Case Study: Data Governance in African Agriculture

"The data our soil produces, the data our seeds produce, the data our labor produces — it should be governed by us. Not by a company in California." — Participant in a Kenyan farmer cooperative workshop (2022)

Overview

Agriculture is the backbone of many African economies, employing over 60% of the continent's workforce and providing livelihoods for hundreds of millions of smallholder farmers. It is also one of the sectors most rapidly being transformed by data — soil sensors, satellite imagery, weather prediction models, yield forecasting algorithms, and market price platforms are reshaping how food is grown, traded, and governed.

But the data revolution in African agriculture has a governance problem. The data generated by African farmers — about their land, their crops, their practices, their markets — is increasingly being collected, aggregated, and monetized by international agribusiness corporations, technology platforms, and development organizations. The farmers themselves receive some benefits (weather alerts, market prices, agronomic advice) but typically have no ownership, no governance voice, and no share of the revenue generated by their data.

This case study examines how data governance operates in African agriculture, the alternatives being developed by farming communities, and the broader implications for data governance in the Global South.

Skills Applied: - Analyzing data extraction dynamics in a specific sector - Evaluating community-based governance alternatives - Applying data cooperative and commons frameworks - Connecting sectoral analysis to broader data colonialism themes

The Data Landscape

What Data Is Collected

Agricultural data in Africa encompasses multiple layers:

Soil and environmental data. Soil composition, pH levels, moisture content, nutrient profiles — collected through sensors, soil testing, and satellite-based remote sensing. Climate data: rainfall patterns, temperature, humidity, wind. These datasets are essential for precision agriculture and increasingly valuable for climate modeling.

Crop data. Crop varieties planted, planting dates, growth stages, pest and disease incidence, yield outcomes. Collected through farmer-reported data (via mobile apps), field sensors, and drone or satellite imagery.

Market data. Commodity prices at local, regional, and international markets. Transaction records. Supply chain information. Storage and transport logistics.

Farmer behavioral data. App usage patterns, financial transactions (through mobile money), input purchasing decisions, compliance with agronomic recommendations. This behavioral data is the most valuable to commercial actors — it enables predictive modeling of farmer decision-making.

Who Collects It

International agribusiness corporations (e.g., Bayer/Monsanto, Syngenta, Corteva) collect data through digital farming platforms that provide agronomic advice in exchange for data about farm operations. The business model is familiar from the platform economy: farmers provide data; the company provides a "free" service; the company monetizes the aggregated data through precision agriculture products, commodity trading insights, and crop insurance modeling.

Technology platforms (e.g., Apollo Agriculture, FarmCrowdy, Twiga Foods) operate in the agritech space, connecting farmers to markets, inputs, and financing through mobile-first platforms. These platforms collect extensive data about farmer behavior, productivity, and financial patterns.

Development organizations (e.g., the World Bank, CGIAR research centers, USAID-funded projects) collect agricultural data for research, policy design, and program monitoring. While their stated purpose is public benefit, the governance of research data — who controls it, how it is shared, whether farmers consent to secondary uses — is often inadequately defined.

Government agencies collect data through agricultural censuses, extension services, and subsidy programs. The quality and governance of government agricultural data varies widely across African countries.

The Extraction Problem

Value Flows

The data extraction dynamic in African agriculture follows a pattern that the chapter identifies as digital extractivism:

  1. Raw data is generated by farmers through their labor, land, and interactions with digital platforms.
  2. Data is collected by intermediaries — platforms, corporations, development projects — that provide services (weather information, market prices, credit scoring) in exchange for data access.
  3. Data is aggregated and analyzed by entities with the computational infrastructure and analytical capability to derive value — commodity trading insights, insurance risk models, precision agriculture products, research publications.
  4. Value accrues to those who control the analysis — shareholders of agribusiness corporations, managers of agricultural investment funds, publishers of research journals — not to the farmers who generated the data.

A specific example illustrates the dynamic. An international development project collects soil data from 50,000 farms across East Africa. The data is used to build a machine learning model that predicts crop yields based on soil composition and weather patterns. The model is published in a high-impact scientific journal and licensed to a commodity trading firm, which uses it to make futures market investments worth millions of dollars. The 50,000 farmers who provided the data receive weather forecasts and basic soil recommendations through a mobile app. They are not informed about the model's development, do not share in the licensing revenue, and cannot access the aggregated dataset.

Information Asymmetry

The information asymmetry in agricultural data governance is extreme. Smallholder farmers typically:

  • Do not know what data is being collected about their farms
  • Do not understand the aggregated value of their data
  • Cannot read the terms of service of the platforms they use (often written in English, not local languages)
  • Have no mechanism to negotiate data terms collectively
  • Lack the infrastructure to store, analyze, or monetize their own data independently

This asymmetry is the agricultural instantiation of the power asymmetry theme: those who generate data have the least power to govern it.

