The following is a guest editorial from Ankit Chandra and Ishani Lal at the University of Nebraska-Lincoln. The views and opinions expressed are those of the author and do not necessarily reflect the official policy or position of Silicon Prairie News, its staff or its affiliates. We welcome diverse perspectives and encourage open dialogue on the topics that shape our startup and innovation community.

In 2025, agtech startups and corporations learned a hard lesson: working technology is not the same as a working business model. Across North America, Europe, Asia, and Africa, we saw a wave of agtech startups shut down.

Some had raised tens or even hundreds of millions of dollars. Others had strong science, promising pilots, and passionate founders. Many were building technologies we desperately need: climate-smart irrigation, robotics, vertical farming, digital agronomy, alternative proteins. 

Yet they failed.

These shutdowns caught a lot of attention, but most discussions focused on individual companies rather than the structural issues behind them. We wanted to understand whether these failures were isolated cases or if they reflected deeper patterns across the sector. 

To do this, we compiled a dataset of 18 agtech startups that publicly shut down in 2025. These were not isolated failures. They reflected a deeper structural issue in the sector. This is not just a story about technical viability or venture capital tightening or macroeconomic headwinds.

It is a story about something deeper: a structural mismatch between innovation and adoption in agriculture. In our research, we found a recurring pattern — what we call the Cost-Adoption Mismatch Effect. We describe this as a structural misalignment between the economic burden of agtech solutions and farmers’ capacity to pay or adopt them at scale.

The lesson is simple: a technology can work in the field and still fail commercially if it does not align with farm economics.

The startups that couldn’t make it in 2025

Failures occurred in both developed and emerging markets. North America (8) and Europe (6) had more shutdowns simply because they host more capital-intensive ventures (See Figure 1), followed by South Asia (4). The failures were not confined to one sector.

Among the 18 shutdowns, seven were in Controlled Environment Agriculture, also referred to as CEA, or vertical farming. Companies such as AeroFarms (USA), Eden Green Technology (USA), Freight Farms (USA), Growy (Singapore), Vertical Future (UK), Jones Food Company (UK), and CleanGreens Solutions (Switzerland) promised pesticide-free, local grown produce year-round.

The agronomy worked. The yields were real. But the energy costs were unforgiving, and scaling required enormous capital. When funding tightened, the model collapsed. 

We observed a comparable pattern in agricultural robotics and automation. U.S.-based companies, including FarmWise and Guardian Agriculture, ceased operations despite having functional, field-tested hardware. These startups built impressive autonomous field equipment. Early pilots were successful. But farmers hesitated to commit to high upfront costs for machines that required new service models, training, and operational adjustments. Trials did not convert to sustained purchase orders. 

Similar dynamics emerged among IoT and sensor technologies, such as Plense Tech and Corral Tech. These companies offered sophisticated, data-driven insights powered by machine learning. Yet, hardware-heavy models required strong after-sales support and integration with existing machinery; startups struggled to build these capabilities at scale. In many cases, the incremental revenue gains for farmers were unclear or too small relative to subscription costs and data-entry burdens.

These stories differ in detail, but the pattern was consistent: innovation moved faster than farm-level economic reality.

Why did they fail?

Our dataset shows that while 30% of the startups failed because they couldn’t raise additional capital, nearly half were directly tied to cost and adoption constraints (See Figure 2). High capital intensity, energy burdens, and weak unit economics repeatedly prevented technically validated products from achieving sustained market traction.

These were not execution mistakes — they were structural mismatches between cost profiles and farmer adoption capacity. A pattern we describe as the Cost-Adoption Mismatch Effect.

Agriculture operates on thin margins and high volatility. Farmers manage weather uncertainty, commodity price swings, labor shortages, and rising input cost. Any new technology must compete not just on innovation, but on immediate, credible return on investment. Consider a Nebraska irrigation startup pricing a $40,000 system upgrade. If commodity prices dip, that investment stalls regardless of environmental benefit.

Beyond cost structure, our broader experience in teaching and working with founders highlights another critical dimension: customer discovery and distribution. The harder question is this — did farmers truly need these technologies in the form they were offered?

Many startups built great solutions before validating whether the underlying problem was urgent, painful, and economically compelling. Product–market fit in agriculture is uniquely difficult because adoption decisions are deeply tied to risk tolerance, seasonal income cycles, and trust networks.

Farmers do not adopt tools in isolation. They operate within ecosystems — equipment dealers, agronomists, co-ops, lenders, crop insurers. Startups that underestimated distribution channels and ecosystem integration faced additional friction even when the technology itself performed well.

What founders must do differently 

The shakeout of 2025 should not discourage agtech entrepreneurs. If anything, it provides clarity.

Start with Economics, not engineering

Model the farm-level economics before perfecting the product. What is the per-acre financial impact? What is the payback period under conservative commodity price assumptions? What happens in a bad year? If the answer depends on ideal conditions, the model is fragile. Founders should also think beyond standalone products. Bundling technology with financing, crop insurance incentives, or guaranteed procurement contracts can transform a product from a cost center into risk mitigation infrastructure.

Design for ecosystem fit

Agriculture rewards technologies that plug into existing systems. Partnerships with equipment manufacturers, cooperatives, input providers, and financial institutions are not optional — they are strategic necessities. Adoption often depends on local relationships and trust channels that startups cannot build alone.

Validate commercial conversion

Too many companies celebrated pilot projects without tracking conversion rates to paid, recurring revenue. A field trial is not a business model. Founders should obsess over:

• Percentage of pilot customers who convert
• Customer acquisition cost relative to lifetime value
• Retention and renewal rates

Practice capital discipline

The Silicon Prairie has an advantage here. Proximity to producers creates real-world feedback loops. Investors and founders in the Midwest often understand commodity cycles and operational realities better than coastal capital chasing exponential narratives. Capital discipline in agtech is strategic strength.

What’s next for agtech?

The failures of 2025 do not signal the end of agtech. They signal maturation. Agriculture has always demanded more rigor than other sectors. It rewards patience, local knowledge, and practical economics. That discipline may now reshape the innovation ecosystem. We are likely to see:

• More modular, incremental technologies rather than capital-intensive moonshots
• Business models that share risk with farmers, including performance-based pricing
• Greater collaboration between startups and established agribusinesses
• Stronger emphasis on measurable ROI over visionary narratives

The next generation of successful agtech companies will not win because they are the most futuristic. They will win because they are economically grounded, operationally practical, and built with farmers. The lesson from 2025 is not that agtech failed. If we learn from it, the years ahead can shape a more disciplined and farmer-aligned phase of agricultural innovation. And that is a future worth building. 

For a deeper dive, the full paper is available here.

About the authors

Ankit Chandra is an agricultural engineer and entrepreneur who has played a pivotal role as co-founder of two agtech startups in India and the U.S. Currently, Ankit works as a Research Program Manager at the Daugherty Water for Food Global Institute and oversees the global agtech entrepreneurship program, which includes a blend of policy research, facilitation, and mentorship.

Ishani Lal is an agricultural economist and applied researcher working at the intersection of farm management, profitability, and risk analysis. She currently serves as an Economic Analyst in the Department of Agricultural Economics at UNL, where she works with the Center for Agricultural Profitability (CAP) and the UNL Testing Ag Performance Solutions (TAPS) program.