While African financial institutions are still stuck with traditional banking models invented in the West, African agricultural value chains have shifted in ways that threaten to render banks irrelevant to agriculture. Rural and agricultural finance modeling is being forced to consider value chain collateral and the existence of a market as opposed to physical forms of collateral. In the majority of African countries with a growing informal sector, informal agriculture markets now play the role of farmer characterization and verification for loan access. The existence of a market constitutes 80% of the collateral. Production factors like availability of suitable land, water draught power, labour and others constitute 10% of the collateral. The other 10% goes to skills and relationships.
It is also no longer about the person but agricultural commodities that can be financed from the market. Assessing an individual in terms of skills, credit history and other personal issues is inadequate without understanding a particular commodity’s performance on the market, especially where farmers are specializing. Financing models from agricultural markets have to be commodity focused rather than focusing on a piece of land, a house or the farmer’s experience. Gone are the days where financial institutions left everything to the borrower without gathering all the insights that ensure a complete picture. You can’t just get comfort from holding onto someone’s title deeds.
Starting from the other end
For a very long time agricultural financing has started from the production side. The new era demands financing should start from markets, traders and processors who pull commodities from production zones. This calls for a balancing act along the value chain. If $100 million is invested in production, more than that figure should be invested on the demand side. The $100 million injected into an agricultural value chain becomes $120 million at the end of the value chain since it accrues profit along the way. The $20 million profit should be shared along the value chain. If farmers produce commodities worth $100 million, they sell to traders and markets for $110 million, earning $10 million profit (10% profit). Traders and the markets should then sell to vendors and processors for $120 million, enabling them to earn $10 million profit (10% profit). Processors and vendors can also put a mark up to the end-users.
But if you inject $100 million in production, without considering the demand side, where will traders get $110 million to buy commodities from farmers? At any given time, before $100 million is injected into production, if the market has its own $200 million worth of commodities, the $200 million is a ratio of 1:1. But if you inject $100 million worth of production, you end up with $100 production against $200 million in the market. Where a farmer was supposed to be paid $1, s/he is paid $0.60. In other words, $300 million worth of commodities negatively affect farmers if production financing is increased without supporting the market with the same amount for purchasing the commodities.
The bottom line is that when banks finance production, they should have adequate finance models on the market. Development organisations and government schemes tend to be major culprits in distorting the market. Injecting fertilizer and other inputs worth $500 million into production can increase market supply by 60%. There should be an equivalent amount of money on the market to buy those commodities. If development organisations inject $100 million into production, 40% of the commodities will cover subsistence, the remaining 60% worth $60 million end up on the market. It means the same amount of money is needed to buy those commodities. Gluts are a result of more goods for a given amount of money. At a given time, Mbare agricultural market in Harare has $2 million in circulation. Indiscriminately bringing agricultural commodities into this market, automatically distorts market dynamics.
The supremacy of data
In order to cope with new trends, banks and other financial institutions have to harness the power of advanced analytics to provide deeper business insights. They have to invest in analytics and data-science expertise. This can rapidly provide knowledge for continuous performance improvement in the agriculture sector. As the pace of change increases, so does the need to transform and rethink current agricultural finance models. The following graphic, based on data gathered in Mbare agriculture market in Harare during the month of May 2016, is an example of insights that should be at the fingertips of financial institutions and other value chain actors:
Chart 1: Top ten highest revenue earning commodities – May 2016
Traditional ways of building financial models are rapidly getting out of date. Financial institutions should embrace the big data revolution spawned by ICTs. Tracking commodities through gathering and analysing data can enable financial institutions and other value chain actors to optimize their performance. In a fast moving economy, it is dangerous for financial institutions to think that they cannot be disrupted by technology and consumer preferences. They have to be always on the edge of innovating and moving fast. A knowledge ecosystem around farming areas is an ideal starting point. Other actors should be part of this ecosystem. For instance, seed companies and other service providers have outlets in rural business centres and growth points.
Markets as sources of knowledge
Informal agriculture markets and traders are reliable sources of knowledge on who to finance, when and where. They know which farmers have been producing for the past years and other related issues. The markets also fulfil an intermediary role. On one hand, the markets have processors and vendors who extend their services to end users. On the other end, the market is in touch with farmers and transporters. This means markets pull produce from farmers and push to processors and vendors.
Building finance models should be directed and advised by each agricultural market and its catchment. That should inform construction of a specific financing model. Each market has its own dynamics, performance pattern, incomes and actors. There are always many finance models ready for scaling up. In most cases, financial institutions do not need to pilot but scale up what exists. It is difficult to get any useful lessons from a pilot because most pilots are not demand-driven. Since financial institutions first of all tailor-make a product and consult end-users before thinking of piloting, it may be ideal to just start implementing from a smaller area.
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