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

Victor Picheny,Evelyne Costes,Pierre Casadebaig,Robert Faivre,David Da Silva,Ronan Trépos,Patrick Vincourt

doi:10.1111/pce.13001

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, that is, ideal values of a set of plant traits, resulting in optimal adaptation for given combinations of environment and management, mainly through the maximization of performance criteria (e.g. yield and 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 modelling approach, which identified paths for desirable trait modification, including direction and intensity.

Highlights

Using simulation models to optimize phenotype Global demand for agricultural products to supply food, feed, and fuel is rapidly increasing (Edgerton 2009)
Design of experiments Our work focused on four key geometrical traits: branching angle (BA), internode length (IL), top shoot diameter (TSD), and leaf area (LA)
This correlation suggests that cultivars that are more efficient at intercepting light for a given leaf area are maintaining their stomatal conductance under water deficit

Summary

Introduction

Using simulation models to optimize phenotype Global demand for agricultural products to supply food, feed, and fuel is rapidly increasing (Edgerton 2009). After decades of increase, many major crops have recently shown slower rates of yield improvement, stagnation, or even loss of productivity (Ray et al 2012) This situation likely results from the negative effects of global climate change and societal prejudice that perceives environmental costs and limits agricultural resources, for example, public policies to reduce the use of chemicals in disease management programs (Sutton 1996). Overcoming these challenges will require that breeders continue the genetic improvement of major crops accounting for changing agricultural practices towards sustainable production systems (Vanloqueren & Baret 2009). Computer-based modeling approaches have recently emerged as a method to save time, labor, and resources and to infer traits value beyond field experiments (Da Silva et al 2014b; Martre et al 2015a; Casadebaig et al 2016b )

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Conclusion