Farms are becoming more data-driven and data-enabled thanks to an increase in smart machines and sensors. Rapid developments in the Internet of Things and cloud computing are propelling the phenomenon known as ‘smart farming’. The data-driven agri-food sector is expected to cause major shifts in power among different players in current food supply chain networks.
Data sharing obstacles pose a real challenge to the continued innovation of the agricultural sector. A lack of data sharing leads to a lack of cooperation, which in turn reduces the number of apps available to farmers and can ultimately result in market fragmentation. Coping with governance issues and defining suitable business models for data sharing in different supply chains is both challenging and essential.
Our approach: creating a governance framework
We first created an overview of the developments in data-driven agri-food business, sketching the stakeholder landscape and how this is expected to change. To do so, we used a conceptual framework of chain network management that describes the drivers and challenges. We then identified several basic business model patterns and applied these to the agri-food sector. These experiences were then translated into agri-business guidelines for coping with data sharing in agri-food networks. Several key questions inspired a governance framework that describes the internal and external factors in the interplay between the governance of data chain processes and institutional settings: What kind of data is available? Who owns that data? Who can access it and what are the conditions for sharing the data within a network?
The outcomes were tested and validated in workshops and in real-life use cases from current projects such as DATA-FAIR and IoF2020. These projects involved key players such as the Dutch Farmer’s Association (LTO), the cooperative data-hub JoinData and European user associations such as COPA COGECA and CEMA. Developments outside Europe (e.g. Australia, USA and New Zealand) served as inspiration for the guidelines.
(Expected) impact of the approach
Research on governance and business models directly influenced the development choices within agri-food businesses and supply chains thanks to the interaction with business parties and real-life use cases. It also influenced the development of codes of conduct, such as the EU Code of conduct on agricultural data sharing by contractual agreement (COPA COGECA). A master class (Towards data-driven agri-food business) is currently being developed with the Wageningen Academy to train agri-businesses on governance and business models for data sharing.
The guidelines, codes of conduct and business model patterns must be further developed and refined in the future, as do any additional issues that are raised. For instance, what are data property rights? Is food production a public concern? Should data be open by definition? And how should we view potential shifts in power as a consequence of data?
There is also a need to further integrate this topic into big data developments, as the primary focus is now on technical challenges instead of on organisational/business challenges.
Facts & Figures
Five business model patterns for data-driven business:
- Basic data sales
- Product innovation
- Commodity swap
- Value chain integration
- Value net creation
Source: Van 't Spijker, A., 2014. The new oil - using innovative business models to turn data into profit. Technics Publications, Basking Ridge.
- Business model canvas
- Business model patterns
- Value Chain Analysis
- VMDbee tool (Value Management Dashboard)