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4Growth high level objective and focus change
Inspirational pitch at the 4Growth project consortium meeing, 17-18 February in Thessaloniki, Greece.
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4Growth high level objective and focus change
- 1. High-level objective of 4Growth– the next waves Consortium Meeting Thessaloniki, 17 February 2025 Sjaak Wolfert – Wageningen Research (WR) This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No. 101134855.
- 2. Digital Transformation ofAgri-Food in 4 areas coming together Cloud Computing Big Data Analytics Internet of Things Linked Data Artificial Intelligence Blockchain Technology 3. Public decision-making Smart Sensing & monitoring Smart Control Smart Analysis & Planning 1. Decision-Making Business/Consumers 2. Food Integrity 4. Science & Technology https://www.linkedin.com/pulse/transdisciplinary-data-driven-research-social-sjaak-wolfert/
- 3. The ‘Battlefield’ ofData of Farming and Food Farming Data Food Data See: Wolfert et al., Agricultural Systems 153 (2017) 69–80 Processors Ag Business Tech Companies Tech Start-up Tech Start-up Ag Tech Retail Venture Capitalists Data Start-up Data Start-up
- 4. current interesting area System of systems Stand-alone application IT Integrationlevel Number of stakeholders Process operator Business ecosystem Single apps Production processes Farm information systems Farm management Chain information systems Food supply chain Data Sharing Initiatives Agri-Food systems Agri-Food Data Economy Data Spaces Evolution of ICT in agri- food See: Wolfert, S., Verdouw, C., van Wassenaer, L., Dolfsma, W., Klerkx, L., 2023. Digital Innovation Ecosystems in Agri-Food: design principles for d eveloping and organizational fra mework. Agricultural Systems 204, 103358. €€€ where to invest? €€€ Artificial Intelligence
- 5. Towards a commonR + I + D infrastructure RESEARCH INNOVATION DEPLOYMENT Partnership AgData CEADS: COMMON EUROPEAN AGRICULTURAL DATASPACE DIGITAL INNOVATION HUBS HORIZON PROJECTS Adapted from: Dr. Doris MARQUARDT, European Commission, DG CNECT private/public investments & effective use IMPACT ‘Draghi report’ The future of European competitiveness Horizon Europe Digital Europe other instruments Common Agricultural Policy EU Regional Development Fund Connecting Europe Facility
- 6. System of systems Stand-alone application IT Integrationlevel Number of stakeholders Process operator Business ecosystem Single apps Production processes Farm information systems Farm management Chain information systems Food supply chain Data Sharing Initiatives Agri-Food systems Agri-Food Data Economy Data Spaces Evolution of ICT in agri- food See: Wolfert, S., Verdouw, C., van Wassenaer, L., Dolfsma, W., Klerkx, L., 2023. Digital Innovation Ecosystems in Agri-Food: design principles for d eveloping and organizational fra mework. Agricultural Systems 204, 103358.
- 7. Data Economy: whendid it start...?
- 8. Business Model patternsin data-driven innovations Basic data sales: Pick up your data generated by your core process and sell it to third parties. Usually not modified. Only anonymized or analysed to turn into a sellable product. Product innovation:Use your data to create additional products and services. Commodity swap: A commodity provider uses the sale or usage of a commodity product or service as a means to generate data. It uses this data to differentiate itself from competing commodity offerings. The data is used to create a new product or service that is inseparable connected to the commodity offering. Value chain interaction: Sharing data with partners in order to save on expenses. Parties involved are part of the same value chain. The data flow creates more value when specific data from various parts of the chain can be combined. Value net creation: Even when companies are not in the same value chain, they can still find a common ground on which to share data and achieve benefits. They all share the same final customer without harming each organisation’s competitive position. Source: Arent van 't Spijker: "The New Oil - using innovative business models to turn data into profit“, 2014
- 9. Basic data salesexample How does it work? - A ‘box’ collects all data - Data is stored by a blockchain in the cloud (DataEngineSM) - Data is being marketed/invested - Farmer pays fee but gets a share of the profit “Farmers think their trust is violated” Their data goes to multinationals that promise high future yields based on big data, while farmers have to pay for everything “Data is our product!”
