How many AgriTech solutions promise to revolutionize smallholder farming with comprehensive digital advisory platforms? The formula is always the same: aspirational technology like sleek mobile apps, blockchain supply chains, or AI chatbots offering advice on everything from soil health to market prices.
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These ideas are piloted with a few thousand farmers, publish glowing case studies, and then quietly struggle to reach beyond 50,000 users.
Meanwhile, this summer, India’s Ministry of Agriculture did something radical. They reached 38 million farmers with a single, accurate weather forecast predicting when the monsoon would arrive.
Not a comprehensive advisory service. Not a super app. Just one critical piece of information delivered via SMS, 30 days in advance, powered by AI models that don’t require supercomputers to run.
Professor Ramesh Chand, Member of Niti Aayog, stressed that “focusing on the needs of farmers when providing weather information is essential.” This should be written on every AgriTech whiteboard.
Do Farmers Need Fancy Digital Services?
Let’s be honest about where we are. According to the State of the Digital Agriculture Sector report, there are nearly 1,400 active ICT4Ag solutions across LMICs. Southeast Asia alone has seen over 60 innovative providers collectively reaching about 2.5 million farmers, or roughly 3% of smallholders in ASEAN.
After a decade of donor funding, private investment, and entrepreneurial energy, we’re still operating at the margins.
Might we be obsessed with building comprehensive platforms when farmers need specific, timely, actionable information they can trust? India’s approach flips our assumptions.
Instead of a startup building a farmer-facing app, the government partnered with university researchers to evaluate which AI weather models best predicted monsoon arrival. They tested seven models across 60 monsoon seasons since 1965. Google’s NeuralGCM and the European Centre for Medium-range Weather Forecasts’ Artificial Intelligence Forecasting System won.
Then they blended these with 100 years of historical rainfall data from the India Meteorological Department to create a probabilistic forecast with 30-day lead time.
The result? Nobel laureate Michael Kremer, who co-directs the Human-Centered Weather Forecasts Initiative, estimates this generates more than $100 for farmers for each dollar invested by the government. That’s the kind of ROI that makes most AgriTech pilots look anemic.
Three Lessons for AgriTech Scale
India’s achievement with 38 million farmers is a proof point for a fundamentally different approach to AgriTech at scale: government-led, infrastructure-aware, focused on specific high-value decisions, co-designed with farmers, and leveraging open-source AI.
1. Leverage existing government infrastructure.
The Ministry of Agriculture delivered forecasts through their existing SMS platform. No new app to download. No digital literacy barrier. No user acquisition costs. This isn’t sexy, but it works.
The development sector’s obsession with “innovative” delivery mechanisms has distracted us from the boring reality that SMS and voice calls, integrated into government systems farmers already interact with, are often the fastest path to scale.
When we analyzed 9 ways AI is improving Nigerian agriculture, the most successful were those integrated into government extension systems, not standalone platforms.
2. Solve one critical decision point exceptionally well.
India didn’t try to build a comprehensive advisory service. They identified the single most valuable piece of information for farmers: when will sustained monsoon rains arrive?
This forecast empowered farmers to make high-stakes decisions about what crops to plant, how much land to cultivate, and whether to seek alternative income.
Parasnath Tiwari, a farmer from Madhya Pradesh who received the forecast, switched to more lucrative crops because the message gave him confidence the season would be long enough. He also shared the information with other farmers in his community, creating organic peer-to-peer diffusion that no marketing budget can buy.
3. Co-design messages with farmers.
Precision Development, the nonprofit that led message design and testing, worked directly with farmers to understand their needs and what types of messages would be most useful. As PxD’s chief economist Tomoko Harigaya noted, “Even the most accurate forecast can fall flat if it’s not communicated clearly.”
This seems obvious, yet how many AgriTech solutions are designed primarily around what’s technically possible rather than what farmers actually need and can act upon?
AI Democratization Opportunity
AI weather models no longer require supercomputers. These models can be run on desktops and can be tuned to the specific weather conditions and needs of the citizens on the ground, all at a fraction of the cost and time.
This represents technological leapfrogging opportunity for LMICs. Countries don’t need to invest in expensive forecasting infrastructure. They can use open-source AI models, blend them with their historical climate data, and generate accurate, localized forecasts at scale.
The 2024 monsoon season proved the value of this approach. The monsoon behaved unusually that year, hitting southern India early, progressing for about a week, then stopping for three weeks before moving again. The AI-powered forecast predicted that pause. Farmers who received and trusted the forecast adjusted their plans accordingly.
How We Need to Change Our Approaches
Nearly two-thirds of the global population live in monsoon climates. The model is proven. The technology is available. The ROI is compelling. Now we need to replicate it across South Asia, Southeast Asia, Sub-Saharan Africa, and Latin America.
For donors and implementing organizations
Stop funding pilots of comprehensive advisory platforms unless they have a clear path to government integration and scale. Instead, support initiatives that help governments leverage AI models for specific, high-value agricultural decisions. The AIM for Scale initiative, backed by the Gates Foundation and UAE, is now working to replicate India’s model in other LMICs.
For AgriTech entrepreneurs
If you’re building a standalone farmer-facing solution without government partnership, you’re choosing the hard path to scale. Consider pivoting to become technical service providers to government agricultural extension services rather than competing with them.
For ministries of agriculture
You don’t need to wait for perfect infrastructure or massive budgets. Open-source AI weather models like Google’s NeuralGCM are available now. Partner with researchers to evaluate which models work best for your context, blend them with your historical data, and use your existing communication channels to reach farmers.
For researchers and technical partners
The Human-Centered Weather Forecasts Initiative offers a replicable model. Rigorous benchmarking of AI models against specific agricultural needs, bias correction with local historical data, and message co-design with farmers. This approach can extend beyond weather to pest forecasts, optimal planting dates, and other critical decision points.
