Experimental data collected on growth and development of plants over a growing season are typically analyzed using a linear mixed model, analogous to a hierarchical linear model in a Bayesian setting. Alternative modeling approaches for repeated measures data involve non-linear models such as logistic regression and ecophysiological dynamic models based on a system of ordinary differential equations (ODE). Yet, current implementations of ODE models are mostly deterministic in nature, which negates recognition of uncertainty in the data generation process and thus impairs inference and prediction. The primary objective of this study was to demonstrate the use of a dynamic ODE model within a Bayesian framework to make stochastic inference on system-level parameters. A secondary objective was to compare the predictive performance of an ODE model relative to methodologies more commonly used for repeated measures data from designed experiments, namely hierarchical linear models and hierarchical non-linear models. Using a hierarchical Bayesian implementation, we fit all three types of models to data on leaf area index (LAI) and biomass from a winter wheat dataset. In the context of this application, none of the modeling approaches clearly outperformed any other in terms of goodness of fit or prediction accuracy as indicated by similar posterior median values for root mean squared error (RMSE), Willmott’s agreement index (d), and Nash-Sutcliffe efficiency (NSE). The prediction statistics for the ODE, linear, and non-linear models respectively, were: RMSEp of 1.38, 1.14, and 1.19; dp of 0.91, 0.93, and 0.93; and NSEp of 0.69, 0.78, and 0.76 for LAI and RMSEp of 274.08, 253.04, and 207.63; dp of 0.95, 0.95, and 0.97; and NSEp of 0.82, 0.84, and 0.89 for biomass. The dynamic ODE model enabled biologically meaningful system-level inferences relevant to the research questions that were not possible when using the hierarchical linear or non-linear modeling approaches.