The Forecast and Assessment of Cropping sysTemS (FACTS) project was first launched in 2015 to provide yield and soil nitrogen predictions at individual fields. In addition to the field scale forecasts, in 2019 we added a regional scale component to provide coverage for Iowa, Illinois and Indiana. The regional scale tool provides the following information:
- Precipitation and growing degree days are presented from May 1 to current day as well as 7 day precipitation and growing degree day forecast. The weather information is updated daily.
- Soil water and soil nitrogen indexes are available. The index indicates optimum (values near 1), below normal (values below 1), and above normal (values greater than 1). These values are updated every Wednesday.
- Additional crop variables such as crop nitrogen uptake rates, grain yield accumulation, root depth, and end of season yield predictions for both corn and soybean will be added.
We use a systems approach to synthesize and convert weather, soil, cultivar relative maturity, and management data into meaningful agronomic parameters to support decision making. We use the Agricultural Production Systems sIMulator (APSIM) and machine learning algorithms.
At the regional scale we simulate 25 to 30 fields per county for a total of 15,000 fields across three states (Iowa, Illinois, and Indiana) and two crops (corn and soybean). Soil information comes from gSSURGO and weather information comes from the Iowa Environmental Mesonet that synthesizes various weather sources. Corn hybrid and soybean variety information per region comes from literature. Management information comes from USDA-NASS and expert opinions.
The regional scale component is a work in progress. As of today, we release 10 maps and the plan is to add more information throughout the growing season and also provide different type of information during to support various decisions.
The Iowa Soybean Association for supporting the modeling project from the beginning. FFAR and ICIA for supporting research to improve the simulation of soil water across the Corn Belt and enabling regional scale modeling. NSF through which we added machine learning techniques into the project. The Plant Sciences Institute, Department of Agronomy, and Agriculture and Natural Resources Extension of Iowa State University for supporting various aspects of the FACTS project. The APSIM Initiative for making the software publically available and for ensuring software quality.