The Aggregation Problem: Why Agro-Processing Plants Underperform Their Financial Models

The analysis draws on Coinletter's engagements with agro-processing clients across grains, oilseeds, dairy, livestock, and horticulture, supplemented by publicly available data from the Central Bank of Nigeria, the Federal Ministry of Agriculture, the National Bureau of Statistics, and academic literature on the Anchor Borrowers Programme.

It focuses on medium-scale and large agro-processing facilities, where feedstock supply is industrial in nature and where investment-stage assumptions about supply are the primary source of post-completion underperformance. It does not address smallholder-level processing or cottage industries, where the economics are different in kind.

Conclusions apply most directly to crops with significant smallholder fragmentation, perishability, and quality variance: pepper, tomato, rice, cassava, oil palm, and feed-grain crops. The structural pattern observed is recurrent across these sectors, even where the specific mechanics of failure differ.

The financial model presented to a credit committee for a typical Nigerian agro-processing facility makes three structural assumptions about feedstock. First, that raw material will be available in the quantities the throughput calculations require. Second, that it will be available at prices in the range the cost-of-goods table projects. Third, that the quality and timing of supply will be consistent enough to keep the plant operating at the assumed capacity utilisation. These three assumptions are treated as parameters of the model, not as the project's primary risk.

In practice, they are the project's primary risk.

Once the facility is commissioned, the operator confronts a procurement reality that the model did not contemplate. Raw material exists in the country in absolute terms, but the supply that physically reaches the gate of a specific processor, at the quality specification its equipment requires, on the dates its production schedule depends on, and at prices that preserve the unit economics, is a different and far more constrained category.

Consider the throughput on which the financial model rests. A tomato paste line designed for 80,000 metric tonnes of fresh tomato annually assumes a defined harvest window, supplier reliability, and a post-harvest loss profile. National post-harvest loss data tells the story bluntly: tomato has a spoilage rate near 48%. A processor's actual purchasable tomato is roughly half of the gross national tomato available. The model that assumed national surplus implies abundance has misread the entire supply curve.

The pattern repeats across categories (pepper, rice, oil palm, fresh dairy), each with its own version of the same structural mismatch. Widening the procurement net to more middlemen increases volumes temporarily but degrades margins, as each layer adds cost and reduces quality and timing control.

By the time the facility produces below capacity in its second year, the letters of intent from aggregators have proved softer than suggested, and the institution discovers that what it underwrote was a manufacturing risk; what it actually owns is an aggregation risk.

The aggregation problem decomposes into three layers, each with its own mechanics and its own structural fix:

1. Fragmentation

The Nigerian smallholder base produces most of the primary agricultural output in plot sizes between 0.5 and 2 hectares. The processor needing 10,000 tonnes of pepper is buying from thousands of independent farms. This fragmentation introduces massive transactional margins and informational gaps.

2. Perishability & Quality Variance

Perishable horticulture experiences 40% to 60% losses before reaching market. Furthermore, quality variance is high: a rice mill calibrated for 14% moisture content cannot process paddy at 18% moisture without major yield loss. Quality risk is borne entirely by the processor at the factory gate.

3. Counterparty Discipline

Even with aggregators, the processor depends on farmers delivering on contracted terms. Side-selling is common: a processor pre-finances inputs, only for the farmer to sell to open spot markets at harvest when prices spike. The Central Bank's Anchor Borrowers Programme, which deployed over ₦1 trillion, encountered this pattern at scale.

Nigeria's experience with the Anchor Borrowers Programme between 2015 and 2023 is the largest natural experiment in industrial-scale outgrower finance the country has conducted.

By 2018, the programme reported coverage of over 862,000 farmers across 835,000 hectares. Yield and productivity gains were real, but repayment rates were low. Farmers frequently diverted inputs or side-sold output when spot prices rose above the anchor's contracted price.

For the agro-processor designing an outgrower scheme today, outgrower credit only works under specific structural conditions:

• Strong cooperative organization: Social accountability structures that enforce delivery.
• Physical control of the harvest gate: Decentralised collection centres operated directly by the processor.
• Competitive harvest pricing: Structuring contracts to remain commercially attractive at harvest time.
• Differentiated input packages: Pre-paid inputs or custom seed varieties that are difficult to sell elsewhere.

Designing for the aggregation problem at the project structuring stage is more efficient than discovering it after commissioning.

"The processor without an aggregation strategy is not building a business; she is renting a market."

Four design choices distinguish operations that approach modelled capacity utilisation:

The constraint on agro-industrialisation is not access to plant capital; it is the absence of an aggregation layer between fragmented smallholders and processing facilities. Promoters must build the aggregation system first, or build it in parallel, but never assume it pre-exists.