01 / Brazilian commodity intelligence

A cargo has
two journeys.

One to the port. One into the price. KAIRO brings forward export basis and logistics risk into the same view for desks trading Brazilian commodities.

Follow a hypothetical soybean cargo from Mato Grosso. The atlas starts with the choice of gateway and ends with the record a desk can review.

FIELD NOTE 01 / SANTOS, BRAZIL

02 / Brazil in world trade

Brazil moves markets.

Whole soybeans and corn, the products of soybean crushing, raw cane sugar and iron ore connect Brazil to several global commodity markets. The trade figures show the scale of the flows behind the choice of cargo, gateway and delivery window.

Net export weight for the selected customs categories, rounded to one decimal place. The raw cane sugar category covers a broader market than the VHP grade. The photographs illustrate the commodities. Source · SECEX / Comex Stat

Trade categories and photo credits

Photographs cropped and resized for this plate.

03 / Market thesis

The first decision is where to send the cargo.

Take a hypothetical soybean cargo in Mato Grosso. Sending it north to Barcarena or south to Santos gives the desk two different delivery exposures.

National balance sheets frame the crop year. The desk still has to choose a gateway and a delivery window, while other commodities may be seeking capacity at the same port.

The commercial choice

Two routes for the same cargo.

01 / North to Barcarena
Start with the available delivery window, then check the freight offer and port exposure for the northern route.
02 / South to Santos
Use the same delivery window and price benchmark to ask whether the Santos basis would offset any difference in costs.
03 / Compare on equal terms
Bring both offers to the same unit, currency and delivery terms. The stronger FOB basis can still leave a weaker net price once route costs enter the comparison.

The map locates the gateways and shows where the comparison starts. Follow the cargo on the map ↓

Field plate / 01From field to vessel.
  1. OriginCargo and availability
  2. Inland movementRoute, mode and cost
  3. Export terminalGateway and delivery window
  4. Ocean legLoading and onward shipment

Illustrative export chain. The inland route and transport mode depend on the corridor.

04 / Export corridors

From origin to port.

Follow the soybean routes from Mato Grosso to Barcarena and Santos. Switch to corn to see how a different origin and gateway change which corridor a desk needs to watch.

Figure 01 / Illustrative

Brazilian commodity flows

Illustrative Brazilian commodity origins and export gateways Schematic paths from example producing areas to ports. Select soybeans or corn for a different set of example routes. The captions provide a text equivalent. Mato Grosso Rio Grande do Sul Mato Grosso Goiás Paraná Arco Norte · Barcarena Santos Paranaguá Rio Grande
Example originExport gatewayIllustrative direction

Soybeans · Mato Grosso to Barcarena and Santos, and Rio Grande do Sul to Rio Grande.

Lines and motion show direction schematically. Boundary source.

Field plate / 02Modes of transport.
  • Road
  • Rail
  • Inland waterway
  • Ocean

A visual key to transport modes. The route lines on the map show direction.

01 / Origin

Start where the cargo is.

The marked origins identify the production areas in the example. The soybean view puts both paths from Mato Grosso on the same plate.

02 / Gateway

Read the lines as choices.

The animation shows direction. The lines are schematic and carry no volume, journey time or live vessel information.

03 / Comparison

Compare delivery terms.

With the gateways in view, compare quotes for the same commodity, delivery window and unit.

05 / Forward basis

At Santos, the delivery window has a price.

Suppose the cargo goes to Santos. Export basis is the difference between the physical FOB price and its reference benchmark. Select a horizon to see how that spread and its range change across delivery windows.

The curve uses illustrative soybean values in US cents per bushel.

Figure 02 / Illustrative

FOB Santos · Soybeans

US ¢/bu

Illustrative forward basis curve A rising median surrounded by a widening band from the 10th to the 90th percentile. Values are illustrative. 100806040 T+0T+1T+2T+3T+4T+5
P10–P90   ━ P50

Central estimate

Start with the median.

P50 is the midpoint of the distribution for that delivery horizon. The example moves from 47 at T+0 to 70 at T+5.

Range of outcomes

Read the band with it.

P10 and P90 bound the central 80% of the distribution. The example widens from 41–54 at T+0 to 41–100 at T+5.

