Model-Aided Adaptation for Future Maize - Novel Plant Traits and New Management

Over the next three decades rising population and changing dietary preferences are expected to increase food demand by 2575%. At the same time climate is also changing with potentially drastic impacts on food production. Breeding for new crop characteristics and adjusting management practices have the potential to mitigate yield loss due to 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 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 use a mechanistic crop model (MAIZSIM) to simulate ~100 trait-management combinations and identify high performing combinations that maximize yield and minimize risk for maize plants in different agro-climate regions within the US. We find that plant trait and management choices that result in longer reproductive stages, or more favorable climate conditions during reproductive stages, lead to high performance. Further, we identify how high performing trait-management combinations under present day climate conditions could shift under projected future climate and investigate the mechanisms behind these shifts. Our analysis identifies the characteristics that lead to high performance under a range of climates which can be leveraged to inform maize breeding targets.