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mindweave/agricultural-crop-yields

Agricultural Crop Yields & Farm Operations Dataset (Free Sample) This is a free sample with 190 rows. The full dataset has 906 rows across 4 tables. Farm operations data for a simulated agricultural cooperative with 25 farms, 150 fields, and 8 crop types across 5 growing seasons (2020-2024). Includes planting records, weather observations, input costs, and harvest yields. Features weather-correlated yield variation, crop rotation patterns, soil quality impact on productivity… See the full description on the dataset page: https://huggingface.co/datasets/mindweave/agricultural-crop-yields.

sourceHugging Facecc-by-nc-4.0updated 5mo agoView on Hugging Face
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Agricultural Crop Yields & Farm Operations Dataset (Free Sample)

This is a free sample with 190 rows. The full dataset has 906 rows across 4 tables.

Farm operations data for a simulated agricultural cooperative with 25 farms, 150 fields, and 8 crop types across 5 growing seasons (2020-2024). Includes planting records, weather observations, input costs, and harvest yields.

Features weather-correlated yield variation, crop rotation patterns, soil quality impact on productivity, and two anomalies — a drought year reducing yields by 35% and a pest outbreak devastating one crop type.

Ideal for: precision agriculture software, yield prediction ML, farm management systems, crop insurance modeling, and AgriTech dashboards.

Sample tables

TableSample Rows
farms5
fields25
plantings100
weather60
Total190

Full dataset

The complete dataset includes all tables with full row counts:

TableFull Rows
farms25
fields139
plantings682
weather60
Total906

Formats included: CSV, Parquet, SQLite

[Get the full dataset on Gumroad](https://mindweavetech.gumroad.com)

About

Generated by Mindweave Technologies -- realistic synthetic datasets for developers, QA teams, and data engineers.

Every dataset features:

  • Enforced foreign key relationships across all tables
  • Realistic statistical distributions (not uniform random)
  • Temporal patterns (seasonal, time-of-day, day-of-week)
  • Injected anomalies for ML training and anomaly detection
  • Deterministic generation (same seed = same output)

Browse all datasets: https://mindweavetech.gumroad.com