SciTransfer
Organization

EXAFAN SA

Spanish livestock technology SME with expertise in precision feeding systems and IoT-enabled smart farming for pig, poultry, and rabbit production.

Technology SMEfoodESSMENo active H2020 projectsThin data (2/5)
H2020 projects
2
As coordinator
0
Total EC funding
€378K
Unique partners
111
What they do

Their core work

EXAFAN SA is a Spanish agri-tech SME based in Zaragoza, a major agricultural hub, specializing in livestock production technology with expertise spanning feed efficiency, precision feeding systems, and the integration of IoT into farm operations. In the Feed-a-Gene project, they contributed direct industry knowledge on feed processing from local resources and by-products, and practical deployment of precision feeding techniques across pig, poultry, and rabbit production systems. Their participation in IoF2020 — one of the EU's largest smart farming pilots — demonstrates a deliberate move into data-driven and IoT-enabled agricultural solutions. As a market-facing SME, they function as an industry end-user and commercial validator in research consortia, bringing real production environments and market uptake pathways that purely academic partners cannot provide.

Core expertise

What they specialise in

Precision livestock feeding and feed efficiencyprimary
1 project

Feed-a-Gene (2015–2020) focused directly on adapting feed composition and feeding techniques using local resources and by-products to improve feed efficiency across pig, poultry, and rabbit systems.

Livestock production systems (pigs, poultry, rabbits)primary
1 project

Feed-a-Gene keywords explicitly cover pigs, poultry, and rabbits alongside genetics and modelling, indicating hands-on operational knowledge of multiple livestock species.

Smart farming and IoT integrationsecondary
1 project

IoF2020 (2017–2021) placed EXAFAN within a large-scale EU pilot deploying IoT platforms across the agri-food chain, including data-driven farming and IoT business integration use cases.

Agricultural data systems and digital farm managementemerging
1 project

IoF2020 keywords include data-driven farming, precision farming, and food chain IoT integration, reflecting active engagement with digital farm management beyond traditional production expertise.

Evolution & trajectory

How they've shifted over time

Early focus
Precision livestock feed efficiency
Recent focus
IoT smart farming integration

EXAFAN's earliest H2020 engagement (2015) was firmly rooted in the physical and biological layer of livestock production — feed composition, by-product valorisation, genetics, and modelling of feeding outcomes for specific species. By 2017, their focus shifted clearly toward the digital layer: IoT connectivity, smart farming platforms, and integrating data flows across the food chain. This is not a break from their roots but a logical extension: a livestock technology company adding digital intelligence on top of operational know-how, moving from "what to feed" toward "how to monitor, decide, and automate" across the farm.

EXAFAN is building toward the intersection of traditional livestock expertise and precision digital agriculture, making them a strong candidate for future projects combining animal production with farm automation, sensor networks, or AI-driven feeding systems.

Collaboration profile

How they like to work

Role: specialist_contributorReach: European19 countries collaborated

EXAFAN has operated exclusively as a consortium participant across both projects, never as coordinator, which positions them as a specialized industry contributor rather than a project architect. Both projects they joined were large European consortia — IoF2020 in particular was a flagship large-scale pilot with dozens of partners — indicating comfort working within complex, multi-actor environments while contributing a defined industry role. For potential partners, this suggests EXAFAN is a reliable and experienced consortium member that brings real-world production context without the administrative overhead of a lead partner.

Despite only two projects, EXAFAN has accumulated 111 unique consortium partners across 19 countries — a remarkably broad network for an SME, driven largely by IoF2020's exceptionally large multi-partner structure. Their reach spans most of the EU, with no apparent concentration in a single region beyond their Spanish base.

Why partner with them

What sets them apart

EXAFAN occupies a niche that few Spanish SMEs fill: direct operational experience in livestock production systems combined with proven participation in large-scale digital agriculture pilots. This dual grounding — feed science and IoT integration — makes them a credible bridge between traditional agri-food industry practice and the precision farming technologies that Europe's agricultural sector is being pushed to adopt. For consortia targeting real-world deployment and market validation in the livestock segment, EXAFAN brings something that research institutes and large agri-corporates typically cannot: a working SME context with skin in the game.

Notable projects

Highlights from their portfolio

  • Feed-a-Gene
    Largest single project by EC funding (EUR 217,038) and the source of EXAFAN's deepest technical expertise, covering precision feeding, genetics, and sustainability across three livestock species.
  • IoF2020
    One of the EU's largest agri-food IoT pilots, giving EXAFAN exposure to a continental-scale smart farming network and marking their strategic pivot toward digital agriculture.
Cross-sector capabilities
Digital and IoT platform deployment in industrial settingsCircular economy and by-product valorisation in agri-industrial supply chainsBiological and genetic modelling applicable to broader life sciences contexts
Analysis note: Profile is based on only 2 projects with limited metadata; no website was available to verify EXAFAN's core product or service offering. The 111-partner network figure is largely an artifact of IoF2020's unusually large consortium structure and should not be interpreted as EXAFAN having 111 bilateral relationships. Expertise inferences are directionally sound but would benefit from review against company website or product catalogues.