Options for calibrating CERES-maize genotype specific parameters under data-scarce environments

Area of Science:

• Agricultural Science
• Agronomy
• Crop Modeling

Background:

• Crop simulation models rely on Genotype Specific Parameters (GSPs) for Genotype × Environment × Management (G×E×M) interactions.
• Estimating GSPs typically requires extensive and costly field experiments.

Purpose of the Study:

• Determine GSPs for 10 new maize varieties in the Nigerian Savannas.
• Compare GSP accuracy from calibration experiments versus existing breeder data.
• Evaluate the CERES-Maize model for simulating grain and tissue nitrogen.

Main Methods:

• Conducted 8 experiments across Nigerian Savanna (2016, rainy/dry seasons).
• Utilized 2 years of breeder evaluation data from 7 locations.
• Calibrated and evaluated the CERES-Maize model using varying nitrogen rates.

Main Results:

• Experimental data calibration yielded high model efficiency (EF: 0.88-0.94) and d-index (0.93-0.98).
• Breeder data calibration showed lower EF (0.58-0.88) and d-index (0.56-0.86).
• Both data types resulted in good agreement for simulated grain yield and nitrogen content.

Conclusions:

• Detailed experimental data provides superior accuracy for CERES-Maize model calibration.
• Breeder trial data offers a viable, albeit less accurate, alternative for GSP estimation when detailed experiments are not feasible.