Agriculture & Environment
Agricultural Commodity Intelligence API
Agricultural markets move on production news from places where the news is slow, inconsistent and published in twelve different formats. Assembling that view is most of the job.
- Grain traders and merchants
- Food manufacturers and procurement teams
- Commodity brokers
- Agricultural lenders
- Co-operatives
What your customers would see
example interfaceMockupIllustrative mockup of the interface we would build. Figures shown are examples, not results from a live deployment.
The problem this solves
Soft commodity markets have an information structure unlike financial markets. The price-moving facts — how much wheat is in the ground in the Black Sea, whether the Brazilian soybean crop is being planted on time, what the Indian government has decided about export restrictions — arrive as statistical releases on inconsistent schedules, in local languages, with revision histories that matter.
Large trading houses solve this by employing people in each origin. Everyone else works from whatever the wire services summarise, which is usually accurate and usually late. For a food manufacturer hedging ingredient cost or a merchant running a basis book, the lag between the fact and the summary is where the money moves.
What can be built is a structured supply and demand picture: production and stock estimates by origin and crop, export pace against programme, price and basis series, and policy events that change trade flows. None of it predicts price on its own. It replaces a scattered manual research process with a consistent dataset, which is a more modest and more defensible claim than a price forecast.
Who buys this
Grain traders and merchants, Food manufacturers and procurement teams, Commodity brokers, Agricultural lenders, Co-operatives
What the API returns
What comes back
- Production and stock estimates by crop, origin and marketing year
- Revision history on every official estimate, since revisions move markets
- Export and import pace against programme and prior-year comparison
- Price series for futures, cash and basis by delivery location
- Supply-demand balance indicators including stocks-to-use ratios
- Trade policy events — export restrictions, tariffs, quota changes
- Crop condition indicators from satellite and official assessments
- Spread and arbitrage indicators between origins and delivery points
Endpoints
| Method | Path | Returns |
|---|---|---|
| GET | /v1/balance/{crop} | Supply-demand balance by crop and marketing year |
| GET | /v1/origins/{origin}/production | Production estimates and revision history |
| GET | /v1/trade/pace | Export and import pace against programme |
| GET | /v1/prices/{crop} | Futures, cash and basis series by location |
| GET | /v1/policy/events | Trade policy events affecting flows |
| WS | /v1/stream | Push new official releases, revisions and policy events |
Request and response
illustrativeRequest
{
"crop": "wheat",
"marketing_year": "2026/27",
"origins": ["RU", "UA", "AU", "CA", "FR"],
"include": ["revisions", "trade_pace", "basis", "policy"]
}Response
{
"crop": "wheat",
"marketing_year": "2026/27",
"as_of": "2026-10-22",
"world_balance": {
"production_mt": 794.2,
"consumption_mt": 801.6,
"ending_stocks_mt": 258.1,
"stocks_to_use": 0.322,
"change_since_prior_estimate_mt": -4.8
},
"origins": [
{
"origin": "RU",
"production_mt": 84.1,
"estimate_source": "national_statistical_agency",
"revisions": [
{ "published": "2026-09-12", "value_mt": 86.5 },
{ "published": "2026-10-10", "value_mt": 84.1, "direction": "down" }
],
"export_pace": { "shipped_mt": 14.2, "pct_of_programme": 31.4,
"vs_prior_year_pct": -8.1 },
"crop_condition": { "source": "satellite_ndvi_anomaly", "band": "below_average" }
}
],
"basis": [
{ "location": "Rouen FOB", "quote_eur_t": 214.50, "vs_matif_eur_t": 6.25 }
],
"policy_events": [
{ "date": "2026-10-04", "origin": "IN", "event": "wheat_import_duty_reduced",
"from_pct": 40.0, "to_pct": 0.0, "expected_flow_effect": "increases_import_demand" }
]
}Where the data comes from
| Official agricultural statistics | Production, stock and trade estimates from national agencies and international bodies. Most are published openly; a few carry terms restricting bulk redistribution |
| Customs and port data | Shipment and lineup data giving export pace ahead of official monthly statistics. Availability and licensing vary sharply by country, and some commercial lineup providers prohibit redistribution outright |
| Futures and cash price data | Exchange futures settlements and physical quotes. Exchange data is licensed with strict terms on redistribution and display, which shapes what can be shown to your end users |
| Satellite crop condition data | Open imagery-derived vegetation anomalies by growing region, used as an independent read on official condition assessments |
| Trade policy publications | Official gazettes and ministry announcements. Open but inconsistently formatted and frequently only in the local language |
| Weather data for growing regions | Open reanalysis and forecasts, used for condition context in the major origins |
The intelligence layer
The difference between a demo and a service is mostly in this section. These are the parts that keep the answer trustworthy once real traffic is hitting it.
