Data on Swiss grapevine growers' production, pest management and risk management decisions

We present survey data from 436 grapevine growers across Switzerland and their production, pest, and risk management decisions. The online survey was conducted in spring 2022 in the three main official languages in Switzerland (German, French, Italian). The survey was used to obtain information on variety choice and farm management strategies, as well as farmer, farm, and spatial environmental characteristics. Moreover, we collected information around fungus-resistant grapevine varieties such as knowledge, attitudes, and perceptions of these varieties. We also elicited the current cultivation and growers' intentions on future acreage under these varieties. In addition, data were collected on growers' pest management strategies against weeds, insects, and fungi. Characteristics of the farm manager collected include education, farming goals, wine-related expertise, and information sources used. Information about the farm consist of marketing channels, labels, direct payment schemes, production systems and pesticide application machinery, among other details. Moreover, risk and time preferences, self-efficacy and locus of control were collected via self-assessed scales. The survey data were matched with spatial climatic data on municipality level (e.g. on temperature, precipitation, the number of yearly hail days, average sunshine duration and relative humidity) as well as pest pressure (e.g. infection risk by Oidium and Peronospora viticola) at weather station level.

Grapevine Downy Mildew Warning System Based on NB-IoT and Energy Harvesting Technology

One major problem that affecting grape production is that of infestations by fungal pathogens, among which Plasmopara viticola is one of the worst, causing grapevine downy mildew. This can cause substantial damage to a vineyard, which leads to economic losses. Methods of predicting disease outbreak rely on the monitoring of meteorological parameters. With the recent development of Internet of Things (IoT) technologies, in situ data can be efficiently collected on a large scale. In this paper, a new model with early warning system implementation for grapevine downy mildew based on Narrow Band IoT (NB-IoT) and energy harvesting is presented. Models of downy mildew warning systems have evolved from the early temperature-based (and later, humidity-based) models to the latest mechanistic models which include rainfall/leaf wetness and hourly monitoring. We added parameters such as ’favorable night condition’ and ’wind speed’ as critical for sporangia spreading. The comparison of the model with the commercial iMetos® warning system and the latest mechanistic model for three specific vineyard locations indicates a high correlation between alarms.

Together for the Better: Improvement of a Model Based Strategy for Grapevine Downy Mildew Control by Addition of Potassium Phosphonates

Grapevine downy mildew is one of the major diseases in viticulture. To control this disease, a more effective strategy has been developed and established based on growth and model data as well as on a combination of fungicides. For this purpose, the systemic plant protection product potassium phosphonate (PP) was combined with two contact fungicides. Treatments were carried out according to the different experimental conditions after the growth of 400 cm², 600 cm², and 800 cm² leaf area per primary shoot. PP increased the effectiveness of the preventive fungicides whenever high infection pressure was the case. The experiments also show that it is possible to extend the treatment intervals from 400 cm² to 600 cm² new leaf area when PP was added. However, none of the tested treatments were sufficient for the extension to intervals of 800 cm². These data show that PP can be a key factor in the reduction of the application of synthetic or copper-based fungicides.