St. Paul, MN. (2/3/2025)— On January 29, Seth Naeve, University of Minnesota Extension soybean agronomist and Anibal Cerrudo, a visiting professor for the last 3 years in the Naeve lab joined UMN Extension crops educator Angie Peltier for a discussion about how using crop modeling can help soybean producers in Minnesota gain important insights into how their management choice could impact yield. This was the fourth weekly episode of the 2025 Strategic Farming: Let’s talk crops! webinars. The series runs through March.
The University of Minnesota team of soybean agronomists are developing crop models with the end goal of helping farmers make data-driven decisions. Crop models incorporate weather, soil condition, crop genetics and management practice data into a series of mathematical equations that can help agronomists or farmers to estimate crop yield. Once enough applied, field-scale data has been compiled and fit to a model, a modeler can run various crop production scenarios through the model without leaving their computer or putting out a field trial. While some of the larger ag companies have developed paid, proprietary tools based on crop modeling of how management conditions can impact their own varieties’ yield, Cerrudo and Naeve’s crop modeling work isn’t designed to work for a specific set of soybean varieties, but is rather designed to provide an open-source set of tools to aid in farmer decision-making based on maturity groups grown in Minnesota. As additional data informs the model, their model’s predictive ability improves.
There are process-driven models and data-driven models and the Naeve lab is working with both, to better understand the interrelatedness of the various factors that can impact soybean yield. Process-driven models require an advanced understanding of the processes that contribute to yield, be they solar radiation, rainfall, planting date, nutrients, soybean growth and development or resource partitioning. Data-driven models use computational power and artificial intelligence (AI) to find relationships between the factors that contribute to yield. According to Cerrudo, instead of being based on knowledge of processes, data-driven models “try to find patterns or relationships from the data” without asking about the processes that underly the data. The webinar showed some of the modeling work that the team has been focused on related to planting date and yield.
For those that missed this session, it is now available to view on YouTube at: http://z.umn.edu/StrategicFarmingRecordings. For more information and to register to attend other weekly session through the end of March, visit https://z.umn.edu/strategic-farming.
