Team members: Skye Crocker, Colin Hascup, Ahmed Malik, Lam Nguyen, Ruilin Zhang
Sponsor: Zalliant | Instructors: Ajay Anand, Ph.D. Cantay Caliskan, Ph.D.
Drying crops before storage as hay or silage is critical for preserving quality, with environmental drying being the preferred method due to its cost-effectiveness. This project, conducted in collaboration with Zalliant, aimed to develop predictive models to optimize crop harvesting and drying decisions based on weather conditions.
The project involved two key models:
- Growing Degree Day (GDD) Calculator: This tool estimates crop maturity by calculating cumulative heat values, helping farmers determine the ideal harvest window. For example, alfalfa is typically harvested at 750–755 GDD. The calculator supports multiple calculation methods (e.g., average, sine, triangle) and customizable temperature thresholds. Validation using USDA harvest reports confirmed alignment between predicted and actual harvest dates.
- Drying Calculation Algorithm: Using a formula derived from agronomic research, this model predicts moisture content over time post-harvest. It assists farmers in deciding when to switch from field drying to mechanical drying. While validation data were limited, the algorithm’s foundation in established literature and adaptability to hourly weather data ensures its potential utility.
Key innovations include a flexible GDD calculator generalizable across U.S. locations, capable of using multiple calculation methods and custom temperature settings. To further optimize harvest dates, we recommend enhancing data collection, including moisture content and harvest timing. Future work involves expanding the models to other crops, refining granularity with hourly data, and validating results through live testing. This project set up a foundation for future work on optimizing the cutting and drying of crops. Zalliant now has a more integrated pathway for GDD and drying calculations.
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