Raw Data team traveled to Ciudad Real to attend Fenavin, one of the leading fairs of the wine sector in Spain.

We were able to conduct a study on viticulture challenges in order to know what the solutions are that big data and prediction models can contribute to the wine sector.

So, especially thanks to the participants in the study for their collaboration and willingness to respond to their interests regarding viticulture and big data challenges.

Conclusions on viticulture challenges solved with Big Data

1.Quality parameters to determine harvest date

89% of respondents responded that Alcoholic Grade, Acidity and PH are the most important quality parameters in viticulture to determine harvest dates.

The remaining % uses organoleptic taste to determine harvest dates.

 

2. Harvest planning managers

95% of the wineries surveyed responded that winemakers and field managers carry out vintage planning.

The rest of the respondents responded that the management or directive performs harvest planning.

 

3. Best prediction models in viticulture that can contribute to a winery

A 78% of respondents responded that knowing the ideal harvest date according to the evolution of the quality parameters is the most useful prediction model.

11% responded that knowing the Production volume per plot is the most useful prediction model in viticulture, so the remaining 11% answered that the prediction models of pests and diseases would be the most useful prediction models to use on their farms.

 

4. Main pests and diseases that affect their plots

44% responded that mildew is their main concern, followed by the oidio, with 38%.

 

If you want to contribute your answers to the survey, please do it in the following link: https://bit.ly/2YzYWoG

Finally, we invite you to see here a real success case with prediction models applied to viticulture or you can also create a Free account to know the benefits of prediction models in Raw Data.