Field Forecasting Tool by WinField United

Dec 09, 2019


Field Forecasting Tool by WinField United
In the growing digital age of agriculture, machine learning and artificial intelligence are becoming more prominent. Using machine learning and a “digital twin” can allow us to see how a crop might respond to different circumstances. Understanding these crop responses allows us to make better use of our inputs to create the best ROI possible. Field Forecasting Tool (FFT) by Winfield United is an example of this technology.

Field Forecasting Tool models expected and necessary nitrogen, potassium and water. Using field specific information like soil properties, production history, tillage practices, and weather data allows FFT to help predict and time nutrient and water application. The tool also uses in-season tissue sampling to help calibrate the model to ensure accuracy.

Using the “digital twin”, a grower can run a single scenario or multiple scenarios to see how a nutrient application, or watering event in an irrigated instance, can affect crop and yield response. This allows for the best possible rate and timing of applications to have the most desired effect.

At Great Bend Co-op, we have tested this tool for the past few years. Most recently in 2019, we were able split a field with a grower to fertilize his standard way vs. what the tool said would have the best possible outcome. Having an outcome better then we had hoped for, the FFT treated portion of the field yielded 18 bushel/acre better than the grower standard with only 10# of additional nitrogen.

In another case, we worked with a grower that had one last fertigation pass to apply to his corn crop. After running a scenario to try to find the best timing and rate to run the last pass, FFT told us that his fertigation pass would have no yield response whatsoever. This allowed the grower to save the time and money from that application to have a higher ROI on the field.

Sometimes the answer might be to put our foot on the gas and push our fertility to achieve a greater yield, while in other cases the best answer might be to pull back on fertility and save the input dollars. In both cases, the grower was able to see a higher return on investment thanks to the crop model.

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