Service Delivery Cost per Farmer vs Crop Type

Strength of Relationship 4/5

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

Key Messages

Contrary to our initial perception, our data shows that the service delivery cost per farmer  is significantly higher in food crops than in cash crops, and in loose value chains than in tight value chains.

We believe that this can be attributed to four main reasons:

  • Nature of the market
  • Enabling environment 
  • Scale and organization 
  • Positioning to farmers 

Keep reading to find out more.

Understanding the crop and value chain dynamics on investment

A widely-shared perception within our sector, and a strong hypothesis we had when developing our first Learning Framework in 2019, is that costs to serve smallholder farmers were higher in cash crops and/or tight value chains than in food crops and/or loose value chains. We expected crop type (food or cash) and value chain organization (loose or tight) to be important drivers of the service delivery cost per farmer, or the investment companies make in smallholder farmers. The results of our analysis, however, show the opposite: in the sample of 100+ models that we analyzed, the (gross) service delivery cost per farmer is significantly higher in food crops and in loose value chains, compared to cash crops and tight value chains, respectively.

Both the mean and median service delivery cost per farmer is over 250% higher in food crops than in cash crops, and over 300% higher in loose than in tight value chains. These differences hold when taking into account the modelled impacts of all other drivers: the triangulation of results using machine learning shows that with all else held equal, models active in food crops or loose value chains have a higher-than-average service delivery cost per farmer, with the opposite being seen in cash crop or tight value chains. Read more about the results from our machine learning analysis.

READ MORE: What do we mean by food/cash crops and loose/tight value chains? And why did we combine these two?

Link to other outcomes

When looking at the relationship between crop type (food/cash) and value chain organization (loose/tight), and the other two outcomes that we analyze in Insights Hub (Direct Cost Recovery and Farmer Value Created), we find that:

  • For Direct Cost Recovery , fascinatingly, the much higher cost (or investment) per farmer in food crops and loose value chains are mirrored by higher cost recovery percentages in those same value chains. In food crops, median and mean cost recovery are 78% and 69%, respectively, compared to 7% and 30% in cash crops, while for loose versus tight value chains the corresponding figures are 74% and 63% versus 7% and 32%, respectively. Click here for more details on these analyses
  • For Farmer Value Created, we find no appreciable differences between food versus cash, or loose versus tight value chains. Click here, for more details these analyses

Diving deeper: what do we think explains these results?

From our experiences analyzing and supporting a wide range of companies, we believe that the higher service delivery cost per farmer and higher direct cost recovery in food versus cash crops and loose versus tight value chains, are explained by the following reasons: 

  1. Nature of the market – More fragmented and competitive markets require more investments into farmer services to secure supply.
  2. Enabling environment – Less availability of goods and services provided by other actors – such as governments – requires heavier investments into farmer services by the private sector in food crops and loose value chains.
  3. Scale and organization – Better organization of farmers and businesses allow for more efficient service delivery in cash crops and tight value chains.
  4. Positioning to farmers – Companies in food crops or loose value chains are often smaller in size with less geographic sourcing diversification. This creates a closer relationship to and dependence on farmers in a particular community and incentives higher investments in these farmers and communities.

Clicking on each of the preceding reasons provides a longer overview of our thinking, including more supporting qualitative and quantitative insights. 

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 next steps

The Insights Hub is a living document that we are constantly updating with new data, new analysis, validation by our partners, and more. 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 number of caveats that we wish to be open about. 

  1. A small number of block farm models are outliers both conceptually and practically
  2. Absolute service delivery cost per farmer may not be an optimal way of comparing across value chains
  3. The classification of models into either food or cash, and loose or tight, is partially subjective

Next steps that we have planned to update these findings in the near future

  1. Incorporation of other service delivery cost per farmer lenses
  2. Organize learning exchanges across food and cash crops, and loose and tight value chains
  3. Revisit the categorization of food/cash and loose/tight
  4. Develop a deep dive analysis into block farms

Suggestions for additional research by our peers and partners

  1. More research into how donor funding is allocated across different types of value chains