- Revision-aware data model where every estimate is stored with its publication date, so a historical analysis uses what was known at the time rather than the restated figure
- Balance sheet reconciliation across origins and the world total, flagging inconsistencies rather than silently forcing them to add up
- Export pace normalised against programme and seasonality, because a raw shipment total early in a marketing year means little without the comparison
- Satellite condition anomalies computed against each region's own historical distribution, giving an independent check on official crop assessments that sometimes lag or flatter
- Document ingestion built for inconsistency: official releases change format, language and schedule, so parsers are versioned and failures alert rather than return stale numbers
- Policy event extraction from official publications with human review before an event affecting trade flow is published, since a misparsed duty change is worse than no data
- Basis series constructed per delivery location with explicit handling of quality specification differences, which otherwise make two quotes look comparable when they are not
- Monitoring of source availability and publication delay, so a missing release is visible as a gap rather than appearing as an unchanged estimate
What it changes for the business
Research efficiency
A consistent dataset replaces a manual round of portal checking across a dozen origins.
Earlier signal
Export pace and satellite condition anomalies move before official monthly estimates confirm them.
Hedging context
Balance indicators and basis series give a procurement team a documented basis for hedge timing.
Credit assessment
Production and price context supports lending decisions in agricultural supply chains.
How it fits into your product
How it embeds
- REST for scheduled pulls, WebSocket for release and revision events
- Release calendar so your systems know when the next official estimate lands
- Point-in-time query support so backtests use the estimates as they stood
- Webhooks on revisions, policy events and condition anomaly thresholds
- Deployment inside your environment where position-adjacent data must stay internal
- Chart-ready balance, pace and basis series for embedding in your research screens
Included in every build
- Authentication, scoped API keys and per-customer rate limiting
- Input validation with predictable, documented error responses
- Versioned endpoints so your integration never breaks without notice
- Request and response logging for audit and dispute resolution
- Latency, error-rate and usage monitoring with alerting
- OpenAPI specification, sandbox environment and client examples
- Backups, failover design and a documented recovery procedure
- An agreed support arrangement once the service is live
How we would build it
about 20 weeksDiscovery and feasibility
Weeks 1–3
This one depends heavily on data access, so we start by proving the sources exist, can be licensed for your use, and actually carry the signal the model needs. If they do not, you find out here.
Architecture and baseline model
Weeks 4–6
A written architecture and a baseline model measured against a holdout period, so you know what accuracy you are buying before the full build starts.
Production build
Weeks 7–16
Data pipelines, model training and serving, historical archive, validation harness, API, security, monitoring and documentation.
Validation and integration
Weeks 17–20
Backtesting against held-out history, accuracy reporting your team can audit, integration support and an agreed retraining cadence.
Commercial models
There is no fixed price list, because the cost is driven by data complexity, integration count, model requirements, request volume and the support level you need. Scope is agreed in writing before development starts.
Fixed-scope prototype
A defined slice of the service, built and priced as one piece of work. The usual first step when the business case needs proving internally.
Fixed-price build
An agreed scope against a written architecture, delivered for an agreed price. Best when the requirements are already clear.
Build then retainer
We build the service, then operate and improve it under a monthly agreement covering monitoring, updates and model maintenance.
White-label product
The service runs under your brand, your domain and your pricing, with MLPivot invisible to your customers.
Questions buyers ask
Does this forecast prices?
No, and that is deliberate. It assembles the supply and demand picture that price responds to. Agricultural price formation also involves speculative positioning, currency moves, freight and policy surprises, and a model fitted on historical balance sheets to predict price would be fitting a relationship that is unstable at best. We would rather deliver a reliable dataset than an unreliable forecast dressed up as one.
How current is the production data?
It is as current as the origin publishes, and that varies from weekly to monthly to effectively never. Some countries publish promptly and revise little; others publish late with large revisions. Satellite condition data fills some of that gap with an independent read. Every figure carries its source and publication date so the staleness is visible rather than averaged into an apparently current number.
Can we display exchange price data to our customers?
Usually not without your own exchange licence, and this is a hard constraint rather than a formality. Futures data redistribution is tightly controlled, with separate terms for display, derived use and non-display applications. Your own licence determines what the API can return to your users, and we build the entitlement logic around it. Derived indicators computed from licensed prices are often permissible where the prices themselves are not.
What about origins with unreliable official statistics?
They are the hardest part of this and we flag them rather than smoothing them. In several significant exporting countries official production figures are revised heavily or are considered unreliable by the trade. We publish the figure, its revision history and the satellite condition anomaly alongside it, and mark source confidence. What we would not do is substitute our own estimate and present it with the authority of an official one.
Can you cover minor crops and local markets?
Only where there is published data. Major grains and oilseeds have reasonable coverage. Minor crops and local cash markets often have no systematic statistics at all, and for those the honest answer is that the dataset would be too thin to be worth integrating. We would scope coverage specifically rather than listing crops we cannot actually serve.
Related blueprints
Crop Yield Forecasting API
Expected yield by crop, location and season, with honest uncertainty, for agritech platforms, insurers, lenders and grain buyers.
Water Availability Intelligence API
Drought risk, water stress and availability indicators by location, for agriculture, lending, insurance and industrial siting.
Crop Disease Risk API
Weather-driven disease and pest risk scoring with outbreak alerts and scouting recommendations, for agronomy and input platforms.
Want this built for your platform?
Tell us what your product does today and what you want another system to be able to ask it. We will come back with an approach, a scope and a price.