Planting Date Records
When each field was planted, by crop type and variety -- two weeks early or late can swing yields 15%, and commodity traders pay to know planting progress county-by-county.
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Find Me This Data →Overview
What Is Planting Date Records?
Planting date records document when each field was planted by crop type and variety, typically at field-level resolution across agricultural regions. This data is critical for agricultural management because planting timing significantly impacts crop development and yield—shifts of just two weeks early or late can swing yields by 15%. Modern planting date datasets are derived from satellite remote sensing combined with ground truth observations, enabling accurate detection across large geographic areas. These records capture spatial and temporal variability in farming decisions, accounting for factors like weather conditions, soil moisture, and regulatory planting windows set by crop insurance policies.
Market Data
2.7 days mean absolute error vs. state-level data
Prediction Accuracy (Corn, HLS Satellite)
Source: ResearchGate
2.5 days mean absolute error vs. state-level data
Prediction Accuracy (Soybeans, HLS Satellite)
Source: ResearchGate
77% of field-level variations
Field-Level Variation Captured (Corn)
Source: ResearchGate
Over 28,000 fields across 12 states (2000–2020)
Training & Validation Ground Data (Corn Belt)
Source: ScienceDirect
30 meters field-level, 3-day revisit (HLS)
Satellite Data Resolution
Source: ResearchGate
Who Uses This Data
What AI models do with it.do with it.
Commodity Traders & Market Analysts
Track planting progress county-by-county to anticipate crop development trajectories and adjust commodity pricing strategies in real time.
Crop Yield Modeling & Research
Analyze relationships between planting dates, weather conditions, and final yields to improve forecasting models and assess climate adaptation in farming practices.
Agricultural Insurance & Risk Management
Support crop insurance underwriting and claims validation by verifying compliance with policy-driven planting windows and calculating growing season length.
Climate & Agricultural Policy Analysis
Evaluate effectiveness of planting windows and assess how farming practices shift over time in response to climate change and environmental conditions.
What Can You Earn?
What it's worth.worth.
Research & Commercial Data Reports
Varies
Market research on AI training datasets (which includes agricultural phenology) priced at €4,034–$4,490 USD for comprehensive reports with regional segmentation and multi-year updates.
Field-Level Dataset Access
Varies
Satellite-derived planting date datasets for specific regions (e.g., Corn Belt, U.S. Midwest) and time periods typically licensed through institutional partnerships or data service subscriptions.
What Buyers Expect
What makes it valuable.valuable.
Sub-10 Day Accuracy vs. Ground Truth
Planting date predictions must achieve mean absolute error (MAE) under 10 days when validated against field-level ground observations, with best-in-class solutions achieving 2.5–2.7 day MAE.
Field-Level or Sub-Field Spatial Resolution
Data should resolve individual field boundaries at 30–250 meter resolution, enabling analysis of within-county variability rather than state-level aggregates alone.
Multi-Year Temporal Coverage
Datasets covering 5–20+ year periods allow trend analysis, climate adaptation tracking, and robust model training across diverse weather and planting scenarios.
Crop-Specific & Variety Codes
Records must distinguish between major commodity crops (corn, soybeans, wheat) and account for management practices (irrigation, cover cropping) that affect planting timing.
Validation Against USDA Standards
Ground truth data should align with USDA NASS crop progress reports and RMA crop insurance records to ensure market credibility and policy relevance.
Companies Active Here
Who's buying.buying.
Real-time planting progress monitoring for agricultural derivatives pricing and supply-chain forecasting.
Ground truth data collection (28,000+ field observations) for training satellite-based planting date detection models.
Validate planting compliance with USDA RMA crop insurance policies and assess growing season risk exposure.
FAQ
Common questions.questions.
Why does planting date matter for agricultural markets?
Planting date directly determines crop development timing relative to weather and growing season length. Research shows that shifts of two weeks early or late can change final yields by 15%, making this data critical for commodity pricing, yield forecasting, and insurance underwriting.
How are modern planting date datasets created?
Current datasets combine satellite remote sensing (Harmonized Landsat/Sentinel-2 at 30m resolution with 3-day revisit) with ground truth validation from 28,000+ field observations. Machine learning models use satellite-derived phenological metrics and weather data to predict planting dates with 2.5–2.7 day accuracy.
What is the typical accuracy of satellite-based planting date detection?
Best-performing models achieve mean absolute error (MAE) of 2.5–2.7 days for corn and soybeans when validated against USDA state-level reports. Accuracy degrades slightly for irrigated fields (5.4–6.1 days MAE) and cover-cropped fields, but remains under 10 days across major commodities.
What geographic coverage exists for planting date records?
Field-level datasets are most mature for the U.S. Corn Belt and Midwest, with coverage spanning 12+ states and 2000–2020+ timeframes. International research shows planting date estimation is possible for rice in Italy and Japan, though highest-resolution commercial datasets focus on U.S. commodity regions.
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