|By Chinwendu Nwani
Agriculture employs 36 percent of Nigeria’s workforce and contributes 24 percent of GDP. Agricultural finance has never exceeded 4 percent of total bank lending. The reason is not risk. It is invisibility.
SOMEWHERE between the farms of Kaduna State’s tomato belt and the shelves of Lagos supermarkets, about 40 percent of Nigeria’s tomato harvest is lost. It rots on trucks, in inadequate storage facilities, at market points without cold chain infrastructure. The farmers who grew it absorb the loss. The traders who moved it absorb the loss. The consumers who needed it pay more for less. The Nigerian economy absorbs the loss; a conservative estimate from the Food and Agriculture Organisation places post-harvest loss across all Nigerian crop categories at approximately $9 billion annually.
The proximate cause of this loss is infrastructure: inadequate storage, unreliable cold chains, poor road connectivity. But behind the infrastructure failure is a financing failure, and behind the financing failure is a data failure. Nigeria’s agricultural sector, which employs 36 percent of the labour force and contributes 24 percent of GDP, has never attracted more than 4 percent of total bank lending, according to Central Bank of Nigeria sector data. The banks know the sector is large. They do not know it in the granular, risk-assessable terms that lending decisions require.
“The data problem in Nigerian agriculture is not that data doesn’t exist,” said Agbetola Ayokanmi Victor, a data analyst who has studied the information infrastructure gaps in emerging market agricultural finance. “Satellite imagery of crop health exists. Weather data exists. Commodity price data exists. What doesn’t exist is the integration layer that connects these data sources to a lending decision. The banks are not able to ask ‘what is this specific farmer’s yield risk this season?’ They can only ask ‘is agriculture risky?’ And the answer to that question, without the data to differentiate within the sector, is always yes.”
What Agricultural Finance Requires That Nigeria Does Not Have
Agricultural lending is inherently data-intensive. Unlike personal credit, which can be assessed primarily through the borrower’s financial history, agricultural credit requires assessment of agronomic risk — weather patterns, soil conditions, pest and disease pressure, commodity price volatility, that is geographically specific, seasonally variable, and requires real-time updating through the crop cycle. A loan extended to a Kano onion farmer in March faces a different risk profile than the same loan extended in November, and a different risk profile than an equivalent loan extended to a maize farmer 200 kilometres east.
Building the data infrastructure to make these distinctions requires integration across at least four categories of data that are, in Nigeria, fragmented across different agencies, private operators, and international research organisations: agronomic data from the National Agricultural Extension and Research Liaison Services, weather data from the Nigerian Meteorological Agency, commodity price data from state marketing boards and commodity exchanges, and smallholder-level production data that, in most cases, exists only in the records of agricultural extension workers or in the transaction logs of input suppliers.
The integration of these sources into a unified data layer accessible to financial institutions has been a stated policy objective of successive Nigerian agricultural finance initiatives, the Anchor Borrowers’ Programme, the Agricultural Credit Guarantee Scheme Fund, the Presidential Fertiliser Initiative, without meaningful operational progress. “Every programme starts from the same position,” said Dr. Funmilayo Adeyemi, an agricultural economics researcher at the University of Ibadan. “We know what the data infrastructure should look like. We know what it should contain. We have never successfully built it in a form that a commercial lender can actually use to make a credit decision.”
“The banks can’t ask ‘what is this specific farmer’s yield risk this season?’ They can only ask ‘is agriculture risky?’ Without the data to differentiate within the sector, the answer is always yes.”
— Agbetola Ayokanmi Victor, data analyst
The Smallholder Visibility Problem
Nigeria’s agricultural workforce is dominated by smallholders, farms of less than two hectares, operated by individual families or small cooperatives. According to the National Agricultural Sample Survey, approximately 14.5 million farm holdings in Nigeria are in this category. They produce the majority of Nigeria’s food output. They are, in terms of formal data infrastructure, largely invisible.
The majority of smallholder farmers in Nigeria do not interact with the formal financial system in ways that produce the data trails a credit assessment requires. They do not have formal land title registrations — an estimated 90 percent of Nigerian land is unregistered, according to the Ministry of Agriculture. They do not sell through formal commodity exchanges that would generate pricing and volume records. They may have mobile money accounts, but the transaction patterns in those accounts do not clearly signal agricultural production cycles without context that the data alone cannot provide.
Agritech startups have attempted to close this gap with varying success. Companies including Farmcrowdy, ThriveAgric, and Babban Gona have developed farmer-facing digital platforms that, as a byproduct of their operations, generate production records, input purchase logs, and yield data for their enrolled farmers. The data exists. The challenge, which each of these companies has confronted independently, is connecting it to a formal financing decision at the scale of Nigeria’s 14.5 million smallholder farms.
Ayokanmi frames the core technical challenge: “The data that agritech platforms generate is valuable but fragmented. Each platform knows its own farmers very well. None of them knows the sector. What you need is a shared data infrastructure, a national smallholder registry with production records, linked to weather and price data, that any lender can query. Every country that has successfully scaled agricultural lending has built something like this. Nigeria hasn’t. We keep trying to solve the financing problem without first solving the data problem that makes the financing problem intractable.”
What a Working System Looks Like
Brazil’s agricultural finance system, built around the Pronaf credit programme and supported by the Sistema Nacional de Cadastro Rural land registry, demonstrates what integration looks like at scale. Lenders making smallholder agricultural credit decisions can access a single data layer containing land registration, crop insurance records, historical production data, and commodity price benchmarks. The system underwrites approximately $14 billion in smallholder agricultural credit annually.
Kenya’s DigiFarm platform, operated by Safaricom in partnership with the International Finance Corporation, has achieved partial integration for enrolled farmers: loan decisions incorporate input purchase history, farm mapping data, and harvest sale records from the platform’s ecosystem. Loan approval rates for enrolled smallholders are 44 percent, compared to an industry average for unregistered smallholders of approximately 8 percent.
Nigeria has the data components for an equivalent system. It does not yet have the political will, the institutional coordination, or the technology governance framework to assemble them into a usable whole. Until it does, the tomatoes will continue to rot on the road from Kaduna to Lagos, and the farmers who grew them will continue to finance next season’s planting from whatever they salvaged from last season’s loss without a bank in sight.
