Service Delivery Cost per Farmer vs Scale
Key Messages
Of all the drivers analyzed, the
While these findings are aligned with FarmFit and our peers’ expectations, the data and insights are valuable in confirming this relationship. It highlights that while small-scale models can be helpful for piloting (new) interventions or business models, medium and large-scale models serving at least 1,000 farmers, and ideally over 10,000 farmers, are likely to be much more efficient. In addition, it confirms the importance of continuing to develop innovative approaches that mitigate quality trade-offs at larger scales.
We believe the results are due to three main reasons:
Efficiency and economies of scale Business model maturity Intensity and quality of service provision
Keep reading to find out more.
Understanding how scale relates to cost
This analysis might be the most straightforward one in the FarmFit Insights Hub, both in terms of expectations and results. Economies of scale are widely accepted as drivers of efficiency.
It’s no different in this case; our data strongly suggests that the more farmers a business works with, the lower the service delivery cost per farmer. Specifically, models working with fewer than 1,000 farmers have a significantly higher cist per farmer than those working with a higher number of farmers.
The differences are substantial; scale seems to be strongly negatively related to service delivery cost per farmer. These results are validated using more rigorous machine learning methods. (
Over 80% of our dataset consists of scattered-plot business models working with many individual smallholder farmers. However, the FarmFit team has also looked at other models such as block farm and nucleus farm models, all of which have, on average, much higher service delivery cost per farmer and are considerably less likely to work with 10,000 or more farmers (large-scale in our categorization). This could distort our results; given that these specific high-cost models tend to be small or medium in scale, they could skew our results. However, even when we control for this variable, the results not only hold, but strengthen further. (
Links to other outcomes
When studying the relationship between scale and the other two outcomes analyzed in the Hub, we find that:
- For
Direct Cost Recovery , our data suggests that on average, larger-scale models recover a larger percentage of their service delivery costs to farmers. Click here for more details - For
Farmer Value Created , we see the opposite result: Smaller-scale models are associated with more absolute value created for farmers than medium or large-scale models. This has important implications for this analysis - while scale can help business models operate more efficiently, part of the reason for lower service delivery costs can also be due to reduced intensity and/or quality of service delivery. Click here for more details
In the following sections we dive deeper into possible explanations and nuances behind these results, as well as their implications.
Diving deeper: what might explain these results?
Our quantitative and qualitative data suggests there are several compelling reasons for why we see these results.
Efficiency and economies of scale – Like businesses in other sectors, we see economies of scale. Working at a larger scale allows companies to work more efficientlyBusiness model maturity – Business models that are at an earlier stage of maturity, such as the piloting or initial set-up phase, exhibit higher costs (for example, in initial design or testing of features). Larger-scale models working with over 10,000 have frequently already passed these initial stages of maturityIntensity and quality of service provision – Larger scale models may reduce the intensity of service provision, such as a lower staff-to-farmer ratio – resulting in a lower cost, but at the expense of quality.
Implications – so what does this mean for you?
Based on our findings to date on this topic, we see the following implications for different audiences:
- From the very start of your smallholder-inclusive business model design process, assess when, with whom and how you can reach sufficient scale for efficiency benefits to kick in. Include this analysis when reaching out to (impact) investors, donors and support organizations, as these are increasingly looking for long-term commercial viability of businesses that they support. If you cannot find a feasible way to reaching scale, reconsider your business model
- Engage and educate your investors, donors and support organizations on how your business operates and ensure they understand how your core business relates to the services you supply to farmers
- Assess whether you can provide the same quality of service delivery at scale. We see that the number of touchpoints with farmers – for instance the staff-to-farmer ratio – often decreases with increased scale. Are there ways in which you can mitigate the risks, for instance through working with intermediaries for last mile delivery or integrating technology to allow you to scale more efficiently without compromising on quality?
- Learn from the best – which peers and other businesses are successfully scaling without a trade-off in farmer value created? With what business model design choices have they achieved this, and how could those lessons benefit you?
- Identify what support – if any – you need in order to achieve scale and reach out to the appropriate partners to overcome any challenges. Be transparent, particularly with investors, on what capital needs will need to be met to reach scale and impact expectations.
- When supporting earlier-stage or smaller-scale models, be aware that longer-term patient capital is needed to allow your investees a sufficient chance to reach commercially viable scale, without compromising farmer value created. Understand the unintended consequences of trying to reach scale too quickly
- Gain insights into what the “tipping points” are; at what scale does efficiency goes at the expense of effectiveness? Support your investees in mitigating this trade-offs, for instance through the use of technology
- Work with potential and current investees to assess the path to scale. Provide support to help overcome barriers to scale, where possible
- Consider the performance of the whole business model, not only the part that engages with farmers. Be willing to make (well-informed) investments into parts of the business that are not directly related to service supply, which can support the growth of the business and therefore (indirectly) the growth and scaling of the service supply
- Our data strongly suggests that scale is a key means of reaching more efficient service delivery, but often involves a trade-off with farmer value. Support research and intelligence efforts to better understand barriers to scale, scale enablers and successful pathways to scale.
- Consider the performance of the whole business model, not only the part that engages with farmers. Be willing to make (well-informed) investments in parts of the business that are not directly related to service supply, which can support the growth of the business and therefore (indirectly) the growth and scaling of the service supply
- Continue to provide patient “scaling” capital, particularly to innovators, to help companies reach scale ‘tipping points’ as they experiment with, and build the foundations of, their business models
- Support the private sector in creating delivery mechanisms (such as technology and intermediated last mile delivery) that retain the quality of service delivery at scale. Our data suggests that the number of touchpoints with farmers are reduced at scale, which could indicate not only greater levels of efficiency, but also reduced quality of service delivery. FarmFit analysis on the relationship between scale and farmer value created supports this theory
- Work with government and other public bodies to build and develop “scale enablers” for private sector engagement. This could include advocating for the right regulatory environment or supporting market-level initiatives such as the development of data assets or of digital b2b models (particularly around distribution), that can help providers reach a larger number of farmers.
- Understand scale-related barriers and needs of private sector actors serving smallholder farmers
- Prioritize investment into public “scale enablers” such as:
- Public infrastructure: storage facilities (including warehouse receipt systems and cold chain services) and rural roads, that are often barriers to scaling for private sector business models
- Digital infrastructure: digital solutions can provide a key way of efficient scaling while maintaining high quality of service provision and sufficient touchpoints with farmers. This can include mobile networks, regulation of digital payments and service provision, and promotion of digital literacy
- Public extension services: Coordinate with the private sector to reduce potential overlaps and waste, for instance with farmer training interventions provided by the private sector
- Consider working the private sector to leverage public assets e.g. public extension officers, for private sector service delivery. This can open up lower cost distribution channels for private sector actors, thereby shortening scale timelines.
Reflections on data limitations and further research
The Hub is an interactive resource that we are constantly updating with new data, new analysis, and validation by our partners. For the results on this page, we would like to emphasize the following:
Major caveats and limitations of our current approach
Our work relies on averages across multiple years Our analysis cannot fully attribute the effects of efficiency and reduced quality or intensity of service delivery
Next steps: Updating FarmFit’s findings
Control for measured and projected data Incorporate scale as an outcome indicator
Suggestions for additional research by our peers and partners
Research innovations that can help scale
Page content
Strength of Relationship 5/5
- Strong relationship between driver and outcome variables
- Results are consistent across analytical models used
- Very few limitations regarding sample or indicator
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