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
+ Author Affiliations
- Author Affiliations
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
Functional Plant Biology 38(3) 246-259 https://doi.org/10.1071/FP09189
Submitted: 22 July 2009 Accepted: 6 January 2011 Published: 29 March 2011
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.
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