Optimization of parameters of the ‘Virtual Fruit’ model to design peach genotype for sustainable production systems

Abstract

Designing sustainable production systems that are respectful of the environment and produce safe food of quality is a challenge for the future. A critical step is to find the best combinations of genetic resources and cultural practices adapted to target environments. The objective of this work is to illustrate how an ecophysiological process-based simulation model could be possibly used to design genotypes and to propose innovative production systems, by applying a methodology of optimization. As example, we studied the peach-brown rot system and used the 'Virtual Fruit', a processbased model that has been extensively tested, to perform virtual experiments. The challenge was to optimize the trade-off between antagonistic criteria of major importance for both fruit quality (increasing fruit mass and sweetness) and sensitivity to brown rot (decreasing skin density of cracks) in four different cultural scenarios. A multiobjective evolutionary algorithm, namely NSGA-II, was applied to solve this multiobjective optimization problem based on the 'Virtual Fruit'. The optimized variables were six parameters of the 'Virtual Fruit', selected on the basis of a sensitivity analysis. This optimization method provided a large diversity of solutions among which the decision-maker can choose the best suited trade-off between criteria according to a particular objective. Most of the optimized solutions were distributed along Pareto fronts suggesting a good convergence of the algorithm. Moreover, it also provided some solutions located in noncrowded zones which constitute some original alternatives for the final decision-maker. The results confirmed the strong antagonism between the criteria considered. Large fruits had a weak sweetness and high crack density and for a given mass, those with improved sweetness had higher crack density. In a current breeding scheme, fruit mass would be the only criteria considered but alternative schemes could be considered for future, favoring organoleptic quality or environment friendly practices. In those cases, some interesting optimized solutions were identified.

Genetics and Genomics of Tree Architecture

“…photosynthesis) or agronomic (e.g. production) traits simultaneously in a multi-objective approach as recently performed by Quilot-Turion et al (2012). In the next future, the use of in silico scenarios and optimization procedures should allow researchers, growers and breeders to better account for antagonistic effects of input parameters, possibly at several scales of tree organization, and their combined effects on multiple objectives.…”

Model-Assisted Estimation of the Genetic Variability in Physiological Parameters Related to Tomato Fruit Growth under Contrasted Water Conditions

“…Many papers have been published on the use of evolutionary algorithms for ideotype model-based design. For the sake of conciseness, we mention only the works of Letort et al (2008) on beech trees, of Qi et al (2010) on maize, of Lu et al (2012) on wheat, of Quilot-Turion et al (2012) and Sidi et al (2014) on peach, and of Ding et al (2016) on rice.…”

Optimization of Allelic Combinations Controlling Parameters of a Peach Quality Model

“…In a case-study on optimizing maturity and density for a sunflower crop, they proved the potential power of the concept. Recent works making the connection of advanced optimization algorithms with process-based models confirmed that the approach is valuable to identify trade-off in complex systems (Qi et al, 2010; Quilot-Turion et al, 2012) and that specific adaptation of genotypes to local environmental conditions confers an advantage over broad adaptation (Hammer et al, 2014). …”

“…Nature-inspired optimization algorithms (e.g., genetic algorithms and particle swarm optimization algorithms) are increasingly used (deVoil et al, 2006; Letort et al, 2008; Qi et al, 2010; Ould-Sidi and Lescourret, 2011; Grechi et al, 2012; Kadrani et al, 2012; Quilot-Turion et al, 2012; Memmah et al, 2015). These algorithms both do not require any derivative information and enable exploring highly dimensional solution spaces.…”