Role of data in accelerating agric transformation
The capacity of data to accelerate agricultural transformation in developing countries is no longer questionable. However, a remaining challenge is limited capacity to set systems for continuous data collection. Without that capacity, it is difficult for policy makers to put in place a set of coherent building blocks for delivering tangible value to farmers, consumers and use agriculture in addressing unemployment.
Data as instruments of culture change
Traditional top down extension approaches are being challenged as mobile technology democratises knowledge. Many farmers who used to depend entirely on extension officers are now able to gather data, seek, sense and share knowledge.
While there are many cases where the value of government extension cannot be ignored, extension officers can creatively use data to expand their coverage and increase their quality of services. Instead of one extension officer engaging with more than 500 farmers on a face to face basis, data and evidence can show which farmers need attention and which ones have become champions and can actually be conduits of advisory services. Farmers who have been producing certain commodities for generation cannot continue to be fed the same advice.
How data can reveal market behaviour and guide investment decisions
It is through data collection and analysis that the entire agricultural sector can see the types, varieties and volumes of commodities flowing from one farming community to specific markets, in ways that can inform investment decisions. If more than 60 percent of fruits come from three out of 10 provinces, a case can be built for setting up processing factories in the three provinces.
However, in the absence of data, policy makers can recommend setting up of fruit processing factories in urban centres, where, although close to bigger markets, moving raw fruits from farming areas for processing in cities can be more costly than if processing happens close to production areas.
On the other hand, data about volumes of commodities from the three provinces may not be enough if it does not show volumes consumed locally. A province can produce high volumes of fruit but most of it can be consumed locally due to high population density. In that case, setting up a local fruit processing factory will be a waste of resources because the factory will compete for raw materials with local consumption such that it will remain dormant for most of the time.
Competitive forces at producer level often force some farmers to sell after knowing the surplus of staple crops. That means a processing factory may not be guaranteed of surplus raw commodities.
Balance between food security and wealth creation
There should be a balance between food security for the nation versus wealth creation for the farmer and the nation. After satisfying food security, what about wealth creation? A model is needed to strike the right balance between food security and he market. Some of the most useful information that should be collected in farming areas goes beyond farm sizes and availability of natural resources like water and favourable climate.
Such resources are not useful without detailed farmer characterisation indicating knowledge, skills base, experience, sources of advice, mechanisation, preferred markets and risk appetite.
Picking the impact of different interventions
The impact of commodities supported by development agencies can also be picked through data, particularly on the market. There have been many cases where, supporting the production of particular commodities by development organisations has led to serious distortions on the market both on the input and output side. For instance, where development agencies provide free inputs, local agro-dealers who sell inputs are short-changed and where small grains production is supported without equal support to the market, gluts become the order of the day.
Important metrics that should be tracked include monthly activities in agricultural markets, monthly active versus daily active ratios, consumption patterns, consumption growth, food supply models, sources and frequency of supply from different sources. Besides informing policy and programming, such data can enable farmers and traders to connect with customers at a much deeper level.
Some of the models that can be fuelled through data include a market demand and supply model that accommodates warehousing, aggregation, cooling, ripening facilities and other value addition activities like processing. All these initiatives have to be backed by credible evidence.
The impact of agriculture on dietary diversity can be seen through longitudinal data collection and analysis. Data can also show the extent to which a country’s diet is broad or narrow. For instance, some evidence is beginning to show that Zimbabwe’s dietary diversity score is based on 12 commodities, although the country has a wide range of food systems.
The country’s rural populations depend on less than six food groups yet they produce a diverse range of foods. Evidence is also beginning to reveal how rural finance does not always lead to better nutrition because people focus on acquiring assets instead of investing in better nutrition.
Longitudinal data can also reveal factors that predict stunting among children. If governments and development agencies want to sustain their achievements, they have to invest in data collection and effective feedback loops. Without data, it is also difficult for academic institutions to become change agents in addressing food and nutrition insecurity.