Comparability

Keep the terms aligned.

When comparing delivery windows, hold the commodity, gateway, benchmark and unit constant so the change reflects the horizon.

06 / Reading the signals

Three questions before a desk commits.

Once the desk has the curve in front of it, the next step is to put the price range alongside the timing and location of the exposure.

01 / Price

Where could basis settle?

A later window in the Santos example has a higher median and a wider band. Examine both before deciding which window deserves more work.

02 / Timing

When does logistics risk merit attention?

Read the logistics regime alongside the forward curve and snapshot date. Then check the delivery window against the desk’s physical commitments.

03 / Location

Where does exposure differ?

Compare the indicators for the relevant gateways on the same cutoff. Review whether the freight and delivery assumptions still hold for each route.

Worked reading. At T+5, the illustrative Santos median rises by 23¢/bu while the lower quantile stays at 41¢/bu. A desk can use that difference to frame a question about the later window, then check logistics and quote terms before drawing a conclusion.

07 / Institutional use cases

From signals to investment decisions.

Five ways institutional desks can use forward basis and Brazilian commodity logistics signals to research potential sources of alpha, stress-test exposures and decide what to investigate next.

  1. 01 / Macro

    Local & global macro funds

    • BRL and the trade balance. Compare reported shipment pace with the trade calendar to update export receipt and trade surplus scenarios. Use those scenarios as inputs to a BRL flow view.
    • Cross-market scenarios. Stress-test how export delays or port queues could affect physical premiums and relevant contracts on CBOT, ICE or SGX.
    • Inflation inputs. Track freight and agricultural derivatives as candidate leading indicators for selected IPCA components.
  2. 02 / Systematic

    Quantitative funds & CTAs

    • Basis momentum. Ingest timestamped FOB basis snapshots via API and test whether changes in the forward curve add information to a systematic strategy.
    • Regime filters. Use regime labels as candidate inputs to exposure and volatility controls. Test whether they add value against the desk’s baseline.
    • Point-in-time testing. Join dated releases using the publication timestamp available at each decision time. The current history endpoint provides two sample cutoffs, 31 March and 30 April 2024.
  3. 03 / Physical

    Commodity merchants & trading desks

    • Demurrage exposure. Use corridor saturation and vessel waiting-time indicators to review berth availability and compare loading windows.
    • Corridor spreads. Compare Santos and Arco Norte premiums on matched delivery terms, then account for inland freight and timing.
    • Farmer selling. Investigate where storage pressure could accelerate producer sales by comparing logistics signals with independent stock and local bid data.
  4. 04 / Equities

    Equity long/short managers

    • Rail and pricing power. Combine corridor signals with reported volumes and route distances to test tonne-kilometre (TKU) and tariff scenarios for operators such as Rumo (RAIL3).
    • Operating assumptions. Use logistics signals alongside company disclosures when reviewing shipment or operating assumptions for SLC Agrícola, Raízen and Vale.
  5. 05 / Credit

    Private credit & FIAGROs

    • Cash conversion. Review how slower throughput could affect inventory turnover, collections and liquidity at cooperatives and distributors that back agribusiness receivables certificates (CRAs).
    • Inventory-backed collateral. Use physical-balance evidence to prioritise stock inspections and compare pledged quantities with independent inventory records.

Field plates / Illustrative data

Four ways to read the signal.

01 / Forward basis

The curve and its risk band

Illustrative soybean basis in US cents per bushel.

906030JUNJULAUGSEP

P10–P90━ P50 median

02 / API snapshot

A signal in JSON

Illustrative excerpt from the public response contract.

{
  "commodity": "SOYBEANS",
  "market_regime": "ELEVATED_LOGISTIC_PRESSURE",
  "corridors": [
    {
      "corridor_id": "SANTOS",
      "saturation_probability": 0.62,
      "freight_pressure_index": 74
    }
  ]
}

Read the API response contract. Throughput is illustrated separately below.

03 / Export corridors

Five corridor views

Illustrative throughput as a share of capacity, with example status labels.

