For much of the last decade, agritech startups in emerging markets were sold on a seductive idea: millions of smallholder farmers, armed with smartphones, would pay for software that helped them farm better. Investors liked the story because it sounded scalable. Build once, distribute widely, grow user numbers fast.
The problem was that the model rarely matched life on the ground.
Across emerging markets, including Southeast Asia, smallholder farmers may need better information, but they are often juggling more urgent constraints: access to affordable inputs, reliable buyers, working capital, weather shocks and unstable prices. A standalone app asking them to pay for advice was rarely competing with another app. It was competing with fertiliser, labour, transport, school fees and debt repayments.
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That mismatch has become harder to ignore since the global funding correction that began in 2022, according to the “AgTech Investment in Emerging Markets 2025” report prepared by AgBase, Briter, and Mercy Corps. As venture capital became scarcer, agritech companies could no longer rely on user registrations or app downloads as proof of progress. Investors started asking a more basic question: who is actually paying, and why?
The answer increasingly points downstream.
Rather than charging farmers directly, a new generation of agritech models is shifting monetisation towards buyers, processors, retailers, exporters and agribusiness corporates. These companies have stronger balance sheets and clearer incentives to pay for tools that improve traceability, climate resilience, supply visibility and compliance. In other words, the farmer remains central to the system, but no longer has to carry the full cost of digitisation.
The limits of farmer-paid software
The old “agri-SaaS” model borrowed too heavily from Western enterprise software. It assumed that smallholders would behave like corporate clients: subscribe, log in regularly, use dashboards and renew. But agriculture in emerging markets is not a neatly digitised office environment. It is fragmented, seasonal, trust-based and physically demanding.
There are an estimated 500 million smallholder farmers across emerging markets. Many operate on thin margins and face risks they cannot control, from droughts and floods to volatile commodity prices. In such a setting, software that addresses only one part of the value chain struggles to become indispensable.
For agritech platforms, the lesson has been blunt. Digital tools need to be bundled with tangible services: input supply, credit, insurance, market access, logistics or guaranteed offtake. Without solving these practical pain points, even useful apps can fail to generate recurring usage, let alone subscription revenue.
This is especially true in Southeast Asia, where agricultural supply chains can be highly localised. A rice farmer in Vietnam, a chilli grower in Indonesia and a durian producer in Malaysia may all benefit from better data, but their routes to market, financing options and buyer relationships differ sharply. A single digital product rarely fits all.
From venture bets to system bets
The funding environment has accelerated this shift. During the pre-2022 liquidity boom, many agritech startups were rewarded for reach. Growth decks highlighted registered farmers, hectares covered or villages reached. Those metrics were not meaningless, but they often obscured weak retention, low willingness to pay and expensive field operations.
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By 2025, the bar has moved. Investors are looking for active usage, stronger unit economics and clearer paths to profitability. They are also more aware that agritech in emerging markets often requires mixed forms of capital. Concessional funding, donor money, commercial equity and corporate partnerships may all be needed to build infrastructure around farmers before a business becomes scalable.
This is a more disciplined phase for the sector. It also means founders must understand what some analysts call the “investable frontier”: the point at which a market’s infrastructure, regulation, logistics and buyer maturity make certain business models viable.
In a more developed agricultural export market, a startup may be able to build a relatively asset-light coordination layer on top of existing logistics and buyer networks. In a less mature market, the same company may need to build warehouses, aggregation centres, transport routes or agent networks before its software has any commercial value.
That difference matters. It explains why copying a model from one region to another often fails. Southeast Asia’s agritech opportunity is not the same as Africa’s, India’s or Latin America’s. Even within the region, Thailand’s export-oriented agriculture, Indonesia’s archipelagic logistics and the Philippines’ fragmented farming base require different operating models.
Why corporates are becoming the payer
Downstream monetisation works because it follows the money. Large agribusinesses, food manufacturers and retailers face growing pressure to know where their products come from, how they are produced and whether supply can withstand climate disruption.
