56: Leveraging Bayesian Hierarchical Model to Combine Domain Knowledge and Big Data to Accelerate Crop Improvement

Thomas M. Menino Convention & Exhibition Center 

Sustained genetic gain in plant breeding requires accurate prediction of varietal performance across diverse environmental and management conditions. Contemporary predictive approaches typically fall into two classes: process-based crop models that encode mechanistic knowledge of plant development, and flexible data-driven models that emphasize predictive accuracy but offer limited interpretability. We propose a hybrid modeling framework that integrates these paradigms within a Bayesian hierarchical model. Outputs from a mechanistic crop growth model are incorporated as structured components alongside genomic covariates, allowing biological knowledge to inform inference while retaining statistical flexibility. The hierarchical structure enables partial pooling across environments and explicit uncertainty quantification.
Model behavior and predictive performance are evaluated using both simulated data and large-scale empirical breeding trial datasets. Results show that the proposed hybrid approach improves prediction and interpretability relative to purely data-driven alternatives, illustrating the value of combining process-based and statistical modeling in high-dimensional agricultural prediction problems.