  • 1 / Santos88% · Tight
  • 2 / Paranaguá73% · Watch
  • 3 / Arco Norte65% · Available
  • 4 / Rio Grande54% · Available
  • 5 / Other corridors48% · Available · Aggregate

04 / Executive tear sheet

A two-page desk briefing

Illustrative report spread with a physical balance and a corridor chart.

01 / Physical balance

Illustrative supply & demand (Mt)
Opening stock2.0
Receipts8.0
Shipments6.4
Closing stock3.6

02 / Corridor capacity

Illustrative throughput / capacity

  • Santos88%
  • Paranaguá73%
  • Arco Norte65%
  • Rio Grande54%

See the public PDF specimen.

08 / Institutional products

From research note to risk feed.

Take the same basis and logistics view into a meeting or bring the dated snapshot into a research workflow.

01 / Macro PMs & trading desks

Executive Tear Sheet

A two-page research note for a meeting or a handover between desks. Read the two pages together to connect the forward spread with the physical market.

  • Page 1 · Interpret the median and dispersion across delivery horizons.
  • Page 2 · Review the corridor and logistics indicators alongside the curve.
Download specimen PDF ↓

Soybeans · Two pages · Illustrative data

Tear sheet page one, reading the basis curve Tear sheet page two, reading logistics risk

02 / Quants, CTAs & systematic desks

Institutional Signals API

Bring delivery horizons, basis quantiles and logistics indicators into a desk workflow. Keep the cutoff and provenance fields with each stored snapshot.

  • Latest · Retrieve the current snapshot for a commodity.
  • History · Inspect the two available sample cutoffs in the current implementation.
GET /v1/signals/SOYBEANS/latest Open API reference ↗
FIELD NOTE 02 / SOYBEAN HARVEST

09 / Coverage

The export universe.

The atlas follows soybeans. The feed also covers corn, soybean products, VHP sugar and iron ore. Use the commodity identifiers below when inspecting the API.

  • SoybeansOilseedSOYBEANS
  • CornGrainCORN
  • Soybean mealCrush productSOY_MEAL
  • Soybean oilCrush productSOY_OIL
  • VHP sugarRaw sugarSUGAR
  • Iron oreBulk mineralIRON_ORE

10 / Market dispatches

Commodity news.

Source-linked news from Brazil’s commodity markets. Each edition brings together developments in crops, processing, minerals, trade and freight.

KAIRO / Public sources

No market brief has been published yet.

11 / Research evidence

What can the desk verify?

Review the product’s presentation, the fields the API returns and the evidence a desk would need before relying on the signal.

Public specimen
Review how the note presents the basis distribution and logistics context. The figures use illustrative values. View the specimen.
Historical samples
The current API reference lists two sample cutoffs. The samples let a desk inspect the contract and the dated fields. View the API reference.
Evaluation
Review the signal quality criteria for assessing forecast accuracy, interval coverage and logistics indicators.

12 / Research and models

From alternative data to models.

Customs records show what was shipped. Crop releases, port activity and freight indicators put those volumes in context. Econometric models offer one way to test whether a signal adds information against a clear reference.

Validation and catalogue coverage

Out-of-sample validation & catalogue coverage.

Soybeans (HS 1201) MAE 0.762 Mt −57.8% vs. Naïve (1.81 Mt)
Corn (HS 1005) MAE 0.578 Mt −55.4% vs. Naïve (1.30 Mt)
Directional Hit Ratio 87.0% / 82.6% Soybeans / Corn turning points
Rank Information Coeff +0.958 t-statistic +15.7 (p < 0.001)

A / Empirical retrospective validation (2023–2024)

Whole soybean & corn exports

A 24-month retrospective comparison for national export volumes. Models were held fixed for January 2023 to December 2024 after fitting to historical series from January 2014 through December 2022. Evaluated against the standard institutional benchmark: Seasonal Naïve (t−12).

