Farmer Value Creation vs Target Group

Key Messages

The organization of farmers into groups has long been touted as a scalable pathway to reducing the cost of working with smallholder farmers; as well as for improving outcomes for smallholder farmers. However, when it comes to the Farmer Value Created, FarmFit’s data shows that businesses engaging with unorganized farmers are associated with creating substantially more value (on an absolute dollar basis) than those working with formal or informal farmer groups. Our evidence suggests that this result is influenced by both contextual and business model design factors:

  • Target group as a symptom of context.
  • More intimate and customized business models.

However, working with individual farmers is often a very costly and a non-scalable approach – we discuss this further in our analysis of Service Delivery Cost and Target Group. Therefore, working with farmer organizations is the norm in the majority of contexts. In such contexts, we find that it is beneficial for a farmer to be part of a farmer organization, where they can benefit from:

  • Better access to services and markets.
  • Improved bargaining power.
  • Improved decision-making control for women and youth.

Therefore, while business models working with unorganized farmers may create more value on average, in many contexts it pays for farmers to be part of a farmer organization.

Keep reading to find out more.

Understanding how target group relates to value creation

It is widely believed that being a member of a farmer organization is generally beneficial for a farmer’s income. The analytical approach that we’ve taken does not directly test this hypothesis, but rather, assesses whether businesses that mainly engage with unorganized farmers create more value at farm-level compared to those working with formal or informal farmer groups. Our analyses show that businesses engaging with unorganized farmers are associated with creating approximately twice as much value on average as those working with formal or informal farmer groups.

Read more: How we define target group

That said, there is broad distribution of farmer value created across business models, particularly those working with unorganized farmer groups. Nonetheless, higher farmer value creation for business models working with unorganized farmers does hold when we use our more rigorous machine learning methods. Target group is in fact one of the most influential drivers when it comes to value creation.

Further investigation into the data suggests there is a degree of nuance behind these results. For this reason, we have triangulated the results with a variation of the farmer value creation indicator, by looking at percentage income uplift

Combining the two results, we can see that:

  • Businesses working with unorganized farmers are associated with the highest absolute value creation and relatively high proportional value creation
  • Businesses working with formal farmer groups are associated with considerably lower than average value creation, but the highest proportional increases in income
  • Businesses working with informal farmer groups tend to underperform in value creation, both on a dollar-value and proportional basis

Ultimately, we see a very nuanced story here. When triangulating our quantitative results with external literature and our own qualitative insights, we find further complexities. In the analysis further below, we dive deeper into the nuances behind the data.

When viewing the relationship between target group and the other two outcomes analyzed in the Hub, we find that:

  • For Service Delivery Cost per Farmer, the cost of service delivery is significantly lower when working with farmer groups, regardless of whether they are informal or formal. Click here for more details
  • For Direct Cost Recovery from Services, our data shows a weak relationship with target group. Direct cost recovery is highest for business models engaging with formal farmer groups, but the differences are small. Click here for more details

In the following sections we investigate possible explanations further, including the nuances behind these results, as well as their implications.

Diving deeper: what do we think explains these results

The data paints a somewhat complicated picture – there’s no shying away from that. At a first glance, it appears that models working with unorganized farmers create significantly more value per farmer on average, compared to those working with farmer groups – a result supported by our more rigorous machine learning results. However, we also see that models engaging with unorganized farmers have a similar relative impact on income as those engaging with formal farmer groups. This is the case when looking at proportional income uplift. Diving deeper into our quantitative and qualitative data highlights two compelling reasons explaining these trends:

  1. Target group as a symptom of context – Business models engaging with unorganized farmers tend to serve farmers that are either already better-off, have larger land holdings or are producing high value crops. Such farmers often also see high value creation on an absolute basis. On the other hand, farmers engaged in business models that target formal farmer groups tend to have lower starting income – these farmers consequently have low absolute value creation but high relative value creation
  2. More intimate and customized business models – Our data highlights that businesses typically adopt more customized and intimate business models when working with unorganized farmers – contributing to higher farmer value creation, but with trade-offs in terms of a high cost to serve

Given that (i) context often dictates whether farmer groups are prominent and (ii) working with unorganized farmers is often too expensive and not scalable for many businesses, most businesses target farmer groups in their business models. Thus, a logical follow-up question is whether farmers are better off as part of a farmer organization in such contexts where farmer organizations are the norm. We believe the answer is generally yes, for three main reasons:

  1. Better access to services and markets – In many value chains, businesses will often only engage with smallholder farmers if they are organized into groups. In such value chains, being part of a group improves, or can even be a prerequisite to access to services and markets.
  2. Improved bargaining power – Being part of a farmer group often means that farmers can negotiate better prices for services as well as for their produce, by marketing their produce collectively.
  3. Improved decision-making control for women and youth – Many farmer organizations are established to allow for certain groups of farmers, such as women and/or youth to coordinate activities with more independence from their households. The emergence of these groups has been shown to create value for their members.

Implications – what does this mean for you?

Based on our findings till date on this topic, we see the following implications for different audiences:

Reflections on data limitations and further research

The Hub is an living document which is constantly updated with new data, new analysis, validation by our partners, etc. For the results on this page, we would like to emphasize the following:

Major caveats and limitations of our current approach 

Although we believe our analyses and insights offer a solid set of insights that can already be used to inform decision-making, there are a couple of caveats that we wish to be open about.

  1. Our quantitative analyses do not fully capture the quality of farmer organizations
  2. Our quantitative analyses employed look at the median farmer

Next steps : Updating FarmFit findings  

  1. Conduct farm-level analyses with our farmer survey data

Suggestions for additional research by our peers and partners

  1. Further research into business development services associated with higher value creation

Strength of Relationship 3/5

  • Strong relationship between driver and outcome variables
  • Results are largely consistent across analytical models used
  • Several limitations regarding sample or indicator