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Work · Agritech / Cooperatives · Nepal
Crop advisory and price-forecast assistant for cooperatives
A blueprint for an assistant that turns weather, soil, market and pest data into plain-language advice a farmer can act on, delivered by SMS, voice or app.
blueprint12 Jun 2026
25%
Typical yield increase from advisory tools
~30%
Income improvement, mid-range benchmark
24%
Higher incomes in Digital Green / J-PAL advisory field trials
This is a solution blueprint — a reference architecture we can build for your business. The figures above are cited industry benchmarks for this class of system, not results claimed for a named client.
The pipeline we'd build
5 stages
Stage 01 · Weather
Local weather and rainfall feeds come in, alongside regional pest and disease alerts.
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 smallholder farmers across developing countries routinely lack access to reliable market-price information, 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.
What we did
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.
The outcome
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.
Stack
Next.jsPythonTime-series forecasting (LSTM/GRU)Weather & market data APIsSMS / IVR gatewayPostgreSQLCloud (AWS or GCP)
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.
At a glance
Client
Solution blueprint
Sector
Agritech / Cooperatives · Nepal
Service
Neural AI
Kind
blueprint
Headline result
25% · Typical yield increase from advisory tools
Handover
Documented, tested code in your repository
Questions we were 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.
Same problem, different business?
We'll send the architecture and a realistic timeline for your version of this — no obligation.