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

TL;DR

This paper presents a modeling framework using the MAIZSIM crop model to explore trait, environment, and management interactions, aiding in identifying optimal strategies for maize yield stability across US regions.

Contribution

The study develops a novel framework for large-scale simulation of maize traits, environments, and management practices to optimize yield and stability.

Findings

Identified trait-management combinations that maximize yield.

Demonstrated the framework's ability to target regional adaptation strategies.

Showed potential to reduce resource-intensive field trials.

Abstract

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.