Knowing the details means knowing the risk.
Preplanting Radar provides highly specific, quantitative risk analysis of soil characteristics, to more accurately inform ceding decisions. Our geographically-specific data provides a sound basis for assessment of relative drought/excess moisture risk, and our data sets include water holding capacity and water availability at multiple depths to better identify pre-planting risks. This risk assessment is based on our proprietary 1.3B samples of yield, carefully collected across millions of acres and 26 states – as well as 20 years of detailed weather matched to the sample locations.
Sophisticated data maps show the effects of long-range weather forecasts on crop development.
HOW IT WORKS
Main Street Data partners with data giant IBM, applying human intelligence to massive amounts of weather and related data. These human insights lend wisdom to data science for immediate, accurate forecasting.
- Nationwide views
- Updated 2x daily for most attributes
- Visualization maps
Access our proprietary data science through our web app, which allows you to view maps and download data. To view the standard SaaS agreement, click here.
Risk Assessment Features
- Forward-looking simulations of how crops react to weather
- Visual representations of future crop development
- Easily accessible and actionable data
Risk Management Benefits
- View the effects of uncontrollable elements on crops, well before planting
- Forecast crop development, accounting for long-range weather impacts
- Simulate future crop growth, as visualized in local maps
What Drives the Preplanting Model?
- Geographically-specific data
- Analysis of water holding capacity and availability at the root zone
- Identification of the relative risk for drought and excess moisture in pre-planting locations
Ag Data Science Done Right
The Main Street Data forecasting model leverages a massive amount of yield and weather data elements to identify and interpret causes, as well as measure, monitor, and forecast a wide variety of outcomes. This crop forecasting model is based on MSD’s proprietary Crop Stage approach, applying machine learning to billions of samples of yield and 10s of billions of field and weather-related data elements. The analytical interpretations derived from this powerful forecasting model are valuable to a wide range of users, including growers, traders, insurers, lenders, agronomists, and co-ops.
See How Human Intelligence is Transforming Data Science.
Data Elements Include:
- soil characteristics
- water holding capacity
- water availability at multiple depths
- trillions of historical weather data elements
- weather forecasts ranging from 15 days to 6 months
- 1.3B yield samples across 26 states
Ag Insights
Knowing Better Means Knowing Sooner
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Bringing Human Intelligence to Data Science in the Ag Space
Main Street Data
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