Using numerical plant models and phenotypic correlation space to design achievable ideotypes

TL;DR

This paper introduces a multi-objective optimization approach combining numerical plant models and phenotypic correlation space to design realistic and optimal plant ideotypes, validated on sunflower and apple tree models.

Contribution

It presents a novel multi-objective optimization method that incorporates trait feasibility based on field data, improving the design of attainable plant ideotypes.

Findings

Successfully characterized key traits in sunflower and apple models

Identified a continuum of optimal solutions balancing feasibility and performance

Provided a proof of concept for realistic ideotype design using this approach

Abstract

Numerical plant models can predict the outcome of plant traits modifications resulting from genetic variations, on plant performance, by simulating physiological processes and their interaction with the environment. Optimization methods complement those models to design ideotypes, i.e. ideal values of a set of plant traits resulting in optimal adaptation for given combinations of environment and management, mainly through the maximization of a performance criteria (e.g. yield, light interception). As use of simulation models gains momentum in plant breeding, numerical experiments must be carefully engineered to provide accurate and attainable results, rooting them in biological reality. Here, we propose a multi-objective optimization formulation that includes a metric of performance, returned by the numerical model, and a metric of feasibility, accounting for correlations between traits based on field observations. We applied this approach to two contrasting models: a process-based crop model of sunflower and a functional-structural plant model of apple trees. In both cases, the method successfully characterized key plant traits and identified a continuum of optimal solutions, ranging from the most feasible to the most efficient. The present study thus provides successful proof of concept for this enhanced modeling approach, which identified paths for desirable trait modification, including direction and intensity.