June 28, 2018

We are pleased to share our initial end-of-season yield forecasts for U.S. corn and soy:
  • Our forecast for end-of-season corn is 172.7 bu/ac. This is 0.8 bu/ac less than our long-term trend-implied yield of 173.5 bu/ac and 2.2% below the record-breaking 2017 harvest of 176.6 bu/ac.
  • Our forecast for end-of-season soy is 50.6 bu/ac. This is 1.1 bu/ac more than our long-term trend-implied yield of 49.5 bu/ac and 3% above the 2017 harvest of 49.1 bu/ac.
The models will be going "live" in Kernel starting next week. At that time, the above forecasts will update daily as our models incorporate live satellite and weather data. They will remain live until mid-October. 
kernel-us-crops

 

 

How do we do it?

Our data science team trains machine-learning models on historical data to forecast what we expect the yield to be this year. We take into account a range of factors, including weather, field conditions, and satellite-derived crop health, to make our predictions as informed and accurate as possible.

 

How do we ensure accuracy?

We backtest our models against historical results to evaluate their accuracy. By testing our models against known results, we can see how well they would have predicted past outcomes. We continue to refine our models to reduce our margin of error while using known techniques to avoid overfitting. We will only publish a model if backtesting suggests more accurate estimates than those of high-quality reference sources such as the Foreign Agricultural Service. (Learn more about backtesting from a previous blog post.)

 

Why TellusLabs?

At TellusLabs, we know that when it comes to actionable information, time is valuable. We provide daily model updates - a much higher frequency than monthly government reports. We use our proprietary predictive models to process large amounts of raw weather and satellite data to make high-quality insights and predictions readily available for our customers.


Want live access to our newest corn and soy models? Check out a free trial of our product, Kernel.



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