From farm data to decision-grade CI output.
A transparent, five-step process: from data collection through calculation and verification to commercial use. Nothing in this process is opaque. Every step is documented.
Data Collection
Gather farm and production data
Producers provide structured practice data through a guided, low-friction input workflow. The goal is to capture what actually drives carbon intensity — not to create administrative burden. Hypocotyl supports multiple data entry methods, including direct input, spreadsheet upload, and API integration with farm management software.
Inputs / Activities
- Fuel use by operation type (tillage, planting, application, harvest)
- Nitrogen fertilizer type, rate, and application timing
- Other input products: herbicide, seed, micronutrients
- Tillage system and pass count
- Crop yield and acres
- Irrigation energy (if applicable)
- Field identifiers for spatial traceability
Data Structuring
Normalize and validate the data
Raw inputs are structured, unit-normalized, and checked for completeness before calculation. Data gaps are flagged — not silently filled with assumptions.
Inputs / Activities
- Unit conversion and normalization (per-acre to per-kg-grain)
- Completeness scoring: which inputs are present vs. assumed
- Outlier flagging for implausible values
- Confidence tier assignment based on data quality
- Input data summary locked for the calculation version
CI Calculation
Calculate carbon intensity using a transparent methodology
The Hypocotyl CI engine applies a documented, boundary-defined methodology to convert practice data into a CI score expressed in kg CO₂e per kg of grain.
Inputs / Activities
- Emission factor application: fuel combustion, fertilizer manufacture, N₂O field emissions
- Boundary: cradle-to-farmgate (all on-farm emissions through to point of sale)
- Allocation methodology: documented and version-controlled
- Land use and carbon stock changes where data supports estimation
- All emission factors sourced from recognized scientific literature, with sources cited
Output Generation
Produce auditable outputs and reporting
The final CI score is packaged with everything needed for a buyer, regulator, or auditor to understand and trust it — not just a number.
Inputs / Activities
- CI score: kg CO₂e / kg grain, with confidence range
- Benchmark comparison: regional average, national average, program baseline
- Boundary statement and scope summary
- Data completeness percentage and key assumption notes
- Input data summary (all values used in calculation)
- Methodology version reference
Commercial Use
Enable commercial use cases
Auditable CI data enables downstream applications that create value for producers, buyers, and the supply chain.
Inputs / Activities
- Premium pricing discussions: auditable CI score as basis for differentiated value
- Low-carbon program participation: structured outputs meeting program data requirements
- Procurement sourcing: buyer-side filtering and comparison of supplier CI data
- Scope 3 reporting: structured data for downstream Scope 3 accounting
- Regulatory and voluntary disclosure support
On Transparency
Nothing we show cannot be explained.
Every claim Hypocotyl makes about a CI score comes with traceable inputs, documented assumptions, and an explicit boundary statement.
Want to walk through the process?
Book a demo to see the full workflow — from data entry to final CI report — for your operation or supply chain.