The term farmers’ association is often used as though it describes one organisational model. It does not. Across agricultural systems, farmer organisations perform very different functions — from representing farmers’ interests, to collectively marketing their produce, to providing commercial services, and, in more sophisticated arrangements, coordinating production itself.
This distinction matters for Farmers United (FU). The question is not simply whether FU should become a “better farmers’ association”. The more fundamental question is what economic function FU should perform within Botswana’s emerging national agricultural pipeline, and what a farmer should be paying for when joining it.
I see three distinct models.
1. The Representative Farmers’ Association
The first model exists principally to give farmers a collective voice.
Its members join because the organisation represents their interests before government, regulators, markets and other institutions. It may provide information, networking, training, advocacy, policy dialogue and access to selected services, but it does not necessarily organise the members’ production or take responsibility for getting their produce to market.
This remains an important function. IFAD describes farmer and producer organisations as membership-based organisations that can provide economic services while also enabling farmers to participate in policy dialogue and advocacy. (Nedac)
The value proposition is therefore essentially:
“Your collective voice is stronger than your individual voice.”
The organisation does not need to know exactly how many tonnes each member will produce next season in order to represent the sector.
Its principal assets are membership, legitimacy, information and collective voice.
2. The Producer or Marketing Organisation
The second model moves beyond representation into economic organisation.
Here farmers come together because collective action gives them advantages that they cannot obtain individually. These can include collective purchasing, aggregation, grading, packaging, processing, storage, transportation, marketing, technical assistance and bargaining with buyers.
The European producer-organisation model is a good example. Recognised producer organisations can undertake joint production planning, concentration of supply, marketing, transportation, packaging, storage and other activities designed to improve members’ economic position. (AgGateway)
The World Bank similarly describes producer organisations as jointly owned, member-controlled organisations established for producers’ economic benefit, with functions including bargaining, inputs, technical assistance, processing and marketing. (Rural Development)
The value proposition becomes:
“Together, we can perform commercially important functions more efficiently and with greater bargaining power than we can individually.”
This is much closer to where FU has been trying to go.
It explains the logic behind distribution, storage, aggregation, collective marketing and possibly processing. The farmer pays because collective organisation can reduce transaction costs and improve commercial returns.
But there is a vulnerability in this model: the organisation can begin accumulating infrastructure and operating costs in order to provide services that may not actually need to be provided in-house.
FU’s experience with rented storage is instructive here. If storage becomes an expensive fixed cost, suffers pilferage and does not correspond to a sufficiently predictable flow of produce, the service can destroy rather than create member value.
3. The Production-System Operator
The third model starts somewhere different.
It begins not with:
“What can farmers collectively sell?”
but with:
“What will customers and the wider economy require in the future, and what productive capacity must exist to meet that requirement?”
This is the model STRLDi is beginning to see as the potential destination for FU.
The organisation becomes a linchpin between forecast demand and organised productive capacity.
Its functions would include:
Forecast demand → aggregate demand → translate demand into production requirements → match requirements against proven capacity → allocate production to geographic clusters → monitor fulfilment → identify variance early → coordinate aggregation and logistics → deliver against forecast → learn from actual performance and improve the next forecast.
This is not the place for the fireman.
It requires the mathematician, forecaster, planner and systems operator.
The distinction is fundamental.
A market intermediary asks:
“Who has tomatoes available now?”
A production-system operator asks:
“What will our customers require six months from now, what production capacity will be available then, where will the gaps occur, and what must we do now to prevent them?”
That is a much more demanding organisational capability.
“Membership — Geographic clusters composed of farmers with demonstrated commercial production histories and the capacity to account for their production over time.”
