What is AgScribe?
AgScribe is a neuro-symbolic AI system that listens to — or reads — conversations between agronomists and farmers, then automatically extracts structured agricultural data that would otherwise require manual data entry. The extracted data feeds directly into carbon quantification models and sustainability reporting platforms such as AgOutcomes.
Agronomists conduct interviews covering crop history, tillage practices, fertilizer and chemical inputs, livestock operations, and more. These conversations are rich with information but arrive in unstructured natural language. AgScribe converts them into typed, structured events — removing a significant data-entry burden and enabling faster, higher-quality carbon program enrollment.
1. Transcription
Agronomist–farmer phone calls are transcribed into speaker-diarized text with timestamps.
2. Event Extraction
A neuro-symbolic LLM pipeline reads the transcript and identifies structured events — field data, crop decisions, practices, inputs, livestock metrics.
3. Carbon Modeling
Extracted data flows into quantification models (e.g., DayCent) to estimate greenhouse gas emissions, sequestration, and sustainability metrics per field.
4. Reporting
Farmers and agronomists receive farm reports showing conservation practices, efficiency metrics, and carbon outcomes — without manual data entry.
Crop Farms
Row-crop operations: field boundaries, multi-year crop rotations, tillage, fertilizer & chemical programs, cover crops, irrigation, yields, and conservation practices.
Dairy Operations
Herd counts (lactating, dry, heifers, calves), milk production, protein & butterfat %, energy use (diesel, electricity), manure application, pasture management, and grazing seasons.
Pork Operations
Barn details, PQA certification, energy use (electricity, propane, natural gas), mortality disposal, renewable energy, employee count, charitable contributions, and associated row crops.
The system identifies and structures over 25 distinct event types from natural conversation.
Handles Natural Speech
Extracts data from messy, conversational transcripts — incomplete sentences, corrections, tangents, and filler words included.
Typed Structured Output
Each extracted item carries a precise event type, relevant fields, values, and units — ready for model ingestion with no manual parsing.
Quality Assessment
Each result includes metadata: whether expected interview sections were present, and what fraction of expected event types were active.
Multi-Species Support
A single pipeline handles crop-only farms, dairy operations, pork operations, and mixed enterprises in the same pass.