Home Food for Thought Data: The invisible gap holding back smallholders

Data: The invisible gap holding back smallholders

Astrophysicists know that there are imperceptible forces like dark matter and dark energy at play in shaping the universe that they can't touch or observe. In much the same way developmental economists are frustrated by the lack of quantifiable data about the real state of smallholder farming in South Africa standing in the way of revolutionising this sector


If we had access to reliable, detailed database reflecting the reality of smallholder and subsistence farmers in Mzansi, we would know where levers were to pull to revolutionise the sector, argues economist Lunathi Hlakanyane. Hard data could eliminate so much of the unspoken bias, hearsay and ignorance that currently informs policy.

It is a conventionally accepted fact in astrophysics circles that the universe, in all its expanding vastness, is teeming with mysteries that evade human comprehension and the advanced cosmo-observation technologies that inform it. 

Consider this for example: Only 5% of our universe consists of ‘normal matter’ or atoms that make up every visible object including the sun, moon, planets, galaxies and farmers. The other 95% consists of dark matter and dark energy

The former is believed to be a non-baryonic constituent of the universe made up of undiscovered subatomic particles that neither absorb, emit nor reflect electromagnetic radiation (therefore unobservable with current scientific instruments) but nevertheless interact with gravity. 

Meanwhile the latter is a repellant force with strong anti-gravity properties, widely believed to be responsible for the universe’s continuously accelerating expansion since the Big Bang. Think of dark matter as a sort of interstellar cement that holds everything in the universe together, and dark energy as the celestial pressure that expands it all in different directions. 

Although science is largely at loggerheads regarding exact properties of dark matter and dark energy, their study nonetheless allows us to make insightful inferences about the nature of the universe and indeed reality itself. 

‘We do not have enough (if any) accurate analytical insights to effect change at farm level.’

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There are graphic similarities, of course, between this phenomenon and one less talked about in agricultural policy frameworks: missing micro-analytical insights regarding the operational and financial performance of smallholder farming enterprises. 

Every agricultural economics paper with the prime object of commercialising smallholder farmers as a rural development strategy opens with an all-too-familiar line: 

South Africa’s agricultural industry is characterised by a systematic dualism consisting of large-scale, industrialised commercial farming establishments alongside poorly-resourced, primarily subsistence smallholder farming units.

The paper(s) would then go on to state that, because of the country’s racially-charged historic policies, the duality in question is divided along ethnic lines, with the distribution of farmers classified as “commercial” overwhelmingly tilted toward white farmers, while for those of “smallholder” disposition toward black farmers. 

The paper(s) would continue to assert that the inherent disparity in resource endowment, output per hectare planted or unit of livestock reared as well as access to mainstream value chains between the two systems is due, in large, to an assortment of technical and institutional constraints confronting smallholder farmers, including but not limited to:

  1. Tragedy of the commons, viz: communal land tenure;
  2. Lack of input subsidies; 
  3. Poor infrastructure;
  4. Adherence to specified industry/market quality standards; 
  5. Market information asymmetry; 
  6. Low or insufficient operating capital; and 
  7. Lack of credit access. 

In closing, the paper(s) would propose an elaborate policy framework through which the above-listed speed humps to smallholder farmer development may be circumvented to forge a fully inclusive, highly commercialised and internationally competitive agricultural industry. 

Also read: Youth + tech = a food secure future

Feeling around in the data-less dark

Rainbows and unicorns. The problem with this all-too-common approach is that it falls short of pointing out a starkly obvious reality — that is, we do not have enough (if any) accurate analytical insights to effect change at farm level. 

Time-tested intervention strategies like rural extension services have only been effective at treating symptoms while consistently remaining oblivious to the proverbial elephant in the room. Quite contrary to what most developmental economists will tell you, the main hurdle to smallholder farmer development is not missing markets, lack of finance or unproductive land, but it is in fact — and I need you to listen carefully — data.

As with astrophysics, the absence of a nationwide smallholder farmer data repository containing insightful variables for individual farmers represents something approximating dark matter/energy in agriculture. 

For example, we have estimates regarding the total number of smallholder farmers, their aggregate livestock herd size, proportion of agricultural output attributed to them (albeit for a minority of commodities such as wool), their demographics as well as anecdotes regarding their prime marketing channel (the so-called “informal market”) and pitfalls thereof. 

However, we do not have micro, individual-level data points that allow for a detailed autopsy of seasonally-adjusted cost of production, marginal returns per hectare/LSU, total farm gross margin, etc. 

Also read: Post-Covid agricultural growth is more crucial than ever

Why is data so crucial?

In keeping with the astrophysics analogy: we know from surveys and general common sense that this on-farm data exists, but we have no quantifiable evidence as to the extent to which it drives growth and indeed rural development, just as we know that dark matter interacts with orbital gravity but we have no observable evidence of its subatomic infrastructure. 

In case you’re wondering why the collection of data is so crucial to smallholder farmer commercialisation, let us take one of the main cited constraints (read: credit access) to smallholder farmer development as an example. 

‘The availability of impartial data, therefore, has the potential to completely eliminate this bias using reliable metrics reflective of actual creditworthiness.’

By their very nature, finance institutions are extremely risk averse. The long-term cost of extending credit to defaulting borrowers is higher than any perceived gains. So, rationally, lenders place their bets on borrowers who are less likely to slip into arrears. 

To do this, they model credit profiles to accurately establish affordability parameters or creditworthiness using such indicators as historic yield trends, total fixed/variable costs as well as overall farm performance taking into account water use, fertiliser/pesticide application, average yield per season and annualised rate of return on investment, etc. 

Now, in the absence of a credible data reservoir containing these insights, the likelihood of risk-shy credit providers extending finance to smallholder farmers dips below zero. With sufficient household-level data, however, the possibilities are endless. 

‘Countless studies have proven that living standards tend to significantly improve in societies where women enjoy fair access to finance.’

For one, we could stratify the data along a wide spectrum of variables such as gender. We know, for example, that credit models have an inherent gender-differentiated credit scoring system with a heavy bias against women — who, by the way, comprise an overwhelming fraction of smallholder farmers, usually occupying the higher plane of the productivity percentile. And yet countless studies have proven that living standards tend to significantly improve in societies where women enjoy fair access to finance. 

The availability of impartial data, therefore, has the potential to completely eliminate this bias using reliable metrics reflective of actual creditworthiness.  Similarly, if the aim is to administer a developmental intervention, in lieu of a blanket approach, the data could be divided into clusters of carefully defined variables. 

Thereafter, tailored intervention strategies could be designed for each cluster to maximise impact and desired outcomes. All of this is entirely feasible with data. 

The agricultural revolution of the 17th century and its spillover effects, the most monumental of which being the industrial revolution that birthed the steam engine, ploughed the way for technological advancements that accelerated human progress on a scale previously unimaginable. 

All things considered, it appears we may be on the cusp of another revolution, mainly driven by big data and corresponding innovations like block chain technology, that promises to unlock possibilities far beyond our wildest expectations. 

We should, therefore, get on the highest crest of this wave and use the opportunity to finally shine the spotlight on agriculture’s dark data as a strategy to eliminate the developmental dichotomy that continues to plague South Africa’s agriculture industry. 

Also read: African women in agriculture hit hardest by Covid-19

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Lunathi Hlakanyane
Lunathi Hlakanyane
Lunathi Hlakanyane is an agri-tech and economics blogger. He holds an MSc in Agricultural economics degree from the University of Stellenbosch.


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