by Adam Pasch
It’s one thing to forecast the weather — it’s quite another to see how weather will affect individual parcels of land in an upcoming planting season. There are, after all, myriad variables at play, including soil quality, drainage ability, type of crop, and much more.
Figure 1: Example of weather data used to simulate crop yield
To that end, our proprietary CIBO Lab Yield Simulator blends observed weather data with state-of-the-art seasonal predictions to simulate a range of possible weather scenarios for the new planting season. Our proprietary software platform simulates crop development, from planting to harvest, under a variety of forecast weather scenarios and management strategies. This work provides a range of outcomes and, as the growing season progresses, the prediction’s accuracy improves.
The end result: farmers, landowners, and speculators can create their own custom inputs to track and predict crop growth at each critical moment in a growing season (read more: How Does CIBO Model Crop Yield?).
A Look at the CIBO Lab Yield Simulator
Figure 2: Screen capture from CIBO Lab Yield Simulator available now to CIBO Plus subscribers
Predicting crop yields involves evaluating land potential using multiple data layers, including satellite imagery, weather, soil, parcel records, and more. CIBO Lab incorporates proprietary machine learning and AI, as well as our subject matter expertise in soil, weather, and agronomy, to predict yields for current and future growing seasons on scales from a sub-field to the continental United States — and beyond. CIBO’s unique capabilities allow for comparisons and experiments to be done across many different potential weather and management scenarios.
We designed the Yield Simulator to help users understand how different factors can impact the predictions of yield and maturity date on any given parcel. Users can customize results by editing inputs in the calculator or clicking Calculate Results with default values based on regionally common farming practices. By inputting their own specific data, users can see the impact of different weather on yields and maturity date.
Let’s look at an example to see this in action.
How the Yield Simulator Works
Suppose we have a parcel of land where corn is to be planted. We can simulate the impacts of different weather scenarios on end-of-season yield and maturity date predictions. Let’s look at the prediction using the Aggregate Scenarios or all possible weather scenarios, which results in predicted yield 13% higher than the 10-year average; and the projected maturity date range is between August 22 and September 8.
Figure 3: Screen capture of CIBO Lab Yield Simulator for an example parcel
There are two scenarios for this particular parcel: Warm and Dry and Warm and Wet. By looking at the Yield Simulator, we can see the impact different types of weather have on predicted yields and maturity dates.
Scenario 1: Warm and Dry
The most likely scenario for this particular parcel is Warm and Dry, which means that the weather predictions are warmer and drier than the 30-year climatology for this particular parcel. The resulting yield is slightly lower at 12% higher than the 10-year average and the predicted maturity range is the same as the Aggregate Scenarios, meaning that most of the possible weather conditions are trending toward being Warm and Dry.
Figure 4: Screen capture of CIBO Lab Yield Simulator for an example parcel for the Warm and Dry conditions
Scenario 2: Warm and Wet
The less likely scenario for this particular parcel is Warm and Wet, which means that the weather predictions are warmer and wetter than the 30-year climatology for this particular parcel. The resulting yield is 18% higher than the 10-year average, and the maturity date range is between August 27 and September 3.
Figure 5: Screen capture of CIBO Lab Yield Simulator for an example parcel for the Warm and Wet conditions
This is just one example of the types of experiments that can be done with the new Yield Simulator. As the season progresses, we learn more about the actual weather conditions. And via remote sensing, we can gain a better idea of how actual planting dates compare to projected planting, how many acres have been lost to adverse conditions, and how crop maturity is progressing. CIBO gives users the ability to save a scenario (a combination of crop, seeding rate, planting date, and nitrogen applications) so that they can come back as more information is known and see how projections evolve over time. To try the CIBO Lab Yield Simulator, click here.
About the Author
Adam Pasch is a Principal Data Scientist in Meteorology at CIBO, a science-driven software startup. He is a Certified Consulting Meteorologist from the American Meteorological Society. Prior to CIBO, Dr. Pasch was the Weather Data Strategy and Operations Manager at The Climate Corporation and a Meteorologist Project Manager at Sonoma Technologies, Inc. He holds a Doctorate, Masters, and Bachelors of Science in Meteorology from Saint Louis University.
For further information about CIBO Technologies‘ approach to corn phenology.
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