One aspect of Agroknow’s work in the context of the BigDataEurope Horizon 2020 project (and as the project is moving forward entering its second year) is to develop a pilot addressing a specific Horizon 2020 Societal Challenge and from a domain specific perspective (in our case that of Societal Challenge 2: Food security, sustainable agriculture and forestry, marine, maritime and inland water research and the bioeconomy). Our pilot is specific to agriculture and more specifically to viticulture and the diversity of grapevine varieties. What we want to do is to explore how we can “support advanced crop data discovery, processing, combining and visualization from distributed and heterogeneous data repositories”.
“Why Viticulture?” you might ask. For many reasons. When it comes to research and viticulture, there are sustainability and biodiversity challenges for with local varieties being lost while at the same time, there is the need to exploit new grapevine varieties and clones in terms of climate change adaptation. Not to mention, of course, the importance of viticultural products on human health. Obviously, the vine and wine sector is a continuously growing market in Europe, in terms of economy, market and commerce.
But let’s get back to our “V” issue. As mentioned in an earlier Agroknow blog post, Big Data is all about the four Vs, one of them being Variety. This extreme variety of data in agriculture has been verified in a workshop organized by Agroknow, FAO and the BigDataEurope project in the context of the Research Data Alliance 6th Plenary Meeting. And viticulture is no exception. Currently, every research being done in viticulture produces a wide variety of heterogeneous (and big) data in every step of the experimental process, whether this research is related to genetic study and identification of grapevine varieties, phenotypic description of grapevine varieties, climate impact on grapevine varieties, precision viticulture, breeding etc.
Grapevine varieties differ from one another in such a degree that researchers may find themselves surrounded by raw data types that need to be managed: genotype data, phenotype data, spectrophotometer data, sensor data, images etc. and many more types of data that need to be translated and integrated into tables, diagrams and other visualized ways in order to present all this information in a simple and meaningful way.
Our use case pilot is a work in progress at the moment so stay tuned for more details and interesting things to come.