As corn approaches physiological maturity and, in some areas, even harvest maturity, farmers face the challenge of deciding when to begin harvest. The ISU Corn DryDown Calculator is a practical tool that helps forecast grain moisture over the next 5 and 10 days, making it easier to schedule harvest operations.
Link to the Calculator: https://facts.extension.iastate.edu/corn-drydown-calculator
The tool covers the entire US Corn Belt. The user first selects a location on the map and then selects a date and a corn grain moisture content on that date. The tool generates a graph showing daily drydown patterns (see Fig 1) and a table summarizing the projected grain moisture content over the next 5 and 10 days (see Table 1). For example, if the grain moisture in central Iowa is 35% today (September 2), the calculator predicts it will drop to 28.6% in 5 days and to 23.8% in 10 days.
Figure 1 - Drydown prediction for central Iowa assuming corn grain moisture is 35% today.
To estimate drydown, the tool uses daily temperature and relative humidity to calculate the atmosphere's drying potential on a given day. This is calculated using an "equilibrium" concept, that is the point where the kernels no longer gain from or lose moisture to the surrounding air. For a detailed description of the science behind the tool, we refer to Martinez-Feria et al. (2019). The calculator uses current and historical weather data from Iowa Environmental Mesonet and forecasted weather data from the Climate Forecast System.
The tool is designed for calculating drydown after corn has reached physiological maturity. This can be determined visually by the formation of the black layer at the tip of corn kernels.
| Today | Expected in 5-days | Expected in 10-days |
|---|---|---|
| 45 | 35.8 | 29.0 |
| 40 | 32.2 | 26.4 |
| 35 | 28.6 | 23.8 |
| 30 | 25.0 | 21.1 |
| 25 | 21.3 | 18.5 |
| 20 | 17.7 | 15.8 |
Reference
Martinez-Feria R, Licht MA, Ordonez RA, Hatfeld JL, Coulter JA, Archontoulis SV, 2019. Evaluating maize and soybean grain dry-down in the field with predictive algorithms and genotype-by-environment analysis. Nature Scientific Reports 9:7167, https://doi.org/10.1038/s41598-019-43653-1
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