The informal agriculture market as dynamite in small packages!

Zimbabwean agriculture has reached a point where almost everyone now knows how to produce maize, sugar beans, tomatoes, cabbages and a diverse range of crops and livestock. However, lack of market intelligence is preventing farmers from translating yields into money in the pocket. Having been socialized to think of markets in terms of big companies and buyers, farmers and policy makers are still to be convinced that informal markets are big market players. Through the right mix of market data, people (farmers, traders and transporters, etc) in informal agriculture markets, eMkambo is surfacing market evidence that used to remain silent. While a single smallholder farmer harvesting two bags of sugar beans can find it difficult to imagine the volume of sugar beans flowing into Mbare Agriculture market daily, it is now possible for serious farmers to know who is bringing what volume of commodities from where. Farmers who don’t care to know about their competitors have not started farming. This analysis brings you snippets of how sugar beans have performed on the informal market based on activities at Mbare Agriculture Market (the biggest informal market in Zimbabwe). Sugar Beans (dried)– Mbare Agriculture Market (Harare): January to October 2014 a Quantities supplied to the market A total of 28719.3 20l buckets of sugar beans were supplied to Mbare Agriculture market. With one 20l bucket weighing approximately 16.9kgs, a total of four hundred and eighty five metric tons (485.36 metric tons) were supplied to the market from January to October 2014 in small doses. b As can be seen in the graph above, supply of sugar beans was high in February. This peak supply in February is attributed to the speculative pricing of the preceding season. Just after February, new harvests will start flowing into the market, knocking prices downward. This is evident from March and the following months. Evidence gathered from farmers indicated that they will be waiting for such peak price periods to earn more income which they then use to pay school fee balances carried over during January school openings. As the months progressed, supply also decreased gradually. In July, supply increased followed by a gradual decrease from August through to October. Sources The sugar beans were supplied from 30 different districts and areas around Zimbabwe from January to October 2014. The table below lists the different sugar beans sources. c Looking at the sources, sugar bean production has invaded many areas of Zimbabwe cutting across all farming regions. While NGOs have contributed to sugar bean production through provision of inputs and extension, farmer to farmer learning also explains why the crop has taken off in the country. Dry areas like Rutenga, Gokwe, Mt Darwin, Muzarabani and Mudzi have shown a commendable appetite to supply commodities to the market. Farmers are realizing that the only way livelihoods can be improved is through increased income not just high yields or bumper harvests. d The price has also been firm on the market. However, decreases in September and October can be attributed to the introduction of pre-paid metres by ZESA which have seen many urban consumers opting for faster – cooking relish which does not increase the energy bill. e The major determinant of sugar bean prices is the demand against supply on the market. The months of January and February attract old stocks onto the market due to demand for a crop that will be out of season. Unlike horticulture crops, shortage of beans on the market attracts more supplies to be released from old stocks due to their non – perishability nature. This explains why prices were high in January and February, with a decline in March as illustrated by the graphs above. However, prices remained almost constant from March through to August, followed by a further decline in September 2014. f The graph above depicts Expected Revenue by month. October saw the lowest revenue while February recorded the highest. g As shown in the above graphic, the bulk of sugar beans supplied were recorded as coming from Harare because they represented stocks by traders who source from around the country in small quantities and store in their warehouses around Harare. In most cases, traders are not comfortable to reveal their sources because they are afraid to lose their competitive advantage. Stocks in Harare are released in small volumes to the market according to demand. This has the effect of keeping the price a bit constant, in the case of February. Why is agriculture market data such a big deal? It is only through data that viable agribusiness models can be crafted in Zimbabwe, resulting in sustainable revenue streams. Technology is now enabling the agriculture sector to reveal its true potential which is no longer in yield per hectare but surplus for the market. By ignoring data from informal markets like Mbare, Malaleni, Sakubva and others, agricultural experts, development agencies and policy makers are missing part of the answers to their solutions. Data has to be converted into accountable agricultural programming. Making use of data can enable the media to steer evidence-based public discourse on agro-based economic development. While there seems to be a glut of information, it’s not the kind of information that will enable farmers to make effective decisions. Traders and farmers know much about agriculture markets than policy makers and development agencies combined. Without integrating expertise from farmers, traders and transporters who operate in the informal market, agriculture development efforts will continue to fall far too short. Reliable data can reveal gaps in the current agriculture system and provide opportunities to see fine-grained agricultural details, indicating choices that would work for farmers and all value chain actors. It is through market data and feedback processes that we are able to see emerging agricultural patterns and commodities that are relevant to farmers in particular areas.   Agricultural inputs worthy thousands of United States Dollars flow to farmers annually. In the absence of the right data and associated performance analytics, it’s impossible to pinpoint the highest performers. Good data will lead to efficient allocation of agricultural resources.

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