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Analysis and modelling of the factors controlling seed oil concentration in sunflower: a review

F. Andrianasolo,P. Debaeke,P. Debaeke,L. Champolivier,P. Maury

Published 2016 in Unknown venue

ABSTRACT

Sunflower appears as a potentially highly competitive crop, thanks to the diversification of its market and the richness of its oil. However, seed oil concentration (OC) – a commercial criterion for crushing industry – is subjected to genotypic and environmental effects that make it sometimes hardly predictable. It is assumed that more understanding of oil physiology combined with the use of crop models should permit to improve prediction and management of grain quality for various end-users. Main effects of temperature, water, nitrogen, plant density and fungal diseases were reviewed in this paper. Current generic and specific crop models which simulate oil concentration were found to be empirical and to lack of proper evaluation processes. Recently two modeling approaches integrating ecophysiological knowledge were developed by Andrianasolo (2014, Statistical and dynamic modelling of sunflower (Helianthus annuus L.) grain composition as a function of agronomic and environmental factors, Ph.D. Thesis, INP Toulouse): (i) a statistical approach relating OC to a range of explanatory variables (potential OC, temperature, water and nitrogen stress indices, intercepted radiation, plant density) which resulted in prediction quality from 1.9 to 2.5 oil points depending on the nature of the models; (ii) a dynamic approach, based on “source-sink” relationships involving leaves, stems, receptacles (as sources) and hulls, proteins and oil (as sinks) and using priority rules for carbon and nitrogen allocation. The latter model reproduced dynamic patterns of all source and sink components faithfully, but tended to overestimate OC. A better description of photosynthesis and nitrogen uptake, as well as genotypic parameters is expected to improve its performance.

PUBLICATION RECORD

  • Publication year

    2016

  • Venue

    Unknown venue

  • Publication date

    2016-03-01

  • Fields of study

    Agricultural and Food Sciences, Biology

  • Identifiers
    DOI 10.1051/OCL/2016004
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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