Cohort 2021

Coffee risk surveillance network


We have two companies joining for this case. CGIAR, a consultative group on international agricultural research. And GIZ provides tailor-made, cost-efficient and effective services for sustainable development. The problem they were focusing on is that, currently, collected farm data is fragmented between various actors and often goes underutilized because it is not shared or analyzed. Through combining multiple data sources, data-driven agronomy could be enabled and recommendations for farmers improved.

Anton Eitzinger

Research leader | CIAT


Make existing solutions in the coffee supply chain interoperable


Problem statement

Smallholder coffee farmers are the least resilient link in the supply chain but the most impacted from shocks. Data collection to better understand and anticipate risks at the farm level is fragmented between various actors and stored in data silos with lack of interoperability. Therefore, most of the collected data is underutilized. Though all actors realize the importance of bringing diverse data resources together to understand risks better and innovate around new information services, what hinders data sharing is technical and governance issues surrounding data interoperability, data privacy, trust, and competitiveness.


In 2023 the Coffee Risk Surveillance Network exists and facilitates data-sharing between all supply-chain actors and is underwritten by blockchain technology. Risk indicators are calculated from established metrics from coffee research. Via the network, location specific risk alerts and insights can be used by all stakeholders to better plan supply chain activities, deliver data-driven targeted farmer services and inform due diligence.

Validated idea

An open-source, transferable data-sharing network tool to deploy spatial risk indicators.

Next steps are to build a prototype aligned with an already ongoing project and raise funding with a interested consortium of stakeholders. The first prototype is for a simple use (1 value chain in 1 region within 1 country). The use case will be with a set data with the follwing risk indicators: composite of production, socio-economic & sensed data.

Want to learn more?

Check out  the full implementation roadmap for this validated idea to get inspired.