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Enabling Infrastructure: This is the foundational layer and includes the core technology assets, human capital and organizational development required to realize the vision. Initially, this work will be led by DG and a core group of collaborators which could include FPOs and promoting orgs or CBBOs (integrating the voice of the farmer is critical). Vision is for these elements to eventually be owned and managed by the community.
Farmstack protocol: Enables secure, P2P consent based data sharing.
Hosting and Security: Where is the data actually stored, how is data security handled (especially PII), what is the uptime, throughput, availability, SLAs, etc.
Data model: An extensible and interoperable model that handles key agri-specific constructs like land record/polygon, agronomic activities, farmer PII (name, contact info, etc.). Critical to get this right so the data can be easily leveraged by others in the ecosystem.
Governance and Financing model: Web3 has lots of exciting potential including blockchains for distributed / decentralized hosting + privacy and Decentralized Autonomous Organization or DAO as an organizing structure. Tokens can play a powerful role in orchestration and as an incentive mechanism. Practically, need to figure out what form of legal entity this will be (non-profit, DAO, Cooperative, Company) based on country specific legal context. This structure along with quantum of funding required will impact funding strategy. Current thinking is that this is initially funded by grants and over time the community monetizes its data assets by selling analytics developed on aggregated data to various ecosystem actors and through membership fees.
Capacity building and inclusivity: This is on-ground work done in collaboration with partners. Core elements include data literacy training for farmer group members, leadership, NGOs (eg, CBBOs in India) and government. We need to take a gender lens in content development and product design and also explore how to bring on-board other marginalized populations including tenant farmers. This is a critical bucket that positions farmer groups to own and really get value from their data. I also include government / policymaker engagement in this bucket for now as how this network interfaces w government who will be a major keeper and user of data is pretty important. That’s a larger topic and may merit a separate thread but the placeholder is here (for now).
User Interfaces: What do farmers groups and their members actually see and interact with when we talk about a farmer data network? Below is a partial list
Data capture and access:- Interfaces used by a data entry operator / PG officer / Village Resource Person / Agri-Entrepreneur who is primary farmer-side user on behalf of a small group and over time move towards a model where farmers contribute data directly and the DEO plays more of a on-boarding and data validation role. This could be a mobile app
Open Protocols: These are small software programs, a few lines of code, owned by the community. The idea is to have a standardized and rigorous way to handle some common data related tasks. Driving standards in how data is captured, accessed and shared supports interoperability and builds trust and transparency among users who consume the data. Below is a partial list of the protocols that would power the FDN. The protocols themselves would be written and maintained by Digital Green and other developers looking to support this work. Third parties can build rich user experiences to make it easier for farmer groups to interact with the protocols. Ideally, the protocols could be easily invoked by existing apps. The protocols could be grouped in a few buckets:
Digitizing some information about the real world: Protocols that create a trusted digital twin of something on the ground. Critical for the real-world process around that digitization to be transparent and audited as this builds trust. Digitizes this info makes it easily discoverable and actionable and over time builds a history that can unlock lots of value. The protocols could be invoked and parameterized through mobile apps developed by DG or a third party (eg, Dimagi or ODK/Kobo). One Another angle to explore is bringing in unstructured data through widely used third party apps Whatsapp/Telegram/IVR/SMS messages and voicenotes and converting these to structured data to reduce barriers to entry.
Data Wallet and Consent management: These tools answer the question, how do farmers know what data exists about them and how can he/she grant informed consent to share it? Practically, this could take the form of a “digi locker” where a few core digital assets (land record, farmer PII, etc.) are managed by farmer groups or more of an account aggregator model where the data lives somewhere else and there is a mechanism for farmers see those assets in a virtual wallet and grant consent for them to be shared.
- Data discovery and access:
A polygon representing not just lat long but actual shape of the plot; could include a digital stamp for a local municipal body when it matches that "official" register. Tenant farmers could link their efforts to these digital plots to build a work history.
Actual yield based on a crop cutting experiment that could be complemented with a remote sensing based estimate (these are not super accurate today but expect they will become more so over time...)
Dates for key agronomic activities like sowing, irrigation events (and type), tillage (timing and depth)
Results of a soil test
A timestamped and geotagged photo as visual proof for one of the above things.
Consent management: Create a template for a digital receipt which reflects that a farmer group has granted informed consent for a particular data asset to be shared (look to the Account Aggregator consent artifacts for reference) ).
Data discovery and access: Farmstack fits in here. Secure P2P data sharing aligned w data governance / usage policies. These protocols answer the question, how do farmers know what data exists about them and how can third parties discover what datasets are available? Initially, ecosystem users (see below) will interact with an API with a well structured data model that serves up high quality data; in a web3 world, this might look different (data on blockchain with smart contracts regulating access and easy for developers to compose new solutions on top of the data itself without needing to call APIs; lots more to figure out here).
Ecosystem: Community owned data assets are shared with Data generated through these open protocols is available to the broader ecosystem (private, government, non-profit, research) - with farmer consent - to accelerate development of value-add products and services. Important to think about the various stakeholders here and prioritize outreach based on which are most compelling farmers and what sort of data assets are most relevant for the providers. Over time, these stakeholders will pay to access information from the network and proceeds go to the farmer owned/controlled entity which manages the underlying protocols and data.
Initial use cases: Over the next couple years, develop farmer facing solutions that get the flywheel in motion and demonstrate protocols that power easy development of solutions that deliver value to farmers groups and validate product market fit. Some success stories will excite and bring in participation from the broader ecosystem. DG will curate high impact use cases and work with partners to implement. For a longer list of ideas, see this post on entry points.
A P2P, video and voice based social / network for farmers (see WeFarm or the General Mills / OpenTEAM digital coffeeshop as a reference)
Remote sensing based yield estimates augmented by ground truth FPO level data to inform procurement decisions and unlock access to loans (see Cropin <-> Waycool <-> Samunnati pilot for reference)
Farmer data registry (Ethiopia)
A coordination tool for FPOs and their members to get an aggregated view of input requirements and marketable surplus; perhaps a jumping off point for additional services like targeted advisory and member payments (Kisan Diary Enterprise)
Hyperlocal weather forecasts and pest management advisories based on lat/long and other parameters (TARA use case)
Crowdsourced input price/performance and offtake pricing data used for benchmarking (see FBN as a reference)
A record of verified farm management practices and estimate of environmental footprint, esp GHG emissions used to access ecosystem services markets and premium sourcing opportunities (see the FRAME project as reference)
Broader ecosystem: Looking ahead, various actors will be interested in leveraging farmer network data to improve their products and services or design whole new applications. This is where we get the flywheel in motion of more users, contributing more data which draws more developers and solution providers which makes the network more powerful for farmers. Some stakeholders to consider:
Input companies for whom the network could provide valuable market intelligence (competitive benchmarking, etc.) and efficient lead generation (assuming the community manages this in a responsible, consent based manner)
"Infrastructure" companies who need sales and distribution support; products might include farm mechanization equipment, solar power/pumps, water efficient irrigation systems, cold-chain, etc.)
Lenders and insurers who need data on current and prospective borrowers
Agribusinesses / large traders or buyers esp those with a focus on climate smart sourcing (eg, members of the Sustainable Rice Platform)
Researchers
Remote sensing companies that need ground truth data or want to market their services to farmers
Providers of advisory services
Carbon offset project developers
...