In VISCA, we have recently published three reports. The first 'seasonal forecasts quality assessment report (D2.1)' assesses the quality of two seasonal forecast systems (European Centre for Middlerange Weather Forecasting (ECMWF) System 4 and SEAS5), two bias correction approaches (calibration and simple bias correction) and a downscaling strategy (calibration). The results obtained show that there is some degree of predictability in the three demo-sites in different variables that can provide value beyond the customary use of climatology. Moreover, the verification / bias-correction / downscaling workflow developed in this task provides the basics of all future refinements that we will conduct during the remaining two years of the project, e. g. through the exploration of new seasonal prediction systems and/or downscaling techniques.
On 2nd-3rd May 2018, the EU AgriResearch Conference took place in Brussels targeting scientists, farmers, rural communities, industry, advisors, policy-makers, citizens and NGO representatives who wish to learn about EU agriculture and rural R&I activities and achievements and have their say on how to shape the future of agriculture R&I after 2020. The conference was also a great occasion to network with around 500 peers.
During the conference, different topics about the H2020 program and priorities in the agriculture sector were presented including presentations about EU-funded projects. The implementation of the strategic approach to EU agricultural R&I and present its first achievements on knowledge produced, linkages established between EU policies or new avenues opened in terms of implementation approaches were discussed.
Few farmers also participated in the event and some discussions on how to better involve them took place concluding that the language and the timing of harvest season should always be taken into consideration.
VISCA project was disseminated during the event by SEMIDE. Exchanges and contacts were gathered in order to make collaborations in the replicability and synergies foreseen in the project.
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 730253.