Community Alternatives

Agricultural Data Cooperatives

Several initiatives are developing cooperative models for agricultural data governance:

The Eastern Africa Grain Council (EAGC) has explored collective data governance models that allow farmers to pool market data and negotiate with buyers from a position of collective strength. Rather than individual farmers checking prices on a platform (and providing behavioral data to the platform operator), the cooperative aggregates price information and makes it available to members.

The GODAN (Global Open Data for Agriculture and Nutrition) Initiative promotes open data principles in agriculture, arguing that certain agricultural data — particularly publicly funded research data and environmental data — should be governed as commons rather than proprietary assets. GODAN's approach distinguishes between data that should be open (public research, environmental monitoring) and data that should be cooperatively governed (farm-level production data).

The CTA (Technical Centre for Agricultural and Rural Cooperation) Data4Ag Project worked with farmer organizations in several African countries to develop data governance frameworks that give farmers collective control over their agricultural data. The project emphasized the importance of local governance institutions, farmer education about data value, and technical infrastructure for community-owned data management.

Design Principles for Agricultural Data Cooperatives

Drawing on Ostrom's commons governance principles and the chapter's analysis, effective agricultural data cooperatives in Africa should address:

Collective ownership. Data generated by farmers belongs to the cooperative, not to individual farmers or to the platform through which data is collected. The cooperative decides how data is used, shared, and monetized.

Benefit sharing. Revenue generated from cooperative data — whether through licensing to researchers, sales to agribusiness companies, or use in precision agriculture services — is shared among members according to cooperatively determined rules.

Governance participation. Members participate in governance decisions about data use. This requires both formal mechanisms (voting, elected governance boards) and practical enablers (meetings in accessible locations, governance communication in local languages, technical literacy support).

Interoperability. Data should be stored in open, interoperable formats that prevent vendor lock-in and allow cooperatives to switch platforms or combine data from multiple sources without losing access.

Privacy protection. Individual farm-level data should be protected; only aggregated data should be available for external use. Cooperatives should have governance mechanisms for deciding when exceptions to this rule are justified (e.g., for research with direct community benefit).

The Broader Implications

Data Governance as Development

The agricultural data governance question in Africa is ultimately a development question. If the current extraction model continues — data generated by African farmers, value captured by international corporations — then the data revolution in agriculture will replicate colonial patterns of resource extraction, producing growth statistics for the continent while concentrating wealth elsewhere.

If alternative governance models succeed — data cooperatives, commons-based governance, community-controlled platforms — they could provide a foundation for rural development that is genuinely participatory, equitable, and sustainable.

Sofia Reyes, drawing on her work at DataRights Alliance, connected the agricultural data governance challenge to the broader data colonialism analysis: "The pattern is the same everywhere. In agriculture, in health, in education — data generated by communities in the Global South flows to corporations in the Global North. The specific sector changes, but the structure doesn't. The governance response has to be structural, too — not just fixing one sector's data practices but building governance infrastructure that works across all of them."

The Role of the African Union

The AU Data Policy Framework explicitly addresses agricultural data governance, recognizing the sector's economic importance and the risks of data extraction. The framework calls for:

  • Data sovereignty — African agricultural data should be governed under African frameworks
  • Benefit sharing — entities that profit from African agricultural data should share benefits with the communities that generated it
  • Capacity building — investment in African data infrastructure, analytical capability, and governance institutions

Implementation of these principles remains uneven. The AU framework provides policy direction, but implementation depends on national governments, which have varying levels of capacity and political will.

Discussion Questions

  1. A farmer cooperative in Kenya is negotiating with an international agribusiness company that wants access to the cooperative's pooled soil and yield data. What terms should the cooperative insist on? How should the negotiation be structured to address the power asymmetry?

  2. Is "open data" the right framework for agricultural data in Africa? Consider who benefits from openness and who benefits from controlled access. Under what conditions should agricultural data be open, and under what conditions should it be cooperatively governed?

  3. How does the agricultural data governance challenge in Africa compare to the VitraMed thread in this textbook? What structural similarities exist between a health data company and an agritech platform?

  4. The chapter discusses "leapfrogging" as a possibility for Global South data governance. Could agricultural data cooperatives in Africa leapfrog the corporate-dominated data governance model that the Global North adopted? What conditions would be necessary?

  5. Dr. Adeyemi asked: "If we believe that democratic governance requires that people have a say in the rules that govern their economic lives, then agricultural data governance is a democratic question, not just a technical one." How would you design a participatory process for governing agricultural data in a specific African community?

Further Investigation

  • Research the GODAN (Global Open Data for Agriculture and Nutrition) Initiative and its principles for agricultural data governance.
  • Investigate the role of mobile money (e.g., M-Pesa in Kenya) in agricultural data governance. How does the data collected through mobile money transactions create governance opportunities and risks for farmers?
  • Compare agricultural data governance models in Africa with indigenous data governance models in Aotearoa New Zealand (Case Study 2, Chapter 39). What principles do they share?