Quantifying physiological determinants of genetic variation for yield potential in sunflower. SUNFLO: a model-based analysis Jérémie Lecoeur A,F , Richard Poiré-Lassus C , Angélique Christophe C , Benoît Pallas B , Pierre Casadebaig D , Philippe Debaeke D , Felicity Vear E and Lydie Guilioni B A Syngenta Seeds SAS, 12 Chemin de l’Hobit, F-31790 Saint-Sauveur, France. B Montpellier SupAgro, Département Sciences du Végétal, 2 Place Viala, F-34060 Montpellier, France. C INRA, UMR 759 LEPSE, 2 Place Viala, F-34060 Montpellier, France. D INRA, UMR 1248 AGIR, BP 52627, F-31320 Castanet Tolosan, France. E INRA, UMR 1095 ASP, Site de Crouël, 234 Avenue du Brézet, F-63100 Clermont-Ferrand, France. F Corresponding author. Email: jeremie.lecoeur@syngenta.com Abstract. Present work focussed on improving the description of organogenesis, morphogenesis and metabolism in a biophysical plant model (SUNFLO) applied to sunflower (Helianthus annuus L.). This first version of the model is designed for potential growth conditions without any abiotic or biotic stresses. A greenhouse experiment was conducted to identify and estimate the phenotypic traits involved in plant productivity variability of 26 sunflower genotypes. The ability of SUNFLO to discriminate the genotypes was tested on previous results of a field survey aimed at evaluating the genetic progress since 1960. Plants were phenotyped in four directions; phenology, architecture, photosynthesis and biomass allocation. Twelve genotypic parameters were chosen to account for the phenotypic variability. SUNFLO was built to evaluate their respective contribution to the variability of yield potential. A large phenotypic variability was found for all genotypic parameters. SUNFLO was able to account for 80% of observed variability in yield potential and to analyse the phenotypic variability of complex plant traits such as light interception efficiency or seed yield. It suggested that several ways are possible to reach high yields in sunflower. Unlike classical statistical analysis, this modelling approach highlights some efficient parameter combinations used by the most productive genotypes. The next steps will be to evaluate the genetic determinisms of the genotypic parameters. Additional keywords: biophysical model, Helianthus annuus, phenotypic characterisation, phenotypic expression of genotypic variability, yield potential. Introduction Recent advances in plant molecular biology have made it possible to collect large genomic datasets for model species such as Arabidopsis thaliana (L.) Heynh. (e.g. The Arabidopsis Genome Initiative 2000) and for crop species such as rice (Oryza sativa (L.); Yu et al. 2002). Consequently, there are a large number of publications concerning identification of genes that could explain the development of plant phenotypes in response to environment. However, difficulties have been encountered in relating, quantitatively, genomic information and its expression in complex phenotypic traits such as plant leaf area or seed yield (Colonna et al. 2004; Sinclair et al. 2004). One way to reduce the gap between molecular and plant levels is the use of mathematical models representing the plant as a biophysical system decomposed as a set of functions determining the response of each genotype in response to environment (Hammer et al. 2006; Jeuffroy et al. 2006). After the break down of plant functions into elementary processes, the parameters of equations used to describe these elementary processes may be compared with genotypic characteristics (Yin et al. 2004) if the environmental variance of the parameters of the equation is low with regards to the genotypic variance (Reymond et al. 2003). It is then possible to use quantitative genetic methods to evaluate genetic control and variability of the parameters of equations. This set of equations describing plant functions may help to account for phenotypic plasticity and to propose new breeding strategies (Hammer et al. 2002). This methodology has been explored for complex traits such as leaf transpiration (Borel et al. 2001) and the rate of leaf expansion (Reymond et al. 2003). However, these examples are simple compared with traits such as canopy biomass accumulation and seed yield. The most suitable plant representations to tackle yield variability would be crop models (Sinclair and Seligman 1996). A recent study has progressed to analyse yield variation by coupling a model which simulates leaf area dynamics using parameters linked to CSIRO PUBLISHING www.publish.csiro.au/journals/fpb Functional Plant Biology, 2011, 38, 246–259 Ó CSIRO 2011 10.1071/FP09189 1445-4408/11/030246