This dissertation aims to develop and implement a climate-analytics framework for the maize
yield predictPability improvement. The statistical genetic-by-environment (GxE) model is
applied to identify how hydroclimate interacts with maize molecular genetic markers through
covariance matrix structure to improve the predictability of and propagate the uncertainty to
maize yield simulations. The central thesis of this research is to evidence the contributions of
climate data science and analytics to the improvement of phenotype predictability. During my
defense, you will see evidence of how climate data enhancement improves the predictability
of maize yields using a GxE model. I will show the sensitivity of the GxE model performance
to climate drivers, illustrating how uncertainty is propagated to the improving maize yield
predictions. Finally, I will set the basis for the consolidation of a multi-dimensional (OMICS
and climate) database for diagnostic and prognostic maize phenotypes users.