WITH frequent reports of the El Niño–Southern Oscillation weather phenomenon worldwide, signals from mass markets are becoming an increasingly important source of knowledge for adapting to climate change.
Rising climate variability, shifting market dynamics, cross-border trade complexities and the growing scale of informal markets demand a more advanced, integrated and responsive early warning system. The rich intelligence embedded in market signals should find its way into decision-making processes.
Converting everyday market observations to structured intelligence
Every African country needs an early warning system that can unlock the value of informal market signals and transform everyday observations into structured intelligence.
Unlike periodic reports from global organisations such as the Famine Early Warning Systems Network (Fews Net), which are often published after many daily changes have already occurred, a market-based and fluid early warning system can inform climate services, food security planning and policy decisions in real time.
This way, the early warning system becomes a strategic platform for strengthening resilience, reducing losses and optimising opportunities across the entire agriculture and food systems landscape, not just a tool for risk reduction.
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In most African countries, agriculture and food systems are increasingly characterised by a wide range of commodities and actors.
Under these circumstances, a consolidated platform that helps to maximise opportunities while minimising losses is urgently needed.
The fact that commodities move together with information and knowledge means that when commodities are scattered, information and knowledge are also scattered, making it difficult to accurately account for gains and losses.
Addressing gluts, shortages, dry spells and disease outbreaks on the market
When supported by real-time alerts, a market-based early warning system can address fluid challenges such as gluts and shortages before they spiral out of control. It can also monitor disease outbreaks, as well as the effects of dry spells and inflation across value chains.
The system can strengthen supply chains by rationalising food distribution and clarifying the roles of different actors.
It can also prevent the concentration of value chain actors and talent in only a few nodes, such as production, when key skills are also needed in areas like logistics, post-harvest handling, quality control, preservation, processing and marketing.
Formalising several economic activities and introducing best practices can also be enabled through a market-driven early warning system.
Facilitating fair trade and financial inclusion
One of the key roles of the early warning system would be to facilitate fair trade through mechanisms such as a pricing committee responsible for demand management.
While food vendors can provide insights into prices at the consumption level, the system’s tracking mechanism could identify unfair trading practices such as arbitrary overcharging.
Currently, consumers and vendors have limited avenues for lodging complaints when they are overcharged. Similarly, farmers often have nowhere to report cases where they are overcharged by transporters or local authorities.
Transport costs have become a major challenge for many farmers located far from markets.
Additional benefits of an early warning system include supporting market-guided production systems that minimise gluts and reduce the waste of agricultural inputs.
Gluts negatively affect GDP by making it difficult for the value of production to be realised through sales. This ultimately undervalues commodities and weakens the agricultural sector.
A consolidated database of all actors would simplify the formalisation of agriculture and food systems. Local authorities would then be able to manage cities, towns, and growth points more effectively by knowing the exact number of agri-businesses, traders and vendors operating in their jurisdictions.
This would improve local planning in terms of vending sites, regulation and revenue generation.
More importantly, the early warning system could enable supply chains to function as a form of collateral. Financial institutions, input suppliers and service providers would gain confidence in working with known and registered actors.
This could help to address persistent challenges such as side marketing, which often leads to losses. Food safety and traceability would also become easier to manage, helping to safeguard public health.
Unlocking the power of aggregation
Beyond mapping production zones and supply areas, the early warning system would guide the aggregation of commodities within production zones.
Aggregation begins with consolidating statistics about farmers and other actors operating in particular production zones.
Once farmers and other actors are captured within the system, it becomes easier to record information such as production plans, harvests and expected harvest volumes.
This data would enable market actors to better plan and manage supply chains by identifying areas with shortages and those experiencing gluts. Commodity volumes could then be adjusted accordingly.
Planning would also allow for price projections based on expected supply volumes relative to demand.
Extension officers would increasingly focus on helping farmers to develop markets informed by production patterns.
Producer groups and lead farmers working with extension services would develop structures that improve the collection of information, ensuring that data does not arrive in fragmented pieces — a process that is both inefficient and costly.
Extension services would also assist with commodity grading and volume monitoring at the production level, while coaching farmers on the importance of markets, trading and aggregation.
Ultimately, the early warning system would evolve into a platform for collecting longitudinal data that informs decision-making from the farmer level up to policy-makers, development organisations and investors by revealing gaps and opportunities along supply chains.
Longitudinal studies would make it easier to differentiate the effects on crops and livestock caused by pests and diseases such as January disease and Fall armyworm, from impacts of floods, droughts or dry spells.