US Harvest Delay Anomaly (vs 5
How far ahead of or behind normal this year's harvest is: current Pct-harvested minus the 5-year average for that same week-of-year, per state and crop. +ve = ahead of schedule, -ve = behind - the 'are we early or late' number grain logistics and basis traders watch each fall.
- Source
- derived
- Licence
- Redistributable open data
- Updates
- Weekly
- Data through
- 2026-07-26
- Quality
- QC clean · 0 failures
- Format
- Apache Parquet + dictionary
Safe to train on
Built only from a named, redistributable official source under a documented open licence - not scraped web data. No copyright grey zone, no personal data. Ships machine-readable Croissant metadata (ML Commons - loads in Hugging Face / Kaggle / Google), an AI training-licence manifest documenting source, licence and provenance for your model's data governance, and a machine-readable data dictionary (drop it into an agent / RAG prompt so the model knows every column) - all inspectable before you buy.
What's included
- Pct-harvested minus its 5-yr same-week average, per state × crop, full history
- fuel = crop; +ve = harvest ahead of normal, -ve = behind
- No look-ahead (trailing 5-yr same-week mean excludes current year)
- Source: USDA NASS QuickStats (public domain, derived)
Schema
| Column | Type | Description | Filled | Distinct |
|---|---|---|---|---|
| zone | str | State (USPS alpha; 'US' national) | 100.0% | 48 |
| ts | timestamp (UTC) | Week-ending Sunday | 100.0% | 721 |
| fuel | str | Crop | 100.0% | 11 |
| harvest_anom_pp | float | Pct-harvested vs 5-yr norm | 100.0% | 909 |
| unit | str | Unit | 100.0% | 1 |
| res_min | int | Native resolution (weekly = 10080) | 100.0% | 1 |
Sample & preview
Every purchase ships as Apache Parquet with a data dictionary and the full QC report. A free sample (first rows + schema) is downloadable here - confirm fit before you buy. Source: derived (redistributable open data; attribution passes through - see our Licence).
Don't trust screenshots - drop the sample into your notebook right now.
import pandas as pd df = pd.read_parquet("us-harvest-delay-anomaly_sample.parquet") df.info() # typed columns, gap-aware, ready to join⬇ Download free Parquet sample or CSV for Excel
Coverage: 48 zones
AL · AR · AZ · CA · CO · CT · DE · FL · GA · IA · ID · IL · IN · KS · KY · LA · MA · MD · ME · MI · MN · MO · MS · MT · NC · ND · NE · NH · NJ · NM · NY · OH · OK · OR · PA · RI · SC · SD · TN · TX · US · UT · VA · VT · WA · WI · WV · WY
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