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

A framework for model-assisted T x E x M exploration in maize