Examining the Corn Seedling Emergence-Temperature Relationship for Recent Hybrids: Insights from Experimental Studies

Area of Science:

• Agricultural Science
• Crop Physiology
• Statistical Modeling

Background:

• Corn seedling emergence significantly impacts crop yields and requires precise prediction for crop models.
• Existing research on corn emergence and temperature lacks focus on modern hybrids.
• Accurate simulation of corn growth and development depends on reliable emergence prediction.

Purpose of the Study:

• To develop and evaluate statistical models for predicting corn seedling emergence in current hybrids.
• To investigate the influence of soil and air temperatures on corn emergence.
• To compare a new growing degree day (GDD) model with existing ones for newer hybrids.

Main Methods:

• Developed linear and quadratic statistical models using data from controlled environment experiments.
• Utilized soil and air temperatures as predictor variables for seedling emergence.
• Created and validated a growing degree day (GDD) based model for contemporary corn hybrids.

Main Results:

• The quadratic model using air temperature demonstrated the highest accuracy (R²: 0.97, RMSE: 0.42 days).
• Quadratic and linear models based on soil temperature also showed high predictive power.
• The new GDD model indicated a 16% higher GDD requirement for emergence in newer hybrids compared to older ones.

Conclusions:

• Statistical models, particularly quadratic ones with air temperature, accurately predict emergence in modern corn hybrids.
• Existing GDD-based models require revision for application to newer corn varieties.
• Integrating the developed emergence model into crop simulation tools can enhance corn management and yield prediction.