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霜霉病预测原位测量的新进展

New Aspects of In Situ Measurements for Downy Mildew Forecasting.

作者信息

Kleb Melissa, Merkt Nikolaus, Zörb Christian

机构信息

Institute of Crop Science, Department of Quality of Plant Products, Viticulture (340e), University of Hohenheim, Emil-Wolff-Strasse 35, 70599 Stuttgart, Germany.

出版信息

Plants (Basel). 2022 Jul 8;11(14):1807. doi: 10.3390/plants11141807.

Abstract

Downy mildew is, globally, one of the most significant diseases in viticulture. Control of this pathogen is achieved through fungicide application. However, due to restrictions (from upcoming regulations) and growing environmental conscientiousness, it is critical to continuously enhance forecasting models to reduce fungicide application. Infection potential has traditionally been based on a 50 h-degree calculation (temperature multiplied by leaf wetness duration) measured by weather stations; the main climatic parameters for forecast modelling are temperature, relative humidity, and leaf wetness. This study took these parameters measured by a weather station and compared them with the same parameters measured inside a grape canopy. The study showed that the temperature readings by the weather station compared to inside the canopy recorded differences during the day but not at night; the relative humidity showed significant differences during both daytime and night; leaf wetness showed the highest differences and was statistically significant during both daytime and night. In conclusion, the measurement differences between inside of the canopy and at the weather station have significant impacts on the precision of forecasting models. Thus, using data from inside of a canopy for the prediction should lead to even less fungicide applications.

摘要

霜霉病在全球范围内是葡萄栽培中最重要的病害之一。通过施用杀菌剂来控制这种病原体。然而,由于(即将出台的法规)限制以及环保意识的增强,持续改进预测模型以减少杀菌剂的使用至关重要。传统上,感染潜力基于气象站测量的50小时积温计算(温度乘以叶片湿润持续时间);预测建模的主要气候参数是温度、相对湿度和叶片湿润度。本研究获取了气象站测量的这些参数,并将其与葡萄树冠层内测量的相同参数进行比较。研究表明,气象站记录的温度读数与树冠层内相比,白天存在差异,但夜间没有;相对湿度在白天和夜间均显示出显著差异;叶片湿润度差异最大,在白天和夜间均具有统计学意义。总之,树冠层内与气象站的测量差异对预测模型的精度有重大影响。因此,使用树冠层内的数据进行预测应能进一步减少杀菌剂的使用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/23ee/9316467/5ec17a4c454c/plants-11-01807-g001.jpg

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