Open data ecosystem in agrifood — GODAN, CGIAR FAIR, USDA Ag Data Commons, Copernicus, SoilGrids

Global (international coordination via GODAN; regional state data layers)

Content

The open data ecosystem in agrifood is structurally important because it is the counter-institutional layer to proprietary data capture. The layer is multi-actor, multi-jurisdictional, and built around the FAIR principles (Findable, Accessible, Interoperable, Re-usable) and increasingly the CARE principles for Indigenous Data Governance (Collective benefit, Authority to control, Responsibility, Ethics).

The named open-data institutions

GODAN — Global Open Data for Agriculture and Nutrition. Founded 2013 (G8-initiated with US, UK). International coordination body for open data in agriculture and nutrition. Current framing: GODAN 2.0 — “advances a holistic framework for transforming food systems through open data, youth-led innovation, and inclusive digital infrastructure.” The 2016 Global Data Ecosystem for Agriculture and Food paper (commissioned by Syngenta with GODAN assistance) articulated the “Five stars of open data” and FAIR principles for the agrifood context.

CGIAR Platform for Big Data in Agriculture. Multilateral research-for-development infrastructure explicitly committed to FAIR data principles. Responsible Data Guidelines for agricultural research for development. Open-access mandate among the strongest of any institution working on agricultural data globally.

USDA Ag Data Commons. Public access research data catalog operated by the National Agricultural Library. FAIR-compliant repository. Replatformed to Figshare-powered institutional portal in January 2024. USDA-funded research data is required (since 2023 federal policy on public access) to deposit in FAIR-compliant repositories.

ISRIC SoilGrids. Global soil property maps at 250m resolution. 14 soil properties. 6 standard depth intervals. ML-based predictions. Africa soil property maps at 250m resolution. The substantive open-data anchor for soil in the field guide.

Copernicus Sentinel. EU’s Earth observation programme. Sentinel-2 for land monitoring (vegetation, soil, water). Open data access via Copernicus Data Space Ecosystem. Substantively used for crop monitoring at continental and global scales.

Other open data of note:

• FAOSTAT — FAO’s global food and agriculture statistics (the standard reference).
• GBIF — Global Biodiversity Information Facility (pollinators, soil biodiversity).
• OpenAQ — Open air quality monitoring.
• Open Climate Fix — open climate/weather data for agriculture.
• HarvestChoice — CGIAR-supported agronomic data.
• Open Soil Atlas — open soil data.

What the open data ecosystem is structurally good at

1. Soil, environmental, and satellite-derived crop data are dominant at the open state level. SoilGrids, Copernicus Sentinel, USDA NASS Cropland Data Layer, FAOSTAT — all state / multilateral open infrastructure.

2. FAIR principles provide a shared standard. Findable, Accessible, Interoperable, Re-usable. Adopted by CGIAR, USDA Ag Data Commons, and increasingly by national agricultural research systems globally.

3. Open access is increasingly a policy mandate. US public investment in agricultural research requires FAIR-compliant deposit; EU and multilateral research funders follow similar trajectories.

What the open data ecosystem is structurally bad at

1. Real-time farm-level data. Open data is aggregated, not real-time. Vendor proprietary platforms (Climate FieldView, John Deere) own the real-time farm-level data.

2. Farmer-level decision support. Open data supports research and policy analysis; not individual farmer decision-making in real time.

3. Capturing the small-farm / developing-country farm context. Most open data is anchored to formal agricultural research institutions, which are stronger in formal research domains than in smallholder / informal contexts.

4. Indigenous Data Governance. FAIR principles address technical interoperability; CARE Principles address Indigenous data sovereignty. The two are complementary but not equivalent.

What this unit is doing in the taxonomy

Anchors the data-substrate × open cell as a framework claim-type — a structured analysis of the open data ecosystem. First unit in the field guide that explicitly captures the open data substrate.

Distinct from:

• Proprietary farm data unit (proprietary-farm-data.md) — proprietary / industry cell.
• Dark data unit (dark-data-agrifood.md) — dark / collected-but-not-surfaced cell.
• Farm-data ownership critical voice unit (farm-data-ownership-critical.md) — the critical-voice layer.

Why it matters for talks

• The open data ecosystem is the counter-institutional layer to proprietary capture. Worth knowing because vendor-only framings of AI in agrifood miss this layer entirely.
• The FAIR principles are a shared standard. Worth knowing because they connect to discussions of interoperability and value capture.
• The state-led open data (USDA, Copernicus, FAO) is substantive and substantial — not just a marginal alternative.
• The CARE Principles (Indigenous Data Governance) are the explicit articulation of Indigenous data sovereignty in agrifood. Distinct from FAIR but complementary.

Critical context

• Open data ecosystem is aggregated, not real-time farm-level. Real-time farm-level data is in proprietary platforms.
• Open data supports research and policy, not individual farmer decisions.
• Smallholder / informal-context data is under-represented in open data infrastructure.
• Indigenous data sovereignty is articulated by CARE Principles, complementary to FAIR.