01 / Retrospective benchmark

Brazilian soybean exports

Monthly net export weight · 2023–2024 · Million metric tonnes (Mt)

Official · SECEX Model forecast 80% Dispersion
Observed and forecast Brazilian soybean export volumes, 2023–2024 The solid line shows observed exports and the dashed line shows a frozen reference forecast. A shaded band represents the central 80% of errors from the earlier validation window. Twenty of the 24 observations fall inside the band. Mt0481216JAN 2023JUL 2023JAN 2024JUL 2024DEC 2024
Pearson correlation
0.9776
Rank-IC (t-stat)
+0.9583 (+15.7)
Inside 80% band
20 / 24

H1 Harvest Peak. Soybeans peak between March and May. 20 of 24 months fall strictly inside the empirical 80% error dispersion band.

02 / Retrospective benchmark

Brazilian corn exports

Monthly net export weight · 2023–2024 · Million metric tonnes (Mt)

Official · SECEX Model forecast 80% Dispersion
Observed and forecast Brazilian corn export volumes, 2023–2024 The solid line shows observed corn exports and the dashed line shows the forecast. Corn showcases a counter-cyclical safrinha peak in Q3/Q4. Mt02.55.07.510JAN 2023JUL 2023JAN 2024JUL 2024DEC 2024
Pearson correlation
0.9684
Rank-IC (t-stat)
+0.9574 (+15.5)
Directional Hit Ratio
82.6%

H2 Safrinha Ramp. Corn shipments peak between August and October, providing a counter-cyclical balance to soybean logistics at export terminals.

03 / Error comparison · Soybeans

Mean absolute error (MAE)

Out-of-sample models on 2023–2024 (24 months) against the seasonal baseline.

  • High-frequency enriched model0.76 Mt
  • Calibrated baseline model0.88 Mt
  • Seasonal benchmark (t−12)1.81 Mt

The enriched model delivers a 57.8% lower MAE vs. Seasonal Naïve (+13.1% gain over baseline) across the 24 retrospective test months.

04 / Error comparison · Corn

Mean absolute error (MAE)

Corn out-of-sample models on 2023–2024 (24 months) against the seasonal baseline.

  • Enriched Corn model0.58 Mt
  • Seasonal benchmark (t−12)1.30 Mt

The corn model achieves a 55.4% lower MAE vs. Seasonal Naïve, capturing both low-water river draft restrictions and rail redirections.

05 / Port logistics risk benchmark

Santos & Paranaguá vessel waiting time

Monthly lineup waiting days · 2024 calendar year · Days per vessel

Median wait (P50) Demurrage tail risk (P90) Expected range
Simulated vessel berth waiting days at Santos and Paranaguá, 2024 Vessel waiting times surge during peak harvest months of April and May, where P90 demurrage tail risk reaches 38 days before stabilizing to 6-12 days off-peak. Days010203040JANMARMAYJULSEPNOVDEC
Peak median wait (P50)
23 days
Tail demurrage risk (P90)
38 days
Off-peak baseline
6 days

Harvest Demurrage Surcharge. Congestion spikes in Q2 create demurrage costs of ~$25,000/day, directly modulating forward FOB export basis differentials.

06 / National throughput distribution

Grain export gateway share

13.45 million metric tonnes reconciled across 22 port facilities in peak month.

  • Arco Norte (Itaqui, Barcarena, Santarém)37.23%5.01 Mt
  • Santos (Central Corridor)35.51%4.78 Mt
  • Paranaguá (Southern Corridor)9.59%1.29 Mt
  • Rio Grande, Vitória & Other Ports17.67%2.37 Mt

Reconciled directly against official ANTAQ microdata with cryptographic receipt verification.

B / Multi-commodity catalogue & taxonomy of evidence

Retrospective evaluation and physical capacity monitoring.

KAIRO classifies empirical evidence under a four-level governance framework. Grains (soybeans and corn) feature audited retrospective evaluations with high-frequency proxies. Derivatives and iron ore operate under zero-leakage physical capacity simulations (lineup_weight = 0.0).

