D4Dairy annual meeting at the Hub


May 29, 2019

D4Dairy project partners gathered at the Complexity Science Hub Vienna

 

In October 2018, the most comprehensive digitization project in Austrian agriculture was started under the lead of the Austrian Cattle Breeder Association (ZAR). The Complexity Science Hub Vienna is one of the 44 project partners. The project, called D4Dairy, wants to make use of Big Data, generated in Austrian dairy stables, to gain insights for breeding and dairy farming practices as well as for health questions.

 

Almost all of the 44 partners from science and industry came together for the first D4Dairy annual meeting at the Hub on May 22, 2019.

 

CSH president Stefan Thurner, the host of this year’s meeting, opened the meeting with an introduction to the research pursued at the Hub. “Processing and analyzing large sets of data—Big Data!—and making meaningful predictions from it, is one of the main tasks at the Hub,” Stefan said. “For D4Dairy, we will bring together data from different fields to discover meaningful, hopefully unexpected connections.” These insights could be used to develop something like a personalized medicine for cows, Stefan continued, “but with much better data than we have for humans!”

 

“One of our aims is to improve communication and data exchange between the operating systems and external data,” said project leader Christa Egger-Danner from ZuchtData. Data integration is crucial for not to collect the same data over and over again.

Nine D4Dairy subprojects

 

The expertise of the Hub is needed to deal with the huge data sets and to generate value from it. The findings will eventually provide new insights for breeders and dairy farmers, including better tools for the early detection of diseases and the optimization of livestock management.

 

The insights will be made available to farmers through easy-to-use new software tools.

 

The project partners contribute in various ways to one or several of the following subprojects:

 

  • Digitization, data integration and building-up of data interfaces
  • Development of online tools to improve livestock management
  • Promotion of ways to reduce antibiotic use
  • Big Data analysis for the early detection of diseases via genetic markers or milk infrared spectral data
  • Impact of stable climate on animal performance, health and welfare
  • Developments in genetics and genomics
  • Detection of mycotoxins in fodder and their impact on milk yield and fertility
  • Data protection
  • Knowledge transfer of the scientific results

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