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Adaptive actions to support vineyard management using VISCA predictions in Douro Valley, Portugal

VISCA DSS is integrating climate and agricultural models with farmers’ management specifications in order to design short practices, medium- and long-term adaptation strategies to climate change.

In the Douro Valley, where our Portuguese demonstration site is located, the combination of abundant rainfall in March and April of 2020 and a very warm winter influenced the precocity of this year’s growth cycle, with budbreak recorded three weeks earlier than average. These conditions caused recurrent and earlier than expected outbreaks of downy mildew. However, with the predictions supplied by VISCA, the Symington viticulture team was able to plan a very effective treatment schedule, which successfully contained the disease.

Flowering also advanced earlier, arriving two weeks ahead of the regional average, and in order to safeguard good fruit set and ensure a healthy crop, the team at Symington continues to rely on timely VISCA predictions to plan ahead and keep disease pressures at bay.

DSS showing that budbreak has been achieved (May 2020)

 

Pictures of Ataíde vineyard of first week of May 2020 showing the phenological stage of bloom

During April, VISCA DSS predicted higher rainfall than normal in Douro. This increase of precipitation would risk the spread of grape diseases causing some loss of crops. To avoid this loss, the Viticulturists of Symington have decided to make 4 treatments sprays instead of 3 with an earlier positioning than usual in the calendar, to block the downy mildew cycle.

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Upcoming Technical Meeting of VISCA Project

Organised by the project coordinator METEOSIM (MET) and Barcelona Supercomputing Center (BSC), VISCA project consortium will hold a technical meeting on 5th-6th April 2018 at BSC premises to discuss the progress of the project and its planned activities for the coming months especially the actions related to the integration of models into the platform.

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This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 730253.