Illustrative impact based on published industry benchmarks — not results from a specific client.
Typical yield increase from advisory tools
Income improvement, mid-range benchmark
Smallholders who lack reliable price forecasts today
The challenge
Most smallholder farmers make big decisions with little information. When to sow, how much fertiliser to use, whether to sell now or hold for a better price, all of it is often guesswork. Research shows over 80% of smallholder farmers in developing countries lack access to reliable market forecasts, and generic advice broadcast to everyone rarely fits a specific plot. For a Nepali cooperative serving hundreds of member farmers, that gap costs real yield and real income, and it drives avoidable post-harvest loss.
Our approach
Here is how we would build it. Several data feeds go in: local weather and rainfall, soil type and the crop calendar, mandi and cooperative price data, and regional pest and disease alerts. An advisory model combines them and produces one clear, specific tip per farmer, in their own language, in a format that works on a basic phone. Price data drives a simple forecast so a farmer can decide whether to sell now or wait. A cooperative agent can review, override or add local context before advice goes out, so the technology supports the human network rather than replacing it.
Expected impact
Published studies of digital advisory services report yield improvements of roughly 23 to 73% and income gains of 18 to 37%, with well-known programmes citing around 25% higher yields and 24% higher incomes, while cutting the cost of reaching each farmer dramatically. Better price forecasts also help farmers time sales and cut post-harvest loss. For a Nepali cooperative, an assistant like this could turn scattered data into advice its members actually use. These are cited industry benchmarks showing what is achievable, not results from a specific deployment.
A cooperative already sits on useful data: weather, prices, what grows well where. The hard part is turning it into advice a single farmer can act on today. This blueprint shows how.
From data to farmer advice
Several data feeds go in, one plain-language tip comes out. Tap a source.
Market prices feed
Mandi and cooperative price feeds let the model forecast whether prices are likely to rise, so a farmer can decide to sell now or hold.
"Tomato prices rising next week. Consider holding your harvest."
The advice goes out in the farmer's own language, in a format that works on a basic phone. A cooperative agent can override or add local context.
Sell now or hold?
Price forecasting is where a lot of value hides. If the model can say prices are likely to rise next week, a farmer with storage can wait and earn more, and the cooperative loses less produce to a rushed sale.
Built with
Frequently asked
- Are these yield and income numbers guaranteed?
- No. This is a solution blueprint. The figures are cited industry benchmarks from digital-advisory studies, framed as what is typically achievable. Real gains depend on crops, region, data quality and how many farmers act on the advice.
- How does advice reach farmers who do not have smartphones?
- The assistant sends advice by SMS or voice as well as an app, so it works on a basic phone. A cooperative agent can also relay and localise the advice in person.
- Where does the data come from?
- From weather and rainfall feeds, soil and crop-calendar records, mandi and cooperative price data, and regional pest and disease reports. The model combines these into one specific tip rather than a generic broadcast.
Ready to build what's next?
Tell us about your project — we'll reply within one business day with a clear plan and a straight answer on fit.