Knowing the benchmark means knowing true performance.
GrowingScore benchmarks grower production by normalizing the influence of weather and other uncontrollable factors, to reveal the actual performance attributable to farming practices. By benchmarking grower production history, firms can highlight meaningful trends. When grower benchmarks are consistent, the result is lower risk and better outcomes.
Understand grower performance within the context of benchmarks.
While this grower’s actual production for 2014 looks low when compared to typical performance, it’s a relatively successful 60th percentile when contextualized with the other years’ benchmarks.
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.
- Subscription cost is $100 per farm, with field boundaries defined by the user
- 10-year benchmarks are included on corn & soybeans, for an unlimited number of fields within each farm
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.
What Drives the Score?
- Individual grower yields by field, crop, practice, and year
- Percentile benchmark scores for each grower
Risk Assessment Features
- Normalized production data without the influence of weather and other uncontrollable factors
- Grower benchmarking for superior risk assessment alongside peers
- Easily accessible and actionable data
Risk Management Benefits
- Develop risk profiles against benchmarks in addition to production history
- Normalize grower production for a true picture of consistent farming practices
- Apply new data set to existing actuarial data, to highlight trends
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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Main Street Data
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