Taming agricultural value chains through data, evidence
Many people who grew up in African communities practising mixed farming, remember how taming young bulls or steers into a span of oxen was difficult.
BY CHARLES DHEWA
The situation was the same with taming a cow in order to milk it, when it had just given birth to its first calf.
In most cases, you would not complete the taming process without suffering serious injuries.
In a dynamic world, where niches are becoming highly competitive, taming agricultural markets requires data and the same tenacity that goes into taming livestock for multi-purposes.
Going beyond obvious ICTs capabilities
Thanks to ICTs and new knowledge, there is now a diversity of techniques for identifying and seizing opportunities across agricultural value chains.
Unfortunately, the majority of farmers and agribusinesses are struggling to optimise their resources for better production.
For instance, new farmers in Zimbabwe are grappling with challenges in combining available inputs and other resources such as water.
This is partly due to lack of appropriate skills and reliable information.
If the ICTs sector is to be fully directed at improving agricultural practices, African universities and colleges should ensure sophisticated advanced data modelling is part of the curricular.
At the moment, African graduates in ICTs are only able to manipulate platforms like websites, mobile applications, mobile money and WhatsApp.
To fully tame agricultural value chains, we badly need experts who can generate accurate market-size forecasts, assemble commodity and input price curves, as well as gather and interpret historical equipment data.
Many farmers buy second-hand or new farming equipment without a thorough understanding of how long the equipment will function optimally.
The absence of many decision-making variables reduces farming into a small game. Business models resulting from accurate data can offer farmers and other value chain actors a precise understanding of the effects of weak value chain nodes.
They can then easily fine-tune the mix of inputs, equipment and harvested commodities, while constantly identifying opportunities for improving their practices.
Without such level of granular detail, value chain actors will not get a clear sense of their needs, constraints, trade-offs and new agribusiness opportunities.
Addressing wrong assumptions through evidence and technology While assumptions that have condemned African countries to the current food insecurity mess may not be obvious to policymakers, evidence and technology can remove most of the blinkers.
This is where trends like machine learning and data analytics can have significant impact.
Where agricultural policymakers may not see the precise importance of logistics, data-driven insights from the market can inform investments in agricultural logistics.
Data and evidence can also reveal the futility of large agribusinesses and multinationals shutting themselves off trends coming from the growing informal agricultural markets.
Where established formal agribusinesses think and act in terms of months, quarters and years, traders and other actors in the informal sector think, plan and act in terms of days and weeks.
They also focus on changing something today rather than waiting to take action next week.
Entrenched thinking in formal institutions has been the main reason why the majority of formal banks cannot penetrate the informal sector in a sustainable manner.
These financial institutions continue to plan in terms of months, quarters and years when their potential clients think and act on their feet in response to the agile environment, where information and responses have become very fluid.
Trying to play it safe by taking years studying the informal sector in order to invest has become very dangerous for formal institutions in these changing times.
They have to embrace new processes for making more efficient decisions.
The transformation headache and tapping into the wisdom of value chain actors
In addition to harnessing ICTs, interdisciplinary collaboration will allow greater agility that makes it easier for agricultural value chain actors to capture new opportunities.
Outside traditional IT and new mobile platforms, the agriculture sector has to develop deep technology expertise.
While many young Africans are embracing ICTs, the main challenge is transforming different technologies from outside so that they meet current and future requirements.
Ensuring a positive public perception of technology talent should be at the forefront of technology development considerations.
Colleges and universities have to produce ICT graduates able to generate an end-to-end view of critical nodes in agricultural value chains.
For that to happen, it is important to build digital centres of agricultural excellence that enable fast and creative knowledge exchange between experts from different backgrounds.
This will ensure everybody remains plugged into the latest trends.
Thousands of university graduates willing to get into the informal sector have to be trained in a new way of thinking and working.
They will have to learn to address quality problems on the spot rather than send them to the market and expect consumers to answer back.
Most consumers may decide not to answer, but look for goods and services from elsewhere.
The youth will have to be on the front line of the agriculture sector in order to understand day-to-day operations better than disconnected value chain actors.
Eventually, they should be able to tell why some agricultural activities are done in a certain way, as well as factors that hold the agricultural sector from moving forward.
Without such insights, it is impossible for them to take an imperfect step forward in a turbulent global market, where all solutions are becoming increasingly imperfect and require real-time evidence.
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