A hierarchical Bayesian model for dynamic phenotypes in multi-environment trials with genotype by environment interaction

In APSIM-simulated longitudinal Triticum aestivum biomass, only the upper asymptote parameter shows clear genotype-by-environment interaction, enabling genotype characterization from DE parameters.

• Why it matters: Longitudinal phenotyping yields dynamic traits, but it is unclear which growth-curve parameters capture genotype specificity across environments for downstream prediction of yield or biomass.
• What they did: A hierarchical Bayesian differential-equation model for log-logistic growth decomposed each DE parameter into genotype, environment, and G x E contributions across multiple environments.
• The result: By fitting the DE model to simulated data and quantifying variance contributions, genotypic and G x E effects within DE parameters characterized genotypes, while environmental and G x E effects informed environmental characterization.

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