In-Season and Predictive Data 2018-06-11T21:38:04+00:00

In-Season and Predictive Cropping

The ag industry is changing. If you want to have a competitive edge, you need the most up-to-date information.

That’s why Farm Market iD is applying our decades of data science to provide you with current season crop data, as well as reliable predictions for next year. Use this data in strategy, marketing and sales to make high-level strategic decisions, as well as on-the-ground tactical decisions.

Contact Us

In-Season and Predictive Cropping

The ag industry is changing. If you want to have a competitive edge, you need the most up-to-date information.

That’s why Farm Market iD is applying our decades of data science to provide you with current season crop data, as well as reliable predictions for next year. Use this data in strategy, marketing and sales to make high-level strategic decisions, as well as on-the-ground tactical decisions.

Contact Us

Past

Historic Data

2011-2017 Crop Data

  • Over seven years of historic data
  • Crop rotation, crop mix, acres farmed, Gross Farm Income and more

Present

In-Season Data

2018 Crop Data

  • In-season crop identification covering corn, soybean, wheat and cotton in 20 states
  • Over 200 million acres and 90 percent coverage of U.S. output of these crops
  • Satellite-based remote sensing using machine learning with ground truthing
  • Available at field and grower level to link grower crops to an overall market view

Future

Predictive Data

2019 Crop Data

  • Leverages eight year field-level crop rotation, crop yields, commodity prices and weather data to project field-level planting decisions
  • Employs multivariate regression analysis and statistical models to analyze and project planting decisions
  • Covers corn, soybean, wheat and cotton in 20 states – over 200 million acres
  • Available in September for the 2019 crop year

Coverage

Farm Market iD’s in-season and predictive data covers four crops in 20 states.

Corn

Projected acres: 82,000,000
Coverage: 90 percent

Soybeans

Projected acres: 75,000,000
Coverage: 89 percent

Wheat

Projected acres: 42,500,000
Coverage: 87 percent

Cotton

Projected acres: 7,800,000
Coverage: 74 percent

States with In-Season and Predictive

Arkansas
Colorado
Indiana
Illinois
Iowa
Kansas
Kentucky
Michigan
Minnesota
Mississippi
Montana
Nebraska
North Carolina
North Dakota
Ohio
Oklahoma
South Dakota
Texas
Wisconsin

Where the Data Comes From

We gather this data from three primary sources, in addition to various other sources. The primary sources are:

  1. Historic and In-Season Satellite Imagery from Landsat and Sentinel Sensors
  2. Historic and In-Season Weather Data Observations
  3. Historic planting information derived from the NASS Cropland Data Layers

Imagery collected in-season is the primary driver of crop identification. Historic and in-season weather information is used to predict crop growth stage and to fine-tune identification. Data drive from the NASS datasets are used to create spectral profiles unique to crops of interests.

Information from satellite imagery is used to distinguish crops early in the growing season. In addition, the crop prediction model is calibrated using ground reference data provided by trusted partners.

We then implement a statistical machine learning model to capture the predominant crop rotation patterns within each state and project those patterns into the future. This model takes into account regional variations in suitability for different crops, commodity prices at the time preliminary planting decisions are made, data from the USDA on planted areas, and weather data that can affect decisions closer to planting time.

With this historically-based prediction as a starting point, satellite imagery from medium resolution sensors is processed chronologically to update the machine learning classification system’s estimate of crop type.

Imagery from previous seasons, pertinent weather data such as growing degree days, soil temperature, and precipitation are used to create the baseline model against which new satellite imagery is compared.

Where the Data Comes From

We gather this data from three primary sources, in addition to various other sources. The primary sources are:

  1. Historic and In-Season Satellite Imagery from Landsat and Sentinel Sensors
  2. Historic and In-Season Weather Data Observations
  3. Historic planting information derived from the NASS Cropland Data Layers

Imagery collected in-season is the primary driver of crop identification. Historic and in-season weather information is used to predict crop growth stage and to fine-tune identification. Data drive from the NASS datasets are used to create spectral profiles unique to crops of interests.

Information from satellite imagery is used to distinguish crops early in the growing season. In addition, the crop prediction model is calibrated using ground reference data provided by trusted partners.

We then implement a statistical machine learning model to capture the predominant crop rotation patterns within each state and project those patterns into the future. This model takes into account regional variations in suitability for different crops, commodity prices at the time preliminary planting decisions are made, data from the USDA on planted areas, and weather data that can affect decisions closer to planting time.

With this historically-based prediction as a starting point, satellite imagery from medium resolution sensors is processed chronologically to update the machine learning classification system’s estimate of crop type.

Imagery from previous seasons, pertinent weather data such as growing degree days, soil temperature, and precipitation are used to create the baseline model against which new satellite imagery is compared.

Get Started Today

In-season and predictive data will be here in July, but you can pre-order now. Click here to get in touch with our team.

Contact Our Team