Audited retrospective metrics (2023–2024, 24 months) and physical monitoring scope by commodity
CommodityResearch statusAudited metrics & monitoring scope
SoybeansRetrospective (Proxies)Retrospective 2023–2024: Enriched MAE = 0.762 Mt (−57.8% vs. Naïve 1.808 Mt), Pearson r = 0.9776, Rank-IC = 0.9583 (t = 15.72), Hit Ratio = 87.0%. Port rain stoppages and silo pace proxies.
CornRetrospective (Proxies)Retrospective 2023–2024: Enriched MAE = 0.578 Mt (−55.4% vs. Naïve 1.295 Mt), Pearson r = 0.9684, Rank-IC = 0.9574 (t = 15.53), Hit Ratio = 78.3% (82.6% turning points). Northern Arc waterway draft drops and rail redirection proxies.
VHP SugarConditioned SimulationConditional simulation. Models berth rain closures and mill crushing pace in Santos and Paranaguá.
Soybean mealConditioned SimulationConditional simulation. Models industrial crush balance (1 t soy → 77.5% meal) and domestic feed absorption.
Soybean oilConditioned SimulationConditional simulation. Models extraction yields (19.5%), port tankage and ANP mandatory biodiesel blending (B14).
Iron oreConditioned SimulationConditional simulation. Models Carajás (Ponta da Madeira) & Southeastern mining corridors (Tubarão), monsoon seasonality and Capesize loading.

C / Portfolio risk research

Common exposures across corridors

The API includes an illustrative breakdown of portfolio risk. Empirically calibrated factor results are unavailable.

D / Logistics scenarios

Compare disruption scenarios

Explore how port rain, shallow waterways or rail outages could affect logistics. Results are conditional model estimates.

E / Interactive Desk Sandbox · On-Demand Stress Testing

Trader Scenario Lab: Stress Testing Logistics Elasticity

Change the assumptions below to compare illustrative cargo flows, waiting times, demurrage and basis. The calculator uses a fixed reference case.

0%
Simulates mudslides or bridge closures deflecting Mato Grosso cargo south.
0 mm
Compulsory hatch closure hours reducing monthly ship loader capacity.
0%
Stimulates immediate farmer selling pace and interior silo evacuation.
Deflected Southern Flow +0.00 Mt Deflected to Santos & Paranaguá
Santos Lineup Queue 12.5 d P90 Tail Risk: 30.7 days
Aggregate Demurrage +$0.0M Incremental fleet demurrage
FOB Basis Dislocation +0.0 c/bu Regime: Normal Flow

Stressed Corridor Throughput Reallocation (Peak Harvest Month)

  • Arco Norte (Barcarena/Itaqui) 5.01 Mt 37.2%
  • Santos (BRSSZ) 4.78 Mt 35.5%
  • Paranaguá (BRPNG) 1.29 Mt 9.6%
  • Rio Grande & Others 2.37 Mt 17.7%

13 / Signal quality

How to judge signal quality.

Quality has two parts. A desk needs to know what information was available at decision time, and whether the signal holds up against what actually happened.

01 / Against observed outcomes

Four questions for a scorecard.

How close was the median?
Compare the mean absolute error of P50 with a seasonal baseline, using observed FOB basis for the same benchmark, gateway and delivery window.
Did it get the direction right?
Count correct up and down calls relative to the prior observation. Report the number of observations and the rule for flat moves.
Did the risk band cover outcomes?
Compare the observed share inside P10–P90 with its nominal 80% coverage. Report the band width alongside coverage to show how informative the interval is.
Did logistics risk materialise?
Compare predicted waiting days with realised waits. Check whether the observed frequency of saturation aligns with the reported probabilities.

14 / Data governance

Know the vintage of each snapshot.

A desk reviewing an earlier decision needs both the information cutoff and the time the snapshot became available. Preserve those fields with the record you received.

as_of_date As-of date
The cutoff associated with the snapshot.
published_at Publication time
When the snapshot was released.
provenance_hash Provenance hash
A SHA-256 identifier included in the snapshot for provenance tracking.

Historical review. For a decision at a known timestamp, check when the snapshot was published before using it. Store the provenance hash with the record so the version can be referenced later.

15 / Institutional access

Review the product with your desk.

Start with the material that fits the review. A desk can read the public specimen and inspect the API contract before using authenticated snapshots or current reports.

Research review

Bring a specimen to the desk.

Pick a commodity and delivery window. Use the two-page format to check whether it supports the questions the desk wants to ask.

See the public specimen ↑

Technical review

Inspect the integration.

Read the endpoint and response example. Access to current snapshots and reports requires a key issued to your institution.

Review access requirements ↗