A smallholder who sells cocoa or coffee to three buyers over a few seasons may be registered three times, have the same plot mapped three times, and answer three surveys asking similar questions. Each buyer pays for its own version of the same first-mile data, and none of them has the full picture. That is the case for letting buyers treat some of this work as pre-competitive, when they choose to.
What does “pre-competitive” mean at the first mile?
Pre-competitive work is what every actor needs, what nobody gains a commercial advantage from doing alone, and what becomes more reliable when done together. At the first mile, four things fit that description, for buyers who decide to pool them:
- Fraud detection across buyers: signals that only appear when data from several buyers is compared.
- Agronomic knowledge: what works on which soils, under which climate, for which crop, drawn from many farms.
- Access to farmers for surveys: a consented, compensated channel to ask farmers questions, instead of each organisation building its own.
- Common farmer records: the basic, verified facts about a farmer that several buyers agree to keep in common rather than collect again.
What stays private by default is everything a buyer brings and competes on: its farmer registry, its plot polygons, prices, volumes, contract terms, premiums, supplier relationships, sourcing strategy. Nothing moves from private to pooled unless the buyer decides so, on terms the participating buyers set together.
Why would buyers choose to pool some first-mile work?
Because duplicating it costs a lot and often produces worse data.
- Cost. Checking for fraud, building agronomic advice and reaching farmers for surveys all take field work and expertise. What buyers pool is paid for once, not by each buyer separately.
- Quality. When buyers agree to keep common farmer records, each correction benefits all of them. When there are five separate versions, errors are fixed in one and persist in four.
- Farmers’ time. When buyers pool their surveys, farmers are asked once, not ten times. Survey fatigue is real, and it degrades answers.
- Consistency. Under the EUDR, the same plot can appear in several buyers’ due diligence statements. A pooled cross-buyer check can flag conflicting versions without any buyer handing over its polygons.
How does sharing reveal fraud that no single buyer can see?
Some of the most important integrity problems are invisible to a single buyer:
- A plot declared by two suppliers, or under two different farmers.
- Volumes that exceed what a farmer’s plots can produce, once all sales across buyers are counted. One buyer sees a plausible volume; the sum across buyers is not plausible.
- Borrowed identities: a compliant farmer’s identity used to launder produce from elsewhere.
- The same farmer registered with different details in different supply chains.
The EUDR explicitly lists the risk of mixing with products of unknown origin, and prevalence of document and data falsification, among the criteria operators must consider in their risk assessment (Article 10(2)(h) and (j)). Fraud detection pooled across buyers makes those risks measurable.
Cross-buyer checks do not require buyers to see each other’s data. When buyers pool fraud detection, the comparison runs on the network and returns only the signal to the party concerned: “this plot is also declared elsewhere”, “this volume is inconsistent with this farmer’s capacity”. The underlying commercial data stays private.
Why pool agronomic knowledge?
Agronomic knowledge improves with the number and diversity of farms it draws on. A buyer with a few thousand farmers in one region learns slowly; buyers who pool what they know across many regions and crops learn faster. Farmers benefit directly: better advice on pests, pruning, shade, soil and climate adaptation, in their language, when they ask for it.
This is also a place where AI helps: answering farmers’ questions from a verified body of agronomic knowledge, supporting the farmers and the teams who work with them.
What about farmers’ privacy and control?
Pre-competitive does not mean public, and it never means imposed. The model works only if:
- Buyers choose: nothing a buyer brings is pooled unless it decides so, on terms the participating buyers set together.
- Farmers consent to what is collected and who can use it, and can see what is held about them.
- Access is purpose-limited: a buyer sees the data of farmers who supply it, for the purposes the farmer agreed to.
- Commercial data stays private to each buyer.
- Data protection law applies. Where EU law is relevant, the General Data Protection Regulation (Regulation (EU) 2016/679) sets the rules on consent, purpose limitation and data subjects’ rights.
For buyers, farmer control is not a constraint to work around. It is a risk reduction: consent is a legal basis, provenance is clear, and data that farmers give knowingly tends to be more accurate. It also avoids lock-in: the data belongs to the farmer, not to whichever vendor collected it.
Why is this the right time?
The requirements of due diligence laws such as the EUDR, and broader frameworks like the CSDDD, all rest on the same first-mile facts: who the farmer is, where they farm, what happened on that land, and how the produce moved. Every buyer needs these facts. Building them separately multiplies cost and fragments the truth.
About 500 million smallholders farm worldwide. No single company, programme or government will register and support them all alone. Private space for each participant, with the option to pool what none of them should pay for twice, is how the effort adds up.
How does Farmer1st make pre-competitive work possible?
Farmer1st is the first-mile infrastructure built around farmers: a farmer-controlled network where companies, governments and organisations collect, verify and share origin data — and build the services they need on top of it.
On Farmer1st, everything a buyer brings is private by default: its farmer registry, plot polygons, prices, volumes and supplier relationships. When buyers in a supply chain agree, the network lets them pool fraud detection across buyers, agronomic knowledge behind the AI agronomist, farmer access for farmer surveys and common farmer records, on terms they set together. Pre-competitive by choice, never as a condition. Each buyer connects the network to the tools it already uses through data integration. Learn more about the pre-competitive network and first-mile traceability.
Frequently asked questions
Does pre-competitive collaboration mean competitors see my suppliers?
No. Your farmer registry and your plot polygons stay private by default. What you pool, and with whom, is your decision, and even pooled checks never reveal who buys from whom. Which farmers supply you, at what price and in what volume stays private to you. Cross-buyer checks, such as detecting a plot declared twice, can run without exposing one buyer's data to another.
Who controls the farmer's data?
The farmer. Farmers consent to what is collected and to who can use it, and can see what is held about them. For buyers, that consent is also a legal basis and a quality signal: data given knowingly by the farmer is more reliable than data collected around them.
Is pre-competitive collaboration compatible with competition rules?
Collaboration on non-sensitive, pre-competitive matters such as fraud detection or agronomic knowledge is common in many sectors. Commercially sensitive information, such as prices, volumes and terms, must stay private, and any collaboration should be reviewed by your own legal counsel.
Updated ·