The three models compared
| Dimension | 1. Representative Farmers’ Association | 2. Producer / Marketing Organisation | 3. Production-System Operator |
|---|---|---|---|
| Primary purpose | Represent farmers | Improve members’ economic position | Synchronise future demand with productive capacity |
| Starting point | Farmer | Farmer + market | Demand + productive capacity |
| Core value | Collective voice | Collective commercial advantage | Forecasting, planning, production assurance and system reliability |
| Membership | Broad farmer membership | Producers meeting defined criteria | Qualified clusters containing proven commercial producers |
| Market role | Advocacy | Collective marketing | Forward demand forecasting and customer coordination |
| Production planning | Limited | May coordinate | Core function |
| Forecasting | Not central | Supporting function | Central capability |
| Clusters | Member/community groups | Production/marketing groups | Geographic production units |
| Farmer commitment | Usually voluntary | May involve marketing agreements | Specific production allocation tracked through to delivery |
| Data | Member information | Production and sales data | Longitudinal production-performance data used for forecasting |
| Storage | Usually not core | May provide/own storage | Used only where the forecasted production flow demonstrates a need |
| Distribution | Usually not core | Member service | Coordinated with forecast volume and timing |
| Infrastructure | Limited | Often important | Owned only where ownership creates a demonstrable advantage |
| Government relationship | Representation | Commercial/regulatory interface | Connection into the national agricultural pipeline |
| Farmer voice | Primary purpose | Important secondary function | Essential feedback into the pipeline, but not the whole purpose |
| Management mindset | Representation and advocacy | Commercial coordination | Mathematics, forecasting, planning and systems operation |
| Economic proposition | Stronger collective voice | Lower transaction costs / greater bargaining power | Lower uncertainty + coordinated productive capacity + predictable supply |
| Member fee definition | Fee for representation, information, networking and advocacy | Fee/service charge for collective commercial services, often supplemented by marketing levies, commissions or transaction-based charges | Fee for access to a forecast-driven production system, with service charges potentially linked to capacity, production or transactions |
The third model should not be understood simply as “Model 2 with more services.” It changes the organisation’s operating logic.
The Starting Point: What Does It Mean to Be a Commercial Farmer?
The discussion that prompted this comparison began with a practical observation: many farmers do not have the quantitative capability required to operate within a highly coordinated production system. My response is not to begin by asking how we can teach every farmer mathematics. The more fundamental question is whether a production organisation such as Farmers United should accept farmers who have not yet demonstrated, through the operation of their farms, that they can sustain and grow a livelihood from farming.
For FU, “skin in the game” needs to mean more than having invested money or having an attachment to farming. It should mean that the farm itself has demonstrated the capacity to sustain the farmer’s livelihood and contribute to the farmer’s growth. A farmer who must maintain two or three other jobs simply to keep the farm going is in a different economic position from a farmer whose farm is already generating the income from which the farmer lives, reinvests and grows.
This is not a judgement on the worth of farmers who are still transitioning into commercial agriculture. It is an organisational design decision about the kind of productive capacity FU is prepared to build its system around. If FU is ultimately expected to forecast demand, allocate production, coordinate volumes and timing, and account for delivery against those requirements, it needs members whose farms can produce a sufficiently reliable history of production, revenue, costs, yields and performance from which those calculations can be made.
The proposed 3–5 year production record therefore becomes more than a membership requirement. It becomes the beginning of FU’s quantitative production intelligence. A farmer who can demonstrate what the farm has produced over time is already generating the information from which FU can begin to understand capacity, reliability, seasonality, yield and growth. The farmer does not have to be a mathematician; the farm must, however, be capable of producing evidence that can be worked with mathematically.
This is also why I would resist the temptation to measure FU’s future success by how many farmers it can bring into the system. The objective is not to fill FU with farmers. It is to build reliable productive capacity. If farmers are encouraged to remain permanently dependent on income from several unrelated activities while the farm itself remains commercially weak, the organisation may increase its membership without increasing the productive economy. And when the productive economy remains weak, the infrastructure built to carry it — including our trains — remains underutilised.
