Yield prediction
Phoeniks runs a process-based crop-model ensemble — APSIM, DSSAT and ORYZA — over a property's soil, weather and remote-sensing history to produce a per-property yield estimate with an explicit confidence level. Wheat and rice process routes are implemented first; the crop catalogue defines what can be modelled where.
How an estimate is produced#
- Crop catalogue and can-grow matrix determine which crops are physically plausible on the parcel.
- Per-listing crop-fit signal ranks those candidates against the local climate and soil envelope.
- The simulation ensemble runs the selected process routes over the property's history.
- The scoring engine converts model output into a comparable score with a confidence band.
- A scenario configuration manager records the assumptions used, so a run can be replayed.
Ensemble, not a single model#
Single-model yield figures are hard to defend in front of a model-risk function. Running three independent process models over the same inputs exposes disagreement between them, and that disagreement is reported rather than averaged away.
Frequently asked
- Which crops are covered?
- Wheat and rice process routes are the implemented core, with the crop catalogue and can-grow matrix defining the wider set of crops that can be assessed for suitability. Coverage varies by country.
- Do you publish accuracy figures?
- Validation metrics are computed under a documented holdout and calibration protocol and delivered in the validation pack. Phoeniks does not publish headline accuracy claims that a reviewer cannot reproduce.
Related pages
Parcel search
Search a normalised farmland inventory built from public portals, with map results, filters, source provenance on every listing and shortlist comparison.
Evidence and diligence
Validation packs, evidence scoring levels, a reproducibility ledger, diligence PDF export and a pre-diligence room for reviewable farmland decisions.
Data and API
Sentinel-1, HLS, AgERA5, CHIRPS, France RPG, IBGE SIDRA and CIMMYT ingestion, exposed through a listing intelligence REST API and an institutional feed.
Carbon MRV evidence
Baseline reconstruction, monitored change with uncertainty and permanence-risk inputs for agricultural carbon projects and buyers.
Insurance analytics
Yield deviation, basis-risk analysis and parametric trigger design built on documented crop models and replayable weather scenarios.
Scope 3 agriculture
Sourcing-region evidence for food and CPG companies reporting agricultural Scope 3 emissions and land-use change.
Commodity intelligence
Regional production trajectories and in-season deviation signals for traders and analysts covering agricultural commodities.
Data sources
The families of data Phoeniks ingests: satellite, weather and reanalysis, soil, land registry, official statistics and market records.
See also
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