  Breeding for new crop characteristics and adjusting management practices are
critical avenues to mitigate yield loss and maintain yield stability under a
changing climate. However, identifying high-performing plant traits and
management options for different growing regions through traditional breeding
practices and agronomic field trials is often time and resource-intensive.
Mechanistic crop simulation models can serve as powerful tools to help
synthesize cropping information, set breeding targets, and develop adaptation
strategies to sustain food production. In this study, we develop a modeling
framework for a mechanistic crop model (MAIZSIM) to run many simulations within
a trait x environment x management landscape and demonstrate how such a
modeling framework could be used to identify ideal trait-management
combinations that maximize yield and yield stability for different agro-climate
regions in the US.