The question is therefore not whether every farmer currently possesses the mathematics required of a production-system operator. The question is whether FU can identify, organise and progressively develop farmers whose farms are already demonstrating the productive discipline from which that capability can be built.
What Do Farmers Actually Pay For?
There is no universal global membership fee for farmer organisations. The fee structure depends heavily on whether the organisation is primarily representative, service-based, commercial, cooperative or producer-controlled.
However, current examples give us useful comparative ranges.
Representative and professional agricultural associations
The Zimbabwe Farmers Union (ZFU) currently charges annual membership fees of US$5 for smallholder farmers, US$20 for small-scale commercial farmers and US$40 for large commercial farmers. The membership card also provides access to discounts from participating firms. (ZFU)
The Zimbabwe Agricultural Society charges US$100 annually for individual members, US$150 for husband-and-wife membership and US$200 for corporate members, with lifetime membership at US$2,000. (ZAS)
Practical Farmers of Iowa charges US$50 for individual membership and US$75 for farm/household membership, with lower-cost access membership at US$25. (Practical Farmers)
The West Virginia Farmers Market Growers Association uses a more commercial basis: full producer membership is US$100–350 annually, based on gross sales. (MFVGA)
These examples illustrate the first model particularly well: membership dues primarily support participation, representation, information and member services.
Producer and commercial organisations
The National Hay Association provides a useful example of a producer-oriented fee model. New producer members pay a minimum of US$325, after which annual dues rise with annual tonnage: US$550 for 5,001–10,000 tons, US$825 for 10,001–25,000 tons and US$1,100 for 25,000+ tons. (National Hay Association)
This is particularly relevant because it demonstrates a principle that could matter for FU:
The fee is related to the economic scale of the member’s participation in the system.
An older agricultural cooperative study in the United States similarly found membership fees ranging from US$25 to US$300, with fees serving partly as member capital and as a mechanism for securing financially responsible members. (Agecon Search)
The European/global producer-group ecosystem also illustrates that membership can be tied to the services and commercial functions of the organisation. For example, GLOBALG.A.P.’s current community membership is €2,550/year for producer groups, producer/supplier associations and cooperative groups, compared with €1,550 for individual producers. This is not a farmers’ association fee in the strict sense, but it demonstrates the higher fee level attached to participation by an organised producer group in a technical/commercial standards ecosystem. (GLOBALG.A.P.)
Comparative Membership-Fee Models
| Organisation / model | Geography | Organisation type | Current fee basis | Published fee | Approx. USD |
|---|---|---|---|---|---|
| Zimbabwe Farmers Union | Zimbabwe | Representative farmers’ union | Farmer category | US$5–40/year | $5–40 |
| Zimbabwe Agricultural Society | Zimbabwe | Agricultural society / farmer community | Membership category | US$100–200/year | $100–200 |
| Practical Farmers of Iowa | USA | Farmer organisation | Individual/farm household | US$25–75/year | $25–75 |
| WV Farmers Market Growers Association | USA | Producer association | Gross sales | US$100–350/year | $100–350 |
| National Hay Association | USA | Producer/marketing association | Annual tonnage | US$325–1,100/year | $325–1,100 |
| GLOBALG.A.P. | Global | Producer/quality ecosystem | Producer group | €2,550/year | ≈$2,950 |
| NEDAC | Asia-Pacific | Agricultural cooperative network | Organisation | US$2,500/year + US$500 admission | $2,500/year |
| FU — proposed Model 3 | Botswana | Production-system operator | To be determined from value/cost of forecast-driven production services | Not yet set | Not yet set |
USD equivalents for euro-denominated fees are approximate and will vary with exchange rates.
The examples demonstrate something important: membership fees range from nominal representation dues to thousands of dollars for organisations receiving specialised commercial, technical or network services. There is no defensible global benchmark that says a farmer organisation should charge “X dollars per farmer.”
The correct question for FU is therefore not:
“What do other farmers’ associations charge?”
It is:
“What measurable economic value does FU create for a participating farmer, and what does it cost FU to provide that value?”