- 10. “The DE4AFS canbe defined as a dynamic, multi-layered ecosystem that connects actors, resources, technologies, and governance structures to create, exchange, and derive value from data within agri-food systems. It integrates data ecosystems and agri-food systems into broader socio-economic, technological, and natural environments, enabling cross-sectoral collaboration and innovation. It supports the optimization of agri-food production, distribution, and consumption while addressing environmental, societal, and economic objectives.” What is the Data Economy for Agri-Food Systems (DE4AFS)? Source: Data4Food2030 project
- 11. Data Economy Data Ecosystem Agri-Food systems Agri-Food ecosystems DataService Ecosystem Agri-Food Service Ecosystem Value co-creation Value-in-use, value-in-context, value-in-exchange,… DE4AFS Service Ecosystem
- 12. “Data Spaces areplatform ecosystems built on federated infrastructures and participative governance structures for sovereign data exchange oriented toward stakeholders' interests”. What are Data Spaces? Schurig et al. (2024)
- 14. Point-to-point integration B A Centralized chainsystems (Data Sharing Initiatives) X B A Data Spaces (federated, decentralized) X B A DSI DSI Authori- zations Access Distributed Ledgers (decentralized Blockchain) A B Common Language Standards Shared Meta Data Architecture Data Sharing approaches
- 15. Example of aData Sharing Initiative (DSI) value net creation business model Cloud DATA platform Farmer Supplier C Supplier A Supplier B Customer X feed sperm milk milking robot data data data data data data data data data data data data data Network Administrative Organization
- 16. System of systems Stand-alone application IT Integrationlevel Number of stakeholders Process operator Business ecosystem Single apps Production processes Farm information systems Farm management Chain information systems Food supply chain Data Sharing Initiatives Agri-Food systems Agri-Food Data Economy Data Spaces Evolution of ICT in agri- food See: Wolfert, S., Verdouw, C., van Wassenaer, L., Dolfsma, W., Klerkx, L., 2023. Digital Innovation Ecosystems in Agri-Food: design principles for d eveloping and organizational fra mework. Agricultural Systems 204, 103358. €€€ where to invest! €€€
- 17. Identifying drivers,barriers and risks for investors and make recommendations to • stimulate uptake of data & digital technologies increase EU competitiveness (Draghi!) • lower barriers and risks Potential Impact: • Boosting the Data Economy for Agriculture and Forestry • Pave the way for innovative AI applications for end users such as Farmers and Foresters Flows of data in the agricultural and forestry data markets (i.e., EO and environmental data, farm-level data, socio-economic data etc.) and their added value! The uptake of Digital and Data Infrastructure (i.e., the use of data sharing/selling platforms, cloud/server capabilities and capacities) in agriculture and forestry, also at a global level (EU ↔ China ↔ USA)
- 18. 1. Split upinto WP teams (WP 2, 3, 4, 5) 2. Individual (5’): post-its with key words/phrases that come to your mind after hearing the pitch • Inspiration, ideas, critics, … • Discuss (10’) them briefly in your group 3. What does it mean for your WP/Task – what could/should you do different? (30’) • analysis, design, approach • (types of) data • scenarios • communication • stakeholder involvement • … 4.Summarize the outcome in one slide to include in your upcoming WP presentation (15’)
- 19. 4Growth-project.eu THANK YOU SUBSCRIBE TO Fundedby the European Union under grant agreement no 101134855. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or REA. Neither the European Union nor the granting authority cannot be held responsible for them. linkedin.com/showcase/4growth-project x.com/4GrowthProject OUR NEWSLETTER
Editor's Notes
- #2 The food production chain - from farm to fork – is becoming increasingly digital. [SLIDE 8] This is due to the so-called cyber-physical management cycle of smart sensing & monitoring, smart analysis & planning and smart control. Objects, such as crops, animals or trucks, are monitored by [CLICK] smart sensors, satellites or drones with cameras. They are smart because their observations go beyond human senses such as sight, smell and hearing. Moreover, they are capable of observation 24/7 without becoming tired. Smart sensing and monitoring results in a lot of data that is [CLICK] analysed through smart algorithms in software applications. The result could be an instruction to fertilize crops or to treat your animals. This planning is executed by [CLICK] smart machines that know the local context and what the optimal treatment for your crops or animals is. The loop is closed again by monitoring the effect of the control actions. In this way you can see if the growth of your animals or crops is going in the right direction. This decision-making cycle can be applied to every object and company in the whole value chain. Also, to consumers who are [CLICK] deciding what cheese they want to buy in the supermarket. This is the first application of digitalization in which data is used for decision-making for business and consumers. [SLIDE 9] The second application of these data is for Food Integrity. Meaning you can [CLICK] pass the data through the whole chain to address questions such as: [CLICK] Where does my food come from? How was it produced? Is it sustainable? [SLIDE 10] The third application of the same data is for public policies. The data could be used to control [CLICK] food safety, [CLICK] support environmental policy or [CLICK] health policies. What if you could plug into these real-time data to monitor the nitrogen emissions or detect a food contamination right at the source? That would be fantastic right? However, currently, most governments have their separate monitoring systems or censuses that are usually lagging behind the actual situation. [SLIDE 11] The fourth and final application area of data is science and technology. Examples of technologies are: artificial intelligence, blockchain, internet of things or big data analytics. Actually, these technologies are main drivers behind this whole digitalization. But at the same time their development depends on the data that are produced through the application of these technologies. You can’t do big data analytics without access to big data!
- #3 This development has so far led to a very dynamic landscape – or battlefield - for the Data of Farming and Food. Competing big AgMachinery and AgBusiness companies (green) were on of the first to collect lots of data from farmers to build their own platforms and applications. Sometimes this was even without farmers knowing their data was used. Thas had led to some reactions from the farmer’s community (e.g. Akkerweb, JoinData in NL) but so far these initiatives remained very small in comparison to the platforms from the big ones. Similar developments took place at the consumer side on food data by large food processors and retailers (orange). More independent platforms that are focusing on transparent information for the consumer (e.g. QuestionMark) were also established, but also for those platforms it is very difficult to survive in this competitive environment. However beside these incumbents new players entered this arena. First, we can distinguish tech-start-ups that were supported – and later often acquired - by large tech companies. A good example of this is the Farmer’s business network in the USA. Second, there were data start-ups supported by venture capitalists that eventually followed the same pathway. Some of them are also now quite dominant players. A good example of this is Farmobile, also in the USA. Sure, these developments have delivered a lot of innovative solutions for the benefit of farmers and consumers, but in most cases the position of these stakeholders is at stake. They feel they are out of control of their own data.
- #6 Key message: although the elements at the lower left of the ‘ladder’ are also still needed, it is expected that the future developments will focus on the upper right part. The current sustainability challenges require integrated solutions which are at the level of food systems and a food data economy that are constituted by data platforms and – in the future – data spaces where data is available, also more in an aggregated/integrated way.
- #16 Key message: although the elements at the lower left of the ‘ladder’ are also still needed, it is expected that the future developments will focus on the upper right part. The current sustainability challenges require integrated solutions which are at the level of food systems and a food data economy that are constituted by data platforms and – in the future – data spaces where data is available, also more in an aggregated/integrated way.