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Non-Invasive Spectral Phenotyping Methods can Improve and Accelerate Cercospora Disease Scoring in Sugar Beet Breeding

Author

Listed:
  • Marcus Jansen

    () (Forschungszentrum Jülich GmbH, Institut für Bio- und Geowissenschaften, IBG-2, Pflanzenwissenschaften, Wilhelm-Johnen-Straße, Jülich 52425, Germany)

  • Sergej Bergsträsser

    () (Forschungszentrum Jülich GmbH, Institut für Bio- und Geowissenschaften, IBG-2, Pflanzenwissenschaften, Wilhelm-Johnen-Straße, Jülich 52425, Germany)

  • Simone Schmittgen

    () (Forschungszentrum Jülich GmbH, Institut für Bio- und Geowissenschaften, IBG-2, Pflanzenwissenschaften, Wilhelm-Johnen-Straße, Jülich 52425, Germany)

  • Mark Müller-Linow

    () (Forschungszentrum Jülich GmbH, Institut für Bio- und Geowissenschaften, IBG-2, Pflanzenwissenschaften, Wilhelm-Johnen-Straße, Jülich 52425, Germany)

  • Uwe Rascher

    () (Forschungszentrum Jülich GmbH, Institut für Bio- und Geowissenschaften, IBG-2, Pflanzenwissenschaften, Wilhelm-Johnen-Straße, Jülich 52425, Germany
    Rheinische Friedrich-Wilhelms-Universität Bonn, Regina-Pacis-Weg 3, Bonn 53113, Germany)

Abstract

Breeding for Cercospora resistant sugar beet cultivars requires field experiments for testing resistance levels of candidate genotypes in conditions that are close to agricultural cultivation. Non-invasive spectral phenotyping methods can support and accelerate resistance rating and thereby speed up breeding process. In a case study, experimental field plots with strongly infected beet genotypes of different resistance levels were measured with two different spectrometers. Vegetation indices were calculated from measured wavelength signature to determine leaf physiological status, e.g., greenness with the Normalized Differenced Vegetation Index (NDVI), leaf water content with the Leaf Water Index (LWI) and Cercospora disease severity with the Cercospora Leaf Spot Index (CLSI). Indices values correlated significantly with visually scored disease severity, thus connecting the classical breeders’ scoring approach with advanced non-invasive technology.

Suggested Citation

  • Marcus Jansen & Sergej Bergsträsser & Simone Schmittgen & Mark Müller-Linow & Uwe Rascher, 2014. "Non-Invasive Spectral Phenotyping Methods can Improve and Accelerate Cercospora Disease Scoring in Sugar Beet Breeding," Agriculture, MDPI, Open Access Journal, vol. 4(2), pages 1-12, May.
  • Handle: RePEc:gam:jagris:v:4:y:2014:i:2:p:147-158:d:35893
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    More about this item

    Keywords

    phenotyping; vegetation index; disease scoring; Cercospora beticola ; resistance breeding;

    JEL classification:

    • Q1 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture
    • Q10 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - General
    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness
    • Q14 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Finance
    • Q15 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment
    • Q16 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - R&D; Agricultural Technology; Biofuels; Agricultural Extension Services
    • Q17 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agriculture in International Trade
    • Q18 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Policy; Food Policy

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