What This Means for FU
This distinction is particularly important because FU’s members would not be joining merely to belong to an association.
Under the emerging Model 3, the farmer would be joining because FU provides something the farmer cannot efficiently create alone:
forward visibility of demand; production planning; allocation of requirements; coordinated aggregation; commercial scheduling; logistics coordination; performance information; and access to a wider production system.
The fee should therefore be defensible in relation to the economic value of those services.
And there is a further discipline that I think is essential.
Storage, Forecasting and Infrastructure
If FU can forecast demand accurately enough to synchronise production and delivery, it may need less storage, less emergency transport, less working capital tied up in inventory and fewer fire-fighting interventions. FU’s previous experience with a rented storage facility becoming costly and vulnerable to looting is therefore not simply a problem to solve by finding a better warehouse. It is evidence that the organisation should first determine what physical infrastructure the future production flow actually requires.
The more effectively FU can forecast demand and synchronise production with the timing of that demand, the less it should need to hold produce simply because production and the market are out of sequence. Storage will not disappear; rather, it should become a calculated buffer for unavoidable timing differences rather than a substitute for poor forecasting. This potentially reduces the amount of storage, emergency transport and working capital FU needs to carry, while also reducing the risks associated with holding produce for which there is not yet a sufficiently certain destination.
This is an important distinction in moving from the producer/marketing organisation to the production-system operator. The latter does not begin by acquiring infrastructure and then finding a use for it. It builds the production system first and lets the mathematics determine what infrastructure the system actually requires.
That leads to a useful design principle:
Do not build infrastructure and then find a use for it. Build the production system first and let the mathematics determine what infrastructure the system requires.
Setting of Membership Fee
The same principle applies to membership.
Do not set the membership fee first and then invent services to justify it. Define FU’s unique value proposition, establish the cost of providing it, measure the economic benefit to the member, and then determine the appropriate fee structure.
For FU, the strategic choice is therefore much bigger than whether it should be a farmers’ association.
It is whether it should remain primarily a voice for farmers, become a collective commercial organisation for farmers, or evolve into something more demanding: a farmer-owned production-system operator capable of forecasting demand, organising productive capacity and making dispersed farms reliably responsive to the requirements of the wider economy.
That is the question the Phase 1 work needs to answer.
The Thinking Behind This Model
This discussion of farmer organisations is part of a wider body of STRLDi work on how productive systems are connected — from national demand and production, through value addition and logistics, to the infrastructure that ultimately carries economic activity.
1. What Will Botswana Put on 40 Locomotives?
A question about what productive economy Botswana must build if its railway is to become an economic corridor rather than an isolated transport asset. (LinkedIn)
Read: What Will Botswana Put on 40 Locomotives?
2. Agriculture Corridor Execution Lattice
The wider architecture for connecting demand, production, aggregation, value addition, logistics and corridor execution — with discipline, standards and time certainty at its centre. (strldi.weebly.com)
Read: Agriculture Corridor Execution Lattice
3. Study Unemployment: The Report
The earlier study from which this line of thinking emerged: understanding unemployment not simply as a labour-market problem, but as an outcome of the design and performance of the national production system. (strldi.weebly.com)
Read: Study Unemployment — The Report
That makes your response to Jimmy much more than a disagreement about whether farmers can do mathematics. It becomes the entry point into the question: what kind of productive organisation does Botswana need if its farmers are to become part of a production system capable of feeding markets, filling corridors and growing the economy?
#SystemsThinking #Agriculture #FarmersOrganisations #FoodSystems #ProductionSystems #AgriculturalTransformation #Botswana #SouthernAfrica #EconomicTransformation #STRLDi
#SystemsThinking #Agriculture #FoodSystems #ProductionSystems #AgriculturalTransformation #Botswana #EconomicTransformation #STRLDi
Systems Thinking Research & Leadership Development Institute (STRLDi) 