16 June 2023

HOLICOW

Small/midsized farms in NWE are suffering for many reasons. One impactful reason is the fact that they can’t reap the benefits of the digital transformation and the efficiency driven “farming 4.0.” Furthermore, the so-called “agri-bashing” does not emphasise the relevance of farming for a resilient and green community. Hence, these farmers are key players in animal welfare and climate change resilience.

Article of "The Local": 
“Twenty years ago, there were ten farms in my commune in the hills 20 miles south of Caen. 
There are now only four farms in the commune. One of the remaining farmers will retire this year .”

The “HoliCow”-team holds a data-driven solution that will adapt and implement existing “Big-Data” solutions and custom-fit/upcale them for small/midsized farms in the entire NWE territory.

It is e.g. new that also external data (climate) and interactive software tools for transparent farming will be integrated. This will enable farmers to stay in their territory and take part in modern farming methods with affordable tools. HoliCow does reveal synergies with other instruments that assists small, rural farms.
A transnational approach is vital to calibrate the wide range of different cows/farms towards standardised solutions and to generate enough data to produce a meaningful alert system. In addition, transnational cooperation will significantly lower the implementation barrier through joint development and joint learning. 

The three main tasks are: 

  • WP1: DATA for farm tools: Big Data integration of external and internal data to new and onsite benefits for animal wellbeing and climate resilience (e.g. heat stress)
  • WP2: TOOLS for farmers: Application of co-created, accessible and remote tools
  • WP3: PEOPLE for farmers: Creating community-based action


As a set of transnational cross-cutting demonstration field lab, a transnational network of pilot farms will be subject to concrete action and joint learning.

CRA-W will mainly participates in WP 1 and 2, by combining in an optimal way the existing data and models, in order to provide valuable information for daily management of dairy farms.

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