10.1007/s00484-006-0043-x. Epub 2006 Aug 15.

Abstract

Six phenological series were available for 'Golden Delicious' apple blooming at six sites in Trentino, an alpine fruit-growing region. Several models were tested to predict flowering dates, all involving a "chilling and forcing" approach. In many cases, application of the models to different climatic conditions results in low accuracy of prediction of flowering date. The aim of this work is to develop a model with more general validity, starting from the six available series, and to test it against five other phenological series outside the original area of model development. A modified version of the "Utah" model was the approach that performed best. In fact, an algorithm using "chill units" for rest completion and a thermal sum for growing-degree-hours (GDH), whose efficiency changes over time depending on the fraction of forcing attained, yielded a very good prediction of flowering. Results were good even if hourly temperatures were reconstructed from daily minimum and maximum values. Errors resulting from prediction of flowering data were relatively small, and root mean square errors were in the range of 1-6 days, being <2 days for the longest phenological series. In the most general form of the model, the summation of GDH required for flowering is not a fixed value, but a function of topoclimatic variables for a particular site: slope, aspect and spring mean temperature. This approach allows extension of application of the model to sites with different climatic features outside the test area.

摘要

在特伦蒂诺(一个高山水果种植区)的六个地点,有六个物候系列可用于“金冠”苹果的开花情况。测试了几个模型来预测开花日期,所有模型都采用“低温和积温”方法。在许多情况下,将这些模型应用于不同的气候条件会导致开花日期预测的准确性较低。这项工作的目的是从现有的六个系列出发,开发一个具有更广泛适用性的模型,并在模型开发原始区域之外的其他五个物候系列上对其进行测试。“犹他”模型的一个修改版本是表现最佳的方法。实际上,一种使用“低温单位”来完成休眠和用热和来计算生长度日(GDH)的算法,其效率会随着时间根据达到的积温比例而变化,对开花情况给出了很好的预测。即使根据每日最低和最高温度重建每小时温度,结果也很好。开花数据预测产生的误差相对较小,均方根误差在1 - 6天范围内,对于最长的物候系列小于2天。在该模型最一般的形式中,开花所需的GDH总和不是一个固定值,而是特定地点的地形气候变量的函数:坡度、坡向和春季平均温度。这种方法使得该模型能够扩展应用于测试区域之外具有不同气候特征的地点。

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