Page Comparison

Opportunity / Purpose

Data about a farmer and the production practices she follows are an increasingly valuable asset. Such information can unlock value for farmers in various ways:

• Access to buyers

• Access to financial services

• Targeted, contextually relevant advisories

• Decision support and planning tools

• Ecosystem services payments

Solution

A platform that generates verified data about farmers and their production practices which farmers can access for their own decision making and easily share with other stakeholders to make credible, trusted claims.

What problem does this solve?

Farmers don’t have digital records about their farms or production practices which means they are unable to tap into data driven analytics solutions or compare performance and practices from prior years.

Farmer self-reported data is not credible to third parties. Leveraging government extension agents as validators addresses this; over time, other empaneled service providers like Agri-Entrepreneurs or firms that do audits / certifications could also play this role.

Getting access to Accessing farmer level data is expensive and time consuming for buyers, policymakers, researchers, service providers.

Product ComponentsDescription

• Data collection and verification methodology / protocol: To engender trust in the accuracy of the data, there needs to be a transparent collection and verification process. Farmers and other data consumers should be able to access information about how data was collected and if/how it has been verified. Initially, front-line extension workers (JEEViKA Village Resource Persons in Bihar) will play the role of data collector and validator and DG will provide a management tool for this process. See here

• Data repository and farmer interface: Farmer data will be stored in the cloud and accessible via a mobile app. That app will provide:

  • Mechanism for farmers to record their own data

  • Visualization of farmer data (primary and derived). This would be information directly recorded and also derived data or outputs / metrics calculated by third party services such as emission estimates. Using an ensemble modeling approach

    • In the Bihar MRV project, practice data will be converted to

    an estimate for CO2e

    • a CO2e estimate using an ensemble approach with three third party models. CO2e is of interest to the market, researchers, and policymakers and once farmers have it for their own

    operation

    • operations, can share it as they see fit.

  • Access to relevant advisory content from Digital Green’s Youtube library. OvertimeOver time, farmers could subscribe to additional advisory providers and receive those messages through the same app or share relevant data through the app and access it through a providers dedicated service.

  • A mechanism to link / catalog important digital artifacts, eg, a panchayat or district level validated land record or soil health card

  • Ability to grant informed consent to share the data. Farmstack provides the mechanism for farmers to grant consent and ensures data is shared in a P2P way with the intended beneficiary in line with farmer preference / usage policy (via the connector)

Users / Stakeholders and Value Prop

Go to Market

• Rely To on-board farmers, rely on JEEViKA VRPs and the farmers they reach for initial ramp

  • By end of 2023, have data profiles including emission estimates for ~25,000 farmers across Bihar

  • Expect a portion of these profiles (20%?) to be actively used by farmers; depends on smartphone access

• Validate farmer value prop, refine product design then explore additional user acquisition channelsCritical to engage stakeholders who can leverage this data in order to provide some value-add for farmers so they see value in the records. Some examples:

  • Olam or other members of the Sustainable Rice Platform source from farmers that can demonstrate their sustainability credentials

  • A provider of targeted agronomy or advisory services delivers relevant messages to farmers based on data available in the wallet

Commercial / Economic Model

• Philanthropic funding for next two years to cover user research, MVP development

  • Sequoia funding is focused on model calibration / validation and engaging policymakers; need to augment with funding for the farmer facing app (potential funders incl Cisco, Microsoft, Global Innovation Fund)

• Vision is for all the farmer data to be maintained by a data trust in which farmers hold shares so that farmers really own and capture the economic value of their data

• Farmer ownership strengthens incentives for accurate data capture; accurate data is more actionable and valuable to the market and farmers participate in the upside that comes from increased trust / credibility of the data

• Look to philanthropic funders, impact investors and government partners to cover hosting, technology development, and scaling costs.

• Given we are relying on the extension system to serve as a validator, need to cover their costs.

• Over time, trust will have a few streams of revenue which it can use to re-capitalize; raise debt and use the proceeds to buy-out a portion of the initial investors. Such a plan could unlock a larger set of initial investors who see some potential of getting repaid with a model return.

• Potential revenue streams.

  • Farmers pay a subscription to maintain their data wallet; there may be a freemium play where individual farmers have free wallets and a farmer group or FPO pays to create an aggregation at the level of its farmers

  • Charge data consumers for access, eg, farmers can opt-in to make their data available to input suppliers who would use the info for marketing / lead gen

  • Build data products and monetize, eg, aggregated and anonymized ground truth data for AI models, and a portion of the sales fund trust operations with remainder paid to farmers

Governance Model

• More research required here

• What role should govt play, especially Dept of Agri? Central vs. State level?

• Some references:

  • Open Data Institute has done a bunch of research on Data Trusts 

  • Mozilla Foundation is working in this space, especially Anouk Rouhaak (see here)

  • There are some blockchain projects and DAOs that can serve as interesting references though I think a token based model is likely too complicated for what we are doing. Some examples are the Streamer (framework for developing a data union with examples of users aggregating and monetizing their browsing behavior) and Brainstrust (a worker owned talent matching platform)

Competitive Landscape

• Traceability apps which might be buyer specific like Olam AtSource or generic like Sourcetrace, Cropin

• Ag advisory services like BharatAgri

• Farmer decision support tools that present risk / reward of different practices. An example from the US is CIBO Technologies which promotes regenerative / sustainable practices and helps farmers monetize changes practices through carbon offsets

• Gradable by FBN

Risks

• Smartphone penetration / access and data connectivity. How should we design a service that might be VRP intermediated for a while?

• Is there genuine farmer interest in this? Is the value proposition compelling?

Open questions

• What is the best entry point to get farmer buy-in? We are starting with production practices and emission estimates given funding sources but is a land record or soil health report a more sensible entry point?

• Does this idea align with or sit in conflict with Agristack?