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Related Experiment Video

Updated: Jan 29, 2026

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
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Options for calibrating CERES-maize genotype specific parameters under data-scarce environments.

A A Adnan1,2,3, J Diels2, J M Jibrin3

  • 1Department of Agronomy, Bayero University Kano, Kano, Nigeria.

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|February 20, 2019
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Summary

Estimating Genotype Specific Parameters (GSPs) for maize models is challenging. Detailed experiments yield higher accuracy than breeder data, though breeder data offers an acceptable alternative for GSP estimation.

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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.