New Aspects of In Situ Measurements for Downy Mildew Forecasting

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.

How can proximal sensors help decision-making in grape production?

“…The self-sustaining characteristic of this device makes it possible to build up a forecasting model for grapevine powdery mildew. In Kleb et al‘s [ 56 ] experiment, proximal sensors measured temperature, relative humidity, and leaf wetness. On the basis of those data, they were able to provide more precise data compared to weather station data, which can make the control of downy mildew faster and more efficient, and therefore cheaper.…”