Variable-rate in corn sowing for maximizing grain yield

Area of Science:

• Agronomy
• Soil Science
• Crop Physiology

Background:

• Sowing density significantly impacts corn yield.
• Soil attributes can influence optimal plant populations.
• Predicting yield based on vegetative indices is crucial for agricultural management.

Purpose of the Study:

• Identify key soil attributes affecting corn grain yield.
• Develop a predictive model for optimal sowing rates based on soil properties.
• Determine the most accurate vegetative growth indices for yield prediction.

Main Methods:

• Conducted field experiments in Chapadão do Céu-GO over two years.
• Evaluated soil attributes including pH, nutrient levels, organic matter, and electrical conductivity.
• Utilized Pearson correlation and path analysis to build decision trees for yield and seeding density estimation.

Main Results:

• Magnesium and apparent electrical conductivity (ECa) were the most significant soil attributes influencing sowing density.
• Developed specific sowing density recommendations based on ECa and magnesium levels.
• Decision tree model accurately predicted optimum plant populations, with no significant yield differences observed among tested populations in validation.

Conclusions:

• Soil-specific adjustments in sowing density are essential for maximizing corn yield.
• Magnesium and ECa are critical parameters for determining optimal corn plant populations.
• The developed decision tree model provides a practical tool for optimizing sowing rates in corn cultivation.