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Increasing the robustness of phenological models for Vitis vinifera cv. Chardonnay

机译:酿酒葡萄的物候模型的稳健性提高。霞多丽

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摘要

Phenological models are important tools for planning viticultural practices in the short term and for projecting the impact of climate change on grapevine (Vitis vinifera) in the long term. However, the difficulties in obtaining phenological models which provide accurate predictions on a regional scale prevent them from being exploited to their full potential. The aim of this work was to obtain a robust phenological model for V. vinifera cv. Chardonnay. During calibration of the sub-models for budburst, flowering and veraison we implemented a series of measures to prevent overfitting and to give greater physiological meaning to the models. Among these were the use of experimental information on the response of Chardonnay to forcing temperatures, restriction of parameter space into physiologically meaningful limits prior to calibration, and simplification of the previously selected sub-models. The resulting process-based model had good internal validity and a good level of accuracy in predicting phenological events from external datasets. Model performance was especially high for the prediction of flowering and veraison, and comparison with other models confirmed it as a better predictor of phenology, even in extremely warm years. The modelling study highlighted a different phenological behaviour at the only mountain station, Cembra. We hypothesised that phenotypical plasticity could lead to growth rates adapting to a lower mean temperature, a mechanism not usually accounted for by phenological models.
机译:物候模型是短期规划葡萄栽培实践以及长期预测气候变化对葡萄(Vitis vinifera)的影响的重要工具。然而,在获得物候模型以提供区域范围的准确预测的困难使它们无法被充分利用。这项工作的目的是为了获得一个可靠的葡萄栽培种的物候模型。霞多丽在对芽生,开花和确证的子模型进行校准期间,我们实施了一系列措施,以防止过度拟合并赋予模型更大的生理意义。其中包括利用霞多丽对强迫温度的响应的实验信息,在校准之前将参数空间限制在生理上有意义的范围内以及简化先前选择的子模型。由此产生的基于过程的模型在从外部数据集中预测物候事件时具有良好的内部有效性和较高的准确性。该模型在预测花期和花期方面的性能特别高,并且与其他模型的比较证实,即使在极端温暖的年份,该模型也可以更好地预测物候。建模研究强调了在唯一的山地站Cembra上不同的物候行为。我们假设表型可塑性可能导致生长速率适应较低的平均温度,这是物候模型通常无法解释的机制。

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