Traceability is no longer a nice-to-have. Export markets are tightening rules on deforestation, labour standards, carbon reporting and food safety. Buyers need better farm-level data to comply with those standards. They also need visibility to protect their own margins when floods, droughts or disease threaten supply.
That creates an opening for agritech startups. Instead of selling generic advice to farmers, they can sell verified data and operational tools to corporates: supply chain transparency, water-efficiency monitoring, carbon measurement, sustainability reporting and quality assurance.
In Southeast Asia, this is particularly relevant for commodities tied to global supply chains, including palm oil, coffee, cocoa, rice, seafood, fruit and rubber. Export-oriented buyers need evidence that production meets increasingly strict standards. Startups that can gather, verify and translate farm-level information into compliance-ready data may find more reliable revenue from buyers than from farmers.
The commercial logic is simple. A farmer may not pay for a traceability dashboard. A multinational buyer facing regulatory risk, reputational damage or supply disruption might.
The return of physical operations
The shift downstream does not mean agritech can become purely digital. If anything, it reinforces the need for “phygital” models: digital systems supported by physical operations and human relationships.
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Agriculture still depends on trust. Farmers need to know who is buying, when payment will arrive, whether inputs are genuine and whether advice is credible. Buyers need confidence that produce quality, volumes and sustainability claims are real. That cannot be solved by code alone.
The most durable models often combine software with field agents, collection points, logistics partners, financing channels or buyer aggregation facilities. In Southeast Asia, startups may be able to use infrastructure already built by cooperatives, distributors, government agencies or large corporates. That allows for more asset-light coordination than in markets where startups must build the “hard rails” themselves.
Digital public infrastructure can also help. Land registries, digital identity systems, e-wallets and government farm databases can reduce the cost of farmer verification, credit scoring and payments. But access to these rails varies widely across the region, which again makes local market design critical.
Impact as unit economics
The new agritech discipline also changes how impact is understood. It is no longer a separate slide at the end of a pitch deck. In smallholder markets, impact often determines whether the business works at all.
If a platform does not improve farmer income, reduce risk or open access to better markets, farmers churn. If a financing product does not bundle insurance, agronomic support or guaranteed offtake, repayment risk rises. The source material suggests that farmer income gains of 20 per cent to 30 per cent may be needed to materially lower churn and build long-term loyalty. Agri-finance models that combine insurance or guaranteed offtake can maintain repayment rates above 95 per cent.
These figures point to a bigger truth: farmer prosperity and startup sustainability are linked. Extractive models fail because they weaken the very supply base they depend on. Stronger models make farmers more productive and less risky, which in turn makes the platform more valuable to lenders, insurers and buyers.
The climate transition as business model
Climate change is likely to make downstream monetisation even more important. Food companies need to secure supply in a world of rising heat, water stress and extreme weather. Governments and regulators are demanding more transparent reporting. Investors are pushing companies to show credible sustainability progress.
Agritech startups that can help corporates measure emissions, manage water use, verify regenerative practices or protect yields will be better positioned than those selling narrow farm-management apps. Biological inputs, satellite monitoring, soil data, carbon accounting and AI-based advisory tools may all have a role, but only if they connect to a paying customer with a real commercial problem.
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The era of vanity metrics is ending. Agritech’s next phase will be judged less by how many farmers download an app and more by whether the company can build a working system around them.
For Southeast Asia, that may be good news. The region’s agricultural sector is fragmented, but it is also deeply connected to global food, commodity and export markets. Startups that can bridge smallholder production with corporate demand for transparency, resilience and sustainability may finally find a path to durable revenue.
The lesson is not that farmers do not matter. It is that charging them directly for software was often the wrong place to start. The future of agritech profitability may depend on helping farmers create more value, while asking those who capture larger margins downstream to pay for the tools that make the system work.
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