Estimating parametric phenotypes that determine anthesis date in Zea mays: Challenges in combining ecophysiological...

Area of Science:

• Agricultural Science
• Computational Biology
• Genetics

Background:

• Ecophysiological crop models use genotype-specific parameters (GSPs) to represent intra-species genotypic properties.
• These GSPs are theoretically interpretable as quantitative traits for genetic analysis.

Purpose of the Study:

• To investigate the estimation of parameters controlling maize anthesis date using the CERES-Maize model.
• To assess the utility of GSPs as mappable traits.

Main Methods:

• Utilized high-performance computing to simulate 356 million maize anthesis dates for 5,266 maize lines across 11 diverse US East Coast environments.
• Estimated four CERES-Maize model parameters to predict anthesis dates.

Main Results:

• Achieved high predictive value for anthesis dates (R² = 0.94).
• Identified significant challenges: expressivity (model failed for some lines), equifinality (multiple parameter sets fit data), and instability (parameter values varied with environment).

Conclusions:

• Despite high predictive accuracy, expressivity, equifinality, and instability issues question the assumption that GSPs represent fundamental genetic traits.
• These challenges must be resolved for robust genetic mapping of GSPs to address the genotype-to-phenotype problem in crops.