Sunflower phenotype optimization under climatic uncertainties using crop models

TL;DR

This paper introduces a novel subset selection and clustering approach to efficiently optimize sunflower phenotypes under climatic variability, reducing computational costs while maintaining accuracy.

Contribution

It presents a generic, non-intrusive method for climate data subset selection and output distribution estimation applicable to environmental models.

Findings

Outperforms naive strategies in sunflower phenotype optimization

Reduces computational costs significantly

Effective for multi-objective, risk-aware optimization

Abstract

Accounting for the annual climatic variability is a well-known issue for simulation-based studies of environmental models. It often requires intensive sampling (e.g., averaging the simulation outputs over many climatic series), which hinders many sequential processes, in particular optimization algorithms. We propose here an approach based on a subset selection of a large basis of climatic series, using an ad-hoc similarity function and clustering. A non-parametric reconstruction technique is introduced to estimate accurately the distribution of the output of interest using only the subset sampling. The proposed strategy is non-intrusive and generic (i.e. transposable to most models with climatic data inputs), and can be combined to most "off-the-shelf" optimization solvers. We apply our approach to sunflower phenotype optimization using the crop model SUNFLO. The underlying optimization problem is formulated as multi-objective to account for risk-aversion. Our approach achieves good performances even for limited computational budgets, outperforming significantly more "naive" strategies.