The scale of Nigeria’s tomato paradox is striking. The country produced an estimated 3.84 million tonnes of tomatoes in 2020 and 3.58 million tonnes in 2021 across more than 844,000 hectares, making it Africa’s second-largest producer after Egypt. Yet national yields average a mere 4 to 7 metric tonnes per hectare, compared with 38.7 tonnes in Egypt and a remarkable 78.7 tonnes in South Africa. That shortfall forces Nigeria to spend roughly 170 million dollars each year importing tomatoes to meet domestic demand, a bitter irony for a nation with fertile rainforest soils and an equatorial climate well suited to the crop.

The roots of the yield gap lie in chronic underinvestment. Fewer than 10 percent of Nigeria’s smallholder farmers have access to formal agricultural credit, and even those who do often face punishing interest rates, stringent collateral demands, and slow administrative procedures. Without reliable financing, farmers cannot purchase improved seeds, fertilizers, irrigation equipment, or post-harvest handling technology. Credit constraints also block adoption of climate-smart and risk-mitigating practices, leaving growers exposed to environmental and market shocks that can wipe out an entire season’s income.

Measuring the true effect of credit is harder than it sounds, and this is where the study makes its methodological contribution. Farmers who obtain loans are not a random sample of the farming population. They tend to be more experienced, better educated, more commercially oriented, and more willing to take risks, and these same traits independently boost productivity. A naive comparison of yields between credit users and non-users would therefore conflate the effect of money with the effect of farmer ability, producing biased estimates. The researchers tackled this problem with an endogenous treatment regression model, which jointly models the decision to access credit using a Probit regression and the resulting yield outcome in a second-stage linear regression, correcting for both observable and unobservable differences between the two groups.

The data came from a cross-sectional survey of 300 smallholder tomato farmers in Oyo and Ondo States, two prominent tomato-producing areas in Southwestern Nigeria. Using a multistage sampling design, the team randomly selected agricultural zones, local government areas, and villages, ultimately interviewing farmers across 24 rural communities during the peak cropping season between August and September 2023. The questionnaire was pretested in non-sampled communities, and reliability checks yielded a Cronbach’s alpha of 0.78, indicating acceptable internal consistency. Respondents averaged 44 years of age, 13 years of farming experience, and 3.72 hectares of farmland, with a mean tomato yield of 518.56 kilograms per hectare.

The raw differences between credit users and non-users were substantial. Farmers with credit access harvested 587.23 kilograms per hectare on average, versus 412.64 kilograms for those without, a gap of nearly 175 kilograms per hectare. Credit users were also more likely to be male, married, better educated, members of agricultural cooperatives, and connected to extension services, and they lived closer to markets. Eighty-three percent of credit users belonged to cooperatives, compared with a significantly lower share among non-users, and 79 percent of credit recipients reported access to extension services. These systematic differences confirmed that simple comparisons would be misleading, and that a rigorous correction for selection bias was essential.

After applying the endogenous treatment framework, the headline result held firm. The average treatment effect of credit access on yield was 8.229, and the average treatment effect on the treated was 8.728, both statistically significant at the 1 percent level. In practical terms, farmers who accessed credit produced roughly eight to nine times more tomatoes than they would have without it, holding other factors constant. To guard against the possibility that this finding was an artifact of one particular model, the researchers ran two robustness checks. Propensity score matching, which compares credit users with statistically similar non-users, produced an ATT of 8.514, while two-stage least squares estimation using cooperative membership, extension access, and formal education as instruments yielded a coefficient of 8.381. The convergence of all three methods strengthens the case that credit genuinely drives productivity rather than merely accompanying it.

The diagnostic tests underpinning the analysis were equally rigorous. A Wu-Hausman test rejected the assumption that credit access is exogenous, confirming that ordinary regression would have produced biased estimates. The Hansen J statistic failed to reject the validity of the instruments, and the first-stage F-statistic of 19.84 exceeded the conventional threshold for instrument strength. The first-stage Probit model also revealed who gets credit and who does not: farming experience, market proximity, extension access, and cooperative membership all increased the likelihood of obtaining loans, while older age and larger numbers of dependents reduced it. The findings expose a troubling pattern in which older farmers and households with heavy dependency burdens are systematically excluded from formal finance, and where women, who made up only 32 percent of the sample and a smaller share of credit recipients, face structural barriers to the resources that drive yields.

The mechanism linking credit to productivity is straightforward but powerful. Liquidity allows farmers to buy improved seeds, fertilizers, and agrochemicals at the right time, hire labor during critical operations, and invest in irrigation, greenhouse technology, and integrated pest management. Credit also supports post-harvest storage and transportation, reducing the spoilage that plagues perishable crops like tomatoes. Beyond the farm gate, the authors argue that expanding credit access would generate benefits across the broader farming population, not just current beneficiaries, and they frame the findings within the Sustainable Development Goals on poverty reduction and zero hunger.

The policy implications are clear. The researchers recommend strengthening extension systems, promoting cooperative membership and group lending, and designing flexible loan products with seasonal repayment schedules and lower collateral requirements tailored to smallholder realities. Gender-sensitive and age-responsive credit schemes are essential to close the productivity gaps the study documented. The authors also acknowledge limitations: the sample is confined to two states and a single crop, the cross-sectional design limits causal inference over time, and unobserved factors such as risk preferences and informal lending arrangements may still play a role. Even so, the message for Nigeria’s policymakers is hard to ignore. With the country bleeding 170 million dollars a year on tomato imports, helping smallholder farmers borrow affordably may be one of the cheapest ways to grow more food at home.

Subject of Research: The effect of agricultural credit access on tomato yield among smallholder farmers in Nigeria

Article Title: Unlocking yield potential through credit access: insights from smallholder tomato farmers in Nigeria

Article References: Kehinde, A. D., Akinola, A., & Tijani, A. (2026). Unlocking yield potential through credit access: insights from smallholder tomato farmers in Nigeria. BMC Agriculture, 2(1), Article 3. https://doi.org/10.1186/s44399-025-00025-7

Image Credits: AI Generated

DOI: 10.1186/s44399-025-00025-7

Keywords: agricultural credit, tomato yield, smallholder farmers, Nigeria, endogenous treatment regression, selection bias, propensity score matching, two-stage least squares, cooperative membership, extension services, food security, rural finance